认知无线环境下的频谱管理：测量、分析和建模
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摘要
随着信号处理与集成电路等相关技术的飞速发展，无线通信在近几十年取得了空前的进步，但如今，无线通信却面临新的挑战——频谱资源匮乏。传统的无线电设计在频谱资源匮乏与应用需求剧增之间的矛盾面前手足无措，但美国联邦通信委员会的调查报告指出，当前无线频谱资源的严重缺乏并非由于无线频谱资源的过度使用造成，相反，缺乏效率的频谱分配政策与无所不在的资源浪费使频谱管理者陷入了尴尬境地。世界各国现行的频率使用政策除分配极少的ISM开放频段外，大多采用许可证制度，即授权用户使用分配的频段，非授权用户不得共享。固定的频谱划分方式使得不可再生的无线频谱资源变得越来越少。
正当业界寻求更先进的无线通信技术，如链路自适应技术、MIMO技术等来提高频谱效率的同时，却发现已授权频段，尤其是信号传播特性良好的低频段频谱利用率极低。为了提高频谱利用率，在不同地区、不同时段能有效利用空闲频段，一种新的频谱使用模式——认知无线电（Cognitive Radio, CR）技术逐渐受到学界和业界的高度关注。认知无线电的基本思想是：在不对拥有频谱的授权用户或非授权用户产生有害干扰的前提下，认知用户择机（opportunistic）接入授权用户频段或非授权频段，以提高频谱利用率。认知无线电技术的出现，为解决频谱资源不足、实现频谱动态管理并提高频谱利用率提供了强有力的技术支持。
认知无线电作为一种资源共享的革命性技术，可显著提高频谱资源利用率，并缓解频谱资源短缺现状。由于认知无线电网络是频谱资源受限网络，需要与主用户网络协商或感知主用户频段的“空隙”从而获取资源完成通信。认知无线电应用的前提是准确认知当前无线频谱的行为和模式，即通过对无线传输场景的测量、分析、建模和判决，获得频谱的可用性知识。
一直以来，对无线传输场景的实地测量是各国频谱监管机构了解和掌握频段使用率、避免通信设备之间相互干扰，进而制定和调整频谱使用政策的基础手段。随着认知无线电技术研究的深入和应用步伐的加快，进一步拓展了无线传输场景测量和分析的应用目的。无线环境下的频谱管理是一种在特定空域和特定时间测量、分析和建模特定频段频谱占用状态的重要措施，通过对无线通信环境的感知，为频谱监管部门调整频谱使用政策提供决策依据，为新的无线通信设备发掘临时可用的频谱资源，进而利用无线资源管理策略为新的无线通信终端提供服务质量（Quility0of Service, QoS）保证。当然，企业界和学术界对开展无线频谱占用测量和分析有着不同的动机，企业界的兴趣主要在于利用认知无线电技术最大化自身效益，而学术界更关注通过发掘低利用率频段，为新的无线通信系统的商用提供更多的频谱可用机会。目前，针对认知无线电相关技术的研究尚处于理论层面和原型样机阶段，利用实验室原型样机验证和测试相关研究成果的性能，频谱实地测量数据可以将原型样机的演示和验证推进到比较实际的应用场景。频谱测量的另一个重要应用在于可用于构建和修正频谱预测模型。
本文研究的第一个贡献在于对尼日利亚几个热点地区的频谱使用率进行了实地测量、统计和分析。全球已有大量国家和地区，包括美国、加拿大、英国、德国、中国、韩国、新加坡、日本以及欧洲等对本国特定地区的无线频谱占用情况进行了实测和统计，建立了符合本国本地区开展移动通信网络规划所需的电波传播或干扰评估模型，而非洲国家（除南非外）几乎从未开展过无线电环境下的频谱占用实测。但从国家安全、社会稳定、经济发展，特别是移动通信技术的推广应用角度出发，开展无线频谱占用和利用率实地测量都是非常必要的。尼日利亚联邦共和国地处西非东南部，非洲几内亚湾西岸顶点。2001年，尼日尼亚放宽了对电信行业的限制，导致了无线通信，特别是蜂窝移动通信网络服务的高速增长，全国现有MTN、Globacom、Airtel、Etisalat和Starcomms等5家移动通信运营商提供3G移动服务。2011年，与中国长城工业公司合作发射了首颗通信卫星——NigComSat-1R，旨在进一步改善通信基础设施。随着人们对移动通信服务需求的进一步提高，还有多家通信运营商计划开展移动通信商用业务。类似于尼日利亚等非洲国家，由于缺乏铜线或光纤等基础部件和材料，对无线通信网络的依赖更重，对无线频谱的需求更显突出。在未来几年。尼日利亚全国有1.5亿人口要求提供通信服务，频谱需求与资源匮乏之间的矛盾日显尖锐。2012年，以尼日利亚为首的非洲地区国家在世界无线电大会上会议强烈要求对该区域无线频谱进行统一调整和规划，并引入新的通信技术手段为民众提供高效的宽带接入服务。2013年，ITU已批准将700HMz频段用于该地区宽带无线接入服务。
为了完成无线频谱使用率的实地测量、统计和分析，笔者构建了一套测试分析平台，包括一台Aaronia AG HF-6060V4频谱分析仪，其频率测量范围为10MHz-6GHz；一付Aaronia AG OmniLOG90200型宽带天线，其适用频率范围为700MHz-2.5GHz；一台笔记本电脑，通过USB接口与频谱分析仪输出连接；一套MCS专用分析软件，运行于Aaronia AG频谱分析仪应用场合。MCS专用分析软件拥有一套完善、直观的图形界面，参数设置和分析显示均能通过界面完成。频谱测量场景选择在3个不同的地点进行，分别是Gwarinpa街区，首都阿布贾（Abuja）的一个主要住宅区；Wuse4区，首都阿布贾的一个住宅金融中心；Gafai区，尼日利亚南部Katsina古城的一部分。针对Gwarinpa街区的实地测量是在2012年暑假进行的。测量范围覆盖了700-2400MHz频带，累计测量超过12小时。为了获得真实准确地频谱占用和利用率信息，测量分白天和夜晚两个单元开展，白天测量从上午9点到晚上9点时段，而夜晚测量则从晚上10点至早上8点时段。Gwarinpa街区位于距Nnamdi Azikiwe国际机场19km附近，频谱测量还记录了航空频段的相关数据。第二个测量地点，Wuse4区位于阿布贾的商业中心区域，该区域包含了大量政府和商业设施以及部分住宅区，测量范围覆盖了700-1200MHz频带，累计测量超过12小时，测量工作也是在2012年夏天进行的。最后一个测量地点是Gafai区，位于人口密集的Katasina古城。此项测量工作是在2013年8月进行的，测量范围覆盖了700-1000MHz频段，测量时间为上午8点到夜晚8点，共12小时。
在三个测量区域所测量的频段中，800MHz频段提供无线通信服务和CDMA应急通信服务；900MHz频段提供GSM移动通信服务；470-960MHz频段提供模拟电视广播服务，包括VHF频段的12个信道以及UHF频段的49个信道共61个信道。测量结果表明，该频段的利用率约为26%，在所测频谱范围内是最高的，主要原因是模拟电视广播活动频繁，GSM网络等移动通信业务繁忙。
1000-1500MHz频段（1350-1550MHz）主要用于微波点对点通信服务，由政府部门和Niger三角区域和Lagos的石油公司使用。测量结果表明，1000-1300MHz和1300-1500MHz的使用率分别为2.13%和1.85%，是所测频率范围内使用率最低的。除了在1350-1450MHz附近的微波点对点通信外，基本上没有其他活动。
1.5GHz-2.4GHz频段主要提供3G移动通信服务。测量结果表明，其频谱利用率约为25.1%，也相对较高。如前所述，尼日利亚全国目前有5家移动通信运营商提供3G移动服务，通信网络采用UMTS规定的WCDMA技术体制，信号的扩频特性使得信号被调制到很宽频带中，其特性类似噪声，很难检测。由于测量是在室内环境实施的，天线接收信号能力可能被阻隔。
高于2.42GHz的ISM频段有着17%的高使用率，但仍可以为第二用户提供潜在的使用机会。通信卫星在2.305-2.32MHz和2.335-2.36MHz的上行和下行活动也能被检测到。
本文研究的第二个贡献是就不同无线通信系统24小时的占用情况进行了实测、统计和分析。3种最常用的频段分别是TV广播电视频段、2G蜂窝网络通信频段和3G蜂窝网络通信频段，其中，TV广播电视频段在白天的占用率高，且不可预测，但在夜晚特别是0点过后占用率显著减少，且不可预测。这一观测结果与大多数电视台在0点过后都会关闭的事实相吻合；2G和3G蜂窝网络通信频段有着随机性的占用率，在白天高度不可预测，而在夜晚中度可预测，分析原因，可能是由于人们在白天使用移动通信更为频繁所致。
