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It is a challenge to apply the precoder design maximizing the mutual information with finite-alphabet inputs to practical multiple-input multiple-output (MIMO) systems, because it needs to iteratively solve an optimization problem, which is difficult to satisfy the requirement of real time. This letter develops a deep learning based precoding scheme, which employs the property of deep neural network (DNN) as approximator of functions. Simulation results show that a DNN can accurately learn the input-output relationship of a nearly optimal precoder achieved by the traditional interior-point method (IPM); moreover, in different MIMO scenarios, a trained DNN of small size offers almost the same performance as the nearly optimal precoder, but with huge improvement in efficiency, especially in cases of higher modulation and more antennas. The improved efficiency makes it possible to be applied to practical communication systems. [ABSTRACT FROM AUTHOR]

Due to the lack of explicit expression of instantaneous mutual information (IMI), online throughput optimization for energy harvesting (EH) MIMO systems with finite alphabet inputs suffers from heavy computation burden and large feedback overhead. We propose a two-layer predictive strategy with the aid of context information, by constructing a precoding codebook offline at both the transmitter and receiver, to relax the time pressure for the online strategy. Two schemes were provided to accelerate the codebook construction. One is to estimate IMI during optimization by using a reference function. The other is by starting the optimization of each precoder from an adjacent precoder. With the channel distribution information (CDI), the feedback overhead is reduced by minimizing the codebook size. By the methods above, the building speed of the precoding codebook can be up to two orders of magnitude faster than no accelerating strategies. Meanwhile, through topology analysis, we propose a higher layer predictive strategy, which determines the index of allocated energy at each slot within a frame. The higher layer strategy takes full advantage of the constructed precoding codebook and context information when estimating the future throughput. Simulation results demonstrate the effectiveness and practicality of the proposed two-layer strategy. [ABSTRACT FROM AUTHOR]

Existing research on a simultaneous wireless information and power transfer (SWIPT) system is based on the assumption of Gaussian inputs. However, the optimal design on Gaussian inputs may lead to dramatic performance loss for practical systems with finite-alphabet inputs. This paper focuses on the precoder design for a SWIPT system with finite-alphabet inputs and instantaneous channel state information over a multiple-input multiple-output channel. We formulate the optimal precoder design as an optimization problem, in which the objective is to maximize the mutual information over the channel from the transmitter to the information receiver under the constraints of transmit power and harvested energy threshold. The formulated problem is NP-hard, so a global optimal solution cannot be found within the polynomial time. The main contributions of this paper are as follows: 1) By using its structure, the NP-hard problem is relaxed to a semidefinite programming problem. Then, a general solving framework for both co-located and separated receiver cases, based on the semidefinite relaxation (SDR) technique, is developed to achieve a near optimal precoder. 2) For the case of co-located receivers, we show that the optimal precoder design is a concave problem with respect to power allocation; then, a specific algorithm for co-located receivers is proposed. Compared with the general SDR-based method, the specific algorithm for co-located receivers exhibits almost the same performance but much lower complexity. 3) The performance of several practical co-located receiver designs is analyzed in SWIPT systems with finite-alphabet inputs. Finally, we provide simulation results to show the efficacy of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

This paper considers an online multiantenna precoder design for energy harvesting transmitter based on finite-alphabet inputs and statistical channel state information (CSI). It formulates the problem by maximizing the sum of average mutual information (AMI) of multiple time slots in one channel frame with causal energy constraint. The formulated problem is a \mbox2Nt^2- dimensional stochastic dynamic programming (SDP) problem with a nonconcave objective function, where Nt is the number of transmit antennas. The \mbox2Nt^2- dimensional SDP problem is prohibitively complex and prevents the development of online algorithm for multiantenna systems. We provide equivalence between the multidimensional SDP problem and a one-dimensional power choice problem. Solving the one-dimensional equivalence decreases the computational burden extraordinarily without loss of optimality. We separate the one-dimensional problem into two cases: 1) the discrete-battery-state–discrete-power-choice (DBDP) problem and 2) the continuous-battery-state–continuous-power-choice (CBCP) problem. We also develop numerical algorithms to solve them, respectively. We further analyze the computational complexity of the developed algorithms and demonstrate the significant performance gains offered by the proposed method when compared with other methods. [ABSTRACT FROM AUTHOR]

