Joint Precoding Research Articles

Joint Precoding Based on Interference Alignment and SVD for MU-MIMO OTFS With MRC Detector

Joint Precoding Based on Interference Alignment and SVD for MU-MIMO OTFS With MRC Detector

SLNR-Based Joint Precoding for RIS Aided Beamspace HAP-NOMA Systems

SLNR-Based Joint Precoding for RIS Aided Beamspace HAP-NOMA Systems

Multi-Agent Reinforcement Learning-Based Joint Precoding and Phase Shift Optimization for RIS-Aided Cell-Free Massive MIMO Systems

Multi-Agent Reinforcement Learning-Based Joint Precoding and Phase Shift Optimization for RIS-Aided Cell-Free Massive MIMO Systems

Joint Precoding Design and Location Optimization in Joint Communication, Sensing and Computing of UAV Systems

Joint Precoding Design and Location Optimization in Joint Communication, Sensing and Computing of UAV Systems

Joint Precoding Design for Sub-Connected Hybrid Beamforming System

Hybrid beamforming has been widely considered in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system, which can greatly reduce power consumption and hardware cost of data paths. Compared to the fully-connected hybrid beamforming architecture, the sub-connected architecture is more practical for its reduced complexity. However, optimal precoding design for the sub-connected architecture is not straightforward due to the specific block-diagonal structure of analog phase shifter network. Algorithms on fully-digital or fully-connected hybrid beamforming architecture cannot be directly applied to sub-connected architecture. Meanwhile, most existing precoding algorithms in such case can only solve the approximate problem, which results in significant performance loss. In this paper, we study the sum rate maximization problem in the sub-connected architecture. We first relax the objective function and derive a relaxed upper bound of the original problem. Then we propose an algorithm to solve the original problem with a local-optimal solution. Simulation results show that the proposed local-optimal algorithm outperforms the baseline algorithms with better sum rate and energy efficiency performance. Besides, the proposed algorithm also converges quickly and is robust.

Joint Precoding and Phase Shift Design for RIS-aided Cell-Free Massive MIMO with Heterogeneous-Agent Trust Region Policy

Joint Precoding and Phase Shift Design for RIS-aided Cell-Free Massive MIMO with Heterogeneous-Agent Trust Region Policy

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.

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.

Joint Precoding for Active Intelligent Transmitting Surface Empowered Outdoor-to-Indoor Communication in mmWave Cellular Networks

Outdoor-to-indoor communications in millimeter-wave (mmWave) cellular networks have been one challenging research problem due to the severe attenuation and the high penetration loss caused by propagation characteristics of mmWave signals. We propose a viable solution to implement the outdoor-to-indoor mmWave communication with the aid of an active intelligent transmitting surface (active-ITS), where the active-ITS allows the incoming signal from an outdoor base station (BS) to pass through the surface and be received by indoor users (UEs) after shifting its phase and magnifying its amplitude. Then, the problem of joint precoding of the BS and active-ITS is investigated to maximize the weighted sum-rate (WSR) of the system. An efficient block coordinate descent (BCD) based algorithm is developed to solve it with the suboptimal solutions in nearly closed-forms. In addition, to reduce the size and hardware cost of active-ITSs, we provide a block-amplifying architecture to partially remove the circuit components for power-amplifying, where multiple transmissive-type elements (TEs) in each block share the same power amplifier. Simulations indicate that active-ITS has the potential of achieving a given performance with much fewer TEs compared to the passive-ITS under the same total system power consumption, which makes it suitable for application to the space-limited and aesthetic-needed scenario, and the performance degradation caused by the block-amplifying architecture is negligible.

Joint User Scheduling and Precoding for XL-MIMO Systems With Imperfect CSI

We propose an algorithm for joint precoding and user scheduling in multiple-input multiple-output systems with extremely-large aperture arrays, assuming realistic channel conditions and imperfect channel estimates. The use of statistical channel state information (CSI) for user scheduling, and a proper selection of the set of users for which CSI is updated allow for obtaining an improved achievable sum spectral efficiency. We also confirm that the effect of imperfect CSI in the precoding vector design and the cost of training must be taken into consideration for realistic performance prediction.

Joint precoding and power allocation in a FSO channel with signal-dependent noise.

