Achieving Global Optimality for Weighted Sum-Rate Maximization in the K-User Gaussian Interference Channel with Multiple Antennas

Characterizing the global maximum of weighted sum-rate (WSR) for the K-user Gaussian interference channel (GIC), with the interference treated as Gaussian noise, is a key problem in wireless communication. However, due to the users' mutual interference, this problem is in general non-convex and thus cannot be solved directly by conventional convex optimization techniques. In this paper, by jointly utilizing the monotonic optimization and rate profile techniques, we develop a new framework to obtain the globally optimal power control and/or beamforming solutions to the WSR maximization problems for the GICs with single-antenna transmitters and single-antenna receivers (SISO), single-antenna transmitters and multi-antenna receivers (SIMO), or multi-antenna transmitters and single-antenna receivers (MISO). Different from prior work, this paper proposes to maximize the WSR in the achievable rate region of the GIC directly by exploiting the facts that the achievable rate region is a "normal" set and the users' WSR is a "strictly increasing" function over the rate region. Consequently, the WSR maximization is shown to be in the form of monotonic optimization over a normal set and thus can be solved globally optimally by the existing outer polyblock approximation algorithm. However, an essential step in the algorithm hinges on how to efficiently characterize the intersection point on the Pareto boundary of the achievable rate region with any prescribed "rate profile" vector. This paper shows that such a problem can be transformed into a sequence of signal-to-interference-plus-noise ratio (SINR) feasibility problems, which can be solved efficiently by existing techniques. Numerical results validate that the proposed algorithms can achieve the global WSR maximum for the SISO, SIMO or MISO GIC.Comment: This is the longer version of a paper to appear in IEEE Transactions on Wireless Communication

Security Supports for Cyber-Physical System and its Communication Networks

A cyber-physical system (CPS) is a sensing and communication platform that features tight integration and combination of computation, networking, and physical processes. In such a system, embedded computers and networks monitor and control the physical processes through a feedback loop, in which physical processes affect computations and vice versa. In recent years, CPS has caught much attention in many different aspects of research, such as security and privacy. In this dissertation, we focus on supporting security in CPS and its communication networks. First, we investigate the electric power system, which is an important CPS in modern society. as crucial and valuable infrastructure, the electric power system inevitably becomes the target of malicious users and attackers. In our work, we point out that the electric power system is vulnerable to potential cyber attacks, and we introduce a new type of attack model, in which an attack cannot be completely identified, even though its presence may be detected. to defend against such an attack, we present an efficient heuristic algorithm to narrow down the attack region, and then enumerate all feasible attack scenarios. Furthermore, based on the feasible attack scenarios, we design an optimization strategy to minimize the damage caused by the attack. Next, we study cognitive radio networks, which are a typical communication network in CPS in the areas of security and privacy. as for the security of cognitive radio networks, we point out that a prominent existing algorithm in cooperative spectrum sensing works poorly under a certain attack model. In defense of this attack, we present a modified combinatorial optimization algorithm that utilizes the branch-and-bound method in a decision tree to identify all possible false data efficiently. In regard to privacy in cognitive radio networks, we consider incentive-based cognitive radio transactions, where the primary users sell time slices of their licensed spectrum to secondary users in the network. There are two concerns in such a transaction. The first is the primary user\u27s interest, and the second is the secondary user\u27s privacy. to verify that the payment made by a secondary user is trustworthy, the primary user needs detailed spectrum utilization information from the secondary user. However, disclosing this detailed information compromises the secondary user\u27s privacy. to solve this dilemma, we propose a privacy-preserving scheme by repeatedly using a commitment scheme and zero-knowledge proof scheme

A parallel implementation on a multi-core architecture of a dynamic programming algorithm applied in cognitive radio ad hoc networks

Spectral resources allocation is a major problem in cognitive radio ad hoc networks and currently most of the research papers use meta-heuristics to solve it. On the other side, the term parallelism refers to techniques to make programs faster by performing several computations in parallel. Parallelism would be very interesting to increase the performance of real-time systems, especially for the cognitive radio ad hoc networks that interest us in this work. In this paper, we present a parallel implementation on a multi-core architecture of dynamic programming algorithm applied in cognitive radio ad hoc networks. Our simulations approve the desired results, showing significant gain in terms of execution time. The main objective is to allow a cognitive engine to use an exact method and to have better results compared to the use of meta-heuristics