In this paper, we consider the interference rejection combining (IRC) receiver, which improves the cell-edge user throughput via suppressing inter-cell interference and requires estimating the covariance matrix including the inter-cell interference with high accuracy. In order to solve the problem of sample covariance matrix estimation with limited samples, a regularization parameter optimization based on the minimum eigenvalue criterion is developed. It is different from traditional methods that aim at minimizing the mean squared error, but goes straight at the objective of optimizing the final performance of the IRC receiver. A lower bound of the minimum eigenvalue that is easier to calculate is also derived. Simulation results demonstrate that the proposed approach is effective and can approach the performance of the oracle estimator in terms of the mutual information metric.

This paper proposes novel linear precoding algorithms for Multiple-Input Multiple-Output Bit-Interleaved Coded Modulation (MIMO-BICM) systems that maximize the achievable rate subject to power constraints. To overcome the nonlinear and nonconvex nature of the optimization problem, we rewrite the achievable rate in terms of the log-likelihood ratio (LLR) and introduce manifold-based gradient ascent (MGA) precoding and low-complexity non-iterative algorithms. Simulation results show significant gains in achievable rate and block error rate compared to existing techniques. Additionally, we extend our investigation to linear precoding with the constraint that the precoding matrix is selected from the codebook type-I adopted in Fifth-Generation New Radio (5G NR) networks. We propose heuristic algorithms that exploit the Kronecker and Discrete Fourier Transform (DFT) structure of the codebook and consider the singular vector decomposition (SVD) precoder as the optimal reference precoder. The traditional exhaustive search methods require a high complexity, especially for large codebook sizes. However, our proposed algorithms apply a combination of direct estimation and a low-dimensional search for deriving the indices, resulting in a reduced number of codebook precoder candidates. Simulation results show that our proposed low-complexity algorithms perform comparably to exhaustive search baselines.

In this paper, we consider the design of joint resource blocks (RBs) and power allocation for dual-mode base stations operating over millimeter wave (mmW) band and microwave (μW) band. The resource allocation design aims to minimize the system energy consumption while taking into account the channel state information, maximum delay, load, and different types of user applications (UAs). To facilitate the design, we first propose a group-based algorithm to assign UAs to multiple groups. Within each group, low-power UAs, which often appear in short distance and experience less obstacles, are inclined to be served over mmW band. The allocation problem over mmW band can be solved by a greedy algorithm. Over μW band, we propose an estimation-optimal-descent algorithm. The rate of each UA at all RBs is estimated to initialize the allocation. Then, we keep altering RB's ownership until any altering makes power increases. Simulation results show that our proposed algorithm offers an excellent tradeoff between low energy consumption and fair transmission.

This paper investigates the hybrid precoding design for millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems with finite-alphabet inputs. The mmWave MIMO system employs partially-connected hybrid precoding architecture with dynamic subarrays, where each radio frequency (RF) chain is connected to a dynamic subset of antennas. We consider the design of analog and digital precoders utilizing statistical and/or mixed channel state information (CSI), which involve solving an extremely difficult problem in theory: First, designing the optimal partition of antennas over RF chains is a combinatorial optimization problem, whose optimal solution requires an exhaustive search over all antenna partitioning solutions; Second, the average mutual information under mmWave MIMO channels lacks closed-form expression and involves prohibitive computational burden; and Third, the hybrid precoding problem with given partition of antennas is nonconvex with respect to the analog and digital precoders. To address these issues, this paper first presents a simple criterion and the corresponding low complexity algorithm to design the optimal partition of antennas using statistical CSI. Then, it derives the lower bound and its approximation for the average mutual information, in which the computational complexity is greatly reduced compared to calculating the average mutual information directly. In addition, it also shows that the lower bound with a constant shift offers a very accurate approximation to the average mutual information. This paper further proposes utilizing the lower bound approximation as a low-complexity and accurate alternative for developing a manifold-based gradient ascent algorithm to find near-optimal analog and digital precoders. Several numerical results are provided to show that our proposed algorithm outperforms the existing hybrid precoding algorithms.

