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Summary: In this paper, a non‐orthogonal multiple access (NOMA)‐based precoded quadrature spatial modulation (PQSM) technique (NOMA‐PQSM) has been proposed for the downlink scenario. In NOMA‐PQSM, two intended receiving antennas are activated at any time instant. One antenna is activated for the in‐phase component of the transmitted signal, and another one is activated for the quadrature phase component, on the basis of data bits. NOMA‐PQSM provides benefits like improved spatial diversity and spectral efficiency in comparison with spatial modulation. This work uses zero forcing (ZF) precoding over downlink flat fading Rayleigh multiple input multiple output (MIMO) channels, to limit the channel's deteriorating effect on transmitted signal, assuming perfect channel state information (CSI) at the transmitter. A low complexity receiver based on the successive interference cancellation is used. An expression for the upper bound of average bit error probability is derived. Moreover, the expressions for the sum mutual information of users and its lower bound are also derived. The proposed scheme is compared with the preprocessing aided spatial modulation (PSM)‐based counterpart. Monte Carlo simulations reveal that the NOMA‐PQSM scheme outperforms its orthogonal counterpart and the PSM scheme. [ABSTRACT FROM AUTHOR]

The rapid development of wireless communication in today's society is facing the development bottleneck of increasingly tense spectrum resources. As the key technology of 5G technology, Massive Multiple Input Multiple Output (MMIMO) technology has been widely studied and applied in recent years. It is expected to bring progress to wireless communication systems. The key characteristic of MMIMO is its large antenna array that results in a high dimension of the wireless system channel. This article examines the use of precoding technology in large-scale multi-antenna MIMO systems to address the issue of Common Channel Interference (CCI). The study starts by introducing the concept of linear digital precoding and provides mathematical demonstrations of three linear digital precoding schemes. The performance of these schemes is then simulated and analyzed using MATLAB, with the results showing a significant improvement in the bit error performance and combining rate of the MIMO system. [ABSTRACT FROM AUTHOR]

Precoding is a critical signal processing technique used in wireless communication systems to enhance transmission performance. This paper initially provides a brief overview of various conventional precoding algorithms. The exploration includes various nonlinear precoding algorithms which outperform linear techniques in high signal-to-noise ratio (SNR) scenarios. Moreover, the paper delves into practical considerations and provides insights into selecting the most suitable technique for specific communication scenarios. The deep learning-based hybrid precoder is designed using the MDL-AltMin algorithm and simulated results show a spectral efficiency of (SE) 40.96 dB compared with different precoding algorithms. The precoder model is modified with a Hybrid Precoding Net algorithm with a spectral efficiency of 21.07 dB for high values of SNR such as −30 and again compared with deep learning precoders. The HPNet model is proposed with a spectral efficiency of 23.04 dB with and without CSI (Channel State Information) at the transmitter. Furthermore, the HPNet model is compared with optimal digital precoders. [ABSTRACT FROM AUTHOR]

In the fifth generation (5G) new radio (NR) wireless communication system, using a multiple-input multiple-output (MIMO) system for signal transmission and reception of the millimeter wave frequency band can effectively improve data rate performance and received signal quality. Besides, precoding is an important method of beamforming in MIMO transmission. With the properly selected precoding matrices, the transmitter and receiver can adjust the transmitting and receiving behaviors in accordance with the changes of the current channel state to reduce the interferences between users and increase the received signal strength. In this paper, a low complexity precoding selection method is proposed based on the steering angle characteristics of the precoding matrices of 5G NR. In addition, for this paper, we used the software defined radio to implement the precoding selection following the requirement of the specifications of 5G. With this implementation, both the transmitter and receiver can determine the suitable precoding matrices according to different precoding selection methods. Results showed that with a little error vector magnitude performance degradation, the proposed precoding selection method can reduce at least 50% of the required computational complexity of the full search method. [ABSTRACT FROM AUTHOR]

【Objective】The symbol decomposition technique aims to decompose Optical Orthogonal Frequency Division Multiplexing (O-OFDM) symbols into multiple symbols with a small Peak to Average Power Ratio (PAPR) to mitigate the non-linear effects of Light Emitting Diodes (LEDs). However, O-OFDM generates more decomposed symbols with a higher PAPR, thereby reducing the information rate and degrading the Bit Error Rate (BER) performance.【Methods】The proposed design of Precoding O-OFDM Adaptive Symbol Decomposition with Serial Transmission (PCO-OFDM-ASDST) system uses precoding scheme to reduce the PAPR of O-OFDM symbols. The proposed method can reduce the average number of symbol decompositions for adaptive symbol decomposition. The theoretical Signal-to-Noise Ratio (SNR) expressions are derived for multipath channels, and the performance of PAPR, BER, and information rate is analyzed using Monte Carlo BER simulations.【Results】The results demonstrate that under 4 Quadrature Amplitude Modulation (QAM) modulation in Asymmetrically Clipped Optical Orthogonal Frequency Division Multiplexing (ACO-OFDM) systems, the symbol power required for PCO-OFDM-ASDST is 7 dB less than that of Adaptive Symbol Decomposition with Serial Transmission (ASDST) when the BER is 10-4. Moreover, under 64QAM, the information rate can be increased by 10 Mbit/s when the symbol power is 20 dBm.【Conclusion】The results indicate that the PCO-OFDM-ASDST outperforms ASDST systems in terms of BER and information transmission rate.

