Optimal Precoder Research Articles

Joint UAV Deployment and Precoder Optimization for Multicasting and Target Sensing in UAV-Assisted ISAC Networks

Joint UAV Deployment and Precoder Optimization for Multicasting and Target Sensing in UAV-Assisted ISAC Networks

Optimized CNN and adaptive RBFNN for channel estimation and hybrid precoding approaches for multi user millimeter wave massive MIMO

ABSTRACT The millimetre-wave (mmWave) communication satisfies the demand for high data rates due to the characteristic of wide bandwidth. Using massive multiple-input multiple-output (MIMO) technology, a significant propagation loss of mmWave communication is effectively compensated. However, it is challenging to provide a specialised radio frequency chain for each antenna due to the constrained physical area with closely spaced antennas and prohibitive power consumption in mmWave massive MIMO systems. This paper presents novel approaches for effective channel estimation and hybrid precoding in mmWave communication systems. To address the challenges of channel estimation, a convolutional neural network (CNN) is utilised, and network parameters are optimised using enhanced whale optimization algorithm (EWOA). The proposed CNN-based channel estimation method aims to accurately estimate the channel in mmWave systems with enhanced efficiency and reduced complexity. By training CNN using EWOA optimisation algorithm, the network parameters are fine-tuned to improve accuracy and generalisation capability of channel estimation process. Furthermore, hybrid precoding is achieved using adaptive radial-basis function neural networks (adaptive RBFNNs) which enables efficient precoding while minimising complexity. Moreover, the adaptive RBFNN approach determines the optimal precoding weights based on channel state information, resulting in a improved performance and a reduced computational overhead. The performance analysis is validated using the MATLAB/Simulink software and offers to provide effectual and reliable mmWave communication systems, facilitating the realisation of high-speed and high-capacity wireless networks.

Numtrical Evaluation of Optimal Precoding Algorithm and Zero Forcing Precoding in MU-MISO Channel with Channel Aging

In paper the numerical investigation of the impact of delayed channel state information (CSI) due to user movement and caused channel aging on the performance of multiuser precoder in downlink MISO system. The time variant CSI is obtained by the least squares channel estimation. We consider Zero Forcing algorithm and numerical optimization based solution of calculating precoder vectors maximizimg sum rate of multiuser system. For numerical simulation the QuaDRiGa channel model reflecting the real propagation conditions for moving users is used. The obtained performance of multiuser Zero Forcing and optimization based beamforming in spatially correlated channel are compared based on the empirical cumulative density function of the sum rate of multiple users.

Jamming precoding in AF relay-aided PLC systems with multiple eavessdroppers

Enhancing information security has become increasingly significant in the digital age. This paper investigates the concept of physical layer security (PLS) within a relay-aided power line communication (PLC) system operating over a multiple-input multiple-output (MIMO) channel based on MK model. Specifically, we examine the transmission of confidential signals between a source and a distant destination while accounting for the presence of multiple eavesdroppers, both colluding and non-colluding. We propose a two-phase jamming scheme that leverages a full-duplex (FD) amplify-and-forward (AF) relay to address this challenge. Our primary objective is to maximize the secrecy rate, which necessitates the optimization of the jamming precoding and transmitting precoding matrices at both the source and the relay while adhering to transmit power constraints. We present a formulation of this problem and demonstrate that it can be efficiently solved using an effective block coordinate descent (BCD) algorithm. Simulation results are conducted to validate the convergence and performance of the proposed algorithm. These findings confirm the effectiveness of our approach. Furthermore, the numerical analysis reveals that our proposed algorithm surpasses traditional schemes that lack jamming to achieve higher secrecy rates. As a result, the proposed algorithm offers the benefit of guaranteeing secure communications in a realistic channel model, even in scenarios involving colluding eavesdroppers.