Hybrid precoding provides a tradeoff between spectral efficiency and power consumption in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. In this paper, we investigate the partially-connected hybrid precoding design for mmWave MIMO broadcast channels with finite alphabet inputs. To enhance the spectral efficiency, a new algorithm is proposed to dynamically optimize the mapping strategy from radio frequency (RF) chains to transmit antennas such that the weighted sum of channel gains is maximized. Then we adopt the inexact alternating minimization method to design hybrid precoding matrices with given optimal mapping strategy and finite-alphabet inputs. Simulation results demonstrate the good performance of our proposed algorithm.

This paper investigates the hybrid precoding design for millimeter wave multiple-input multiple-output systems with finite-alphabet inputs. The precoding problem is a joint optimization of analog and digital precoders, and we treat it as a matrix factorization problem with power and constant modulus constraints. This paper presents three main contributions. First, we present a sufficient condition and a necessary condition for hybrid precoding schemes to realize unconstrained optimal precoders exactly when the number of data streams Nssatisfies Ns= min{rank(H), Nrf}, where H represents the channel matrix and Nrfis the number of radio frequency chains. Second, we show that the coupled power constraint in our matrix factorization problem can be removed without loss of optimality. Third, we propose a Broyden-Fletcher-Goldfarb-Shanno-based algorithm to solve our matrix factorization problem using gradient and Hessian information. Several numerical results are provided to show that our proposed algorithm outperforms existing hybrid precoding algorithms.

This paper investigates the hybrid precoding design for millimeter wave (mmWave) multiple-input multiple- output(MIMO) systems with finite alphabet inputs. The precoding problem is a joint optimization of analog and digital precoders,and it imposes nonconvex constant modulus constraints on the analog precoder. We treat this problem as a matrix factorization problem with constant modulus constraints. The main contributions of our work are listed as follows: First, we propose sufficient and necessary conditions for hybrid precoding schemes to realize any unconstrained optimal precoders exactly when the number of data streams is equal to the number of radio frequency chains. Second, we show that the power constraint in the hybrid precoding problem can be removed without loss of optimality. Third, we present a trust region Newton method to solve our problem using gradient and Hessian information, and the proposed algorithm converges to a stationary point satisfying the first and second order necessary optimality conditions. Several numerical examples are provided to show that the proposed algorithm outperforms existing hybrid precoding algorithms.

This paper investigates a new class of nonconvex optimization, which provides a unified framework for linear precoder design. The new optimization is called generalized quadratic matrix programming (GQMP). Due to the non-deterministic polynomial time (NP)-hardness of GQMP problems, we provide a polynomial time algorithm that is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) point. In terms of application, we consider the linear precoder design problem for spectrum-sharing secure broadcast channels. We design linear precoders to maximize the average secrecy sum rate with finite-alphabet inputs and statistical channel state information (CSI). The precoder design problem is a GQMP problem and we solve it efficiently by our proposed algorithm. A numerical example is also provided to show the efficacy of our algorithm.

This paper investigates the linear precoder design for cognitive multiple-access wiretap channel (CMAC-WT), where two secondary-user transmitters (STs) communicate with one secondary-user receiver (SR) in the presence of an eavesdropper and subject to interference threshold constraints at primary-user receivers (PRs). It designs linear precoders to maximize the ergodic secrecy sum rate for multiple-input multiple-output (MIMO) CMAC-WT under finite-alphabet inputs and statistical channel state information (CSI). For this non-convex problem, a two-layer algorithm is proposed by embedding the convex-concave procedure into an outer approximation framework. The key idea of this algorithm is to reformulate the approximated ergodic secrecy sum rate as a difference of convex (DC) functions, and then generate a sequence of simpler relaxed sets to approach the non-convex feasible set. In this way, near optimal precoding matrices are obtained by maximizing the approximated ergodic secrecy sum rate over a sequence of relaxed sets. Numerical results show that the proposed precoder design provides a significant performance gain over the Gaussian precoding method in the medium and high SNR regimes.