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Spectral efficiency (SE) and energy efficiency (EE) are two main metrics for the transmit design in Visible Light Communications (VLC). However, the SE-optimal and EE-optimal strategies may be in conflict with each other to some extent. In this paper, we investigate the the tradeoff between EE and SE in rate splitting multiple access (RSMA)-aided VLC systems to maximize the system resource efficiency (RE) taking both perfect and imperfect channel state information (CSI) into consideration. For the scenarios with perfect CSI, we explore the joint precoding design and common rate allocation to maximize the RE of RSMA-aided VLC systems under the constraints of quality of service (QoS) and linear operation region (LoR) of LED, and propose a primal-dual-gradient-based precoding strategy. Furthermore, in the presence of CSI estimation errors, we propose a worst-case robust precoding design by exploiting quadratic transform and $\mathcal {S}$-Procedure. Numerical results indicate that the proposed resource efficient RSMA algorithm achieves the tradeoff between EE and SE.

Precoding is a technique employed to enhance transmission rates in various communication systems, including massive multiple-input multiple-output (MIMO) and optical wireless communication (OWC). In this study, we focus on network massive MIMO OWC (NM-MIMO-OWC) systems and investigate the precoder design to enhance the sum rate and improve the system performance. We present the network’s massive MIMO OWC framework. By utilizing this framework, we are able to calculate the achievable sum rate. Subsequently, we consider the precoding design for maximizing the sum rate while adhering to the total power constraint. To solve this optimization problem, we provide a necessary condition of the optimal solution based on the Karush–Kuhn–Tucker (KKT) conditions, and propose a low-complexity algorithm to further enhance the efficiency of the proposed precoding technique. The numerical results demonstrate that the proposed precoder design significantly improves the transmission rate and effectively maximizes the sum rate.

Low Earth Orbit (LEO) satellite communications (SATCOM) are important for wireless networks and offer better global wireless access services than higher altitude SATCOM options. SATCOM performance is affected by the number of antennas, which is usually constrained by the satellite space. In this paper, the application of dual-polarized technology is considered to assist LEO SATCOM. Compared with single-polarized antennas, dual-polarized antennas utilize two different polarization orientations to transmit and receive data. This can improve the quality of service (QoS) performance, as it allows for twice the amount of data to be transmitted over the same time–frequency resources. Specifically, we characterize the dual-polarized channel in massive multiple-input multiple-output (MIMO) LEO SATCOM and investigate the precoding design for enhancing the system performance. In particular, a precoding optimization problem is formulated, which aims to jointly optimize the system throughput and the QoS performance. We divide the problem into two stages and develop novel algorithms for each stage to obtain the precoding vectors. The numerical results demonstrate that the dual-polarized technology can improve the system throughput, and the proposed algorithm leads to a higher percentage of users achieving their QoS requirements compared to conventional approaches.

Multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM), confirmed as an efficient technique, has been applied in optical wireless communications (OWCs) to improve the transmission rate. However, the disadvantage of high peak-to-average power ratio (PAPR) also brings into the OWC systems, especially for multi-user communications, resulting in low power efficiency. This paper proposes beam domain MIMO-OFDM OWCs with PAPR reduction. We describe the MIMO-OFDM OWC system architecture and derive the achievable sum rate and PAPR for multi-user OFDM systems, which are dominated by the precoding vectors. To reduce the PAPR, we design the precoding vectors to maximize the sum rate under the PAPR constraint. We propose an iterative process to obtain a candidate optimal solution by the sequential parametric convex approximation (SPCA) method. Furthermore, we prove that the PAPR increases with the number of users and propose a low complexity beam scheduling and power allocation algorithm. Numerical results validate that the proposed schemes can efficiently improve the transmission rate and reduce the PAPR.

Extending massive multiple-input multiple-output (MIMO) technique to low earth orbit (LEO) satellite communication systems enables satellites to implement fl exible beamforming, which can make full use of the spatial degrees of freedom of massive MIMO and signifi cantly improve the spectral effi ciency and energy effi ciency of LEO satellite communication systems.Several related issues in massive MIMO-based LEO satellite communication systems were studied.The characteristics of massive MIMO LEO satellite channels were analyzed.The important diff erences between them and terrestrial wireless channels were revealed.With the aim of addressing the problems on channel information acquisition, multi-user transmission and user scheduling in massive MIMO LEO satellite communication, the main difficulties faced by the current work were discussed, and how to use the characteristics of massive MIMO LEO satellite channels to design tailored solutions with low implement complexity was conceived.

