Tadilo Endeshaw Bogale

Massive MIMO and mmWave for 5G Wireless HetNet: Potential Benefits and Challenges

There has been active research worldwide to develop the next-generation, i.e., fifth-generation (5G), wireless network. The 5G network is expected to support a significantly large amount of mobile data traffic and a huge number of wireless connections and achieve better costand energy-efficiency as well as quality of service (QoS) in terms of communication delay, reliability, and security. To this end, the 5G wireless network should exploit the potential of new developments, including superdense and heterogeneous deployment of cells and massive antenna arrays [i.e., massive multiple-input, multiple-output (MIMO) technologies] and utilization of higher frequencies, particularly millimeter-wave (mmWave) frequencies. This article discusses the potential benefits and challenges of the 5G wireless heterogeneous network (HetNet) that incorporates massive MIMO and mmWave technologies.

Beamforming for multiuser massive MIMO systems: Digital versus hybrid analog-digital

This paper designs a novel hybrid (a mixture of analog and digital) beamforming and examines the relation between the hybrid and digital beamformings for downlink multiuser massive multiple input multiple output (MIMO) systems. We assume that perfect channel state information is available only at the transmitter and we consider the total sum rate maximization problem. For this problem, the hybrid beamforming is designed indirectly by considering a weighed sum mean square error (WSMSE) minimization problem incorporating the solution of digital beamforming which is obtained from the block diagonalization technique. The resulting WSMSE problem is solved by applying the theory of compressed sensing. The relation between the hybrid and digital beamformings is studied numerically by varying different parameters, such as the number of radio frequency (RF) chains, analog to digital converters (ADCs) and multiplexed symbols. Computer simulations reveal that for the given number of RF chains and ADCs, the performance gap between digital and hybrid beamformings can be decreased by decreasing the number of multiplexed symbols. Moreover, for the given number of multiplexed symbols, increasing the number of RF chains and ADCs will increase the total sum rate of the hybrid beamforming which is expected.

Cognitive Radio Techniques Under Practical Imperfections: A Survey

Cognitive radio (CR) has been considered as a potential candidate for addressing the spectrum scarcity problem of future wireless networks. Since its conception, several researchers, academic institutions, industries, and regulatory and standardization bodies have put their significant efforts toward the realization of CR technology. However, as this technology adapts its transmission based on the surrounding radio environment, several practical issues may need to be considered. In practice, several imperfections, such as noise uncertainty, channel/interference uncertainty, transceiver hardware imperfections, signal uncertainty, and synchronization issues, may severely deteriorate the performance of a CR system. To this end, the investigation of realistic solutions toward combating various practical imperfections is very important for the successful implementation of cognitive technology. In this direction, first, this survey paper provides an overview of the enabling techniques for CR communications. Subsequently, it discusses the main imperfections that may occur in the most widely used CR paradigms and then reviews the existing approaches toward addressing these imperfections. Finally, it provides some interesting open research issues.

On the Number of RF Chains and Phase Shifters, and Scheduling Design With Hybrid Analog–Digital Beamforming

This paper considers hybrid beamforming (HB) for downlink multiuser massive multiple-input multiple-output (MIMO) systems with frequency selective channels. The proposed HB design employs sets of digitally controlled phase (fixed phase) paired phase shifters (PSs) and switches. For this system, first we determine the required number of radio frequency (RF) chains and PSs such that the proposed HB achieves the same performance as that of the digital beamforming (DB) which utilizes N (number of transmitter antennas) RF chains. We show that the performance of the DB can be achieved with our HB just by utilizing rt RF chains and 2rt(N-rt + 1) PSs, where rt ≤ N is the rank of the combined digital precoder matrices of all subcarriers. Second, we provide a simple and novel approach to reduce the number of PSs with only a negligible performance degradation. Numerical results reveal that only 20-40 PSs per RF chain are sufficient for practically relevant parameter settings. Finally, for the scenario where the deployed number of RF chains (Na) is less than rt, we propose a simple user scheduling algorithm to select the best set of users in each subcarrier. Simulation results validate theoretical expressions, and demonstrate the superiority of the proposed HB design over the existing HB designs in both flat fading and frequency selective channels.This paper considers hybrid beamforming (HB) for downlink multiuser massive MIMO systems with frequency selective channels. For this system, first we quantify the required number of radio frequency (RF) chains and phase shifters (PSs) such that the proposed HB achieves the same performance as that of the digital beamforming (DB) which utilizes N (number of transmitter antennas) RF chains. We show that the performance of the DB can be achieved with our HB just by utilizing rt RF chains and 2rt(N − rt + 1) PSs, where rt ≤ N is the rank of the combined digital precoder matrices of all sub-carriers. Second, we provide a simple and novel approach to reduce the number of PSs with only a negligible performance degradation. Numerical results reveal that only 20− 40 PSs per RF chain are sufficient for practically relevant parameter settings. Finally, for the scenario where the deployed number of RF chains (Na) is less than rt, we propose a simple user scheduling algorithm to select the best set of users in each sub-carrier. Simulation results validate theoretical expressions, and demonstrate the superiority of the proposed HB design over the existing HB designs in both flat fading and frequency selective channels.