将实地测量获得的700-2400MHz的原始功率数据转换为包含信道占用率的时间序列，该序列揭示了一个信道在给定时间区间究竟有多少时间是空闲的。信道空闲时间定义为信道占用序列数据中连续0的个数，分析信道空闲时间发现，信道空闲分布服从指数分布，尽管这些信道并非独立分布的。本文获得的该研究成果的贡献不在于信道空闲的经验分布，而是获得了这些频段上的空闲时隙概率分布。分析结果表明，尽管有丰富的使用机会，但实际上的时隙/频谱机会要比预想的少很多。本质上，频谱被严格分割开，且散布在时间序列中，极大地减少了动态频谱接入的频谱可用机会。
信道空闲时间提供了一个特定信道的平均信息，而服务阻塞率指标（SCR）则提供了频谱在特定瞬间的短时信息，比如某第二用户需要在3G蜂窝网络1800MHz上行和2G蜂窝网络900MHz上行之间选择频谱接入机会，假设两张蜂窝网络的总体占用率相似，且信道空闲时间相同，此时就需要一个指标来提供瞬时信息，以便第二用户可以做出正确决策。为了达到这个目的，必须依据2G和3G蜂窝通信网络通信频段的上行和下行数据来评估服务阻塞率。
对频率信道间的相关性检测和预测是本文研究的第三个贡献。频率信道的相关性能反映不同频率信道能提供的服务质量等重要信息，由于信道使用率特征不可预测，对其进行相关性分析必须在更长时间段进行。本文在频谱相关性分析中，随机选取并测量获得了5个信道1小时的原始数据，分析结果表明，所有信道的相关系数平均为0.5，这意味着各频谱使用率模式之间有一定程度的独立性。值得注意的是，所选取的部分信道在测试时间内一直处于空闲状态。通过对比分析Katsina镇和阿布贾城的临时频段占用相关性，进一步综合考虑2G蜂窝网络上行频段、下行频段和TV广播电视频段的技术特征，结果表明，不同服务之间没有或很少有相关性，2G蜂窝网络上行频段、下行频段和TV广播电视频段的相关系数分别0.1023、0.4999和0.2303，这些发现与频谱使用率只在地点上相关这一事实相符。
基于人工神经网络进行频谱预测的主要优势在于，认知无线电可以学习历史信息，并改变自身工作参数而无需重新设计系统。不同于在无线通信系统设计中广泛运用的马尔科夫链预测模型，人工神经网络模型只需要将最近的数据作为输入，数据获取时间短、数据量小、运算复杂度低。机器学习算法被公认为构建认知无线电网络中认知引擎的核心算法之一，人工神经网络模型的高预测精度能减少感知整个频段的时间，相应的运算处理能耗也会大幅下降。在人工神经网络建模的学习和训练过程中，有两个重要因素必须重点关注：一个是相互连接的初始权重，另一个是训练更新的权重改变方式。一般而言，BP ANN的相互连接初始权重是随机、盲目产生的，可能导致网络进入局部最优，而降低了取得最优解的概率，如果采用Delta准则来修改BP ANN的相互连接权重，其收敛速度慢，甚至有时无法收敛。本文深入研究了BP ANN的上述问题，针对局部最小化问题，通过引入遗传算法来调整相互连接初始权重。遗传算法是一个非线性优化方法，有很强的全局搜索能力。在研究中，本文针对5家无线通信和广播网络进行。预测结果表明，Etisalat公司拥有的GSM900网络上行信道平均预测错误率仅为0.035；其他GSM900网络下行信道的平均预测错误率为0.005；TV广播电视频段和3G蜂窝移动通信网络下行信道的平均预测错误率分别为0.0004和0.0007。
Marconi’s demonstration at the turn of the20th century revolutionized the way wecommunicate forever while also ushering the birth of radio communication. The growthof radio communication highlighted the problem of interference amongst transmittersoperating in the same geographical area. Earlier efforts aimed at resolving this issuehighlighted the importance of spectrum as a scarce and renewable natural resource.Regulatory bodies were then set up to manage the planning, allocation and overallmanagement of this scarce resource at the national level. Currently, the InternationalTelecommunication Union (ITU) along with the representatives of each country’sregulatory body converge at the World Radio Conference (WRC) periodically to mapout policies for international and regional usage as well establishing world standards.
Spectrum management since the early days of radio communication has alwaysbeen about minimizing interference. Under this regime, spectrum bands are allocated toa particular service over a large geographical area in some cases a country, withprovision for band guards to alleviate the problem of interference. This approach hasbeen very efficient as far as maintaining non-interference communication is concerned.However, with the proliferation of several wireless standards over the years, the demandfor spectrum has grown tremendously so is its economic value. It has therefore becomenecessary to review the current command and control approach to spectrummanagement. One of the solutions being proposed is a regime whereby licensed andunlicensed users could share the spectrum in a non-interference fashion; this is knownas Cognitive radio. It was first envisioned by Mitola in1999. To realize this concept,the secondary user also known as the unlicensed user must be able to sense the channelprior to initiating transmission and also vacate the channel in the event the primary useror licensed user returns to transmit. This process termed as spectrum sensing is thecornerstone of successful deployment of cognitive radio.