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Abstract This paper discusses the operation of a massive multiple‐input multiple‐output (MIMO) system from a different perspective. The system is performed in the presence of an eavesdropper who is trying to disrupt the message being transmitted between the base station (BS) and the users. An attempt has been made to maximize the secure energy efficiency (EE) of the system by allocating appropriate power to each user. The channel state information (CSI) between users and the BS is considered imperfect and the employed precoding scheme is the zero forcing (ZF). The CSI related to the eavesdropper is considered to be perfect and its precoding is assumed to be the maximum ratio transmission (MRT). The problem of optimization that is for maximizing the EE has two constraints including the maximum transmission power and the minimum user data rate. To improve the performance of system, the cell division technique is applied, which results in approaching the performance of system to the without eavesdropper scenario. Five various scenarios have been investigated in this research, and it is proved that the proposed method improves the system performance in all cases. The numerical and simulation results obtained from implementation of the proposed algorithm confirm the claim.

This paper discusses the operation of a massive multiple‐input multiple‐output (MIMO) system from a different perspective. The system is performed in the presence of an eavesdropper who is trying to disrupt the message being transmitted between the base station (BS) and the users. An attempt has been made to maximize the secure energy efficiency (EE) of the system by allocating appropriate power to each user. The channel state information (CSI) between users and the BS is considered imperfect and the employed precoding scheme is the zero forcing (ZF). The CSI related to the eavesdropper is considered to be perfect and its precoding is assumed to be the maximum ratio transmission (MRT). The problem of optimization that is for maximizing the EE has two constraints including the maximum transmission power and the minimum user data rate. To improve the performance of system, the cell division technique is applied, which results in approaching the performance of system to the without eavesdropper scenario. Five various scenarios have been investigated in this research, and it is proved that the proposed method improves the system performance in all cases. The numerical and simulation results obtained from implementation of the proposed algorithm confirm the claim. [ABSTRACT FROM AUTHOR]

The paper studies the multi-user precoding problem as a non-convex optimization problem for wireless multiple inputs and multiple outputs (MIMO) systems. In our work, we approximate the target Spectral Efficiency function with a novel computationally simpler function. Then, we reduce the precoding problem to an unconstrained optimization task using a special differential projection method and solve it by the Quasi-Newton L-BFGS iterative procedure to achieve gains in capacity. We are testing the proposed approach in several scenarios generated using Quadriga-open-source software for generating realistic radio channel impulse response. Our method shows monotonic improvement over heuristic methods with reasonable computation time. The proposed L-BFGS optimization scheme is novel in this area and shows a significant advantage over the standard approaches. The proposed method has simple implementation and can be a good reference for other heuristic algorithms in this field. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is an anticipated technology for fifth-generation networks for increasing mass connectivity, spectrum efficiency, user-fairness, and higher capacity. NOMA allows end-clients to share indistinguishable radio resources such as spreading code, subcarrier, and time slots simultaneously. Thus, the main challenge involved in conceptualizing effective NOMA design is selection of resource allocation (i.e., user clustering, power allocation, and quality-of-service (QoS) assurance) algorithms. NOMA can be easily integrated with current fifth-generation multi-access methodologies. In this survey paper, the NOMA methodologies are discussed, and provide an overview of the methodologies and algorithms designed for optimizing power allocation, interference management, and network selection management in the heterogenous multiple carrier NOMA. The survey highlights the current limitation of the existing resource provisioning framework and presents a solution to overcome the current limitation. [ABSTRACT FROM AUTHOR]

This paper deals with the design of optimal linear precoder for multi-user multiple-input multiple-output (MU-MIMO) system. The singular value decomposition (SVD) precoder under the ideal channel condition is introduced. However, in the circumstance with general conditions, due to co-channel interference (CCI), the system capacity degradation of MU-MIMO system is influenced by the efficacy decreasing of the precoder. Therefore, a linear precoder based on particle swarm optimization (PSO) algorithm to conduct optimal searching of the precoding matrix for the individual users is proposed in this paper. With the objective function for maximizing output signal to interference plus noise ratio, the proposed PSO-searching based (PSB) precoder can suppress CCI and improve system capacity effectively. For the purpose to enhance the accuracy and speed of the optimal solution searching, the improved PSO-searching based (IPSB) precoder has been provided, which depends on the marginal effect of capability of the global exploration. Several computer simulations are provided for illustration and comparison. [ABSTRACT FROM AUTHOR]

Massive MIMO technology has significantly enhanced the performance of 5G networks, and its potential is poised for further expansion in the upcoming 6G standard. Large antenna arrays at the transmitter, combined with multiple antennas at each user can enable multi-user multi-layer MIMO data transmission. In this paper, we study the potential benefits of a cylindrical array arrangement in a 3D scenario, compared to the conventional trisector planar array configuration. In fact, cylindrical arrays allow for more uniform coverage of the cell, don’t suffer from beam broadening, and can mitigate the sector-edge problem. For this purpose, we assess the effectiveness of multi-user and multi-layer precoding and combining strategies using cylindrical arrays outfitted with both single and dual-polarized antenna elements, under the most recent 3GPP-compliant 3D channel model for 5G, and by taking into account impairments due to an imperfect channel estimation. Results and comparisons with planar arrays in terms of sum-rate, spectral efficiency and outage are provided for outdoor Line-of-Sight (LOS), outdoor Non-LOS, and indoor users. They highlight the benefits of using cylindrical arrays in realistic 3D scenarios, even when the planar arrays of the different sectors are coordinated to operate as a single array. This makes them an interesting solution for next-generation 6G networks.

