Perfect Global Channel State Information Research Articles

Multi-Antenna Aided Secrecy Beamforming Optimization for Wirelessly Powered HetNets

The new paradigm of wirelessly powered two-tier heterogeneous networks (HetNets) is considered in this paper. Specifically, the femtocell base station (FBS) is powered by a power beacon (PB) and transmits confidential information to a legitimate femtocell user (FU) in the presence of a potential eavesdropper (EVE) and a macro base station (MBS). In this scenario, we investigate the secrecy beamforming design under three different levels of FBS-EVE channel state information (CSI), namely, the perfect, imperfect and completely unknown FBS-EVE CSI. Firstly, given the perfect global CSI at the FBS, the PB energy covariance matrix, the FBS information covariance matrix and the time splitting factor are jointly optimized aiming for perfect secrecy rate maximization. Upon assuming the imperfect FBS-EVE CSI, the worst-case and outage-constrained SRM problems corresponding to deterministic and statistical CSI errors are investigated, respectively. Furthermore, considering the more realistic case of unknown FBS-EVE CSI, the artificial noise (AN) aided secrecy beamforming design is studied. Our analysis reveals that for all above cases both the optimal PB energy and FBS information secrecy beamformings are of rank-1. Moreover, for all considered cases of FBS-EVE CSI, the closed-form PB energy beamforming solutions are available when the cross-tier interference constraint is inactive. Numerical simulation results demonstrate the secrecy performance advantages of all proposed secrecy beamforming designs compared to the adopted baseline algorithms.

Resource Allocation Based on Interference Alignment With Clustering for Data Stream Maximization in Dense Small Cell Networks

In this paper, by allocating the limited subchannel resources based on interference alignment (IA) with clustering to mitigate the severe co-tier interference in dense small cell networks (SCNs), we aim at maximizing the number of data streams achieved by all small cell user equipments (SUEs) while guaranteeing the data stream requirement for each SUE. As the corresponding optimization problem is NP-hard, we propose a two-phases algorithm based on graph theory to obtain a suboptimal but efficient solution, which requires much lower complexity and notably reduced feedback overhead. In the first phase, all SBS-SUE pairs are grouped into disjoint clusters by the recursive partition of the constructed graph, where the edge weight is only determined by the path losses from small cell base stations (SBSs) to SUEs, so the estimation of perfect global channel state information (CSI) is avoided. Furthermore, when guaranteeing that IA is feasible within each cluster, the number of formed clusters and the size of each cluster are determined by the distributions of dense SCNs instead of being specified. In the second phase, by further treating each cluster as a new vertex, graph coloring algorithm is proposed for subchannel allocation, which only requires to estimate the perfect CSI within each cluster. The analysis of computational complexity demonstrates that the proposed two-phases algorithm has a much lower complexity compared with the optimal solution obtained by the exhaustive search. Numerical results show that the proposed solution outperforms other related schemes and exhibits a performance close to the optimal solution.

Secure Communications for Dual-Polarized MIMO Systems

To enhance secure communications, we deploy the dual-polarized antenna arrays at communication nodes of the multi-input multioutput (MIMO) system, where the base station communicates with multiple legitimate users in the presence of an eavesdropper. We also adopt the dual-structured precoding in which a preprocessing matrix based on the polarized array spatial correlation and a linear precoding based on the instantaneous channel state information (CSI) are concatenated. We design this dual-structured multiuser linear precoding under three cases. In the first case, given perfect global CSI, the secrecy rate optimization problem is formulated and transformed into the weighted minimum mean square error (MSE) problem, which can be effectively solved by the block coordinate decent method. In the second case, where the eavesdropper's CSI is unavailable, an artificial noise is generated to confuse the eavesdropper by minimizing the information transmit power subject to a preset MSE threshold for the recovered confidential signals, which can be solved by an efficient iterative algorithm. In the third case of imperfect global CSI, the robust optimization for secure communications is performed by minimizing the largest received MSE among the users subject to the total transmit power constraint, which can be reformulated into a biconvex semidefinite programming problem and solved by an efficient alternating convex optimization. Simulation results are included to demonstrate the excellent performance of our proposed designs over the conventional single-polarized array-based designs, in terms of achievable secrecy rate, minimum transmit power, and the MSE of recovered confidential signals.