Abstract In this paper, massive multiple‐input‐multiple‐output (MIMO) wireless communication systems are considered to investigate joint transceiver beamforming. A base station (BS) equipped with a uniform planar array (UPA) serves several multi‐antennas users in a single cell. Based on the channel state information (CSI), the low complexity design of transceiver beamforming to minimize the transmit power subject to some quality of service (QoS) constraints is investigated. As the upper bound of the transmit power performance, the existing iteration‐based algorithms are leveraged as a reference. A general deep learning (DL)‐based framework and deep neural network (DNN) structure are proposed to reduce the complexity of the existing algorithms, where the properly trained DNN structure can learn directly from CSI. Consider the complexity of the DNN structure itself, a heuristic algorithm is proposed to replace the DNN structure, which takes the max‐eigenvalue‐eigenvector of the CSI as the direction of receive beamforming directly. The DNN structure is trained in the offline stage, therefore, only the complexity in the online stage is taken into consideration. Based on the numerical simulation, the complexity of the proposed DL‐based framework and the transceiver beamforming algorithms is reduced significantly while maintaining nearly the optimal performance compared with the existing iterative algorithms.

In this paper, we investigate a user terminal (UT) fingerprint positioning problem in massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing systems under non-line-of-sight scenario. Exploiting the advantages of a large-scale array and wide bandwidth, we introduce a spatial refined beam-based channel model and advocate a beam domain channel amplitude matrix as location-related fingerprint embedding abundant and stationary multi-path information, including amplitude, angle of arrival and delay of arrival, which are favorable to positioning. Taking the fingerprints as the input, we propose a novel machine learning enabled positioning method to localize the 2-dimension position of UTs. Specifically, we propose two fully-connected neural networks (NNs) to complete the task. The first NN is used to classify the position coordinates of the UTs, and the second NN functions as the regression module to estimate the UTs’ position coordinates. The simulation results present that the first NN can classify the UTs efficiently and the second NN can reliablely regress the UTs’ location. With the increasing of beams, the positioning performance of the proposed methods can achieve superior accuracy compared with conventional approaches.

Satellite communication is an indispensable part of future wireless communications given its global coverage and long-distance propagation. In satellite communication systems, channel acquisition and energy consumption are two critical issues. To this end, we investigate the tradeoff between the total energy efficiency (TEE) and minimum EE (MEE) for robust multigroup multicast satellite communication systems in this paper. Specifically, under the total power constraint, we investigate the robust beamforming aimed at balancing the TEE-MEE, so as to achieve the balance between the fairness and total performance on the system EE. For this optimization problem, we first model the balancing problem as a nonconvex problem while deriving its approximate closed-form average user rate. Then, the nonconvex problem is handled by solving convex programs sequentially with the help of the semidefinite relaxation and the concave-convex procedure. In addition, depending on the solution rank value, Gaussian randomization and eigenvalue decomposition method are applied to generate the feasible solutions. Finally, simulation results illustrate that the proposed approach can effectively achieve the balance between the TEE and MEE, thus realizing a tradeoff between fairness and system EE performance. It is also indicated that the proposed robust approach outperforms the conventional baselines in terms of EE performance.

Narrowband internet of things (NB-IoT) technology is widely used in data communications for a large number of low-speed and low-power devices, but it is diff cult to provide services for remote areas that cannot be covered by terrestrial networks.With its global coverage and low cost, the LEO satellite constellation can supplement and expand the terrestrial NB-IoT infrastructure, providing an eff ective solution for the space-based internet of things.Facing the application requirements of space-based internet of things, on the basis of reviewing the NB-IoT system architecture, the challenges of channel environment high dynamic delay and frequency off set faced by the application of NB-IoT technology in the LEO satellite constellation scenario were analyzed.For the uplink random access process, an eff ective user detection and timing synchronization method was designed, which had the advantages of high performance and low complexity, and provided an eff cient receiving method for the LEO satellite NB-IoT uplink random access synchronization.