The successful deployment of the cognitive radio paradigm hinges on conciseknowledge of the spectrum; its behavior, usage patterns, and the availability ofspectrum holes. It is generally hoped in the research community that cognitive radiowill dramatically reduce spectrum usage inefficiency thereby increasing spectrumutilization. Knowledge of the spectrum has therefore become the first step towardsachieving this goal. Field measurements of the radio environment have been performed over the years to determine frequency channel usage information for national regulatoryauthorities which they have been using for formulating spectrum usage policies wherethe sole aim is interference minimization. These measurements provide a thoroughknowledge of network activities that can provide a platform for understanding the usagepattern which is invaluable in cognitive radio deployment. Prior to the cognitive radioera, the main aim of spectrum occupancy measurements have been to provideinformation for policy makers and regulatory bodies to formulate policies as well asminimize interference respectively. However, with the advent of cognitive radioresearch, the aim of spectrum occupancy measurements have broadened to includequantitative information on underutilized spectrum bands for possible cognitive radiodeployment and also the establishment of the degree of efficiency of spectrum usage.The economic and research communities might have different motives for conducting aspectrum occupancy measurement, with the economic community mostly interested inthe investments required to develop cognitive radio so that the investments are notmisapplied if they do not take into account the current realities. On the hand, theresearch community is mostly interested in determining the bands that experience lowutilization so that they can be analyzed and characterized in time, frequency and space.This information will be vital for the deployment of future cognitive radio systems.Current works on cognitive radio have all been theoretical with no practicaldemonstration as to the real applicability of these schemes. Spectrum measurement datacan be used to actually determine the feasibility of these schemes. Another importantapplication of these measurements lies in the development of spectrum predictionmodels for deployment in cognitive radio engines with the aim of saving both energyand sensing time. It can therefore be said that successful deployment of cognitive radiorequires a thorough understanding of the spectrum, spectrum occupancy statistics fromthe measurements and reliable models capable of predicting future usage with little orno errors.
Spectrum prediction models have been researched over the years, where modelswere developed for the High Frequency HF bands. Due to the opportunistic nature ofspectrum, successful deployment of these models will depends on the performance ofthe secondary networks which will heavily rely on the spectrum usage patterns of theprimary user. It has therefore become necessary to investigate the nature and pattern ofspectrum usage so that successful models could be furnished. Recently, models aimed atpredicting the spectrum opportunities have been presented with varying degree of accuracy. Part of the objective of this work presented herein is to contribute to thiseffort by improving the prediction accuracy of these models.
The first contribution of this work is to provide an insight into the spectrum usagein Nigeria with the aim of providing a platform for future measurements and thepossible deployment of the cognitive radio paradigm. Several spectrum occupancymeasurements have been performed all over the world; USA, United Kingdom, China,Singapore, Japan, are some of the countries were these measurements have been made.Few of these measurements can be found in Africa with the exception of South Africain the research community. It has therefore become necessary to perform thesemeasurements for the socio-economic benefit of the populace. The deregulation of thetelecommunication sector in Nigeria in2001has lead to a remarkable growth in thewireless communication services sector especially cellular communication. With thelaunch of the country’s first satellite the Nigersat-1which is basically a remote sensingsatellite the government signaled its intention to develop this vital area of the economy.NigComSat-1R a communication satellite was re-launched in2011after earlier attemptsin2007failed. The satellite was launched in partnership with China Great Wall IndustryCorporation with the aim of further improving the communication infrastructure.Spectrum scarcity has been a dilemma recently; African countries suffer more in thisregard because of their heavy reliance on wireless communication services due to non-availability of other vital infrastructure such as copper and fiber optic networks.Therefore it could be said that the transmission channel is not readily available thus theovercrowded nature of the spectrum allocated for broadband communication. InNigeria’s case, the population of over150million and over100million active lines hasaffected the network quality of these services, to increase quality, capacity must beincreased, to increase capacity there must be more spectra. Even though other servicescould be considered, the affordability across users must be taken into consideration asmost users won’t be able to afford expensive services. These factors and others lead tothe African region headed by Nigeria to demand for further spectrum allocation duringthe World Radio Conference2012. They argued that the allocation will improve thequality of broadband services and also increase mobile internet penetration amongst thepopulace. Currently, the ITU has granted the700MHz for efficient delivery ofbroadband services for this region. It has therefore become extremely important tostudy the spectrum utilization, behavior, and analyze the data obtained from these measurements in order to formulate and plan for the future due to the uniqueness ofNigeria’s case.
The measurement setup involved an Aaronia AG HF-6060V4spectrum analyzerwith a range of10MHz-6GHz, an Aaronia AG OmniLOG90200antenna with a rangeof700MHz to2.5GHz, a laptop system that is connected to the spectrum analyzer via aUSB cable, and an MCS software specially designed to run on Aaronia AG spectrumanalyzers. The MCS software has a graphical interface whereby all the parametersrequired for accurate measurement are set. The software is also used for controlling themeasurements based on the parameters set. The measurement was conducted indoors inprimarily three different locations; at Gwarinpa District a primarily residential district inAbuja the capital of Nigeria, Wuse Zone4a residential/commercial activity centre alsoin Abuja, and Gafai quarters which is part of the ancient city of Katsina, a town in thenorthern part of Nigeria.. The measurement conducted in Gwarinpa Estate Abuja wasconducted during the summer holidays of2012i.e. July-August2012covering the700-2400MHz band over12hour periods. The measurements were further divided into twocategories i.e. daytime and nighttime. Daytime measurements cover the duration from9am-9pm while nighttime covers the duration from10pm-8am. Gwarinpa estate isapproximately19km from Nnamdi Azikiwe International Airport; we could therefore beable record some of the activity in the aeronautical bands due to the relatively closeproximity of the location to the airport.The second location was situated in the centralbusiness district of Abuja. It contains most of the government and businessestablishments with some residential areas sparsely situated. The dataset from thislocation consists of data from700-1200MHz band also obtained during the summer of2012(July-August2012). The third and last dataset was obtained from Gafai quarters inthe ancient city of Katsina a densely populated area of the ancient town. Themeasurements were conducted in the summer of2013(August) over the700-1000MHzband over a period of12hours from8am-8pm. The first band considered was the700-1000MHz. It comprises the800MHz band used for trunk radio services, emergencyservices, CDMA (fixed),900MHz for GSM and also the470-960MHz for analoguetelevision broadcasting. In the VHF band there are12channels where as the UHF bandconsists of49channels making a total of61channels. This band has the highestutilization level experienced at26%due to the activities of the analogue broadcasting(part of it to be precise) GSM operations and the radio trunk services. The1000-1500MHz band is mostly used for microwave point to point communication(1350-1550MHz), government agencies and oil companies in the Niger delta region andLagos. The1000-1300MHz and1300-1500MHz with a utilization level of2.13%and1.85%respectively are among the bands with the lowest utilization level. Apart frommicrowave point to point transmission observed around1350-1450MHz; there isvirtually no activity at all.
