Search

In this paper, we consider a coordinated beamforming design for a multiple-input single-output bursty interference channel. In practical scenarios, the distributed medium access control mechanisms or the decentralized networking protocols across different users contribute to the bursty nature of network traffic. The potential gains provided by such burstiness can be exploited by feeding back the interference state information to the transmitters. Errors may occur during such feedback, thus causing uncertainties in the user traffic and resulting in complicated beamforming design problems. In this paper, we model such traffic uncertainties by using a hidden Markov model (HMM). Assuming that perfect channel state information (CSI) is available at the transmitters, our goal is to maximize the average system utility subjected to average power constraints under traffic uncertainties. We also extend the optimization problem to the imperfect CSI case. However, the resulting problem is highly nonconvex. Hence, we apply a series of convex approximation techniques. We further improve the approximation accuracy by using our proposed successive convex approximation (SCA) algorithms (HMM-SCA). In our simulation results, we observe that our proposed HMM-SCA provides a $46.3\%$ improvement in weighted sum rate over the SCA algorithm, in which the interference is assumed to be always present (NB-SCA). For the average weighted geometric mean rate, we observe that our proposed HMM-SCA also provides a $41.3\%$ improvement over the NB-SCA when user fairness is considered. Therefore, it is crucial to exploit bursty traffic for a practical wireless communication system when channel estimation errors are considered. [ABSTRACT FROM AUTHOR]

In this paper, we study a secure cooperative transmission system in which a transmitter (Alice) intends to send a secret message to an authorized receiver (Bob), with the aid of a friendly private cooperative jammer (Oscar), in the presence of a malicious eavesdropper (Eve). Specifically, artificial noise (AN) assisted secrecy beamforming is utilized for secure transmission. Under the imperfect channel state information (CSI) of legitimate channels and the statistic CSI of illegitimate channels, the optimal power allocation ratio between the information-bearing signal and the AN signal for maximizing secrecy rate is derived. Through mathematical analysis, we notice that the high channel estimation error is greatly detrimental to the realization of the positive secrecy rate in secure system. For overcoming this drawback, back propagation neural network (BPNN) is employed for a high accuracy channel estimation to simulate a realistic security communication scenario. The numerical results show that the channel estimation errors result in a decrease in secrecy rate, but the BPNN channel estimator still can guarantee secure transmission that verifies the feasibility of the application of physical layer security. Analytical derivations and numerical simulations are presented to validate the correctness of obtained expressions and our analysis. [ABSTRACT FROM AUTHOR]

In this paper, a multiple-input multiple-output wiretap channel model is studied, where the information packet sent from the transmitter to the desired receiver is wiretapped by a potential eavesdropper. When ideal channel state information (CSI) is available, we design the secure beamforming using a proposed iterative closed-form updating scheme based on majorization–minimization approach. Numerical results demonstrate that the proposed scheme acquires the same secrecy rate as the existing algorithms whereas it has much lower complexity. When ideal CSI is not available and channel errors are modeled by deterministically bounded model, we investigate robust secure beamforming (RSB) optimization problem whose goal is worst-case secrecy rate maximization. In the formulated optimization problem, the objective function includes the expression of a logarithm of a determinant ($\log \det$) minus $\log \det$. Instead of approximating the $\log \det$ expression as a trace, we propose to linearize two $\log \det$ terms. After linearization, epigraph reformulation is used to deal with deterministically bounded channel errors and semi-infinite constraints are recast as linear matrix inequalities. Finally, an alternating optimization algorithm is utilized to find the RSB design. After obtaining the RSB design, we derive the worst-case secrecy rate. Numerical results illustrate that the proposed RSB design is superior to the nonrobust one. [ABSTRACT FROM AUTHOR]

Consider the single-group multicast beamforming problem, where multiple users receive the same data stream simultaneously from a single transmitter. The problem is NP-hard and all existing algorithms for the problem either find suboptimal approximate or local stationary solutions. In this paper, we propose an efficient branch-and-bound algorithm for the problem that is guaranteed to find its global solution. To the best of our knowledge, our proposed algorithm is the first tailored global algorithm for the single-group multicast beamforming problem. Simulation results show that our proposed algorithm is computationally efficient (albeit its theoretical worst-case iteration complexity is exponential with respect to the number of receivers) and it significantly outperforms a state-of-the-art general-purpose global optimization solver called Baron. Our proposed algorithm provides an important benchmark for performance evaluation of existing algorithms for the same problem. By using it as the benchmark, we show that two state-of-the-art algorithms, semidefinite relaxation algorithm and successive linear approximation algorithm, work well when the problem dimension (i.e., the number of antennas at the transmitter and the number of receivers) is small but their performance deteriorates quickly as the problem dimension increases. [ABSTRACT FROM PUBLISHER]