As a promising alternative to traditional underwater communication methods, underwater wireless optical communication (UWOC) offers higher data rates, lower latency, and enhanced security. However, the practical implementation of high-speed UWOC systems typically faces challenges arising from bandwidth-limited optical devices and adverse effects of absorption, scattering, and oceanic turbulence. To tackle these challenges, we propose a novel joint probabilistic shaping and precoding scheme that simultaneously optimizes the mutual information (MI) and reduces the peak-to-average power ratio (PAPR) of multiple-input single-output (MISO) orthogonal frequency-division multiplexing (OFDM) UWOC systems. Specifically, the proposed approach probabilistically shapes the transmitted symbols and appropriately allocates weights to both multi-light sources and multi-carriers based on precoding to match the channel characteristics. Numerical results demonstrate its superiority over existing schemes, particularly showcasing its potential to significantly improve the performance of UWOC systems via MI optimization and PAPR reduction. Moreover, the results verify that the proposed approach exhibits satisfactory robustness against oceanic turbulence.

Device-to-Device (D2D) communications facilitate direct data exchange between two devices, bypassing the necessity of routing through a central base station. This proximity-based communication offers advantages such as high data rates, low latency, reduced power consumption, and efficient frequency reuse. In the cellular spectrum, D2D operations encompass overlay D2D and underlay D2D. Overlay D2D allocates orthogonal radio resources to D2D and cellular users, resulting in inefficient frequency reuse. On the other hand, underlay D2D shares the same radio resources among D2D and cellular users, necessitating centralized coordination and extensive exchanges of channel and traffic information for effective interference management. To address the limitations of overlay and underlay D2D, we introduce a scheme that allows D2D users to simultaneously and non-orthogonally transmit both D2D and cellular signals. This approach resembles underlay D2D in terms of high frequency reuse but eliminates the need for centralized interference management. In this paper, we investigate various system design aspects based on this scheme, including power allocation, multiple-antenna extension, scheduling constraints, and different interference cancellation techniques. Incorporating a coordinated scheduling method, the proposed scheme enhances the sum rate of a conventional multiple-antenna overlay D2D system by 2.14 times, with only a negligible impact on the cellular communication rate. Furthermore, the proposed scheme improves the sum rate by up to 87% within conventional underlay D2D systems, all without the need for additional centralized coordination.

This paper explores the potential of applying Zero Forcing (ZF) and Block Diagonalization (BD), two linear precoding techniques commonly utilized in communications, to a Multi-User Multiple Input Multiple Output (MU-MIMO) Integrated Sensing and Communications (ISAC) setup. The parallels between communications and sensing are highlighted to show the feasibility of extending ZF and BD to ISAC. To enable the implementation of these techniques, the concept of sensing channel is exploited, enabling the treatment of the sensing receiver as an additional user. Furthermore, two user selection strategies extend existing communication-only algorithms to accommodate sensing requirements. The efficacy of the proposed techniques is substantiated through comprehensive simulations. With the proposed solution, a high beampattern gain is maintained for the sensing while keeping good communication performance, only limited by the power allocation between the two functionalities.

There has been an increasing interest in physical layer security (PLS), which, compared with conventional cryptography, offers a unique approach to guaranteeing information confidentiality against eavesdroppers. In this paper, we study a joint design of adaptive M-ary pulse amplitude modulation (PAM) and precoding, which aims to optimize wiretap visible-light channels’ secrecy capacity and bit error rate (BER) performances. The proposed design is motivated by higher-order modulation, which results in better secrecy capacity at the expense of a higher BER. On the other hand, a proper precoding design, which can manipulate the received signal quality at the legitimate user and the eavesdropper, can also enhance secrecy performance and influence the BER. A reward function that considers the secrecy capacity and the BERs of the legitimate user’s (Bob) and the eavesdropper’s (Eve) channels is introduced and maximized. Due to the non-linearity and complexity of the reward function, it is challenging to solve the optical design using classical optimization techniques. Therefore, reinforcement learning-based designs using Q-learning and Deep Q-learning are proposed to maximize the reward function. Simulation results verify that compared with the baseline designs, the proposed joint designs achieve better reward values while maintaining the BER of Bob’s channel (Eve’s channel) well below (above) the pre-FEC (forward error correction) BER threshold.

This article considers the context of multiuser massive MIMO downlink precoding with low-resolution digital-to-analog converters (DACs) at the transmitter. This subject is motivated by the consideration that it is expensive to employ high-resolution DACs for practical massive MIMO implementations. The challenge with using low-resolution DACs is to overcome the detrimental quantization error effects. Recently, spatial Sigma-Delta ($\Sigma \Delta$) modulation has arisen as a viable way to put quantization errors under control. This approach takes insight from temporal $\Sigma \Delta$ modulation in classical DAC studies. Assuming a 1D uniform linear transmit antenna array, the principle is to shape the quantization errors in space such that the shaped quantization errors are pushed away from the user-serving angle sector. In the previous studies, spatial $\Sigma \Delta$ modulation was performed by direct application of the basic first- and second-order modulators from the $\Sigma \Delta$ literature. In this paper, we develop a general $\Sigma \Delta$ modulator design framework for any given order, for any given number of quantization levels, and for any given angle sector. We formulate our design as a problem of maximizing the signal-to-quantization-and-noise ratios (SQNRs) experienced by the users. The formulated problem is convex and can be efficiently solved by available solvers. Our proposed framework offers the alternative option of focused quantization error suppression in accordance with channel state information. Our framework can also be extended to 2D planar transmit antenna arrays. We perform numerical study under different operating conditions, and the numerical results suggest that, given a moderate number of quantization levels, say, 5 to 7 levels, our optimization-based $\Sigma \Delta$ modulation schemes can lead to bit error rate performance close to that of the unquantized counterpart.