Above1.5GHz, majority of the utilization can be observed in the3G mobilestandards. With a utilization level of around25.1%it has one of the highest utilizationlevel amongst the bands considered for this work. In Nigeria, there are currently fivemobile companies delivering3G mobile services in Nigeria: MTN, Globacom, Airtel,Etisalat and Starcomms. Networks employing UMTS use WCDMA technology, thespread spectrum nature of the signals where by the signals are modulated over a widebandwidth thus making them having a noise-like character due to the very lowtransmission power makes them difficult to detect. This makes it difficult for thespectrum analyzer to determine such signals. Similarly, since the measurements’ wereconducted indoors, the ability of the antenna to receive signals might be hindered.Above2.42GHz, with17%utilization, the ISM band shows considerable utilization butit could also provide some opportunity for secondary usage. As the measurement wasdone indoors it was able to detect much of the signals due to the short nature of signalsin this band. Some activity on the satellite uplink and downlink bands were alsodetected at a frequency of2.305-2.32MHz and2.335-2.36MHz.
The utilization pattern over24hours was also investigated. For this analysis, bandscontaining the three most utilized bands i.e. TV broadcasting,2G cellular band and the3G cellular bands were investigated. The TV broadcasting band was found to betransmitting during the day time with the occupancy being highly unpredictable,however during the night especially after00:00there seems to be less transmissionswith the occupancy being predictable. This observation is in tune with the normaloperating manner of such bands as most of the TV stations switch off their transmissionafter00:00. The2G and3G cellular bands indicate a random occupancy from beinghighly unpredictable during the day time to being moderately predictable at night. Thispattern is also understandable with people utilizing the bands during the day and lesspeople using the services at night.
The conversion of the raw data which is a collection of power levels across700-2400MHz considered as a time series containing yields the channel occupancy. It indicates how long a channel is actually free at a given time before the primary userresumes transmission. Channel vacancy duration can be defined as the number ofconsecutive0’s in the channel occupancy series; this is the stage after thresholdingwhich is the conversion of power level to0’s and1’s. Channel vacancy durationanalysis was performed, the channel vacancy distribution was found to follow anexponential-like distribution even though the channels are not independently distributed.The coefficient of determination R2is well approximated by an exponential-likedistribution with values around0.93in all the locations considered. The significance ofthis analysis lies not in the empirical distribution of the channel vacancy distribution butin the distribution of vacancy timeslots amongst the bands analyzed. It was found outthat despite abundant opportunities the amount of timeslots/spectrum opportunitiesactually presented are much lower than initially expected. In essence, the spectrum isheavily fragmented and scattered across time thus greatly reducing the amount ofspectrum available for dynamic spectrum access.
While channel vacancy duration provides long term information about the averageinformation for a particular channel, the Service Congestion Rate SCR metric providesthe short term or instantaneous picture of the spectrum at a particular instant. A situationmight arise whereby a secondary user needs to select between two services say3G1800Uplink and2G900Uplink, assuming their overall occupancy for the day is similar andalso their CVD is identical, a metric is needed to provide instantaneous information sothat the secondary user might be able to make an informed decision. For the purpose ofthis analysis, data from cellular band (both2G and3G) were used to determine the SCRvalues of these services both in the uplink and downlink paths. Five channels wereselected randomly from each service for the purpose of this analysis.
The need to examine the correlation among frequency channels is of paramountimportance as it will provide valuable information on the similarities/differences inusage/behavior amongst different services analyzed. The correlation should be over alonger period of time due to the unpredictable nature of usage as this will provide acalculated insight into the actual correlation of the channels in question. For spectralcorrelation analysis, an hour long data from five channels were randomly selected andprocessed. From the results obtained from the spectral correlation analysis, it can beseen that there is average correlation coefficients of around approximately0.5in allcases considered. This implies a degree of independence in the spectrum utilizationpatterns across the services. It should be noted that some of channels used especially in the cellular bands are idle during the whole time because they serve as band gaps whichare used to avoid interference amongst services adjacent to each other. However, forthe temporal correlation analysis spectrum occupancy results from Katsina town andAbuja city are compared and correlation analysis performed amongst the servicescommon to both cities. The2G uplink,2G downlink and the broadcasting bands wereconsidered, results indicates that there is little or no correlation amongst the services.The correlation results were found to be0.1023,0.4999, and0.2303for2G uplink,2Gdownlink, and broadcasting band respectively. These findings are in tune with thegenerally known fact that spectrum utilization is dependent on location.
The main attraction of using neural network based spectrum prediction lies in thefact that cognitive radios can learn and train itself from historical information obtainedby the cognitive radio without redesigning the whole system completely as is the casewith other methods. Unlike other models such as the Markov chain approach tospectrum prediction, the neural network model need only to be updated with the mostrecent data as its input. This approach saves power, sensing time and manpower.Machine learning has already been proposed as an integral part of future cognitiveradios, the high prediction accuracy realized in this model will greatly reduce the timerequired to sense whole bands. In addition, the processing power required at the basestation will be also reduced. Two factors generally influence modeling a network duringthe learning and training session. One is the initial interconnecting weights of thenetwork, and another is their modified quantities. Generally the initial interconnectingweights of BP ANN are often stochastically and blindly produced, this might cause thenetwork to run into partial optimization and therefore decrease the probability to obtainthe optimal solutions. Moreover, because the Delta rule is always adopted to modify theinterconnecting weights of BP ANN, the convergence velocity is always slow, orsometimes the network does not even converge. These shortages of BP ANN are quitenecessary to be optimized and improved. The problem of partial minimum of a BPANN can be solved by adjusting the initial interconnecting weights of the network. Thiscan be achieved through the application of Genetic algorithm because the problem is anon linear problem. GA is a nonlinear optimization method that has very strong abilityof global searching. To the best of our knowledge this problem has not been addressedin neural network based spectrum prediction. For the purpose of this work, five popularservices were considered. The GSM900uplink channels licensed to Etisalat had a meanprediction error of about0.035over five channels that were selected randomly. We observed a mean prediction error of about0.005in the GSM900downlink band,0.0179prediction error was recorded in the3G downlink band. Errors of0.0004and0.0007were observed in the broadcasting band and3G downlink band (licensed to StarcommsNigeria) respectively. The low prediction errors are the lowest recorded in the works wehave considered so far. Further analysis on energy utilization reveals bands with higherutilization level record higher sensing energy reduction as compared to bands withlower utilization that record lower energy reduction.
Cooperative spectrum sensing has already been shown to improve the reliability ofspectrum sensing by exploiting the spatial dimension through cooperation. This hasbeen shown to increase the detection probability because the probability that all userswill experience deep fading has been reduced. Successful deployment of cognitive radiowill depend on the development of highly reliable spectrum sensing techniques ofwhich cooperative spectrum sensing fits the bill. The concept of cooperative spectrumsensing has already been extended to spectrum occupancy measurements. Mostspectrum occupancy measurements were done using a single device, the data captured isthus from a single device and can be viewed as unreliable especially in harsh channelconditions. All in all, the problem of weight selection was resolved using geneticalgorithm, thereby improving the overall accuracy, secondly, the data used wasobtained from a cooperative spectrum measurement which involved two devices unlikethe traditional spectrum occupancy measurement that involves a single device thisimproves the reliability of the model.