Weighted Sum Rate Optimization for Downlink Multiuser MIMO Coordinated Base Station Systems: Centralized and Distributed Algorithms

This paper considers the joint linear transceiver design problem for the downlink multiuser multiple-input-multiple-output (MIMO) systems with coordinated base stations (BSs). We consider maximization of the weighted sum rate with per BS antenna power constraint problem. We propose novel centralized and computationally efficient distributed iterative algorithms that achieve local optimum to the latter problem. These algorithms are described as follows. First, by introducing additional optimization variables, we reformulate the original problem into a new problem. Second, for the given precoder matrices of all users, the optimal receivers are computed using minimum mean-square-error (MMSE) method and the optimal introduced variables are obtained in closed form expressions. Third, by keeping the introduced variables and receivers constant, the precoder matrices of all users are optimized by using second-order-cone programming (SOCP) and matrix fractional minimization approaches for the centralized and distributed algorithms, respectively. Finally, the second and third steps are repeated until these algorithms converge. We have shown that the proposed algorithms are guaranteed to converge. We also show that the proposed algorithms require less computational cost than that of the existing linear algorithm. All simulation results demonstrate that our distributed algorithm achieves the same performance as that of the centralized algorithm. Moreover, the proposed algorithms outperform the existing linear algorithm. In particular, when each of the users has single antenna, we have observed that the proposed algorithms achieve the global optimum.

Robust Transceiver Optimization for Downlink Multiuser MIMO Systems

This correspondence addresses the joint transceiver design for downlink multiuser multiple-input multiple-output (MIMO) systems, with imperfect channel state information (CSI) at the base station (BS) and mobile stations (MSs). By incorporating antenna correlation at both ends of the channel and taking channel estimation errors into account, we solve two robust design problems: minimization of the weighted sum mean-square-error (MSE) and minimization of the maximum weighted MSE. These problems are solved as follows: first, we establish three kinds of MSE uplink-downlink duality by transforming only the power allocation matrices from uplink channel to downlink channel and vice versa. Second, in the uplink channel, we formulate the power allocation part of each problem ensuring global optimality. Finally, based on the solution of the uplink power allocation and the MSE duality results, for each problem, we propose an iterative algorithm that performs optimization alternatively between the uplink and downlink channels. Computer simulations verify the robustness of the proposed design compared to the nonrobust/naive design.

Pilot optimization and channel estimation for multiuser massive MIMO systems

This paper proposes novel pilot optimization and channel estimation algorithm for the downlink multiuser massive multiple input multiple output (MIMO) system with K decentralized single antenna mobile stations (MSs), and time division duplex (TDD) channel estimation which is performed by utilizing N pilot symbols. The proposed algorithm is explained as follows. First, we formulate the channel estimation problem as a weighted sum mean square error (WSMSE) minimization problem containing pilot symbols and introduced variables. Second, for fixed pilot symbols, the introduced variables are optimized using minimum mean square error (MMSE) and generalized Rayleigh quotient methods. Finally, for N = 1 and N = K settings, the pilot symbols of all MSs are optimized using semi definite programming (SDP) convex optimization approach, and for the other settings of N and K, the pilot symbols of all MSs are optimized by applying simple iterative algorithm. When N = K, it is shown that the latter iterative algorithm gives the optimal pilot symbols achieved by the SDP method. Simulation results confirm that the proposed algorithm achieves less WSMSE compared to that of the conventional semi-orthogonal pilot symbol and MMSE channel estimation algorithm which creates pilot contamination.