In this paper, we investigate channel estimation and robust detection for uplink massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with in-phase and quadrature-phase imbalances (IQI). We represent the received signal with a real-valued model, where the combination of the IQI and the wireless channel is treated as the effective channel. Based on the real-valued model, we then perform the minimum mean square error (MMSE) criterion based estimation for the effective channel. We use adjustable phase shift pilots (APSPs) to reduce the pilot overhead and verify that the MSE of the effective channel estimation can be minimized by scheduling the pilot phase shifts properly. We propose a pilot phase shift scheduling algorithm based on the covariance matrices of the effective channels. Since the channel estimation performance might be degraded due to using APSPs, we develop an MMSE criterion based data detection scheme which is robust to the channel estimation error. Using operator-valued free probability theory, we derive an asymptotic deterministic equivalent for the ergodic achievable sum rate of the proposed detection scheme. The performance of the channel estimation with APSPs and the robust data detection is demonstrated via extensive numerical results.

Network slicing is a promising technology that can tailor the network to support diverse types of services over a common physical network infrastructure, and tenants can lease the customized slices from network providers to provide customized services for their users. This paper focuses on tenant profit optimization in 5G Radio Access Network (RAN) slicing and analyzes slice resource reservation and reconfiguration operation. A slice cost model is proposed, in which a base cost is included to compensate reconfiguration management overhead for the network providers. Increasing the accuracy of tenant resource requirement prediction can decrease tenant cost while guaranteeing QoS of its users. Further, we analyze the price strategy based on user traffic, and propose several alternative price scheme. In slicing across multiple Base Stations (BSs) scenario, we study two slice management operations, called slice mergence/split, similar but not identical to the breathing behavior of conventional cellular BS management. The tenant profit optimization problem across adjacent BSs is formulated, which can be used in slice mergence/splilt decision making. Taking VR (Virtual Reality) application as an example, simulation in allusion to the typical user arrival process is performed. The simulation indicates how these two operations can increase tenant profits.

In infrared optical wireless communications, narrow beams require the perfect calibration of the base station (BS) and user terminal (UT), which cannot support multiple moving users. This paper proposes a fiber lens based optical antenna, which employs a fiber unit array and a transmit lens to generate multiple beams, achieving full beam coverage. Specifically, we first design a fiber lens to extend the spreading angle of the emitted lights. We employ the wave optics to analyze the characteristic of the fiber lens and use the geometric optics to design the parameters of the lens. With the fiber lens, we then design the optical antenna consisting of a hexagonal fiber unit array and a transmit lens. Each fiber unit generates a beam to a specific direction and different fiber units irradiate different directions. All the generated beams can fully cover the communication area. Thus, the BS has the potential to support multi-user communication via multiple beams. Simulation results verify the performance of the designed fiber lens and the optical antenna achieving full beam coverage.

Based on the vision on the 6G wireless communication network,i.e.,global coverage,all spectrums and all applications,we comprehensively survey 6G related wireless channel measurements,channel characteristics,and channel models for all frequency bands and all scenarios.Millimeter wave (mmWave),terahertz (THz),optical band,satellite,unmanned aerial vehicle (UAV),maritime,underwater acoustic,high-speed train (HST),vehicle-to-vehicle (V2V),massive/ultra-massive multiple-input multiple-output (MIMO),orbital angular momentum (OAM),and industry Internet of things (IoT) communication channels were particularly investigated.The related 6G channel measurement and modeling results were also given.Finally,future research challenges on 6G channel measurements and modeling were pointed out.

In this paper, we investigate channel estimation with pilot reuse for massive MIMO with in-phase and quadrature-phase imbalances (IQI). Firstly, we obtain an augmented real-valued representation for the received signal by processing the real and imaginary parts individually with relative compensation. Then based on the obtained augmented real-valued representation, we perform minimum mean square error (MMSE) estimation for the effective channel which comprises the IQI matrix and wireless channel coefficients. Pilots are reused to reduce the pilot overhead. A lower bound of the effective channel estimation MSE is obtained. Motivated by the optimal conditions under which the lower bound can be achieved, we propose a pilot schedule algorithm. Finally, a least square based method to obtain the relative IQI compensation coefficients is provided. Numerical results verify the performance of the proposed effective channel estimation with pilot reuse.

Decrease of the pilot overhead is an important issue in vehicle-to-vehicle (V2V) underlay wideband massive multiple-input multiple-output (mMIMO) communication. To reduce the pilot overhead for channel acquisition, we propose phase shift adjustable pilots (PSAPs) for V2V underlay wideband mMIMO systems employing orthogonal frequency division multiplexing (OFDM). Motivated by the sparse property of the angle-delay (AD) domain channels in mMIMO, we first investigate channel acquisition for V2V underlay mMIMO-OFDM with PSAPs, and derive the optimal conditions to achieve the sum mean square error (sMSE) minimums of channel estimation and channel prediction, respectively. The optimal conditions indicate that the minimum sMSE can be achieved when the AD domain channel power spectra of cellular users (CUs) and transmitters in V2V communication pairs are non-overlapping after appropriate allocation of the available pilot phase shifts. Then an AD domain channel power spectrum based pilot allocation (ADCPS-PA)algorithm is developed according to the optimal conditions. Simulation results indicate that the achievable spectral efficiency for both cellular and V2V links can be significantly improved with PSAPs compared with that obtained by the conventional phase shift orthogonal pilot approach.

