This paper presents a systematic approach to exploiting TV white space (TVWS) for device-to-device (D2D) communications with the aid of the existing cellular infrastructure. The goal is to build a location-specific TVWS database, which provides a lookup table service for any D2D link to determine its maximum permitted emission power (MPEP) in an unlicensed digital TV (DTV) band. To achieve this goal, the idea of mobile crowd sensing is first introduced to collect active spectrum measurements from massive personal mobile devices. Considering the incompleteness of crowd measurements, we formulate the problem of unknown measurements recovery as a matrix completion problem and apply a powerful fixed point continuation algorithm to reconstruct the unknown elements from the known elements. By joint exploitation of the big spectrum data in its vicinity, each cellular base station further implements a nonlinear support vector machine algorithm to perform irregular coverage boundary detection of a licensed DTV transmitter. With the knowledge of the detected coverage boundary, an opportunistic spatial reuse algorithm is developed for each D2D link to determine its MPEP. Simulation results show that the proposed approach can successfully enable D2D communications in TVWS while satisfying the interference constraint from the licensed DTV services. In addition, to our best knowledge, this is the first try to explore and exploit TVWS inside the DTV protection region resulted from the shadowing effect. Potential application scenarios include communications between internet of vehicles in the underground parking and D2D communications in hotspots such as subway, game stadiums, and airports.
The idea of opportunistic access of TVWS for cellular networks has also received increasing interests. A related work in @cite_50 proposed a spectrum sensing-based mechanism to explore TVWS for cellular users, where a simplified circular DTV coverage model was assumed and collaborative sensing was done among neighboring cellular BSs with fixed topology. In contrast, our proposed approach introduces mobile crowd sensing to collect spectrum measurements from massive personal devices and exploits TVWS in a much finer granularity by considering the practical irregular DTV coverage.
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Collaborative Sensing is an important enabling tech- nique for realizing opportunistic spectrum access in white space (cognitive radio) networks. We consider the security ramifications of crowdsourcing of spectrum sensing in presence of malicious users that report false measurements. We propose viewing the area of interest as a grid of square cells and using it to identify and disregard false measurements. The proposed mechanism is based on identifying outlier measurements inside each cell, as well as corroboration among neighboring cells in a hierarchical structure to identify cells with significant number of malicious nodes. We provide a framework for taking into considera- tion inherent uncertainties, such as loss due to distance and shadowing, to reduce the likelihood of inaccurate classification of legitimate measurements as outliers. We use simulations to evaluate the effectiveness of the proposed approach against attackers with varying degrees of sophistication. The results show that depending on the attacker-type and location parameters, in the worst case we can nullify the effect of up to 41 of attacker nodes in a particular region. This figure is as high as 100 for a large subset of scenarios. 1 We analyze the performance of a wireless system consisting of a set of secondary users opportunistically sharing bandwidth with a set of primary users over a coverage area. The secondary users employ spectrum sensing to detect channels that are unused by the primary users and hence make use of the idle channels. If an active secondary user detects the presence of a primary user on a given channel, it releases the channel and switches to another idle channel, if one is available. In the event that no channel is available, the call waits in a buffer until either a channel becomes available or a maximum waiting time is reached. Spectrum sensing errors on the part of a secondary user cause false alarm and mis-detection events, which can potentially degrade the quality-of-service experienced by primary users. We derive system performance metrics of interest such as blocking probabilities. Our results suggest that opportunistic spectrum sharing can significantly improve spectrum efficiency and system capacity, even under unreliable spectrum detection. The proposed model and analysis method can be used to evaluate the performance of future opportunistic spectrum sharing systems. Motivated by the Federal Communications Commission's recent approval of commercial unlicensed operations of some television (TV) spectrum, we propose to integrate cognitive radios (CRs) that operate on unoccupied TV bands with an existing cellular network to increase bandwidth for mobile users. The existing cellular infrastructure is used to enable the operation of such CRs. Because base stations (BSs) can sense spectrum and exchange the sensed information for the reliable detection of primary users (PUs) and white spaces, we propose a collaborative sensing mechanism based on cell topology, where the BS declares its cell to be PU-free when neither the BS nor its neighboring BSs detect any PU. This way, in a PU-free cell, the following two types of channels are available: 1) channels that are originally licensed for the cellular system and 2) CR channels that are discovered through spectrum sensing. Because the CR channels that operate on TV bands usually suffer less path loss than the cellular channels, we derive two important results. First, each user gains more capacity when accessing a cellular channel than an empty TV channel, as long as intercell interferences are caused by the same sources. Second, assigning TV bands to cell-edge users is better in maximizing cell capacity. These two effects and the performance of the proposed sensing mechanism are verified through numerical evaluation. For achieving optimized spectrum usage, most existing opportunistic spectrum sensing and access protocols model the spectrum sensing and access problem as a partially observed Markov decision process by assuming that the information states and or the primary users' (PUs) traffic statistics are known a priori to the secondary users (SUs). While theoretically sound, the existing solutions may not be effective in practice due to two main concerns. First, the assumptions are not practical, as before the communication starts, PUs' traffic statistics may not be readily available to the SUs. Second and more serious, existing approaches are extremely vulnerable to malicious jamming attacks. By leveraging the same statistic information and stochastic dynamic decision-making process that the SUs would follow, a cognitive attacker with sensing capability can sense and jam the channels to be accessed by SUs, while not interfering PUs. To address these concerns, we formulate the antijamming, multichannel access problem as a nonstochastic multi-armed bandit problem. By leveraging probabilistically shared information between the sender and the receiver, our proposed protocol enables them to hop to the same set of channels with high probability while gaining resilience to jamming attacks without affecting PUs' activities. We analytically show the convergence of the learning algorithms and derive the performance bound based on regret . We further discuss the problem of tracking the best adaptive strategy and characterize the performance bound based on a new regret . Extensive simulation results show that the probabilistic spectrum sensing and access protocol can overcome the limitation of existing solutions and is highly resilient to various jamming attacks even with jammed acknowledgment (ACK) information.