Search

This paper considers a method for improving physical layer security of wireless networks with full-duplex radio. In particular, fast algorithms are developed to compute power allocations in subcarriers, subject to power and rate constraints, to maximize the secrecy capacity of a three-node network consisting of a source, a full-duplex destination, and an eavesdropper. A residual level of radio self-interference channel is considered. The optimal power allocation at the destination is found to be significant especially when its power budget is high. Also studied in this paper are a network with multiple full-duplex destinations and another network with multiple sources. Using the algorithms developed in this paper, we are able to show that a multiuser strategy that optimizes the power distributions among the users (in terms of either the sources or the destinations) can yield a substantial gain of secrecy capacity over a single-user strategy. [ABSTRACT FROM PUBLISHER]

This paper considers coordinated multicast beamforming in a multicell multigroup multiple-input single-output system. Each base station (BS) serves multiple groups of users by forming a single beam with common information per group. We propose centralized and distributed beamforming algorithms for two different optimization targets. The first objective is to minimize the total transmission power of all the BSs while guaranteeing the user-specific minimum quality-of-service targets. The semidefinite relaxation (SDR) method is used to approximate the nonconvex multicast problem as a semidefinite program (SDP), which is solvable via centralized processing. Subsequently, two alternative distributed methods are proposed. The first approach turns the SDP into a two-level optimization via primal decomposition. At the higher level, intercell interference powers are optimized for fixed beamformers, whereas the lower level locally optimizes the beamformers by minimizing BS-specific transmit powers for the given intercell interference constraints. The second distributed solution is enabled via an alternating direction method of multipliers, where the intercell interference optimization is divided into a local and a global optimization by forcing the equality via consistency constraints. We further propose a centralized and a simple distributed beamforming design for the signal-to-interference-plus-noise ratio (SINR) balancing problem in which the minimum SINR among the users is maximized with given per-BS power constraints. This problem is solved via the bisection method as a series of SDP feasibility problems. The simulation results show the superiority of the proposed coordinated beamforming algorithms over traditional noncoordinated transmission schemes, and illustrate the fast convergence of the distributed methods. [ABSTRACT FROM PUBLISHER]

In an interference limited network, joint power and admission control (JPAC) aims at supporting a maximum number of links at their specified signal-to-interference-plus-noise ratio (SINR) targets while using minimum total transmission power. Various convex approximation deflation approaches have been developed for the JPAC problem. In this paper, we propose an effective polynomial time non-convex approximation deflation approach for solving the problem. The approach is based on the non-convex \ell q~(0< q< 1) approximation of an equivalent sparse \ell 0 reformulation of the JPAC problem. We show that, for any instance of the JPAC problem, there exists a \bar q\in (0,1) such that it can be exactly solved by solving its \ell q approximation problem with any q\in (0, \bar q]. We also show that finding the global solution of the \ell q approximation problem is NP-hard. Then, we propose a potential reduction interior-point algorithm, which can return an \epsilon -KKT solution of the NP-hard \ell q approximation problem in polynomial time. The returned solution can be used to check the simultaneous supportability of all links in the network and to guide an iterative link removal procedure, resulting in the polynomial time non-convex approximation deflation approach for the JPAC problem. Numerical simulations show that the proposed approach outperforms the existing convex approximation approaches in terms of the number of supported links and the total transmission power, particularly exhibiting a quite good performance in selecting which subset of links to support. [ABSTRACT FROM PUBLISHER]

This paper presents a novel accurate array response control algorithm, abbreviated as \text A^2\text RC, and its application to array pattern synthesis. The proposed \text A^2\text RC algorithm deals with the problem of how to accurately control the array response at a given direction. Starting from the adaptive array theory, a deep analysis of the optimal weight vector is carried out. It is found that the normalized response at a given direction can be accurately adjusted to an arbitrary level, by means of making some simple modification to the initial weight vector. On this basis, all possible weight vectors, which have a specific form and can make the normalized response at the given direction equal to the prescribed value, are first figured out. Then, an effective approach to selecting the most appropriate one, which would cause the least pattern distortion, is devised. By applying the \text A^2\text RC algorithm, a new pattern synthesis approach for arbitrary arrays is developed. In this approach, the array pattern is adjusted in a point-by-point manner by successively modifying the weight vector. Contrary to the conventional approaches that assign artificial interferences in an ad hoc way, our approach is able to obtain the weight vector without iteratively determining the powers of the artificial interferences. Extensive simulation results are provided to demonstrate the performance of the \text A^2\text RC algorithm in array response control and the effectiveness of this algorithm in pattern synthesis under various situations. [ABSTRACT FROM PUBLISHER]

We design an efficient robust multi-group multicast beamforming scheme for massive multiple-input multiple-output (MIMO) systems. Assuming only estimates of the channel covariance matrices are available at the base station with a bounded error, we formulate the robust quality-of-service (QoS) problem, which is to minimize the transmit power subject to the worst-case minimum signal-to-interference-plus-noise-ratio (SINR) guarantee. We directly solve the worst-case SINR problem and convert the robust QoS constraint into a number of non-convex constraints. Based on the recent convergence result of the alternating direction method of multipliers (ADMM) for non-convex problems, we develop an ADMM-based fast algorithm to directly tackle the reformulated non-convex problem with a convergence guarantee. The algorithm contains two layers of ADMM procedures. We design the outer-layer ADMM to decompose the problem into three convex subproblems and solve them alternatingly. We further develop an inner-layer consensus-ADMM-based algorithm to efficiently solve one subproblem. By exploring each subproblem structure and developing the special optimization techniques, we obtain closed-form or semi-closed-form solutions to each subproblem. These results lead to a fast iterative algorithm, which is guaranteed to converge to a stationary point of the original robust QoS problem. Simulation shows that our proposed algorithm provides a favorable performance compared with existing alternative methods with magnitudes of computational complexity reduction. [ABSTRACT FROM AUTHOR]

Efficient optimization techniques are important to manage interference in emerging dense wireless networks. Here, we address interference management through power control as a general utility maximization problem. For the class of utility functions that are concave in the logarithm of the optimization variables, we propose a power control algorithm based on fixed-point iterations. The iterations converge to the globally optimal power vector. One key benefit is that, for a network with N transmitters and a centralized implementation of the power control algorithm, the computational complexity per iteration of the algorithm is \cal O(N^2). When implemented in a distributed fashion and allowing for a signaling complexity of N messages per iteration, the computation complexity is reduced to \cal O(N). We show that the proposed centralized and distributed versions of the algorithm converge to the optimal power vector at a linear rate. Our numerical results suggest that in most instances, the algorithm takes fewer than ten iterations to converge, even fewer if the initialization is close to the optimal power vector. The proposed algorithm is, therefore, very efficient for power control in slowly fading channels. Furthermore, unlike previous works in the literature, the proposed algorithm does not require the objective function to be separable into a sum of individual utilities. As an example, we present results for power control in a two-hop decode-and-forward cooperative relay network and illustrate the performance gains due to interference management. [ABSTRACT FROM AUTHOR]