CELL-FREE massive multiple-input multiple-output (MIMO) is a promising technology for next-generation communication systems. This work proposes a novel partially coherent (PC) transmission framework to cope with the challenge of phase misalignment among the access points (APs), which is important for unlocking the full potential of cell-free massive MIMO technology. With the PC operation, the APs are only required to be phase-aligned within clusters. Each cluster transmits the same data stream towards each user equipment (UE), while different clusters send different data streams. We first propose a novel algorithm to group APs into clusters such that the distance between two APs is always smaller than a reference distance ensuring the phase alignment of these APs. Then, we propose new algorithms that optimize the combining at UEs and precoding at APs to maximize the downlink sum data rates. We also propose a novel algorithm for data stream allocation to further improve the sum data rate of the PC operation. Numerical results show that the PC operation using the proposed framework with a sufficiently small reference distance can offer a sum rate close to the sum rate of the ideal fully coherent (FC) operation that requires network-wide phase alignment. This demonstrates the potential of PC operation in practical deployments of cell-free massive MIMO networks.

The recently emerged symbol-level precoding (SLP) technique is a promising solution in multi-user wireless communication systems due to its ability to transform harmful multi-user interference (MUI) into useful signals, thereby improving system performance. Conventional symbol-level precoding designs have a significant computational complexity that makes their practical implementation difficult and imposes excessive computational complexity on the system. To deal with this problem, we suggest a new deep learning (DL) based approach that utilizes low-complexity designs of symbol-level precoding. This paper focuses on DL-based one-bit precoding approaches for downlink massive multiple-input multiple-output (MIMO) systems, where one-bit digital-to-analog converters (DACs) are used to reduce cost and power. Unlike previous works, the optimized one-bit precoder for multiuser massive MIMO system (HDL-O1PmMIMO) for a wide range of signal-to-noise-ratio (SNR) has a low computational complexity, making it suitable for real precoding scenarios. In this paper, we first design an unsupervised DL-based precoder (UDL-O1PmMIMO) to address the low SNR scenarios, using which we then design a hybrid DL-based precoder (HDL-O1PmMIMO) to address both low and high SNR scenarios. The method suggested in this article utilizes a novel residual DL network structure, which helps overcome the problem of training very deep networks. Additionally, a novel customized cost function, specifically for one-bit precoding in massive MIMO systems, is introduced to optimize the performance of the system in handling interference. The results of an experiment conducted on a general test set using Python and MATLAB show that the proposed approach outperforms existing methods in three aspects: it has a lower bit error rate, it takes less time to generate the precoded vector, and it is more resistant to imperfect channel estimation.

Massive MIMO is a key technology for the current and next-generation cellular standards to achieve multi-fold improvements in the spectral efficiency of the network. Various demonstrations of this Massive MIMO technology in the existing literature transmit data to a maximum of 15 users simultaneously in the downlink while using 64 to 100 antenna ports at the base station. In this paper, we present in detail the design and implementation of our Large-scale Massive MIMO testbed, where we demonstrate a further maximization of the user pairing to realize the high network capacities achievable with massive MIMO technology. This is the first testbed implementation that demonstrates as high as 24 user pairing in downlink by utilizing 192 antenna elements (48 antenna ports) at the base station. This high user pairing capability helps the network operators to support as many users in a limited amount of spectrum and achieve multifold improvement in the network capacity. Further, this is a first step in the direction of understanding the real-world limitations of Massive MIMO systems in a variety of deployment scenarios and under constraints such as antenna array sizes, antenna array types such as circular versus rectangular, and indoor versus outdoor deployment, among others. All the analysis is backed by detailed experiments that are described in this paper.

Synchronizing the local oscillators in multibeam satellites with the objective of coherent communications is still an open challenge. It has to be addressed to implement full-frequency reuse approaches, such as precoding techniques using the already deployed multibeam satellites. This article addresses the required phase synchronization to enable precoding techniques in multibeam satellite systems. It contains the detailed design of a frequency and phase compensation loop based on the proportional-integral controller, which deals with the phase drift introduced by the hardware components. Specifically, the phase noise of the local oscillators used for up and down conversion at each system element (gateway, satellite, and user terminals). The implementation of the two-state phase noise model used to emulate this phase drift is included in the article. Besides, a comparative analysis of several methods to combine the frequency and phase measurements obtained from the user terminals is also included. Finally, the performance of the proposed closed-loop synchronization method is validated through simulations using our in-house developed MIMO end-to-end satellite emulator based on SDR platforms.

Photon counting has been proven to possess excellent signal detection capabilities at low power levels and has extensive potential applications in sixth-generation (6G) communications. However, the inherent dependency between the signal and noise complicates system analysis, and optimizing achievable rates in photon-counting visible light communication (VLC) systems remains unresolved. This paper introduces a new method aimed at minimizing multi-user interference (MUI) through a zero-forcing (ZF) scheme and maximizing the weighted sum rate of the proposed downlink multi-user photon-counting multiple-input multiple-output (MU-PhC-MIMO) VLC system by solving an optimization problem. The key point lies in our utilization of the ZF approach to derive a reasonable asymptotic approximation expression for the weighted sum rate. Subsequently, we use variable substitution and methods like successive convex approximation (SCA) to iteratively convexify the non-convex optimization problem and maximize the weighted sum rate under the ZF form. Compared to other algorithms, this approach can save 2.5 dB of transmission power to achieve the same system-weighted sum rate and significantly outperforms the repetition coding scheme at sufficient transmission power. [ABSTRACT FROM AUTHOR]