Asymptotic SEP Analysis and Optimization of Linear-Quantized Precoding in Massive MIMO Systems

Asymptotic SEP Analysis and Optimization of Linear-Quantized Precoding in Massive MIMO Systems

Precoder Optimization Using Data Correlation for Wireless Data Aggregation

Precoder Optimization Using Data Correlation for Wireless Data Aggregation

On the Application of Quasi-Degradation to Network NOMA in Downlink CoMP Systems

The application of network non-orthogonal multiple access (N-NOMA) technique to coordinated multi-point (CoMP) systems has attracted significant attention due to its superior capability to improve connectivity and maintain reliable transmission for CoMP users simultaneously. Based on the concept of quasi-degraded channel for N-NOMA, this paper studies the precoding design for downlink N-NOMA scenarios with two base stations (BSs) equipped with multiple antennas. In specific, under quasi-degraded channels, simple linear precoding based N-NOMA can achieve the same minimal total transmission power as theoretically optimal but complicated dirty paper coding (DPC) scheme, when the users’ target rates and minimal transmission power of each BS are given. In this paper, the channel quasi-degradation (QD) condition is first rigorously derived for the scenario with single CoMP user and two NOMA users. The closed-form optimal precoders for N-NOMA under quasi-degraded channels are also provided. Then, based on QD condition, a novel hybrid N-NOMA (H-N-NOMA) scheme is proposed, which is a mixture of N-NOMA and conventional zero-forcing beamforming (ZFBF) scheme. Further, for the scenarios with more users, a low-complexity QD based user pairing (QDUP) algorithm is proposed, which is then combined with H-N-NOMA to make a novel scheme termed H-N-NOMA/QDUP into being. Numerical results are presented to reveal the impact factors on QD channels, and also demonstrate the superior performance of the proposed H-N-NOMA/QDUP scheme. It is shown that the proposed H-N-NOMA/QDUP scheme can effectively exploit the benefit of multi user diversity.

A Meta-Learning-Based Precoder Optimization Framework for Rate-Splitting Multiple Access

A Meta-Learning-Based Precoder Optimization Framework for Rate-Splitting Multiple Access

Transmitter Precoder Design to Improve the Performance of the MUSIC Algorithm

Using the MUSIC (MUltiple SIgnal Classification) algorithm to estimate the angle-of-arrival (AoA), we derive a new asymptotic error variance bound when the transmitted signal can be pre-processed. We next propose a precoder design to achieve this bound. However, such an optimal precoder requires channel state information at the transmitter (CSIT) exclusive of the receiver array which cannot be separately estimated practically. A more feasible precoder design, which leverages on the feedback CSIT estimated at the receiver, is next proposed. Using the performance of the optimal precoder which achieves the bound asymptotically as a benchmark, the practical precoder design performs closely to the optimal precoder even in high-resolution scenario. Both precoder schemes exhibit performance improvement compared with the case when no precoder is used.

Rate-Splitting Multiple Access With Finite-Alphabet Constellations: Precoder Optimization and Achievable Rate Performances

Rate-Splitting Multiple Access With Finite-Alphabet Constellations: Precoder Optimization and Achievable Rate Performances

Precoding-Based Downlink OAM-MIMO Communications With Rate Splitting

Orbital angular momentum (OAM) and rate splitting (RS) are the potential key techniques for the future wireless communications. As a new orthogonal resource, OAM can achieve the multifold increase of spectrum efficiency to relieve the scarcity of the spectrum resource, but how to enhance the privacy performance imposes crucial challenge for OAM communications. RS technique divides the information into private and common parts, which can guarantee the privacies for all users. In this paper, we integrate the RS technique into downlink OAM-MIMO communications, and study the precoding optimization to maximize the sum capacity. First, the concentric uniform circular arrays (UCAs) are utilized to construct the downlink transmission framework of OAM-MIMO communications with RS. Particularly, users in the same user pair utilize RS technique to obtain the information and different user pairs use different OAM modes. Then, we derive the OAM-MIMO channel model, and formulate the sum capacity maximization problem. Finally, based on the fractional programming, the optimal precoding matrix is obtained to maximize the sum capacity by using quadratic transformation. Extensive simulation results show that by using the proposed precoding optimization algorithm, OAM-MIMO communications with RS can achieve higher sum capacity than the traditional communication schemes.