This paper considers the precoder design for multiantenna secure cognitive radio networks. We use finite-alphabet inputs as the signaling and exploit statistical channel state information (CSI) at the transmitter. We maximize the secrecy rate of the secondary user and control the transmit power and the power leakage to the primary receivers that share the same frequency spectrum. The secrecy rate maximization is important for practical systems, but challenging to solve, mainly due to two reasons. First, the secrecy rate with statistical CSI is computationally prohibitive to evaluate. Second, the optimization over the precoder is a nondeterministic polynomial-time hard (NP-hard) problem. We utilize an accurate approximation of the secrecy rate to reduce the computational effort and then propose a global optimization approach based on branch-and-bound method. The idea is to define a simplex and transform the secrecy rate into a concave function. The derived concave function converges to the secrecy rate when the defined simplex shrinks down. Using this feature, we solve a sequence of concave maximization problems over iteratively shrinking simplices and eventually attain the globally optimal solution that maximizes the approximation of the secrecy rate. When the complexity is concerned, a low-complexity variant with limited number of iterations can be used in practice. We demonstrate the performance gains when compared with others through numerical examples. [ABSTRACT FROM PUBLISHER]

This paper proposes an online resource allocation algorithm for weighted sum rate maximization in energy harvesting downlink multiuser multiple-input–multiple-output (MIMO) systems, where the base station transmitter is powered by both a regular energy source and an energy buffer that is connected to an energy harvester. Taking into account the discrete nature of the modulation and coding rates (MCRs) used in practice, we formulate a stochastic dynamic programming (SDP) problem to jointly design the MIMO precoders, select the MCRs, assign the subchannels, and optimize the energy consumption over multiple time slots with causal and statistical energy arrival information and statistical channel state information. Solving this high-dimensional SDP entails several difficulties: the SDP has a nonconcave objective function, the optimization variables are of mixed binary and continuous types, and the number of optimization variables is on the order of thousands. We propose a new method to solve this NP-hard SDP by decomposing the high-dimensional SDP into an equivalent three-layer optimization problem and develop efficient algorithms to solve each layer separately. The decomposition reduces the computational burden and breaks the curse of dimensionality successfully. We analyze the complexity of the proposed algorithm and demonstrate the performance gains based on numerical examples. [ABSTRACT FROM PUBLISHER]

This work investigates the design of linear precoders in cooperative multi-cell MIMO downlink coverage for finite-alphabet source signals. Traditional design of multi-cell MIMO downlink precoder relies on Gaussian input assumption, which may lead to performance loss when true data inputs consist of discrete non-Gaussian symbols. This work presents optimized precoders for finite-alphabet input by maximizing the sum rate under per-base station power constraints. Specifically, we propose two distributed algorithms: a finite-alphabet signal Gaussian interference gradient projection algorithm and a block diagonalization alternating optimization algorithm while supporting interference cancellation. Our numerical results demonstrate considerable performance gain in terms of approximate transmission data rate as well as decoded bit error rate over precoding schemes designed by using the non-realistic Gaussian input assumption. [ABSTRACT FROM PUBLISHER]

This paper investigates the linear precoder design for spectrum sharing in multi-antenna cognitive radio networks with finite-alphabet inputs. It formulates the precoding problem by maximizing the constellation-constrained mutual information between the secondary-user transmitter and secondary-user receiver while controlling the interference power to primary-user receivers. This formulation leads to a nonlinear and nonconvex problem, presenting a major barrier to obtain optimal solutions. This work proposes a global optimization algorithm, namely Branch-and-bound Aided Mutual Information Optimization (BAMIO), that solves the precoding problem with arbitrary prescribed tolerance. The BAMIO algorithm is designed based on two key observations: First, the precoding problem for spectrum sharing can be reformulated to a problem minimizing a function with bilinear terms over the intersection of multiple co-centered ellipsoids. Second, these bilinear terms can be relaxed by its convex and concave envelopes. In this way, a sequence of relaxed problems is solved over a shrinking feasible region until the tolerance is achieved. The BAMIO algorithm calculates the optimal precoder and the theoretical limit of the transmission rate for spectrum sharing scenarios. By tuning the prescribed tolerance of the solution, it provides a trade-off between desirable performance and computational complexity. Numerical examples show that the BAMIO algorithm offers near global optimal solution with only several iterations. They also verify that the large performance gain in mutual information achieved by the BAMIO algorithm also represents the large gain in the coded bit-error rate. [ABSTRACT FROM AUTHOR]