In this article, we investigate in different scenarios the feasibility of using massive multiple-input–multiple-output (mMIMO) with reconfigurable intelligent surfaces (RISs) to increase the throughput and coverage with high energy efficiency, considering sub-6 GHz, mmWave, and THz bands. With that objective, a centralized radio access network (C-RAN) suitable for beyond fifth-generation (B5G) systems is considered, where we integrate the base stations (BSs) with multiple RISs and IoT devices or user equipment. RISs with a large number of quasi-passive reflecting elements constitute a low-cost approach capable of shaping radio wave propagation and improving wireless connectivity. We consider a scenario where multiple RISs are combined with mMIMO in the uplink in order to provide connectivity to a smart city (with thousands of active low-power IoT devices), wirelessly, in the 3.6 GHz and 28 GHz bands. We also address a scenario where RISs are adopted with mMIMO in the downlink so as to offer connectivity to a stadium with a pitch, (and thousands of active users' equipment) in the 28 GHz band. Finally, we also studied the connectivity at 100 GHz of a factory in which several RIS panels, replacing most of the BSs equipped with mMIMO, assure improved throughput and coverage. We concluded that RISs are capable of improving the performance in any of these analyzed scenarios at the different frequency bands, justifying that they are a key enabling technology for 6G. [ABSTRACT FROM AUTHOR]

Cell-free massive multi-input-multi-output (MIMO) systems comprise a large number of distributed access points (APs) to serve a small number of user equipments (UEs). In this paper, compute-and-forward (CF) is investigated for uplink in cell-free massive MIMO. We propose a novel reverse CF precoding scheme for downlink transmission for cell-free massive MIMO. The APs send the linear equations of messages over a multiple access channel, and the UEs recover messages without interferences. The max–min power allocation is investigated to provide uniformly good service to all UEs. Numeral results demonstrate the performance improvement provided by the proposed reverse CF precoder against zero-forcing, conjugate beamforming, and linear minimum mean-square error precoder.

Abstract Hybrid precoding can significantly reduce the number of required radio frequency (RF) chains in massive MIMO systems. However, due to the large number of antennas and the high‐dimensional channel, the existing hybrid precoding algorithms usually require very high complexity. In this paper, we propose a convolutional neural network (CNN)‐based hybrid precoding scheme by utilizing unsupervised learning to reduce both the computational complexity and space complexity. Specifically, we first design a low‐complexity CNN structure to achieve the analog precoder, where the size of the convolution kernel and the number of channels are reduced by the further improved inception network. Then, we obtain the digital precoder by the classical zero‐forcing (ZF) algorithm. Simulation results show that, the proposed CNN‐based hybrid precoding can approach the sum‐rate performance of the classical two‐stage hybrid precoding but has much lower complexity.

Abstract Due to the limitation of mobility and cost, it is inappropriate to utilize ground base station (GBS) to provide service in emergency scenarios or harsh environments. Unmanned aerial vehicle (UAV) is considered as a feasible way to solve the problem. Millimetre wave communication and UAV can be used together to offer wireless communications flexibly and rapidly for target area. However, fuselage jittering and energy supply would affect the quality and reliability of UAV communication seriously. To overcome the weakness of UAV, the authors prefer to use multiple UAVs to provide wireless communication service simultaneously. This paper investigates the design of hybrid beamforming in the scenarios including multiple UAVs and one end user. Cooperation strategy and alternating optimization method are applied in this design. The distributed UAV base stations are regarded as an entirety and the reference beamforming scheme obtained. Then, the beamforming problem for each UAV is formulated. Moreover, residual error minimization (REM) algorithm is proposed to find the solution of hybrid beamformers at UAV side. Finally, the beamformer of the receiver is optimized with the assist of effective channel, which includes the gain of both channel and beamformers of the transmitters. Simulation results show that the proposed algorithm can achieve near‐optimal spectral efficiency in different scenarios.

Orthogonal frequency division multiplexing (OFDM) based massive multiuser (MU) multiple input multiple output (MIMO) system is popularly known as high peak-to-average power ratio (PAPR) issue. The OFDM-based massive MIMO system exhibits large number of antennas at Base Station (BS) due to the use of large number of high-power amplifiers (HPA). High PAPR causes HPAs to work in a nonlinear region, and hardware cost of nonlinear HPAs are very high and also power inefficient. Hence, to tackle this problem, this manuscript suggests a novel scheme based on the joint MU precoding and PAPR minimization (PP) expressed as a convex optimization problem solved by steepest gradient descent (GD) with μ-law companding approach. Therefore, we develop a new scheme mentioned to as MU-PP-GDs with μ-law companding to minimize PAPR by compressing and enlarging of massive MIMO OFDM signals simultaneously. At CCDF = 10−3, the proposed scheme (MU-PP-GDs with μ-law companding for Iterations = 100) minimizes the PAPR to 3.70 dB which is better than that of MU-PP-GDs, (iteration = 100) as shown in simulation results. [ABSTRACT FROM AUTHOR]

Modern 5G wireless cellular networks use massive multiple-input multiple-output (MIMO) technology. This concept entails using an antenna array at a base station to concurrently service many mobile devices that have several antennas on their side. In this field, a significant role is played by the precoding (beamforming) problem. During downlink, an important part of precoding is the power allocation problem that distributes power between transmitted symbols. In this paper, we consider the power allocation problem for a class of precodings that asymptotically work as regularized zero-forcing. Under some realistic assumptions, we simplify the spectral efficiency functional and obtain tractable expressions for it. We prove that equal power allocation provides optimum for the simplified functional with total power constraint (TPC). We propose low-complexity Intersection methods (IM) that improve equal power allocation in the case of per-antenna power constraints (PAPC). On simulations using Quadriga, the proposed IM method in combination with widely-studied water filling (WF) shows a significant gain in spectral efficiency while using a similar computing time as the reference equal power (EP) solution. [ABSTRACT FROM AUTHOR]