A Unified Rate-Splitting Framework for Secure Spectrum Sharing via Joint Precoding Optimization

To handle the security problem of cognitive radio (CR) systems, secondary users (SUs) are able to be designed to assist the primary user (PU) in secure transmission. To realize the spectrum sharing among CR users, we adopt the rate-splitting multiple access (RSMA) properly, with nonorthogonal multiple access (NOMA) and space division multiple access (SDMA) as its special cases. In this article, we set up a unified rate-splitting framework to guarantee the safe spectrum sharing through multiple multiaccess strategies, without the knowledge of wiretap channels. The precoding vectors at the transmitter are conjointly designed to boost the transmission of common stream, which is from both PU and SU. Besides, the private stream of the downlink CR system can be well secreted in the high-power common signal. As for NOMA and SDMA, when the message of SU is regarded as the friendly jamming signal, the security transmission requirement of PU can be similarly guaranteed via precoding design. Simulations validate that the rate-splitting framework we established can be effective in ensuring the spectrum sharing security for the secure users through RSMA, and the security for NOMA and SDMA is also able to be promoted through adjusting our designed framework.

Adaptive Precoding for Aeronautical Communication Systems in Public Channels

The family of aeronautical communication systems is considered as one of the core technologies for the beyond-the-fifth-generation (B5G) networks, thus attracting rapidly increasing attention from both industry and academia. In this paper, a new multiple-input multiple-output (MIMO) precoding scheme is proposed for aeronautical communication systems, where the transmit power for the aerial user equipment (UE) approaching the target cell is maximized based on the direction information. Specifically, we first transform the precoding optimization problem into a linear programming problem, and then develop two algorithms to derive the precoders for two transmission modes, namely the multi-stream and single-stream transmissions. For the multi-stream case, an algorithm based on manifold optimization is proposed, which guarantees the equal power allocation for each antenna. For the single-stream case, we derive a precoder by a numerically stable algorithm that extends the Newton-Raphson method. Simulation results show that the proposed precoding schemes can improve the link performance of the UEs with known directions, while ensuring that the performance of the UEs with unknown directions can still be close to that achievable by conventional schemes.

Joint Precoding and CSI Dimensionality Reduction: An Efficient Deep Unfolding Approach

A recently proposed unified precoding and pilot design optimization (UPPiDO) framework offers a reduction in both training and feedback overhead of acquiring channel state information (CSI) and an enhancement in robustness (to CSI uncertainties) at the expense of a more computationally demanding precoding optimization. To address this increased complexity, in this paper we first propose an unfolding-friendly iterative algorithm, which can efficiently address a family of non-convex and non-smooth problems. Then, we develop an efficient approach to unfold the iterative algorithm designed. Besides being applicable to important and typical iterative optimization algorithms, a pivotal advantage of the proposed unfolding approach is that the trainable parameters are scalars (rather than matrices). This, in turn, reduces the number of training samples required and makes it suitable for rapidly fluctuating wireless environments. We apply the algorithm unfolding (AU) techniques developed to our UPPiDO-based symbol-level precoding and block-level precoding. Our complexity analysis indicates that the computational complexity is scalable both with the numbers of served users and antennas. Our simulation results demonstrate that the number of outer iterations (or layers) required is about 1/3 of that of the original iterative algorithms.

Deep neural network-enabled resolution enhancement for the digital light field display based on holographic functional screen

The digital light-field display suffers from low resolution due to lack of planar pixels transformed into spatial information, which limits three-dimensional (3D) visual effect obviously. Herein, a pre-coding optimization of rendering the elemental image array (EIA) using a resolution-enhanced deep neural network (RE-DNN) is proposed, and it is used to enhance each reconstructed view perspective with high clarity besides using the holographic functional screen (HFS). The RE-DNN is used to cooperate with the HFS to form additional effective visual pixels/sub-pixels without increasement of physical planar resolution. The simulation and experimental presented 3D images with high clarity are demonstrated, verifying the feasibility and superiority of the proposed resolution enhancement method.

A Unified Joint Optimization of Training Sequences and Transceivers Based on Matrix-Monotonic Optimization

Channel estimation and data transmission constitute the most fundamental functional modules of multiple-input multiple-output (MIMO) communication systems. The underlying key tasks corresponding to these modules are training sequence optimization and transceiver optimization. Hence, we jointly optimize the linear transmit precoder and the training sequence of MIMO systems using the metrics of their effective mutual information (MI), effective mean squared error (MSE), effective weighted MI, effective weighted MSE, as well as their effective generic Schur-convex and Schur-concave functions. Both statistical channel state information (CSI) and estimated CSI are considered at the transmitter in the joint optimization. A unified framework termed as joint matrix-monotonic optimization is proposed. Based on this, the optimal precoder matrix and training matrix structures can be derived for both CSI scenarios. Then, based on the optimal matrix structures, our linear transceivers and their training sequences can be jointly optimized. Compared to state-of-the-art benchmark algorithms, the proposed algorithms visualize the bold explicit relationships between the attainable system performance of our linear transceivers conceived and their training sequences, leading to implementation ready recipes. Finally, several numerical results are provided, which corroborate our theoretical results and demonstrate the compelling benefits of our proposed pilot-aided MIMO solutions.