正当业界寻求更先进的无线通信技术，如链路自适应技术、MIMO技术等来提高频谱效率的同时，却发现已授权频段，尤其是信号传播特性良好的低频段频谱利用率极低。为了提高频谱利用率，在不同地区、不同时段能有效利用空闲频段，一种新的频谱使用模式——认知无线电（Cognitive Radio, CR）技术逐渐受到学界和业界的高度关注。认知无线电的基本思想是：在不对拥有频谱的授权用户或非授权用户产生有害干扰的前提下，认知用户择机（opportunistic）接入授权用户频段或非授权频段，以提高频谱利用率。认知无线电技术的出现，为解决频谱资源不足、实现频谱动态管理并提高频谱利用率提供了强有力的技术支持。
认知无线电作为一种资源共享的革命性技术，可显著提高频谱资源利用率，并缓解频谱资源短缺现状。由于认知无线电网络是频谱资源受限网络，需要与主用户网络协商或感知主用户频段的“空隙”从而获取资源完成通信。认知无线电应用的前提是准确认知当前无线频谱的行为和模式，即通过对无线传输场景的测量、分析、建模和判决，获得频谱的可用性知识。
一直以来，对无线传输场景的实地测量是各国频谱监管机构了解和掌握频段使用率、避免通信设备之间相互干扰，进而制定和调整频谱使用政策的基础手段。随着认知无线电技术研究的深入和应用步伐的加快，进一步拓展了无线传输场景测量和分析的应用目的。无线环境下的频谱管理是一种在特定空域和特定时间测量、分析和建模特定频段频谱占用状态的重要措施，通过对无线通信环境的感知，为频谱监管部门调整频谱使用政策提供决策依据，为新的无线通信设备发掘临时可用的频谱资源，进而利用无线资源管理策略为新的无线通信终端提供服务质量（Quility0of Service, QoS）保证。当然，企业界和学术界对开展无线频谱占用测量和分析有着不同的动机，企业界的兴趣主要在于利用认知无线电技术最大化自身效益，而学术界更关注通过发掘低利用率频段，为新的无线通信系统的商用提供更多的频谱可用机会。目前，针对认知无线电相关技术的研究尚处于理论层面和原型样机阶段，利用实验室原型样机验证和测试相关研究成果的性能，频谱实地测量数据可以将原型样机的演示和验证推进到比较实际的应用场景。频谱测量的另一个重要应用在于可用于构建和修正频谱预测模型。
本文研究的第一个贡献在于对尼日利亚几个热点地区的频谱使用率进行了实地测量、统计和分析。全球已有大量国家和地区，包括美国、加拿大、英国、德国、中国、韩国、新加坡、日本以及欧洲等对本国特定地区的无线频谱占用情况进行了实测和统计，建立了符合本国本地区开展移动通信网络规划所需的电波传播或干扰评估模型，而非洲国家（除南非外）几乎从未开展过无线电环境下的频谱占用实测。但从国家安全、社会稳定、经济发展，特别是移动通信技术的推广应用角度出发，开展无线频谱占用和利用率实地测量都是非常必要的。尼日利亚联邦共和国地处西非东南部，非洲几内亚湾西岸顶点。2001年，尼日尼亚放宽了对电信行业的限制，导致了无线通信，特别是蜂窝移动通信网络服务的高速增长，全国现有MTN、Globacom、Airtel、Etisalat和Starcomms等5家移动通信运营商提供3G移动服务。2011年，与中国长城工业公司合作发射了首颗通信卫星——NigComSat-1R，旨在进一步改善通信基础设施。随着人们对移动通信服务需求的进一步提高，还有多家通信运营商计划开展移动通信商用业务。类似于尼日利亚等非洲国家，由于缺乏铜线或光纤等基础部件和材料，对无线通信网络的依赖更重，对无线频谱的需求更显突出。在未来几年。尼日利亚全国有1.5亿人口要求提供通信服务，频谱需求与资源匮乏之间的矛盾日显尖锐。2012年，以尼日利亚为首的非洲地区国家在世界无线电大会上会议强烈要求对该区域无线频谱进行统一调整和规划，并引入新的通信技术手段为民众提供高效的宽带接入服务。2013年，ITU已批准将700HMz频段用于该地区宽带无线接入服务。
为了完成无线频谱使用率的实地测量、统计和分析，笔者构建了一套测试分析平台，包括一台Aaronia AG HF-6060V4频谱分析仪，其频率测量范围为10MHz-6GHz；一付Aaronia AG OmniLOG90200型宽带天线，其适用频率范围为700MHz-2.5GHz；一台笔记本电脑，通过USB接口与频谱分析仪输出连接；一套MCS专用分析软件，运行于Aaronia AG频谱分析仪应用场合。MCS专用分析软件拥有一套完善、直观的图形界面，参数设置和分析显示均能通过界面完成。频谱测量场景选择在3个不同的地点进行，分别是Gwarinpa街区，首都阿布贾（Abuja）的一个主要住宅区；Wuse4区，首都阿布贾的一个住宅金融中心；Gafai区，尼日利亚南部Katsina古城的一部分。针对Gwarinpa街区的实地测量是在2012年暑假进行的。测量范围覆盖了700-2400MHz频带，累计测量超过12小时。为了获得真实准确地频谱占用和利用率信息，测量分白天和夜晚两个单元开展，白天测量从上午9点到晚上9点时段，而夜晚测量则从晚上10点至早上8点时段。Gwarinpa街区位于距Nnamdi Azikiwe国际机场19km附近，频谱测量还记录了航空频段的相关数据。第二个测量地点，Wuse4区位于阿布贾的商业中心区域，该区域包含了大量政府和商业设施以及部分住宅区，测量范围覆盖了700-1200MHz频带，累计测量超过12小时，测量工作也是在2012年夏天进行的。最后一个测量地点是Gafai区，位于人口密集的Katasina古城。此项测量工作是在2013年8月进行的，测量范围覆盖了700-1000MHz频段，测量时间为上午8点到夜晚8点，共12小时。
在三个测量区域所测量的频段中，800MHz频段提供无线通信服务和CDMA应急通信服务；900MHz频段提供GSM移动通信服务；470-960MHz频段提供模拟电视广播服务，包括VHF频段的12个信道以及UHF频段的49个信道共61个信道。测量结果表明，该频段的利用率约为26%，在所测频谱范围内是最高的，主要原因是模拟电视广播活动频繁，GSM网络等移动通信业务繁忙。
1000-1500MHz频段（1350-1550MHz）主要用于微波点对点通信服务，由政府部门和Niger三角区域和Lagos的石油公司使用。测量结果表明，1000-1300MHz和1300-1500MHz的使用率分别为2.13%和1.85%，是所测频率范围内使用率最低的。除了在1350-1450MHz附近的微波点对点通信外，基本上没有其他活动。
1.5GHz-2.4GHz频段主要提供3G移动通信服务。测量结果表明，其频谱利用率约为25.1%，也相对较高。如前所述，尼日利亚全国目前有5家移动通信运营商提供3G移动服务，通信网络采用UMTS规定的WCDMA技术体制，信号的扩频特性使得信号被调制到很宽频带中，其特性类似噪声，很难检测。由于测量是在室内环境实施的，天线接收信号能力可能被阻隔。
高于2.42GHz的ISM频段有着17%的高使用率，但仍可以为第二用户提供潜在的使用机会。通信卫星在2.305-2.32MHz和2.335-2.36MHz的上行和下行活动也能被检测到。
本文研究的第二个贡献是就不同无线通信系统24小时的占用情况进行了实测、统计和分析。3种最常用的频段分别是TV广播电视频段、2G蜂窝网络通信频段和3G蜂窝网络通信频段，其中，TV广播电视频段在白天的占用率高，且不可预测，但在夜晚特别是0点过后占用率显著减少，且不可预测。这一观测结果与大多数电视台在0点过后都会关闭的事实相吻合；2G和3G蜂窝网络通信频段有着随机性的占用率，在白天高度不可预测，而在夜晚中度可预测，分析原因，可能是由于人们在白天使用移动通信更为频繁所致。