In this paper, the precoding-based peak-to-average power ratio (PAPR) reduction methods are studied for the massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system in the angular domain. The expectation maximization generalized approximate message passing algorithm and the proposed optimized alternating direction method of multipliers (OADMM) scheme are adopted here to explore the system characteristics in terms of the symbol error ratio (SER) performance, the PAPR reduction efficiency, and the inter user interference (IUI). Specifically, the high PAPR problem is reduced to no more than 0.18 dB by using the inherent property of the massive MIMO-OFDM angle division multiple access (ADMA) system with relatively reduced computational complexity. Moreover, the value of SER is around 10 − 5 dB when the proposed technique is performed. Computer numerical simulation results verify the efficiency of the proposed technique from the perspective of SER, PAPR suppression, and IUI. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT), most communication systems are difficult to implement on a large scale due to their high complexity. Multiple-input multiple-output (MIMO) precoding is a generally used technique for improving the reliability of free-space optical (FSO) communications, which is a key technology in the 6G era. However, traditional MIMO precoding schemes are typically designed based on the assumption of additive white Gaussian noise (AWGN). In this paper, we present a novel MIMO precoding method based on reinforcement learning (RL) that is specifically designed for the Poisson shot noise model. Unlike traditional MIMO precoding schemes, our proposed scheme takes into account the unique statistical characteristics of Poisson shot noise. Our approach achieves significant performance gains compared to existing MIMO precoding schemes. The proposed scheme can achieve the bit error rate (BER) of 10 − 5 in a strong turbulence channel and exhibits superior robustness against imperfect channel state information (CSI). [ABSTRACT FROM AUTHOR]

Intelligent reflecting surfaces (IRS) have been proposed as a revolutionary technology owing to its capability of adaptively reconfiguring the propagation environment in a cost-effective and hardware-efficient fashion. While the application of IRS as a passive reflector to enhance the performance of wireless communications has been widely investigated in the literature, using IRS as a passive transmitter recently is emerging as a new concept and attracting steadily growing interest. In this paper, we propose two novel IRS-based passive information transmission systems using advanced symbol-level precoding. One is a standalone passive information transmission system, where the IRS operates as a passive transmitter serving multiple receivers by adjusting its elements to reflect unmodulated carrier signals. The other is a joint passive reflection and information transmission system, where the IRS not only enhances transmissions for multiple primary information receivers (PIRs) by passive reflection, but also simultaneously delivers additional information to a secondary information receiver (SIR) by embedding its information into the primary signals at the symbol level. Two typical optimization problems, i.e., power minimization and quality-of-service (QoS) balancing, are investigated for the proposed IRS-based passive information transmission systems. Simulation results demonstrate the feasibility of IRS-based passive information transmission and the effectiveness of our proposed algorithms, as compared to other benchmark schemes.

This article presents a quick overview of conventional precoding and beamforming techniques in the cellular networks and proposes a novel coordinated beamforming technique for coordinated multipoint (CoMP) networks, which is based upon signal-to-leakage-plus-noise ratio (SLNR) metric. The proposed scheme has reduced computational cost and offers improved performance compared to the conventional multi-cell precoding schemes in the noise-dominated environments. Simulation results reveal that proposed scheme offers a spectrally efficient, low complexity solution with improved average user throughput and sum rate in the CoMP enabled networks.

Intelligent reflecting surfaces (IRSs) have emerged as a revolutionary solution to enhance wireless communications by changing propagation environment in a cost-effective and hardware-efficient fashion. In addition, symbol-level precoding (SLP) has attracted considerable attention recently due to its advantages in converting multiuser interference (MUI) into useful signal energy. Therefore, it is of interest to investigate the employment of IRS in symbol-level precoding systems to exploit MUI in a more effective way by manipulating the multiuser channels. In this article, we focus on joint symbol-level precoding and reflecting designs in IRS-enhanced multiuser multiple-input single-output (MU-MISO) systems. Both power minimization and quality-of-service (QoS) balancing problems are considered. In order to solve the joint optimization problems, we develop an efficient iterative algorithm to decompose them into separate symbol-level precoding and block-level reflecting design problems. An efficient gradient-projection-based algorithm is utilized to design the symbol-level precoding and a Riemannian conjugate gradient (RCG)-based algorithm is employed to solve the reflecting design problem. Simulation results demonstrate the significant performance improvement introduced by the IRS and illustrate the effectiveness of our proposed algorithms.

We study closed-form interference-exploitation precoding for multi-level modulations in the downlink of multi-user multiple-input single-output (MU-MISO) systems. We consider two distinct cases: first, when the number of served users is not larger than the number of transmit antennas at the base station (BS), we mathematically derive the optimal precoding structure based on the Karush-Kuhn-Tucker (KKT) conditions. By formulating the dual problem, the precoding problem is transformed into a pre-scaling operation using quadratic programming (QP) optimization. We further consider the case where the number of served users is larger than the number of transmit antennas at the BS. By employing the pseudo inverse, we show that the optimal solution of the pre-scaling vector is equivalent to a linear combination of the right singular vectors corresponding to zero singular values, and derive the equivalent QP formulation. We also present the condition under which multiplexing more streams than the number of transmit antennas is achievable. For both considered scenarios, we propose a modified iterative algorithm to obtain the optimal precoding matrix, as well as a sub-optimal closed-form precoder. Numerical results validate our derivations on the optimal precoding structures for multi-level modulations, and demonstrate the superiority of interference-exploitation precoding for both scenarios.

