Integrated Sensing and Communication (ISAC) improves resource utilization efficiency by sharing resources between communication and radar sensing. With the development of ISAC technology, there is a growing focus on how to protect information at the physical layer to ensure communication security. In this context, the physical-layer security (PLS) of the near-field ISAC system is investigated, operating in nonorthogonal multiple access (NOMA) scenario. In our proposed system, the base station (BS) transmits confidential messages to multiple communication users (CUs) while wirelessly sensing a target. Consider a joint transmission beamforming design to support secure communication and ensure sensing requirements. Specifically, we derive the Cramér-Rao bound (CRB) for joint distance and angle sensing in the near-field, and maximise the secrecy rate for CUs under the CRB constraint imposed by target sensing. Since the beamforming problem is nonconvex and challenging to be solved, semi-definite relaxation (SDR) and successive convex approximation (SCA) algorithms are investigated to transform the objective problem into a convex one, providing local optimal solution after acceptable iterations. The beamforming and overall system performance are also evaluated and studied by means of analysis and simulation. Simulation results show that our proposed method leverages near-field joint angle and distance beamforming design, enhancing the performance of secure communication while meeting the sensing requirements in the ISAC system.

Abstract We consider a cognitive radio network which coexists with multiple primary users (PUs) and secondary users (SUs) transmit over time‐varying channels. In this scenario, one problem of the existing work is the poor performances of throughput and fairness due to variances of SUs' channel conditions and PUs' traffic patterns. To solve this problem, we propose a novel prediction‐based MAC‐layer sensing algorithm. In the proposed algorithm, the SUs' channel quality information and the probability of the licensed channel being idle are predicted. Through the earlier predicted information, we schedule the SUs to sense and transmit on different licensed channels. Specifically, multiple significant factors, including network throughput and fairness, are jointly considered in the proposed algorithm. Then, we formulate the prediction‐based sensing scheduling problem as an optimization problem and solve it with the Hungarian algorithm in polynomial time. Simulation results show that the proposed prediction‐based sensing scheduling algorithm could achieve a good tradeoff between network throughput and fairness among SUs. Copyright © 2014 John Wiley & Sons, Ltd.

It is well known that broadband wireless communications (BWC) are necessary for high speed vehicles since passengers need many broadband wireless multimedia services. However, one design challenge is to identify sufficient spectrum resource to support BWC in high speed vehicles. Recently, cognitive radios (CRs) are being considered as a promising technology to solve scarcity problem of spectrum resource. Therefore, we investigate the spectrum resource allocation problem in the CR based high speed vehicle network (CR-HSVN) in this paper. Specifically, we propose a spectrum resource allocation framework, where high speed vehicles could effectively utilize the TV white spaces. Subsequently, we formulate the allocation of spectrum resource as an optimization problem to maximize the available spectrum resources (i.e., TV white spaces) utilized by the CR-HSVN, and investigate the property of the CR-HSVN to reduce the complexity by separation computing method without loss of optimality. Furthermore, we analyze the optimal solution based on branch and bound method, the suboptimal solutions based on single channel and linear programming, respectively. Simulation results show that the proposed framework could offer excellent performances of the spectrum utilization and fairness for the CR-HSVN. Meanwhile, the aggregated interference from vehicles to primary users is constrained.

In this paper, we propose a novel framework of cognitive radio assisted cooperation (CRAC) for downlink transmissions in orthogonal frequency-division multiple access (OFDMA) - based cellular networks. In the proposed CRAC framework, relay stations are deployed in each cell and have spectrum sensing capability. In turn, they can access unoccupied white space to opportunistically obtain additional sub-channels to assist relaying information for cellular users. One of promising novelties is that the proposed CRAC considers joint resource allocation which includes transmission mode selection, relay station allocation, and transmit power/sub-channel allocation, to cost-effectively provide services and applications. Specifically, we first formulate the joint resource allocation as a sum utility maximization problem with power constraints on the base station and relay stations, which is a mixed integer programming problem. Then, we leverage dual decomposition method and derive a centralized optimal solution. Extensive simulation results are presented and demonstrate that the proposed CRAC can achieve a significant performance improvement in terms of the downlink network throughput while maintaining comparable fairness among cellular users in contrast to the traditional relay-based cooperation approach.