This paper investigates the linear precoder design that maximizes the average mutual information of multiple-input multiple-output fading channels with statistical channel state information known at the transmitter. It formulates the design from the standpoint of finite-alphabet inputs, which leads to a problem that is very important in practice but extremely difficult in theory: First, the average mutual information lacks closed-form expression and involves prohibitive computational burden. Second, the optimization over the precoder is nonconcave and thus easily gets stuck in local maxima. To address these issues, this study first derives lower and upper bounds for the average mutual information, in which the computational complexity is reduced by several orders of magnitude compared to calculating the average mutual information directly. It proves that maximizing the bounds is asymptotically optimal and shows that, with a constant shift, the lower bound actually offers a very accurate approximation to the average mutual information for various fading channels. This paper further proposes utilizing the lower bound as a low-complexity and accurate alternative for developing a two-step algorithm to find a near global optimal precoder. Numerical examples demonstrate the convergence and efficacy of the proposed algorithm. Compared to its conventional counterparts, the proposed linear precoding method provides significant performance gain over existing precoding algorithms. The gain becomes more substantial when the spatial correlation of MIMO channels increases. [ABSTRACT FROM AUTHOR]

This paper considers the precoder design for dual-hop amplify-and-forward relay networks and formulates the design from the standpoint of finite-alphabet inputs. In particular, the mutual information is employed as the utility function, which, however, results in a nonlinear and nonconcave problem. This paper exploits the structure of the optimal precoder that maximizes the mutual information and develops a two-step algorithm based on convex optimization and optimization on the Stiefel manifold. By doing so, the proposed algorithm is insensitive to initial point selection and able to achieve a near global optimal precoder solution. Besides, it converges fast and offers high mutual information gain. These advantages are verified by numerical examples, which also show the large performance gain in mutual information also represents the large gain in the coded bit-error rate. [ABSTRACT FROM PUBLISHER]

In this paper, we study linear precoding for multiple-input multiple-output (MIMO) multiple access channels (MAC) with finite discrete inputs. We derive the constellation-constrained capacity region for the MIMO MAC with an arbitrary number of users and find that the boundary can be achieved by solving the problem of weighted sum rate maximization with constellation and individual power constraints. Due to the non-concavity of the objective function, we obtain a set of necessary conditions for the optimization problem through Karush-Kuhn-Tucker analysis. To find the optimal precoding matrices for all users, we propose an iterative algorithm utilizing alternating optimization strategy. In particular, each iteration of the algorithm involves the gradient descent update with backtracking line search. Numerical results show that when inputs are digital modulated signals and the signal-to-noise ratio is in the medium range, our proposed algorithm offers considerably higher sum rate than non-precoding and the traditional method which maximizes Gaussian-input sum capacity. Furthermore, a low-density parity-check coded system with iterative detection and decoding for MAC is presented to evaluate the bit error rate (BER) performance of precoders. BER results also indicate that the system with the proposed linear precoder achieves significant gains over the non-precoding system and the precoder designed for Gaussian inputs. [ABSTRACT FROM PUBLISHER]

A low-complexity, near-optimal transmit antenna selection algorithm is proposed for multi-relay networks where all nodes are equipped with multiple antennas. We first establish a system model and a unified capacity maximization framework for a two-hop opportunistic relaying scheme where the source node (S) transmits signals to multiple relay nodes (R) in the first time slot, and the selected relay antennas and their corresponding relay nodes receive, decode and forward the messages to the destination (D) in the second time slot. Based on the system model, we develop a transmit antenna selection algorithm that maximizes the network capacity assuming that the channel state information is available at the receivers but not available at the transmitters, and total transmit power constraints are imposed on source/relay transmitters. The proposed algorithm first constructs a sorted list of relay antennas with decreasing S-R capacities, then iteratively maximizes the R-D capacity over a candidate antenna set using a low-complexity, near-optimal antenna selection scheme. The candidate set is reduced in the next iteration according to the selected antenna set of the current iteration. The overall network capacity is computed for the selected antenna sets of all iterations, and the set yielding the highest S-R-D capacity is the solution to the maximization problem. We show that this novel iterative algorithm achieves near-optimal solution and has a polynomial-time complexity. We also derive the lower and upper bounds of the achievable network capacity for both average capacity and outage capacity. Numerical examples show the significant performance gains obtained via the proposed scheme compared to its conventional counterparts. [ABSTRACT FROM PUBLISHER]