Based on the concept of constructive interference (CI), multiuserinterference (MUI) has recently been shown to be beneficial for communicationsecrecy. A few CI-based secure precoding algorithms have been proposed that useboth the channel state information (CSI) and knowledge of the instantaneoustransmit symbols. In this paper, we examine the CI-based secure precodingproblem with a focus on smart eavesdroppers that exploit statisticalinformation gleaned from the precoded data for symbol detection. Moreover, theimpact of correlation between the main and eavesdropper channels is taken intoaccount. We first modify an existing CI-based preocding scheme to betterutilize the destructive impact of the interference. Then, we point out thedrawback of both the existing and the new modified CI-based precoders whenfaced with a smart eavesdropper. To address this deficiency, we provide ageneral principle for precoder design and then give two specific designexamples. Finally, the scenario where the eavesdropper's CSI is unavailable isstudied. Numerical results show that although our modified CI-based precodercan achieve a better energy-secrecy trade-off than the existing approach, bothhave a limited secrecy benefit. On the contrary, the precoders developed usingthe new CI-design principle can achieve a much improved trade-off andsignificantly degrade the eavesdropper's performance.

Low-cost passive intelligent reflecting surfaces (IRSs) have recently been envisioned as a revolutionary technology capable of reconfiguring the wireless propagation environment through carefully tuning reflection elements. This paper proposes deploying an IRS to cover the dead zone of cellular multiuser full-duplex (FD) two-way communication links while suppressing user-side self-interference (SI) and co-channel interference (CI). This approach, allowing the base station (BS) and all users to exchange information simultaneously, can potentially double the spectral efficiency. To ensure network fairness, we jointly optimize the precoding matrix of the BS and the reflection coefficients of the IRS to maximize the weighted minimum rate (WMR) of all users, subject to maximum transmit power and unit-modulus constraints. We reformulate this non-convex problem and decouple it into two subproblems. Then the optimization variables in the equivalent problem are alternately optimized by adopting the block coordinate descent (BCD) algorithm. In order to further reduce the computational complexity, we propose the minorization-maximization (MM) algorithm for optimizing the precoding matrix and the reflection coefficient vector by defining minorizing functions in the surrogate problems. Finally, simulation results confirm the convergence and efficiency of our proposed algorithm, and validate the advantages of introducing IRS to improve coverage in blind areas.

AbstractMassive multiple input multiple output (Ma-MIMO) is one of the basic enabler technologies for 5 G wireless communication networks to provide higher spectral efficiency (SE) by using large number of antennas and serves many users simultaneously in the same frequency—time resources. In a time division duplex (TDD) Ma-MIMO downlink (DL) system, the base station (BS) estimates the channel using uplink (UL) training but a pilot contamination (PC) is the major challenge in multi-cell Ma-MIMO system. This challenge causes severe inter-cell interference at the home cell and then limits the performance of the system. Precoding techniques with pilot reuse factor (PRF) are used to mitigate the PC effect. The performance of DL multi cell Ma-MIMO system is analyzed under spatial correleted Rayleigh fading channel with both perfect and imperfect channel state information (CSI). Precoding is used to mitigate the intra-cell interferences and PRF reduces the PC effect by assigning orthogonal pilots to neighboring cells. SE performance of multi cell Ma-MIMO system is analyzed with maximum ratio transmitter (MRT), zero forcing (ZF) and minimum mean square error (MMSE) precoding techniques and combined with PRF. Performance comparison of precoding techniques showed that MMSE provides the best SE performance for both CSI and it has highest SE at PRF of four among all when the number of antennas are changed. When both the number of users (K) and antennas (M) are increased simultaneously and [Formula: see text], the SE is improved by more than 25% and also system performance with perfect CSI is better than imperfect CSI.

Beamspace MIMO-NOMA is an effective way to improve spectral efficiency. This paper focuses on a downlink non-orthogonal multiple access (NOMA) transmission scheme for a beamspace multiple-input multiple-output (MIMO) system. To increase the sum rate, we jointly optimize precoding and power allocation, which presents a non-convex problem. To solve this difficulty, we employ an alternating algorithm to optimize the precoding and power allocation. Regarding the precoding subproblem, we demonstrate that the original optimization problem can be transformed into an unconstrained optimization problem. Drawing inspiration from fraction programming (FP), we reconstruct the problem and derive a closed-form expression of the optimization variable. In addition, we effectively reduce the complexity of precoding by utilizing Neumann series expansion (NSE). For the power allocation subproblem, we adopt a dynamic power allocation scheme that considers both the intra-beam power optimization and the inter-beam power optimization. Simulation results show that the energy efficiency of the proposed beamspace MIMO-NOMA is significantly better than other conventional schemes. [ABSTRACT FROM AUTHOR]

Hybrid precoding can significantly reduce the number of required radio frequency (RF) chains in massive MIMO systems. However, due to the large number of antennas and the high‐dimensional channel, the existing hybrid precoding algorithms usually require very high complexity. In this paper, we propose a convolutional neural network (CNN)‐based hybrid precoding scheme by utilizing unsupervised learning to reduce both the computational complexity and space complexity. Specifically, we first design a low‐complexity CNN structure to achieve the analog precoder, where the size of the convolution kernel and the number of channels are reduced by the further improved inception network. Then, we obtain the digital precoder by the classical zero‐forcing (ZF) algorithm. Simulation results show that, the proposed CNN‐based hybrid precoding can approach the sum‐rate performance of the classical two‐stage hybrid precoding but has much lower complexity. [ABSTRACT FROM AUTHOR]