Information and energy can be transferred over the same radio-frequency channel. In the power-splitting (PS) mode, they are simultaneously transmitted using the same signal by the base station (BS) and later separated at the user (UE)’s receiver by a power splitter. In the time-switching (TS) mode, they are either transmitted separately in time by the BS or received separately in time by the UE. In this paper, the BS transmit beamformers are jointly designed with either the receive PS ratios or the transmit TS ratios in a multicell network that implements wireless information and power transfer (WIPT). Imposing UE-harvested energy constraints, the design objectives include: 1) maximizing the minimum UE rate under the BS transmit power constraint, and 2) minimizing the maximum BS transmit power under the UE data rate constraint. New iterative algorithms of low computational complexity are proposed to efficiently solve the formulated difficult nonconvex optimization problems, where each iteration either solves one simple convex quadratic program or one simple second-order-cone-program. Simulation results show that these algorithms converge quickly after only a few iterations. Notably, the transmit TS-based WIPT system is not only more easily implemented but outperforms the receive PS-based WIPT system as it better exploits the beamforming design at the transmitter side. [ABSTRACT FROM PUBLISHER]

We consider a point-to-point multiple-input-single-output (MISO) system where a receiver harvests energy from a transmitter. To achieve high-efficiency wireless power transfer (WPT), the transmitter performs energy beamforming by using an instantaneous channel state information (CSI). The CSI is estimated at the receiver by training via a preamble and fed back to the transmitter. In this paper, we address the key challenge of balancing the time resource used for channel estimation and WPT to maximize the harvested energy and also investigate the allocation of energy resource used for WPT. First, we consider the general scenario where the preamble length is allowed to vary dynamically depending on channel conditions. The optimal preamble length is obtained online by solving a dynamic programming (DP) problem. The DP problem is proved to reduce to an optimal stopping problem. The optimal policy is then shown to depend only on the channel estimate power. Next, we consider the scenario in which the preamble length is fixed by an offline optimization. Furthermore, we derive the optimal power allocation schemes. For the dynamic-length-preamble scenario, the power is allocated according to both the optimal preamble length and the channel estimate power, while for the fixed-length-preamble scenario, the power is allocated according to only the channel estimate power. By numerical simulations, our results show that with optimal power allocation, the energy harvested by using the optimized fixed-length preamble is close to that harvested by using a dynamic-length preamble, hence allowing a low-complexity yet close-to-optimal WPT system to be implemented in practice. [ABSTRACT FROM AUTHOR]

This paper studies a multi-antenna wiretap channel with a passive eavesdropper and an external helper, where only quantized channel information regarding the legitimate receiver is available at the transmitter and helper due to finite-rate feedback. Given a fixed total bandwidth for the two feedback channels, the receiver must determine how to allocate its feedback bits to the transmitter and helper. Assuming zero-forcing transmission at the helper and random vector quantization of the channels, an analytic expression for the achievable ergodic secrecy rate due to the resulting quantization errors is derived. While direct optimization of the secrecy rate is difficult, an approximate upper bound for the mean loss in secrecy rate is derived and a feedback bit allocation method that minimizes the average upper bound on the secrecy rate loss is studied. A closed-form solution is shown to be possible if the integer constraint on the bit allocation is relaxed. Numerical simulations indicate the significant advantage that can be achieved by adaptively allocating the available feedback bits. [ABSTRACT FROM PUBLISHER]

The interference alignment (IA) is a promising technique to efficiently mitigate interference and to enhance capacity of a wireless communication network. This paper proposes an interference alignment scheme for a network with multiple cells and multiple multiple-input and multiple-output (MIMO) users under a Gaussian interference broadcast channel (IFBC) scenario. We first extend a grouping method already known in the literature to a multiple-cells scenario and jointly design transmit and receiver beamforming vectors using a closed-form expression without iterative computation. Then we propose a new approach using the principle of multiple access channel (MAC) - broadcast channel (BC) duality to perform interference alignment while maximizing capacity of users in each cell. The algorithm in its dual form is solved using interior point methods. We show that the proposed approach outperforms the extension of the grouping method in terms of capacity and basestation complexity. Finally, a rate balancing technique is introduced to maintain fairness among users. [ABSTRACT FROM PUBLISHER]

The communication between a multiple-antenna transmitter and multiple receivers (users) with either a single or multiple-antenna each can be significantly enhanced by providing the channel state information at the transmitter (CSIT) of the users, as this allows for scheduling, beamforming and multiuser multiplexing gains. The traditional view on how to enable CSIT has been as follows: In time-division duplexed (TDD) systems, uplink (UL) and downlink (DL) channel reciprocity allows the use of a training sequence in the UL direction, which is exploited to obtain an UL channel estimate. This estimate is in turn recycled in the next downlink transmission. In frequency-division duplexed (FDD) systems, which lack the UL and DL reciprocity, the CSIT is provided via the use of a dedicated feedback link of limited capacity between the receivers and the transmitter. In this paper, we focus on TDD systems and put their classical approach in question. We show that the traditional TDD setup above fails to fully exploit the channel reciprocity in its true sense. In fact, we show that the system can benefit from a combined CSIT acquisition strategy mixing the use of limited feedback and that of a training sequence. This combining gives rise to a very interesting joint estimation and detection problem for which we propose two iterative algorithms. An outage rate based framework is also developed which gives the resource split between training and feedback. We demonstrate the potential of this hybrid combining in terms of the improved CSIT quality under a global training and feedback resource constraint. [ABSTRACT FROM PUBLISHER]