To enhance the throughput of visible light communication (VLC), this paper investigates precoding and power allocation jointly in a free space optical channel with signal-dependent noise (SDN-FSO). With the presence of SDN from a light source, a more realistic multi-user multiple-input single-output (MU-MISO) VLC channel model is introduced. First, based on the entropy power inequality, a capacity bound for the SDN-FSO channel is derived under a MU-MISO scenario. Second, due to the nonnegativity of SDN-FSO input, a zero forcing constraint is introduced to alleviate multi-user interference. Third, from the perspective of resource allocation, min-max and total power budget constraints are taken into consideration. A non-convex joint optimization problem is formulated and divided into two subproblems, i.e.,the precoding subproblem and power allocation subproblem. Moreover, an iterative algorithm based on a concave and convex program and a simplified high signal-to-noise ratio approximation method are iteratively implemented in the precoding subproblem. For the power allocation subproblem, successive convex approximation is employed to ensure its optimality. Simulation results indicate that the proposed joint optimization methods can converge after about two iterations and accomplish a throughput improvement of up to 1∼3n a t s/s/H z as compared with the benchmarks for four different spacing scenarios. When channel state information is interfered with noise, the proposed methods still demonstrate their superior performance with varying degrees of noise.

Robust Joint Precoding/Combining Design for Multiuser MIMO Systems With Calibration Errors

We consider the downlink of a multiuser system operating in the time-division duplexing mode, for which base station (BS) and users are equipped with multiple antennas, and provide a robust precoding/combining design against imperfect channel state information (CSI) and calibration errors due to hardware mismatch. Towards this end, we first formulate a robust joint precoder and combiner design as a stochastic minimum mean squared error optimization problem. Then, employing an alternating optimization approach, we propose an algorithm to obtain the precoding and combining matrices assuming imperfect CSI and calibration errors at both the BS and the user sides. We also provide asymptotic closed-form expressions for the mean squared error (MSE) and the achievable sum-rate in the massive MIMO regime. The results indicate that while the MSE linearly increases with the calibration errors at the user side, the sum-rate is asymptotically independent of them. Extensive simulation results show that the proposed robust joint precoder/combiner outperforms the existing solutions while having the same order of complexity. Moreover, when the BS sends a quantized version of the combining coefficients to the users, it is observed that the proposed solution is more robust to the quantization errors than the existing algorithms.

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.

Joint Precoding and Array Design for Broadcast in the Internet of Unmanned Aerial Vehicles

To promote energy and spectrum efficient communications in multi-antenna channels, broadcast can provide a substantial gain in system throughput. However, the hardware constraints and strong line-of-sight (LoS) limit the implementation and performance of multi-antenna broadcast in the Internet of unmanned aerial vehicles (UAVs). Based on the pseudo-Doppler principle, we propose a joint precoding and antenna array design to reduce the number of radio frequency (RF) chains required by the broadcast and free the LoS path from the inter-stream interference. The reduction of RF chains is realised by designing the precoding matrix that makes at least one of the transmit antennas have null inputs during any broadcast and, moreover, the LoS path is formed to match the obtained precoding matrix through antenna array design at the broadcasting UAV. The algorithms with low computational complexity for optimising this design are developed to minimise the transmit power within the UAV broadcast paradigms. Theoretical formulation and numerical results in the metrics of sum data rate and bit error rate substantiate the validity of our proposed design, specifically in the Internet of UAVs with strong LoS.

C-RAN Zero-Forcing With Imperfect CSI: Analysis and Precode & Quantize Feedback

Downlink joint transmission by a cluster of remote radio-heads (RRHs) is essential for enhancing throughput in future cellular networks. This method requires global channel state information (CSI) at the processing unit that designs the joint precoder. To this end, extensive CSI must be shared between the RRHs and that unit. This paper proposes two contributions. The first is a new upper bound on the rate loss, which implies a lower bound on the achievable rate for an RRHs-cluster employing joint zero-forcing (ZF) with incomplete CSI with single-user decoding (SUD) at the mobile stations (MSs). The second contribution, which follows insights from the bound, is a new CSI sharing scheme that drastically reduces the significant overhead associated with acquiring global CSI for joint transmission. In a nutshell, each RRH applies a local precoding matrix that creates low-dimensional effective channels that can be quantized more accurately with fewer bits, thereby reducing the overhead of sharing CSI. In addition to the CSI sharing-overhead, this scheme reduces the data rate delivered to each RRH in the cluster.