In the era of rapidly expanding wireless communication systems, the demand for high-performance, low-latency, and energy-efficient solutions is paramount. One technology that has emerged as a transformative force in addressing these requirements is Massive Multiple-Input Multiple-Output (Massive MIMO) precoding. This abstract delves into the key aspects of Massive MIMO precoding, highlighting its role in enhancing spectral efficiency, mitigating interference, and improving the overall performance of wireless networks. Massive MIMO precoding leverages a substantial number of antennas at the transmitter, allowing for the creation of highly focused spatial beams. These beams can be dynamically optimized to cater to the specific requirements of individual users or devices, maximizing the spectral efficiency by spatially multiplexing multiple streams. This technique offers significant advantages in terms of increasing network capacity and achieving higher data rates, especially in dense network scenarios. Furthermore, Massive MIMO precoding excels in interference mitigation. By spatially directing signals toward intended recipients and steering nulls towards interferers, it reduces the impact of co-channel interference, enhancing network reliability and quality of service. This is particularly valuable in scenarios where network congestion and interference pose significant challenges, such as urban environments and crowded event venues. The research delves into the role of Massive MIMO precoding in improving the signal-to-noise ratio, which directly translates to extended coverage areas and reduced power consumption. Additionally, we explore the implications of Massive MIMO precoding in enabling efficient communication in massive Internet of Things (IoT) deployments and its potential for 5G and beyond. Massive MIMO precoding is poised to reshape the wireless communication landscape. It promises to deliver unprecedented gains in spectral efficiency, interference management, and energy efficiency. As the demand for high-speed, reliable, and ubiquitous connectivity continues to surge, this research plays the pivotal role that Massive MIMO precoding plays in meeting these demands, ushering in a new era of wireless communication. [ABSTRACT FROM AUTHOR]

This paper presents two precoding techniques for a reconfigurable intelligent surface (RIS)-assisted multi-user (MU) multiple-input multiple-output (MIMO) double quadrature spatial modulation (DQSM) downlink transmission system. Instead of being applied at the remote RIS, the phase shift vector is applied at the base station (BS) by using a double precoding stage. Results show that the proposed RIS-MU-MIMO-DQSM system has gains of up to 17 dB in terms of bit error rate (BER) and a reduction in detection complexity of 51% when compared with the conventional MU-MIMO system based on quadrature amplitude modulation (QAM). Compared with a similar system based on amplify and forward (AF) relay-assisted technique, the proposed system has a gain of up to 18 dB in terms of BER under the same conditions and parameters. [ABSTRACT FROM AUTHOR]

Due to the limitation of mobility and cost, it is inappropriate to utilize ground base station (GBS) to provide service in emergency scenarios or harsh environments. Unmanned aerial vehicle (UAV) is considered as a feasible way to solve the problem. Millimetre wave communication and UAV can be used together to offer wireless communications flexibly and rapidly for target area. However, fuselage jittering and energy supply would affect the quality and reliability of UAV communication seriously. To overcome the weakness of UAV, the authors prefer to use multiple UAVs to provide wireless communication service simultaneously. This paper investigates the design of hybrid beamforming in the scenarios including multiple UAVs and one end user. Cooperation strategy and alternating optimization method are applied in this design. The distributed UAV base stations are regarded as an entirety and the reference beamforming scheme obtained. Then, the beamforming problem for each UAV is formulated. Moreover, residual error minimization (REM) algorithm is proposed to find the solution of hybrid beamformers at UAV side. Finally, the beamformer of the receiver is optimized with the assist of effective channel, which includes the gain of both channel and beamformers of the transmitters. Simulation results show that the proposed algorithm can achieve near‐optimal spectral efficiency in different scenarios. [ABSTRACT FROM AUTHOR]

Due to its many advantages, orthogonal frequency division multiplexing (OFDM) is frequently utilised in optical wireless systems to achieve rapid data transmission. Hybrid asymmetrically clipped optical OFDM (HACO-OFDM) is a widely utilised approach in OFDM-based visible light communication (VLC) systems because it allows for high-rate burst transmission while maintaining reliability in a multi-path fading environment. Meanwhile, the congenital nonlinear characteristic of LEDs is a critical hindrance in VLC OFDM systems due to the high peak-to-average power ratio (PAPR) of OFDM signals. The high PAPR necessitates a large input back-off power to operate LEDs in the linear zone, lowering the BER of OFDM-based optical devices. In order to reduce PAPR and ameliorate non-linearity in the HACO-OFDM system, this work offers a hybrid precoder with μ -law compander approach. The auto-correlation association of modulated data symbols is suppressed by pre-processing in the frequency domain using a preset precoder matrix (PM), resulting in a low PAPR score. PAPR is further reduced by compressing the precoded signal in the time domain with the μ -law compressor. The PAPR of the proposed design is just 5.1 dB. It also reduces the non-uniform SNR spread among data symbols and mandates a low SNR for a 10 - 5 reference bit error rate (BER). Furthermore, with a reference BER of 10 - 3 , the hybrid approach reduces the optical bit energy required for high-speed data transmission. The metric used to calculate the system's non-linearity is the percentage error vector magnitude (EVM %). For 0 dB input back-off (IBO), the hybrid technique has an EVM of 5.59%, compared to 30% for a traditional HACO system. The reduction in LED nonlinearity reduces in-band and out-of-band distortion, allowing high-data-rate services to be supported. [ABSTRACT FROM AUTHOR]