Jingran Lin

Robust Cooperative Beamforming and Artificial Noise Design for Physical-Layer Secrecy in AF Multi-Antenna Multi-Relay Networks

This paper is concerned with an optimization problem in a two-hop relay wiretap channel, wherein multiple multi-antenna relays collaboratively amplify and forward (AF) information from a single-antenna source to a single-antenna destination, and at the same time emit artificial noise (AN) to improve physical-layer information security in the presence of multiple multi-antenna eavesdroppers (or Eves). More specifically, the problem is to simultaneously optimize the AF matrices and AN covariances for secrecy rate maximization, with robustness against imperfect channel state information of Eves via a worst-case robust formulation. Such a problem is nonconvex, and we propose a polynomial-time optimization solution based on a two-level optimization approach and semidefinite relaxation (SDR). In particular, while SDR is generally an approximation technique, we prove that SDR is optimal in the specific context here. This desirable result is obtained by careful reformulation and Karush-Kuhn-Tucker optimality analysis, where, rather interestingly, AN is found to be instrumental in providing guarantee of SDR optimality. Simulation results are provided, and the results show that the proposed joint AF-AN solution can attain considerably higher achievable secrecy rates than some existing suboptimal designs.

Joint power allocation, base station assignment and beamformer design for an uplink SIMO heterogeneous network

Consider the max-min problem for an uplink SIMO heterogeneous network, where the base stations (BS) are coordinated dynamically for joint reception under some backhaul overhead constraints. We formulate this problem in the perspective of joint power allocation, BS assignment and beamformer design, and develop an efficient algorithm based on alternating optimization. In particular, we transfer the joint BS assignment and beamformer design subproblem into a group LASSO problem by applying the alternating direction method of multipliers (ADMM). Consequently, the problem is solved in a partially distributed manner and in each iteration a simple closed-form solution is derived. Numerical simulations demonstrate the effectiveness and efficiency of the proposed algorithm.

Joint Multirelay Selection, Power Allocation, and Beamformer Design for Multiuser Decode-and-Forward Relay Networks

Consider a two-hop relay network that consists of multiple pairs of single-antenna users and multiple multiantenna relays. We assume that the relays employ a linear decode-and-forward (DF) strategy to cooperatively forward information from sources to their destinations. To provide fairness among users and alleviate the network backhaul load, we consider a signal-to-interference-plus-noise-ratio (SINR)-based max-min fairness (MMF) problem by jointly optimizing the power allocation at sources, the receive and transmit beamformers at relays, and the size of the cooperating relays. This problem is a nonlinear mixed-integer program, which is challenging to solve. As a compromise, we seek some efficient approximate solutions to it. Specifically, we first approximate the hard backhaul constraint by a group sparse constraint and then employ the uplink-downlink duality transformation, advocated by Luo et al. [IEEE Transactions on Wireless Communications 2015], to eliminate trivial nonsparse solutions. From there, a custom-made distributed algorithm is developed for the approximated problem by fitting it into the alternating direction method of multipliers (ADMM) framework. Each iteration of ADMM can be computed in closed form, thus giving it very low complexity. The effectiveness of the proposed algorithm was validated by extensive numerical simulations.

Full-Duplex Bidirectional Secure Communications Under Perfect and Distributionally Ambiguous Eavesdropper's CSI

Consider a full-duplex (FD) bidirectional secure communication system, where two communication nodes, named Alice and Bob, simultaneously transmit and receive confidential information from each other, and an eavesdropper, named Eve, overhears the transmissions. Our goal is to maximize the sum secrecy rate (SSR) of the bidirectional transmissions by optimizing the transmit covariance matrices at Alice and Bob. To tackle this SSR maximization (SSRM) problem, we develop an alternating difference-of-concave (ADC) programming approach to alternately optimize the transmit covariance matrices at Alice and Bob. We show that the ADC iteration has a semiclosed form beamforming solution, and is guaranteed to converge to a stationary solution of the SSRM problem. Besides the SSRM design, this paper also deals with a robust SSRM transmit design under a moment-based random channel state information (CSI) model, where only some roughly estimated first- and second-order statistics of Eves CSI are available, but the exact distribution or other high-order statistics is not known. This moment-based error model is different from the widely used bounded-sphere error model and the Gaussian random error model. Under the considered CSI error model, the robust SSRM is formulated as an outage probability-constrained SSRM problem. By leveraging the Lagrangian duality theory and difference-of-concave (DC) programming, a tractable safe solution to the robust SSRM problem is derived. The effectiveness and the robustness of the proposed designs are demonstrated through simulations.

Dynamic base station clustering and beamforming for an uplink SIMO cloud radio access network

Consider the throughput maximization problem for an uplink SIMO cloud radio access network (C-RAN), where the base stations (BSs) can be clustered dynamically to perform joint reception under some backhaul constraints. In this paper, it is formulated as a sparse optimization problem based on dynamic BS clustering and beamforming. Instead of solving this problem via a series of feasibility checking steps (bisection), we design an efficient algorithm here. After the reformulation based on the semidefinite relaxation (SDR) and the Charnes Copper transformation (CCT), the BS clusters as well as the beamformers can be determined by solving only two simple convex SDP problems. Then an additional debiasing operation is introduced so that the throughput performance can be further improved. The effectiveness and efficiency of the proposed algorithm are demonstrated by some numerical examples.