Joint Precoding and Artificial Noise Design for MU-MIMO Wiretap Channels

Secure precoding superimposed with artificial noise (AN) is a promising transmission technique to improve security by harnessing the superposition nature of the wireless medium. However, finding a jointly optimal precoding and AN structure is very challenging in downlink multi-user multiple-input multiple-output wiretap channels with multiple eavesdroppers. The major challenge in maximizing the secrecy rate arises from the non-convexity and non-smoothness of the rate function. Traditionally, an alternating optimization framework that identifies beamforming vectors and AN covariance matrix has been adopted; yet this alternating approach has limitations in maximizing the secrecy rate. In this paper, we put forth a novel secure precoding algorithm that jointly and simultaneously optimizes the beams and AN covariance matrix for maximizing the secrecy rate when a transmitter has either perfect or partial channel knowledge of eavesdroppers. To this end, we first establish an approximate secrecy rate in a smooth function. Then, we derive the first-order optimality condition in the form of the nonlinear eigenvalue problem (NEP). We present a computationally efficient algorithm to identify the principal eigenvector of the NEP as a suboptimal solution for secure precoding. Simulations demonstrate that the proposed methods improve secrecy rate significantly compared to the existing methods.

Two-Timescale Joint Precoding Design and RIS Optimization for User Tracking in Near-Field MIMO Systems

Two-Timescale Joint Precoding Design and RIS Optimization for User Tracking in Near-Field MIMO Systems

Design of a Reconfigurable Intelligent Surface Algorithm Based on Multiple-Input Multiple-Output

Reconfigurable Intelligent Surface (RIS) can improve the security of the physical layer of wireless communication by adjusting the phase of the reflective unit. After analyzing the common theoretical models and potential problems, this study proposes an alternative iterative model based on multiple input multiple output (MIMO), and designs the transmitter, channel and receiver. In addition, passive beamforming precoding matrix of RIS was jointly optimized, and the Lagrangian dual relaxation (LDR) was adopted to decouple the nonconvex problem. After that, the active and passive beamforming matrices were subjected to iterative calculation, and the beamforming was optimized through cyclic programming at the base station (BS). In addition, the high convergence of the proposed algorithm was proved by MATLAB simulation. The results of simulation demonstrate that the joint precoding framework algorithm can maximize the weighted sum rate (WSR), which in turn demonstrates the feasibility of our method. Finally, the authors analyzed the strong applicability of the RIS technology in complex wireless networks with different volatility, revealing the possibility of future development.

Joint Precoder and Beamformer Design for Secure Relay Networks With Finite-Alphabet Inputs and Statistical CSI of Eve

Because of discrepant deteriorations of the intended receiver and the unintended receiver, artificial noise (AN) can be invoked in conjunction with the precoder to wireless transmissions for enhancing the secrecy rate (SR) performance as long as we elaborately frame them. This paper studies a secure transmission strategy by jointly designing the precoder and AN beamformer at the relay network, where a passive eavesdropper and finite-alphabet inputs are taken into account. We propose a pair of solutions for low-order modulation and high-order modulation, respectively. To solve the first optimization problem, we propose a low-complexity algorithm with the aid of the invoked cut-off rate. Interestingly, we find that the phase of an optimum precoder for maximizing the SR has a correlation to both the channels spanning from the transmitter to the relays and spanning from the relays to the legitimate receiver. Furthermore, we reveal that the AN beamformer vector has at most one non-zero component, which only locates at the position corresponding to the minimum element of the channel between the relays and the intended receiver. According to these findings, the SR maximization problem over the two vectors is simplified as one only related to a pair of scalars. As for the high-order modulation, a new solution is further proposed for circumventing the predicament that the computational complexity exponentially increases as the size of the adopted modulation increases, where we not only eliminate two-layer summation over the legitimate symbols but also conceive a concave maximization SR problem. Finally, simulation results demonstrate the efficiency of the proposed algorithms in terms of the SR performance.

Beamforming Optimization for Intelligent Reflecting Surface-Assisted MIMO Systems

To improve the spectral efficiency of symmetry-based intelligent reconfigurable surface (IRS)-assisted MIMO communication systems, this paper investigates the joint precoding and passive beamforming optimization problem for millimeter-wave point-to-point MIMO systems under both ideal and practical IRS phase shifts. For the ideal IRS phase shifts setup, we derive a simplified approximate spectral efficiency expression based on Jensen’s inequality and propose a minimum mean square error (MMSE)-based algorithm to transform the simplified non-convex problem into a solvable convex function. For the practical IRS phase shifts setup, we propose a multi-start stepwise optimization algorithm to obtain the passive beamforming by stepwise iterative search. Finally, with the above-obtained passive beamforming, the optimal precoding is derived by performing the singular value decomposition (SVD) on the effective channel and water-filling power allocation. The simulation results verify our performance analysis and demonstrate that spectral efficiency can be effectively improved compared to various benchmark schemes.