将实地测量获得的700-2400MHz的原始功率数据转换为包含信道占用率的时间序列，该序列揭示了一个信道在给定时间区间究竟有多少时间是空闲的。信道空闲时间定义为信道占用序列数据中连续0的个数，分析信道空闲时间发现，信道空闲分布服从指数分布，尽管这些信道并非独立分布的。本文获得的该研究成果的贡献不在于信道空闲的经验分布，而是获得了这些频段上的空闲时隙概率分布。分析结果表明，尽管有丰富的使用机会，但实际上的时隙/频谱机会要比预想的少很多。本质上，频谱被严格分割开，且散布在时间序列中，极大地减少了动态频谱接入的频谱可用机会。
信道空闲时间提供了一个特定信道的平均信息，而服务阻塞率指标（SCR）则提供了频谱在特定瞬间的短时信息，比如某第二用户需要在3G蜂窝网络1800MHz上行和2G蜂窝网络900MHz上行之间选择频谱接入机会，假设两张蜂窝网络的总体占用率相似，且信道空闲时间相同，此时就需要一个指标来提供瞬时信息，以便第二用户可以做出正确决策。为了达到这个目的，必须依据2G和3G蜂窝通信网络通信频段的上行和下行数据来评估服务阻塞率。
对频率信道间的相关性检测和预测是本文研究的第三个贡献。频率信道的相关性能反映不同频率信道能提供的服务质量等重要信息，由于信道使用率特征不可预测，对其进行相关性分析必须在更长时间段进行。本文在频谱相关性分析中，随机选取并测量获得了5个信道1小时的原始数据，分析结果表明，所有信道的相关系数平均为0.5，这意味着各频谱使用率模式之间有一定程度的独立性。值得注意的是，所选取的部分信道在测试时间内一直处于空闲状态。通过对比分析Katsina镇和阿布贾城的临时频段占用相关性，进一步综合考虑2G蜂窝网络上行频段、下行频段和TV广播电视频段的技术特征，结果表明，不同服务之间没有或很少有相关性，2G蜂窝网络上行频段、下行频段和TV广播电视频段的相关系数分别0.1023、0.4999和0.2303，这些发现与频谱使用率只在地点上相关这一事实相符。
基于人工神经网络进行频谱预测的主要优势在于，认知无线电可以学习历史信息，并改变自身工作参数而无需重新设计系统。不同于在无线通信系统设计中广泛运用的马尔科夫链预测模型，人工神经网络模型只需要将最近的数据作为输入，数据获取时间短、数据量小、运算复杂度低。机器学习算法被公认为构建认知无线电网络中认知引擎的核心算法之一，人工神经网络模型的高预测精度能减少感知整个频段的时间，相应的运算处理能耗也会大幅下降。在人工神经网络建模的学习和训练过程中，有两个重要因素必须重点关注：一个是相互连接的初始权重，另一个是训练更新的权重改变方式。一般而言，BP ANN的相互连接初始权重是随机、盲目产生的，可能导致网络进入局部最优，而降低了取得最优解的概率，如果采用Delta准则来修改BP ANN的相互连接权重，其收敛速度慢，甚至有时无法收敛。本文深入研究了BP ANN的上述问题，针对局部最小化问题，通过引入遗传算法来调整相互连接初始权重。遗传算法是一个非线性优化方法，有很强的全局搜索能力。在研究中，本文针对5家无线通信和广播网络进行。预测结果表明，Etisalat公司拥有的GSM900网络上行信道平均预测错误率仅为0.035；其他GSM900网络下行信道的平均预测错误率为0.005；TV广播电视频段和3G蜂窝移动通信网络下行信道的平均预测错误率分别为0.0004和0.0007。
Marconi’s demonstration at the turn of the20th century revolutionized the way wecommunicate forever while also ushering the birth of radio communication. The growthof radio communication highlighted the problem of interference amongst transmittersoperating in the same geographical area. Earlier efforts aimed at resolving this issuehighlighted the importance of spectrum as a scarce and renewable natural resource.Regulatory bodies were then set up to manage the planning, allocation and overallmanagement of this scarce resource at the national level. Currently, the InternationalTelecommunication Union (ITU) along with the representatives of each country’sregulatory body converge at the World Radio Conference (WRC) periodically to mapout policies for international and regional usage as well establishing world standards.
Spectrum management since the early days of radio communication has alwaysbeen about minimizing interference. Under this regime, spectrum bands are allocated toa particular service over a large geographical area in some cases a country, withprovision for band guards to alleviate the problem of interference. This approach hasbeen very efficient as far as maintaining non-interference communication is concerned.However, with the proliferation of several wireless standards over the years, the demandfor spectrum has grown tremendously so is its economic value. It has therefore becomenecessary to review the current command and control approach to spectrummanagement. One of the solutions being proposed is a regime whereby licensed andunlicensed users could share the spectrum in a non-interference fashion; this is knownas Cognitive radio. It was first envisioned by Mitola in1999. To realize this concept,the secondary user also known as the unlicensed user must be able to sense the channelprior to initiating transmission and also vacate the channel in the event the primary useror licensed user returns to transmit. This process termed as spectrum sensing is thecornerstone of successful deployment of cognitive radio.