A green cellular network with maximum user number based on joint base station assignment and power allocation

In this paper, the traditional power minimization problem under some QoS constraints in a SISO cellular network is considered. However, the research is distinguished by two points. (1) The association relationship between base stations (BS) and users is no longer known and fixed. (2) When the QoS constraints are not satisfied, instead of simply claiming the problem is infeasible, the network tries to serve as many users as possible. To achieve this goal, a two-stage algorithm is proposed in this paper, which manages the network via joint BS assignment and power allocation. In the first stage, based on the identified polynomial time solvable BS assignment model, the BS assignment problem is solved by the auction algorithm. Then the power allocation is determined easily in the second stage. Moreover, if the candidate solution does not satisfy the QoS constraints, these constraints are relaxed by introducing an auxiliary sparse vector. Then, after performing the sparse optimization, the network can serve as many users as possible. Numerical examples demonstrate that compared with many conventional algorithms in this field, the proposed one achieves better performance in terms of power consumption and active user number.

Constant modulus beamforming for large-scale MISOME wiretap channel

The multi-input single-output multi-eavesdropper (MISOME) wiretap channel is one of the generic wiretap channels in physical layer security. In Khisti and Wornells classical work [1], the optimal secure beamformer for MISOME has been derived under the total power constraint. In this work, we revisit the MISOME wiretap channel and focus on the large-scale transmit antenna regime and the constant modulus beamformer design. The former is motivated by the significant spectral efficiency gains provided by massive antennas, and the latter is due to the consideration of cheap hardware implementation of constant modulus beamforming. However, from an optimization point of view, the secrecy beamforming with constant modulus constraints is challenging, more specifically, NP-hard. In light of this, we propose two methods to tackle it, namely the semidefinite relaxation (SDR) method and the ADMM-Dinkelbach method. Simulation results demonstrate that the ADMM-Dinkelbach method outperforms the SDR method, and can attain nearly optimal secrecy performance for the large-scale antenna scenario.

Joint base station activation, user admission control and beamforming in a green downlink cooperative MISO network

We consider the green communication problem in a downlink cooperative MISO network consisting of multiple base stations (BSs) and multiple users. We employ power efficiency as the performance metric, defined as the ratio of total user rate to total power consumption. The total user rate is related to the number of active users, while the total power consumption includes the transmit power and the device maintenance power, determined by the beamformers and the number of active BSs, respectively. Motivated by this observation, we consider a power efficiency maximization problem by jointly optimizing the active BSs, the admissible users and the cooperative beamform-ers. This problem is challenging due to the fractional objective and the mixed-integer program. To seek for some efficient approximate solution, we first formulate it as a convex sparse problem balancing the transmit power, the number of active BSs and the number of admissible users. A two-stage algorithm is then developed to solve the approximate problem. Simulations demonstrate the effectiveness of the proposed algorithm.

Adaptive Beamforming with Robustness against Both Finite-Sample Effects and Steering Vector Mismatches

A novel approach of robust adaptive beamforming (RABF) is presented in this paper, aiming at robustness against both finite-sample effects and steering vector mismatches. It belongs to the class of diagonal loading approaches with the loading level determined based on worst-case performance optimization. The proposed approach, however, is distinguished by two points. (1) It takes finite-sample effects into account and applies worst-case performance optimization to not only the constraints, but also the objective of the constrained quadratic equation, for which it is referred to as joint worst-case RABF (JW-RABF). (2) It suggests a simple closed-form solution to the optimal loading after some approximations, revealing how different factors affect the loading. Compared with many existing methods in this field, the proposed one achieves better robustness in the case of small sample data size as well as steering vector mismatches. Moreover, it is less computationally demanding for presenting a simple closed-form solution to the optimal loading. Numerical examples confirm the effectiveness of the proposed approach.

Joint base station activation, user admission control and beamforming in downlink green networks☆☆☆

Abstract Consider a multicell downlink network, where the base stations (BSs) in different cells cooperate in the precoder level, while those in the same cell are coordinated for joint processing (JP). To achieve green communication, we optimize the network power consumption under some quality-of-service (QoS) constraints. Different from the conventional approaches based on BS activation and beamforming, we further integrate user admission control into network management, and thus minimize the power consumption by jointly optimizing the active BSs, the admissible users and the transmit beamformers. This strategy has two advantages. First, it ensures that the network still works even when some users cannot achieve the QoS requirements. Second, it helps select a subset of users whose QoS requirements can be satisfied with relatively low power cost, thereby improving the power efficiency. However, this problem is challenging due to the mixed-integer programming. To pursue some efficient approximate solution, we first reformulate it as a convex sparse optimization problem and then develop a distributed algorithm to iteratively solve the problem, utilizing the alternating direction method of multipliers (ADMM). The proposed algorithm has very low complexity since each step can be computed in closed form. Its efficacy and efficiency are demonstrated by numerical simulations.