Robust Transceiver Optimization for Downlink Coordinated Base Station Systems: Distributed Algorithm

This paper considers the joint transceiver design for downlink multiuser multiple-input single-output (MISO) systems with coordinated base stations (BSs) where imperfect channel state information (CSI) is available at the BSs and mobile stations (MSs). By incorporating antenna correlation at the BSs and taking channel estimation errors into account, we solve two robust design problems: (1) minimizing the weighted sum of mean-square-error (MSE) with per BS antenna power constraint, and (2) minimizing the total power of all BSs with per user MSE target and per BS antenna power constraints. These problems are solved as follows. First, for fixed receivers, we propose centralized and novel computationally efficient distributed algorithms to jointly optimize the precoders of all users. Our centralized algorithms employ the second-order-cone programming (SOCP) approach, whereas, our novel distributed algorithms use the Lagrangian dual decomposition, modified matrix fractional minimization and an iterative method. Second, for fixed BS precoders, the receivers are updated by the minimum mean-square-error (MMSE) criterion. These two steps are repeated until convergence is achieved. In all of our simulation results, we have observed that the proposed distributed algorithms achieve the same performance as that of the centralized algorithms. Moreover, computer simulations verify the robustness of the proposed robust designs compared to the nonrobust/naive designs.

Hybrid Analog-Digital Channel Estimation and Beamforming: Training-Throughput Tradeoff

This paper develops hybrid analog-digital channel estimation and beamforming techniques for multiuser massive multiple-input multiple-output (MIMO) systems with limited number of radio frequency (RF) chains. For these systems, first, we design novel minimum-mean-squared error (MMSE) hybrid analog-digital channel estimator by considering both cases with perfect and imperfect channel covariance matrix knowledge. Then, we utilize the estimated channels to enable beamforming for data transmission. When the channel covariance matrices of all user equipments (UEs) are known perfectly, we show that there is a tradeoff between the training duration and throughput. Specifically, we exploit the fact that the optimal training duration that maximizes the throughput depends on the covariance matrices of all UEs, number of RF chains, and channel coherence time (Tc). We also show that the training time optimization problem can be formulated as a concave maximization problem where its global optimal solution can be obtained efficiently using existing tools. The analytical expressions are validated by performing extensive Monte Carlo simulations.

Robust Sum MSE Optimization for Downlink Multiuser MIMO Systems With Arbitrary Power Constraint: Generalized Duality Approach

This paper considers linear minimum mean-square-error (MMSE) transceiver design problems for downlink multiuser multiple-input multiple-output (MIMO) systems where imperfect channel state information is available at the base station (BS) and mobile stations (MSs). We examine robust sum mean-square-error (MSE) minimization problems. The problems are examined for the generalized scenario where the power constraint is per BS, per BS antenna, per user or per symbol, and the noise vector of each MS is a zero-mean circularly symmetric complex Gaussian random variable with arbitrary covariance matrix. For each of these problems, we propose a novel duality based iterative solution. Each of these problems is solved as follows. First, we establish a novel sum average mean-square-error (AMSE) duality. Second, we formulate the power allocation part of the problem in the downlink channel as a Geometric Program (GP). Third, using the duality result and the solution of GP, we utilize alternating optimization technique to solve the original downlink problem. To solve robust sum MSE minimization constrained with per BS antenna and per BS power problems, we have established novel downlink-uplink duality. On the other hand, to solve robust sum MSE minimization constrained with per user and per symbol power problems, we have established novel downlink-interference duality. For the total BS power constrained robust sum MSE minimization problem, the current duality is established by modifying the constraint function of the dual uplink channel problem. And, for the robust sum MSE minimization with per BS antenna and per user (symbol) power constraint problems, our duality are established by formulating the noise covariance matrices of the uplink and interference channels as fixed point functions, respectively. We also show that our sum AMSE duality are able to solve other sum MSE-based robust design problems. Computer simulations verify the robustness of the proposed robust designs compared to the nonrobust/naive designs.