Energy Efficient Transmission in Multi-User MIMO Relay Channels With Perfect and Imperfect Channel State Information

We design novel transmission strategies to maximize the energy efficiency (EE) of the uplink multi-user multiple-input and multiple-output relay channel. In this channel, $K$ multi-antenna users communicate with a multi-antenna base station (BS) through a multi-antenna relay. To achieve the goal of EE maximization, we propose new iterative algorithms to jointly optimize the multi-user precoder and the relay precoder under transmit power constraints for two cases. In the first case, the perfect global channel state information (CSI) is available, while in the second case, the CSI between the relay and the BS is imperfect. To surmount the non-convexity of our formulated EE optimization problems in both cases, we introduce the parameter subtractive function into the proposed algorithms. Then, the EE parameter in the parameter subtractive function is updated by Dinkelbach’s algorithm in the perfect CSI case, and by the bisection method in the imperfect CSI case. Moreover, in the perfect CSI case, the relay precoder is optimized by the diagonalization operation and the multi-user precoder is optimized based on the weighted minimum mean square error method. Differently, in the imperfect CSI case, we apply the sign-definiteness lemma to promote the semidefinite programming formulation of the EE optimization problem. Furthermore, we present the numerical results to demonstrate that our proposed iterative algorithms have a good convergence rate in both cases. In addition, we show that our proposed iterative algorithms achieve a higher EE performance than the existing algorithms in both CSI cases.

Ergodic Interference Alignment With Limited Feedback: Power Control and Rate Adaptation

Considering the time-varying $K$ -user single-antenna interference channel (IC), it has been shown that, when terminals have perfect global channel state information (CSI) and they can tolerate asymptotically long delay, applying an ergodic interference alignment (EIA) scheme can achieve half of the interference-free achievable rate. However, in practice, obtaining such CSI is challenging, and only a limited delay is acceptable. This paper addresses data transmission over the IC by taking these concerns into account. Specifically, we consider the case that each transmitter attains only quantized CSI via limited feedback signals. This causes imperfect interference alignment and a degraded performance. We propose adaptive schemes to compensate the impact of the CSI uncertainties. We first study a power control problem which is concerned with communicating at fixed rates using minimum transmit powers. A power control algorithm is used to reach the solution. Next, we address a throughput maximization problem when the transmit powers are fixed. Through the analysis of system outage probability, we propose a rate adaptation scheme to maximize throughput. Finally, we quantify the throughput loss in delay-limited systems. Our results show that, even with limited feedback, performing the EIA scheme with proper power control or rate adaptation strategies can still outperform conventional orthogonal transmission approaches.

Capacity Analysis of Interference Alignment With Bounded CSI Uncertainty

Interference alignment (IA) has been demonstrated to achieve the degree-of-freedom (DoF) of an interference channel given perfect global channel state information (CSI). In this letter, we consider the case of imperfect CSI with bounded errors and derive a capacity lower bound of the channel using IA. We show that this lower bound is within 1 bps/Hz of the capacity of the perfect CSI case up to a certain signal-to-noise ratio (SNR) which we refer to it as the saturating SNR. Further, we introduce a new metric called modified DoF (mDoF) in order to characterize the multiplexing performance of IA with imperfect CSI at finite SNR. Simulation results for the 3-user case are provided to illustrate the region within which the actual capacity of IA falls.

A channel estimation based opportunistic scheduling scheme in wireless bidirectional networks

Generally speaking, there are two assumptions in the previous bidirectional networks. The first one is assuming that no direct transmission paths exist between the source and destination nodes due to high shadow fading or large separation. The second one is assuming that either channel parameters are pre-established or perfect global channel state information (CSI) can be obtained by the relay nodes. In this paper, we get rid of these limitations and estimate channel gains for both analog network coding (ANC) and time division broadcasting (TDBC) schemes based on outage probability. As the channel gain of each link varies from time to time in wireless networks, we first estimate the channel gain according to outage probability, then propose an opportunistic scheduling scheme to optimize the sum rate values by maximizing the minimum link transmission rate. Simulation results are presented to demonstrate the effectiveness of our channel estimation scheme, and the performance of our proposed opportunistic scheduling scheme is compared with the round robin scheduling (RRS) scheme for both ANC and TDBC schemes.