Robust Precoding for HF Skywave Massive MIMO

In this paper, we investigate the robust precoding with imperfect channel state information (CSI) for high frequency (HF) skywave massive multiple-input multiple-output (MIMO) communications. Starting with a sparse beam based <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a posteriori</i> channel model for the available imperfect CSI at the base station (BS), we prove that the robust precoder for ergodic sum-rate maximization can be designed by optimizing the beam domain robust precoder (BDRP) without any loss of optimality. Furthermore, the asymptotic optimal precoder is beam structured for a sufficiently large number of antennas at the BS, involving a low-dimensional BDRP. As a result, the beam structured robust precoding is asymptotic optimal and can be efficiently implemented based on chirp z-transform. We then derive an iterative algorithm to design the BDRP using majorization-minimization (MM). Furthermore, we develop a low-complexity BDRP design with an ergodic sum-rate upper bound, simplifying the MM based design algorithm. Based on our simulation results, the proposed beam structured robust precoding can achieve a near-optimal performance with significantly reduced complexity in various scenarios.

Deep learning based relay selection and precoders design for IoT cognitive relay networks

This paper focuses on designing a multiple-input multiple-output (MIMO) Internet-of-Things (IoT) oriented cognitive multi relay network (CMRN), where the secondary system (SS) consists of IoT nodes. Randomly placed multiple IoT nodes help in data transmission between two IoT users, i.e., a secondary transmitter (ST) and a secondary receiver (SR). Specifically, the problem of designing energy-efficient precoders at ST and SR is considered. The problem is addressed in two steps, first, a closed-form solution for the precoders is derived. Secondly, optimal precoders weight are used to select the best relay that assists the transmission between ST and SR nodes, ensuring maximum end-to-end energy efficiency (E2E-EE) at the SS. However, as the number of relays increases, the computational complexity of the solution also increases. Therefore, achieving a balance between the number of relays and computational feasibility is essential for optimal performance. To ensure fast communication system, deep learning (DL) based framework is employed for precoder design and relay selection, which offers solution with high precision and fast execution time. This approach effectively minimizes power consumption in the IoT nodes while maintaining the Quality of Service (QoS) of the primary and secondary systems. Numerical results demonstrate the impact of relay selection and precoder design on the achievable E2E-EE at SS. The DL scheme achieves comparable performance and much lower complexity than the conventional method while demonstrating lower runtime, particularly when the number of relays is large.

Over-the-Air Computation With Multiple Receivers: A Space-Time Approach

The emerging concept of Over-the-Air (OtA) computation has shown great potential for achieving resource-efficient data aggregation across large wireless networks. However, current research in this area has been limited to the standard many-to-one topology, where multiple nodes transmit data to a single receiver. In this study, we address the problem of applying OtA computation to scenarios with multiple receivers, and propose a novel communication design that exploits joint precoding and decoding over multiple time slots. To determine the optimal precoding and decoding vectors, we formulate an optimization problem that aims to minimize the mean squared error of the desired computations while satisfying the unbiasedness condition and power constraints. Our proposed multi-slot design is shown to be effective in saving communication resources (e.g., time slots) and achieving smaller estimation errors compared to the baseline approach of separating different receivers over time.

Outage Probability Oriented Secrecy Rate Optimization for Finite-Alphabet Inputs

This paper aims at investigating the secrecy outage probability (SOP) performance and formulating an associated precoder optimization problem to maximize the achievable secrecy rate (SR) for finite-alphabet inputs. As a passive eavesdropper is taken into account, we derive that: 1) the phase of the optimal precoder is orthogonal to that of the channel spanning from the transmitter to the intended receiver; 2) the modulus of the optimal precoder vector also has a linear correlation to that of the mentioned channel. Benefited from these findings, the original optimization problem is simplified to one only related to a pair of scalars, which thus dramatically reduces the computational complexity. Our simulation results finally verify the effectiveness of the proposed precoder design scheme.