The successful deployment of the cognitive radio paradigm hinges on conciseknowledge of the spectrum; its behavior, usage patterns, and the availability ofspectrum holes. It is generally hoped in the research community that cognitive radiowill dramatically reduce spectrum usage inefficiency thereby increasing spectrumutilization. Knowledge of the spectrum has therefore become the first step towardsachieving this goal. Field measurements of the radio environment have been performed over the years to determine frequency channel usage information for national regulatoryauthorities which they have been using for formulating spectrum usage policies wherethe sole aim is interference minimization. These measurements provide a thoroughknowledge of network activities that can provide a platform for understanding the usagepattern which is invaluable in cognitive radio deployment. Prior to the cognitive radioera, the main aim of spectrum occupancy measurements have been to provideinformation for policy makers and regulatory bodies to formulate policies as well asminimize interference respectively. However, with the advent of cognitive radioresearch, the aim of spectrum occupancy measurements have broadened to includequantitative information on underutilized spectrum bands for possible cognitive radiodeployment and also the establishment of the degree of efficiency of spectrum usage.The economic and research communities might have different motives for conducting aspectrum occupancy measurement, with the economic community mostly interested inthe investments required to develop cognitive radio so that the investments are notmisapplied if they do not take into account the current realities. On the hand, theresearch community is mostly interested in determining the bands that experience lowutilization so that they can be analyzed and characterized in time, frequency and space.This information will be vital for the deployment of future cognitive radio systems.Current works on cognitive radio have all been theoretical with no practicaldemonstration as to the real applicability of these schemes. Spectrum measurement datacan be used to actually determine the feasibility of these schemes. Another importantapplication of these measurements lies in the development of spectrum predictionmodels for deployment in cognitive radio engines with the aim of saving both energyand sensing time. It can therefore be said that successful deployment of cognitive radiorequires a thorough understanding of the spectrum, spectrum occupancy statistics fromthe measurements and reliable models capable of predicting future usage with little orno errors.
Spectrum prediction models have been researched over the years, where modelswere developed for the High Frequency HF bands. Due to the opportunistic nature ofspectrum, successful deployment of these models will depends on the performance ofthe secondary networks which will heavily rely on the spectrum usage patterns of theprimary user. It has therefore become necessary to investigate the nature and pattern ofspectrum usage so that successful models could be furnished. Recently, models aimed atpredicting the spectrum opportunities have been presented with varying degree of accuracy. Part of the objective of this work presented herein is to contribute to thiseffort by improving the prediction accuracy of these models.
The first contribution of this work is to provide an insight into the spectrum usagein Nigeria with the aim of providing a platform for future measurements and thepossible deployment of the cognitive radio paradigm. Several spectrum occupancymeasurements have been performed all over the world; USA, United Kingdom, China,Singapore, Japan, are some of the countries were these measurements have been made.Few of these measurements can be found in Africa with the exception of South Africain the research community. It has therefore become necessary to perform thesemeasurements for the socio-economic benefit of the populace. The deregulation of thetelecommunication sector in Nigeria in2001has lead to a remarkable growth in thewireless communication services sector especially cellular communication. With thelaunch of the country’s first satellite the Nigersat-1which is basically a remote sensingsatellite the government signaled its intention to develop this vital area of the economy.NigComSat-1R a communication satellite was re-launched in2011after earlier attemptsin2007failed. The satellite was launched in partnership with China Great Wall IndustryCorporation with the aim of further improving the communication infrastructure.Spectrum scarcity has been a dilemma recently; African countries suffer more in thisregard because of their heavy reliance on wireless communication services due to non-availability of other vital infrastructure such as copper and fiber optic networks.Therefore it could be said that the transmission channel is not readily available thus theovercrowded nature of the spectrum allocated for broadband communication. InNigeria’s case, the population of over150million and over100million active lines hasaffected the network quality of these services, to increase quality, capacity must beincreased, to increase capacity there must be more spectra. Even though other servicescould be considered, the affordability across users must be taken into consideration asmost users won’t be able to afford expensive services. These factors and others lead tothe African region headed by Nigeria to demand for further spectrum allocation duringthe World Radio Conference2012. They argued that the allocation will improve thequality of broadband services and also increase mobile internet penetration amongst thepopulace. Currently, the ITU has granted the700MHz for efficient delivery ofbroadband services for this region. It has therefore become extremely important tostudy the spectrum utilization, behavior, and analyze the data obtained from these measurements in order to formulate and plan for the future due to the uniqueness ofNigeria’s case.
The measurement setup involved an Aaronia AG HF-6060V4spectrum analyzerwith a range of10MHz-6GHz, an Aaronia AG OmniLOG90200antenna with a rangeof700MHz to2.5GHz, a laptop system that is connected to the spectrum analyzer via aUSB cable, and an MCS software specially designed to run on Aaronia AG spectrumanalyzers. The MCS software has a graphical interface whereby all the parametersrequired for accurate measurement are set. The software is also used for controlling themeasurements based on the parameters set. The measurement was conducted indoors inprimarily three different locations; at Gwarinpa District a primarily residential district inAbuja the capital of Nigeria, Wuse Zone4a residential/commercial activity centre alsoin Abuja, and Gafai quarters which is part of the ancient city of Katsina, a town in thenorthern part of Nigeria.. The measurement conducted in Gwarinpa Estate Abuja wasconducted during the summer holidays of2012i.e. July-August2012covering the700-2400MHz band over12hour periods. The measurements were further divided into twocategories i.e. daytime and nighttime. Daytime measurements cover the duration from9am-9pm while nighttime covers the duration from10pm-8am. Gwarinpa estate isapproximately19km from Nnamdi Azikiwe International Airport; we could therefore beable record some of the activity in the aeronautical bands due to the relatively closeproximity of the location to the airport.The second location was situated in the centralbusiness district of Abuja. It contains most of the government and businessestablishments with some residential areas sparsely situated. The dataset from thislocation consists of data from700-1200MHz band also obtained during the summer of2012(July-August2012). The third and last dataset was obtained from Gafai quarters inthe ancient city of Katsina a densely populated area of the ancient town. Themeasurements were conducted in the summer of2013(August) over the700-1000MHzband over a period of12hours from8am-8pm. The first band considered was the700-1000MHz. It comprises the800MHz band used for trunk radio services, emergencyservices, CDMA (fixed),900MHz for GSM and also the470-960MHz for analoguetelevision broadcasting. In the VHF band there are12channels where as the UHF bandconsists of49channels making a total of61channels. This band has the highestutilization level experienced at26%due to the activities of the analogue broadcasting(part of it to be precise) GSM operations and the radio trunk services. The1000-1500MHz band is mostly used for microwave point to point communication(1350-1550MHz), government agencies and oil companies in the Niger delta region andLagos. The1000-1300MHz and1300-1500MHz with a utilization level of2.13%and1.85%respectively are among the bands with the lowest utilization level. Apart frommicrowave point to point transmission observed around1350-1450MHz; there isvirtually no activity at all.
