Wence Zhang

Large-Scale Antenna Systems With UL/DL Hardware Mismatch: Achievable Rates Analysis and Calibration

This paper studies the impact of hardware mismatch (11M) between the base station (BS) and the user equipment (UE) in the downlink (DL) of large-scale antenna systems. Analytical expressions to predict the achievable rates are derived for different precoding methods, i.e., matched filter (MF) and regularized zero-forcing (RZF), using large system analysis techniques. Furthermore, the upper bounds on achievable rates of MF and RZF with 11M are investigated, which are related to the statistics of the circuit gains of the mismatched hardware. Moreover, we present a study of 11M calibration, where we take zero-forcing (ZF) precoding as an example to compare two 11M calibration schemes, i.e., Pre-precoding Calibration (Pre-Cal) and Post-precoding Calibration (Post-Cal). The analysis shows that Pre-Cal outperforms Post-Cal schemes. Monte-Carlo simulations are carried out, and numerical results demonstrate the correctness of the analysis.

Distributed Energy-Efficient Power Optimization for CoMP Systems With Max-Min Fairness

This letter considers the power optimization problem for downlink transmission in coordinated multi-point (CoMP) systems. We aim to maximize the minimum weighted energy efficiency (EE) with QoS constraint and limited intercell coordination. The established optimization problem is converted to the standard form of max-min fractional programming problem first. Then we discuss feasibility of the Dual Dinkelbach-type Algorithm (DDA). A distributed algorithm is proposed to solve the subproblem in DDA with limited intercell coordination, where only a small number of positive scalars are shared between base stations. Simulation results show that the proposed algorithm outperforms previous schemes in terms of both the minimum EE of all the BSs EE and fairness.

Weighted Sum Energy Efficiency Maximization in Ad Hoc Networks

In this letter, we propose a distributed adaptive-pricing algorithm aimed at solving the weighted sum energy efficiency (EE) maximization problem in ad hoc networks. It is theoretically proven that the proposed distributed algorithm strictly converges to the Karush-Kuhn-Tucker (KKT) point of the problem. Significant performance enhancement is observed by numerical results with fast convergence. Moreover, it is shown that the proposed algorithm degrades gracefully when decreasing overhead of information exchange.

Power Minimization in Multi-Band Multi-Antenna Cognitive Radio Networks

This paper aims to design an optimal set of beam-vectors for multi-band multi-antenna cognitive radio networks that jointly allocate power over both space and frequency, so that the sum power of secondary users (SUs) is minimized, subject to rate demands at the SUs, as well as the interference constraints imposed by primary users. Unlike the rate maximization problems, which are always feasible, this power minimization (PM) problem may be infeasible due to the rate constraints. Therefore, we provide a complete analysis of the PM problem by splitting the solution into two separate phases. In phase I, a novel method is developed to check the feasibility of the PM problem by considering an alternative problem, where one additional variable is introduced. This alternative problem is always feasible and one algorithm based on network duality and geometric programs is developed to solve it. In phase II, a novel algorithm is developed to solve the PM problem. This algorithm can be implemented in an online fashion. Furthermore, this algorithm is proved to converge to a Karush-Kuhn-Tucker point of the PM problem. Simulation results show that the proposed algorithms converge in only a few iterations and significantly outperform the existing single-band method in terms of both the feasibility probabilities and power savings.

Widely Linear Precoding for Large-Scale MIMO with IQI: Algorithms and Performance Analysis

In this paper, we study widely linear precoding techniques to mitigate in-phase/quadrature-phase (IQ) imbalance (IQI) in the downlink of large-scale multiple-input multiple-output (MIMO) systems. We adopt a real-valued signal model, which considers the IQI at the transmitter, and then develop widely linear zero-forcing (WL-ZF), widely linear matched filter, widely linear minimum mean-squared error, and widely linear block-diagonalization (WL-BD) type precoding algorithms for both single- and multiple-antenna users. We also present a performance analysis of WL-ZF and WL-BD. It is proved that without IQI, WL-ZF has exactly the same multiplexing gain and power offset as ZF, while when IQI exists, WL-ZF achieves the same multiplexing gain as ZF with ideal IQ branches, but with a minor power loss, which is related to the system scale and the IQ parameters. We also compare the performance of WL-BD with BD. The analysis shows that with ideal IQ branches, WL-BD has the same data rate as BD, while when IQI exists, WL-BD achieves the same multiplexing gain as BD without IQ imbalance. Numerical results verify the analysis and show that the proposed widely linear type precoding methods significantly outperform their conventional counterparts with IQI and approach those with ideal IQ branches.

Joint TX/RX IQ imbalance parameter estimation using a generalized system model

The joint estimation and compensation of IQ imbalance (IQI) parameters at both transmitter (TX) and receiver (RX) is studied in this paper. We develop a generalized system model with a reduced number of parameters (RNP) that covers a wide range of mobile communications scenarios. We devise efficient direct least-squares (DLS) and alternating least-squares (ALS) techniques for IQI parameter estimation based on the generalized system model. For the ALS based method, we prove that the algorithm will converge to a local optimal solution of the optimization problem. Numerical results show that compared with a previously reported method, the proposed DLS-RNP achieves similar performance with a reduced computational complexity, and the proposed ALS-RNP algorithm has significantly better performance with comparable complexity, with a gain over 5 dB for QPSK and 10 dB for 64QAM in the high signal-to-noise-ratio (SNR) region.

Simplified matrix polynomial-aided block diagonalization precoding for massive MIMO systems

In the downlink of Massive Multiple-Input-Multiple-Output (MIMO) systems, the high computational cost of precoding is a major challenge for real-time data transmission. In this paper, we propose a simplified matrix polynomial-aided block diagonalization (SMP-BD) precoding scheme to simplify the conventional block diagonalization (BD) type precoding schemes for users with multiple antennas. By replacing the singular value decomposition (SVD) operation with a matrix inversion, which is then approximated by matrix polynomials with optimized coefficients, SMP-BD is shown to be hardware-efficient, reduce the transmission overhead and obtain high performance. The optimized coefficients can be calculated offline and the convergence of the matrix polynomials is guaranteed. Simulations show that SMP-BD with polynomial order L = 3 performs close to previously reported algorithms based on matrix inversions, while being much simpler.

Achievable rate analysis of large scale antenna systems with hardware mismatch in UL/DL

This paper studies the impact of hardware mismatch (HM) between base station (BS) and user equipment in the downlink of large scale antenna systems. Analytical expressions to predict the achievable rates are derived for different precoding methods, i.e. matched filter (MF) and regularized zero-forcing (RZF), using large system analysis techniques. Furthermore, the asymptotic downlink signal to interference plus noise ratio (SINR) under HM is investigated, which is only related to the variances of circuit gains in most practical scenarios. Monte-Carlo simulations are carried out, and numerical results demonstrate the correctness of the analysis.

Correlation-driven optimized Taylor expansion precoding for massive MIMO systems with correlated channels

Hardware-efficient low-complexity precoding is very important in the downlink of Massive MIMO systems for mitigating interference and optimizing performance. In this paper, we propose a correlation-driven optimized Taylor expansion (CD-OTE) precoding scheme to simplify linear minimum mean square error (MMSE) precoding. In order to simplify the hardware-expensive matrix inversion involved in the linear MMSE pre-coder, a Taylor expansion with optimized polynomial coefficients and selection of the most relevant correlation coefficients is proposed. We take into consideration the correlation between different users channels and develop a general design criterion. Both convergence and complexity analyses are carried out. Simulation results show that the proposed CD-OTE precoder is significantly better than previously reported techniques, while requiring a similar cost.