Amplify-and-Forward Two-Way Relay Networks: Error Exponents and Resource Allocation

In a two-way relay network, two terminals exchange information over a shared wireless half-duplex channel with the help of a relay. Due to its fundamental and practical importance, there has been an increasing interest in this channel. However, there has been little work that characterizes the fundamental tradeoff between the communication reliability and transmission rate across all signal-to-noise ratios. In this paper, we consider amplify-and-forward (AF) two-way relaying due to its simplicity. We first derive the random coding error exponent for the link in each direction. From the exponent expression, the capacity and cutoff rate for each link are also deduced. We then put forth the notion of bottleneck error exponent, which is the worst exponent decay between the two links, to give us insight into the fundamental tradeoff between the rate pair and information-exchange reliability in the two-way relay network. As applications of the error exponent analysis to design a reliable AF two-way relay network, we present two optimization framework to maximize the bottleneck error exponent, namely: i) the optimal rate allocation under a sum-rate constraint and its closed-form quasi-optimal solution that requires only knowledge of the capacity and cutoff rate of each link; and ii) the optimal power allocation under a total power constraint and perfect global channel state information, which is shown equivalently to a quasi-convex optimization problem. Numerical results verify our analysis and the effectiveness of the optimal rate and power allocations in maximizing the bottleneck error exponent, i.e. the network information-exchange reliability.

Robust Power Allocation Algorithms for Wireless Relay Networks

Resource allocation promises significant benefits in wireless networks. In order to fully reap these benefits, it is important to design efficient resource allocation algorithms. Here, we develop relay power allocation (RPA) algorithms for coherent and noncoherent amplify-and-forward (AF) relay networks. The goal is to maximize the output signal-to-noise ratio under individual as well as aggregate relay power constraints. We show that these RPA problems, in the presence of perfect global channel state information (CSI), can be formulated as quasiconvex optimization problems. In such settings, the optimal solutions can be efficiently obtained via a sequence of convex feasibility problems, in the form of second-order cone programs. The benefits of our RPA algorithms, however, depend on the quality of the global CSI, which is rarely perfect in practice. To address this issue, we introduce the robust optimization methodology that accounts for uncertainties in the global CSI. We show that the robust counterparts of our convex feasibility problems with ellipsoidal uncertainty sets are semi-definite programs. Our results reveal that ignoring uncertainties associated with global CSI often leads to poor performance, highlighting the importance of robust algorithm designs in practical wireless networks.

Robust Wireless Relay Networks: Slow Power Allocation With Guaranteed QoS

In wireless networks, power allocation is an effective technique for prolonging network lifetime, achieving better quality-of-service (QoS), and reducing network interference. However, these benefits depend on knowledge of the channel state information (CSI), which is hardly perfect. Therefore, robust algorithms that take into account such CSI uncertainties play an important role in the design of practical systems. In this paper, we develop relay power allocation algorithms for noncoherent and coherent amplify-and-forward (AF) relay networks. The goal is to minimize the total relay transmission power under individual relay power constraints, while satisfying a QoS requirement. To make our algorithms practical and attractive, our power update rate is designed to follow large-scale fading, i.e., in the order of seconds. We show that, in the presence of perfect global CSI, our power optimization problems for noncoherent and coherent AF relay networks can be formulated as a linear program and a second-order cone program (SOCP), respectively. We then introduce robust optimization methodology that accounts for uncertainties in the global CSI. In the presence of ellipsoidal uncertainty sets, the robust counterparts of our optimization problems for noncoherent and coherent AF relay networks are shown to be an SOCP and a semi-definite program, respectively. Our results reveal that ignoring uncertainties associated with global CSI often leads to poor performance. We verify that our proposed algorithms can provide significant power savings over a naive scheme that employs maximum transmission power at each relay node. This work highlights the importance of robust algorithms with practical power update rates in realistic wireless networks.