This paper investigates the optimal channel exploration in multiple spectrum band covert communication. Different from most existing covert communication works that only consider the static wardens, we also take the reactive wardens that release real-time suppression tracking jamming based on their receiving power into consideration. In channel exploration period, the covert transmitter Alice aims to find a channel with high channel gain while guaranteeing its transmission covertness. However, obtaining channel gains on different bands before transmission takes Alice time and Alice has to choose the right time to stop exploring for throughput maximization. Firstly, to address the threats of the inactive and reactive wardens, the strategies of channel inversion power control and feedback based anti-jamming are respectively adopted. Then, we formulate the channel exploration problem with optimal stopping theory after analyzing Alice’s transmission covertness performance. Furthermore, since the conventional approach to this problem requires intensively computation, the one stage look ahead (1-SLA) rule is adopted to reduce the computation complexity. In particular, we mathematically prove that this rule is optimal in maximizing Alice’s expected throughput. At last, the simulation results are provided to validate the analytical results and the superiority of the proposed scheme compared with the benchmarks.

Neighbor discovery is the procedure to establish a first contact between two wireless devices. For duty-cycled low-power devices, energy consumption is closely related to neighbor discovery latency. Actually, in recent protocols, such as Bluetooth low energy (BLE) or ANT+, neighbor discovery latency is determined by the parameters used by the devices, such as advertising interval, scan window, scan interval, and so on. A fundamental problem of the BLE-like protocol is that the exact relation between parameters and discovery latency has not been fully analyzed. In this article, we propose a Euclidean-division-based low-complexity precise analytical approach that can derive the mathematical expressions of both worst-case latency and average latency for any parameter groups. It is confirmed by simulation results that our solution can make highly accurate predictions about the value of latencies. Simulation results also show that the proposed solution has an extremely low complexity. Moreover, we derive the lower bound of latency for given duty cycles, which provides useful guidelines for the choice of energy-efficient parameter groups for BLE.

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.

This paper investigates the issue of cooperative spectrum sensing with a crowd of low-end personal spectrum sensors (such as smartphones, tablets, and in-vehicle sensors), where one critical challenge is the uncertainty of the quality of sensing data from crowd sensors that may be unreliable, untrustworthy, or even malicious. Moreover, due to either unexpected equipment failures or malicious behaviors, every crowd sensor could sporadically and randomly contribute abnormal data, which makes the existing defense schemes ineffective. To tackle these unique challenges, we propose a robust spectrum sensing scheme by developing a data cleansing framework, where the underutilization of licensed spectrum bands and the sparsity of nonzero abnormal data are jointly exploited to robustly cleanse out the potential nonzero abnormal data component from the original corrupted sensing data. Simulation results demonstrate that the proposed robust sensing scheme outperforms the state-of-art schemes under various abnormal data parameter configurations.

Starting from Neyman-Pearson criterion, this paper derives an optimal spectrum sensing scheme which exploits spatial diversity among multiple cognitive sensors and temporal diversity among consecutive time slots jointly. In the proposed scheme, the impact of the imperfect reporting channel on the design of the spectrum sensing scheme is effectively integrated. Simulation results show that compared with singular (either spatial or temporal) diversity-based sensing schemes, the proposed scheme brings not only improvement of sensing performance, but also significant reduction of sensing overhead.

In this paper, decentralized cooperative spectrum sensing in cognitive radio networks is studied based on the recent advances in unsupervised learning. To balance a tradeoff between the sensing reliability and the cooperation overhead (e.g., energy, delay, and signaling, etc.), a distributed clustering algorithm, without any central coordinator, is introduced for inducing the sensors with the best detection performance to join together and take charge of cooperative spectrum sensing. Numerical results show that the proposed scheme can obtain detection performance comparable to that of optimal soft combination scheme with reduced cooperation overhead. Moreover, the proposed scheme does not require any priori knowledge of spectrum sensors' received signal-to-noise-ratios (SNRs) or locations.