Above1.5GHz, majority of the utilization can be observed in the3G mobilestandards. With a utilization level of around25.1%it has one of the highest utilizationlevel amongst the bands considered for this work. In Nigeria, there are currently fivemobile companies delivering3G mobile services in Nigeria: MTN, Globacom, Airtel,Etisalat and Starcomms. Networks employing UMTS use WCDMA technology, thespread spectrum nature of the signals where by the signals are modulated over a widebandwidth thus making them having a noise-like character due to the very lowtransmission power makes them difficult to detect. This makes it difficult for thespectrum analyzer to determine such signals. Similarly, since the measurements’ wereconducted indoors, the ability of the antenna to receive signals might be hindered.Above2.42GHz, with17%utilization, the ISM band shows considerable utilization butit could also provide some opportunity for secondary usage. As the measurement wasdone indoors it was able to detect much of the signals due to the short nature of signalsin this band. Some activity on the satellite uplink and downlink bands were alsodetected at a frequency of2.305-2.32MHz and2.335-2.36MHz.
The utilization pattern over24hours was also investigated. For this analysis, bandscontaining the three most utilized bands i.e. TV broadcasting,2G cellular band and the3G cellular bands were investigated. The TV broadcasting band was found to betransmitting during the day time with the occupancy being highly unpredictable,however during the night especially after00:00there seems to be less transmissionswith the occupancy being predictable. This observation is in tune with the normaloperating manner of such bands as most of the TV stations switch off their transmissionafter00:00. The2G and3G cellular bands indicate a random occupancy from beinghighly unpredictable during the day time to being moderately predictable at night. Thispattern is also understandable with people utilizing the bands during the day and lesspeople using the services at night.
The conversion of the raw data which is a collection of power levels across700-2400MHz considered as a time series containing yields the channel occupancy. It indicates how long a channel is actually free at a given time before the primary userresumes transmission. Channel vacancy duration can be defined as the number ofconsecutive0’s in the channel occupancy series; this is the stage after thresholdingwhich is the conversion of power level to0’s and1’s. Channel vacancy durationanalysis was performed, the channel vacancy distribution was found to follow anexponential-like distribution even though the channels are not independently distributed.The coefficient of determination R2is well approximated by an exponential-likedistribution with values around0.93in all the locations considered. The significance ofthis analysis lies not in the empirical distribution of the channel vacancy distribution butin the distribution of vacancy timeslots amongst the bands analyzed. It was found outthat despite abundant opportunities the amount of timeslots/spectrum opportunitiesactually presented are much lower than initially expected. In essence, the spectrum isheavily fragmented and scattered across time thus greatly reducing the amount ofspectrum available for dynamic spectrum access.
While channel vacancy duration provides long term information about the averageinformation for a particular channel, the Service Congestion Rate SCR metric providesthe short term or instantaneous picture of the spectrum at a particular instant. A situationmight arise whereby a secondary user needs to select between two services say3G1800Uplink and2G900Uplink, assuming their overall occupancy for the day is similar andalso their CVD is identical, a metric is needed to provide instantaneous information sothat the secondary user might be able to make an informed decision. For the purpose ofthis analysis, data from cellular band (both2G and3G) were used to determine the SCRvalues of these services both in the uplink and downlink paths. Five channels wereselected randomly from each service for the purpose of this analysis.
The need to examine the correlation among frequency channels is of paramountimportance as it will provide valuable information on the similarities/differences inusage/behavior amongst different services analyzed. The correlation should be over alonger period of time due to the unpredictable nature of usage as this will provide acalculated insight into the actual correlation of the channels in question. For spectralcorrelation analysis, an hour long data from five channels were randomly selected andprocessed. From the results obtained from the spectral correlation analysis, it can beseen that there is average correlation coefficients of around approximately0.5in allcases considered. This implies a degree of independence in the spectrum utilizationpatterns across the services. It should be noted that some of channels used especially in the cellular bands are idle during the whole time because they serve as band gaps whichare used to avoid interference amongst services adjacent to each other. However, forthe temporal correlation analysis spectrum occupancy results from Katsina town andAbuja city are compared and correlation analysis performed amongst the servicescommon to both cities. The2G uplink,2G downlink and the broadcasting bands wereconsidered, results indicates that there is little or no correlation amongst the services.The correlation results were found to be0.1023,0.4999, and0.2303for2G uplink,2Gdownlink, and broadcasting band respectively. These findings are in tune with thegenerally known fact that spectrum utilization is dependent on location.
The main attraction of using neural network based spectrum prediction lies in thefact that cognitive radios can learn and train itself from historical information obtainedby the cognitive radio without redesigning the whole system completely as is the casewith other methods. Unlike other models such as the Markov chain approach tospectrum prediction, the neural network model need only to be updated with the mostrecent data as its input. This approach saves power, sensing time and manpower.Machine learning has already been proposed as an integral part of future cognitiveradios, the high prediction accuracy realized in this model will greatly reduce the timerequired to sense whole bands. In addition, the processing power required at the basestation will be also reduced. Two factors generally influence modeling a network duringthe learning and training session. One is the initial interconnecting weights of thenetwork, and another is their modified quantities. Generally the initial interconnectingweights of BP ANN are often stochastically and blindly produced, this might cause thenetwork to run into partial optimization and therefore decrease the probability to obtainthe optimal solutions. Moreover, because the Delta rule is always adopted to modify theinterconnecting weights of BP ANN, the convergence velocity is always slow, orsometimes the network does not even converge. These shortages of BP ANN are quitenecessary to be optimized and improved. The problem of partial minimum of a BPANN can be solved by adjusting the initial interconnecting weights of the network. Thiscan be achieved through the application of Genetic algorithm because the problem is anon linear problem. GA is a nonlinear optimization method that has very strong abilityof global searching. To the best of our knowledge this problem has not been addressedin neural network based spectrum prediction. For the purpose of this work, five popularservices were considered. The GSM900uplink channels licensed to Etisalat had a meanprediction error of about0.035over five channels that were selected randomly. We observed a mean prediction error of about0.005in the GSM900downlink band,0.0179prediction error was recorded in the3G downlink band. Errors of0.0004and0.0007were observed in the broadcasting band and3G downlink band (licensed to StarcommsNigeria) respectively. The low prediction errors are the lowest recorded in the works wehave considered so far. Further analysis on energy utilization reveals bands with higherutilization level record higher sensing energy reduction as compared to bands withlower utilization that record lower energy reduction.
Cooperative spectrum sensing has already been shown to improve the reliability ofspectrum sensing by exploiting the spatial dimension through cooperation. This hasbeen shown to increase the detection probability because the probability that all userswill experience deep fading has been reduced. Successful deployment of cognitive radiowill depend on the development of highly reliable spectrum sensing techniques ofwhich cooperative spectrum sensing fits the bill. The concept of cooperative spectrumsensing has already been extended to spectrum occupancy measurements. Mostspectrum occupancy measurements were done using a single device, the data captured isthus from a single device and can be viewed as unreliable especially in harsh channelconditions. All in all, the problem of weight selection was resolved using geneticalgorithm, thereby improving the overall accuracy, secondly, the data used wasobtained from a cooperative spectrum measurement which involved two devices unlikethe traditional spectrum occupancy measurement that involves a single device thisimproves the reliability of the model.
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