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Cognitive radio (CR) is considered a promising technology for addressing the issue of spectrum scarcity and has been receiving increasing attention in recent years. One of its "cognitive" capabilities is environment awareness, which gathers useful information from the radio environment, such that CR devices can accordingly change the operating (e. g., spectrum sensing) strategies. Spectrum measurement is an effective means for radio environment awareness since ground-truth observation of spectrum usage is more convincing than any theoretical assumption. In this paper, we perform in-depth statistical analysis on wireless signal strength in mobile service (MS) bands and propose a novel statistical model based on characteristic function (CF), which characterizes both wireless fading channels and MS dynamics. We validate the proposed model through empirical studies on the spectrum measurement data previously collected in Guangdong Province of China. We statistically fit the CF of the overall signal strength in MS (GSM uplink) band and estimate both Nakagami fading parameters of wireless channels and the arrival rate of MS. Then, we further utilize the estimated information to optimize the spectrum sensing strategy, which aims at maximizing the secondary user's (SU's) expected achievable throughput. The simulation results show that the optimal sensing strategy that we propose significantly improves the long-term average achievable throughput since it fully utilizes the estimated information on wireless fading channels and MS dynamics.

In this paper, we present digital dividend (DD) spectrum capacity gain brought about by switching from analogue terrestrial television (ATT) to digital terrestrial television (DTT) which is now ongoing in China. DD spectrum represents total of TV White Space (TVWS) and the released spectrum in our paper. Since spectrum measurement is the most effective method to evaluate the capacity, we use measured TV UHF band data from 470 MHz to 798 MHz from diverse locations to acquire a quantitative DD capacity. Our results confirm the existence of at most 168 MHz DD capacity in urban areas which will provide good opportunities for Dynamic Spectrum Access (DSA) applications. We observe that during the transition period introducing DTT to rural areas tends to provide less spectrum compared with purely ATT broadcasting. Hence, rural areas have less DD capacity compared with urban areas, which totally contradicts previous measurement findings. Our data validates the practicality of fixed power level threshold rules for idle TV channel detection. We find field strength is another threshold choice for determining TV channel states. Our spectrum measurement methodology and findings are applicable to spectrum planning and spectrum regulations, especially for developing countries that are in DTT transition periods.

Nowadays, cyber-physical system (CPS) relies on wireless networks for devices control and information backhaul. But the mass deployment CPS devices make operators' spectrum scarce situations even more worse. Hence, cellular network operators anticipate the Dynamic Spectrum Access (DSA) technology to solve the spectrum shortage problem in the context of cognitive radio (CR). Femtocells, acting as gateways in CPS, integrate CPS devices into cellular networks in a seamless manner. The concept of cognitive femtocell can solve the spectrum congestion problem even within a massive network on the CPS scale. However, in practical systems, cellular white space spectrum should be quantitatively measured to guide cognitive femtocell access algorithms design. We are the first to conduct a comprehensive measurement study for the purpose of measurement, discovery and model features of GSM white space spectrum. We evaluate availabilities of extra 21.4 MHz capacity in GSM white space spectrum as a reason of artificial GSM network spectrum planning. In our study, we find out that perfect results can hardly be obtained because of inherent measurement trade-offs, even when extremely high sweep speed of 16 GHz/s is applied at the receiver. Based on statistical analysis of real-scene traces, we propose an Efficient Duty Cycle (EDC) model to accurately characterize the white space in GSM network by considering missdetection probabilities. Cross-validating evaluation results show that the EDC model can well decrease interference probabilities at high time-granularity measurement periodicity scenarios. Our results confirm the feasibility of cognitive femtocells access in an intra-operator scenario and can be applied to future wireless networks.

Dynamic spectrum access has been a subject of extensive study in recent years. The increasing volume of literatures calls for a deeper understanding of the characteristics of current spectrum utilization. In this paper, we present a detailed spectrum measurement study, with data collected in the 20 MHz to 3 GHz spectrum band and at four locations concurrently in Guangdong province of China. We examine the statistics of the collected data, including channel vacancy statistics, channel utilization within each individual wireless service, and the spectral and spatial correlation of these measures. Main findings include that the channel vacancy durations follow an exponential-like distribution, but are not independently distributed over time, and that significant spectral and spatial correlations are found between channels of the same service. We then exploit such spectrum correlation to develop a 2D frequent pattern mining algorithm that can predict channel availability based on past observations with considerable accuracy.

Nowadays, cyber-physical system (CPS) relies on wireless networks for devices control and information backhaul. But the mass deployment CPS devices make operators' spectrum scarce situations even more worse. Hence, cellular network operators anticipate the Dynamic Spectrum Access (DSA) technology to solve the spectrum shortage problem in the context of cognitive radio (CR). Femtocells, acting as gateways in CPS, integrate CPS devices into cellular networks in a seamless manner. The concept of cognitive femtocell can solve the spectrum congestion problem even within a massive network on the CPS scale. However, in practical systems, cellular white space spectrum should be quantitatively measured to guide cognitive femtocell access algorithms design. We are the first to conduct a comprehensive measurement study for the purpose of measurement, discovery and model features of GSM white space spectrum. We evaluate availabilities of extra 21.4 MHz capacity in GSM white space spectrum as a reason of artificial GSM network spectrum planning. In our study, we find out that perfect results can hardly be obtained because of inherent measurement trade-offs, even when extremely high sweep speed of 16 GHz/s is applied at the receiver. Based on statistical analysis of real-scene traces, we propose an Efficient Duty Cycle (EDC) model to accurately characterize the white space in GSM network by considering missdetection probabilities. Cross-validating evaluation results show that the EDC model can well decrease interference probabilities at high time-granularity measurement periodicity scenarios. Our results confirm the feasibility of cognitive femtocells access in an intra-operator scenario and can be applied to future wireless networks.

In this paper, we present digital dividend (DD) spectrum capacity gain brought about by switching from analogue terrestrial television (ATT) to digital terrestrial television (DTT) which is now ongoing in China. DD spectrum represents total of TV White Space (TVWS) and the released spectrum in our paper. Since spectrum measurement is the most effective method to evaluate the capacity, we use measured TV UHF band data from 470 MHz to 798 MHz from diverse locations to acquire a quantitative DD capacity. Our results confirm the existence of at most 168 MHz DD capacity in urban areas which will provide good opportunities for Dynamic Spectrum Access (DSA) applications. We observe that during the transition period introducing DTT to rural areas tends to provide less spectrum compared with purely ATT broadcasting. Hence, rural areas have less DD capacity compared with urban areas, which totally contradicts previous measurement findings. Our data validates the practicality of fixed power level threshold rules for idle TV channel detection. We find field strength is another threshold choice for determining TV channel states. Our spectrum measurement methodology and findings are applicable to spectrum planning and spectrum regulations, especially for developing countries that are in DTT transition periods.

Cognitive radio (CR) for dynamic spectrum sensing and access has been a hot research topic in recent years. To avoid collision with the primary users, secondary users need to sense the channels before transmitting on them, which is referred to as sensing time overhead. Our previous work shows that the spectral correlations between the channels within the same service are sufficiently high for accurate prediction, which can further be used to reduce the sensing time. With such motivation, in this paper, we propose a new definition, i.e., channel availability vector (CAV), to characterize the state information of a group of licensed channels by introducing spectrum prediction while focusing on the scenario of a single secondary user with multiple channels and leverage it by formulating the throughput optimization problem as a Markov decision process, which is further solved by our optimal sensing scheme and verified with the real spectrum measurement data. The results show that our prediction-based sensing scheme outperforms one existing work.

Dynamic spectrum access has been a subject of extensive study in recent years. The increasing volume of literature calls for a deeper understanding of the characteristics of current spectrum utilization. In this paper we present a detailed spectrum measurement study, with data collected in the 20MHz to 3GHz spectrum band and at four locations concurrently in Guangdong province of China. We examine the first and second order statistics of the collected data, including channel occupancy/vacancy statistics, channel utilization within each individual wireless service, and the temporal, spectral, and spatial correlation of these measures. Main findings include that the channel vacancy durations follow an exponential-like distribution, but are not independently distributed over time, and that significant spectral and spatial correlations are found between channels of the same service. We then exploit such spectrum correlation to develop a 2-dimensional frequent pattern mining algorithm that can accurately predict channel availability based on past observations. Copyright 2009 ACM.