Two-stage precoding is a promising transmission strategy for frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) systems due to its large multiplexing gain with significant overhead reduction in both downlink training and feedback. In this paper, we propose a new agglomerative clustering method to significantly simplify the user clustering process. In order to suppress the residual inter-cluster interference in realistic two-stage precoding transmission, we propose an average signal-to-leakage-plus-noise ratio (SLNR)-based iterative cluster scheduling and outer precoder design scheme to achieve a balance between providing high multiplexing gain and improving per-user rate. For uniform linear arrays (ULAs), a fast implementation of the scheme with discrete Fourier transformation (DFT) approximation is proposed. The numerical results demonstrate the performance improvement of the proposed methods over the existing methods.

The integration of satellite and terrestrial 5G networks aims to provide ubiquitous coverage, improve service reliability, and enable the network scalability. However, the inherent characteristics of satellite channels bring challenges on the air interface design of integrated terrestrial-satellite networks. For example, for low earth orbits (LEO) mobile satellite communication (SatCom) system, it is unclear so far whether the 5G new radio (5G-NR) synchronization signals could meet the requirement of timing and frequency offset estimation in the presence of large Doppler shifts. In this paper, we investigate time and frequency synchronization for the downlink transmission of 5G-NR signals over LEO satellite channels. Starting from the maximum log-likelihood criterion for timing offset estimation given the observation of the received primary synchronization signals (PSS), we derive an upper bound of the objective function for simplicity. With a priori information that the maximum Doppler shift of LEO satellite-ground link is within a specific range, we construct the local synchronization sequence via using the modulated discrete prolate spheroidal sequences (DPSS) vectors. Then the timing offset estimation can be recast into a one-dimensional peak search problem. Moreover, the cyclic prefix (CP) structure of orthogonal frequency division multiplexing (OFDM) can be utilized to improve the estimation performance further. Once the timing and frequency offset are captured in the above initial synchronization phase, the tracking synchronization can be much simplified as the variation of both the timing and frequency offset is very small between two adjacent synchronization blocks. Simulation results show that by using the proposed algorithms, the 5G-NR signals can achieve near optimal downlink time and frequency synchronization in typical LEO SatCom systems.

Abstract For the uplink multicell massive multiple-input multiple-output (MIMO) block fading systems, a two-dimensional smoothed l 0 channel estimation method (2D-SL0-CE) with the aid of virtual channel representation is firstly exploited in this paper, which can jointly estimate the desired multiuser channels of the target cell and the interference links from neighbor cells without inducing pilot contamination. Then, a 2D-SL0 signal detection method (2D-SL0-SD) with the aid of sparse decomposing and the modified 2D s l 0 recovery algorithm is proposed, which can jointly decode M-ary phase-shift keying (MPSK) signal block for whole desired users. Moreover, an improved 2D-SL0-SD is also proposed to remove multiuser interference of neighbor cells in high SNR scenario. Simulation results show that the 2D-SL0-CE method can remove performance floor induced by pilot contamination and need less pilot overhead than the conventional least square (LS) method. When detecting QPSK signal blocks at 12 dB SNR, the 2D-SL0-SD method with perfect channel state information (CSI) can obtain 10−2 BER. Moreover, in the case of 8PSK signals, the 2D-SL0-SD joining with the 2D-SL0-CE can obtain 10−2 BER at 20 dB SNR.

We investigate non-orthogonal unicast and multicast (NOUM) transmission for massive multiple-input multiple-output systems, where only statistical channel state information of all user terminals is available at the base station. We adopt a weighted sum of the achievable ergodic unicast rate and multicast rate as the design objective. We first show the closed-form eigenvectors of the optimal unicast and multicast transmit covariance matrices, respectively, which reveals the optimality of beam domain transmission and simplifies the large-dimensional matrix-valued NOUM transmission design into a beam domain power allocation problem. Via invoking the concave-convex procedure, we, then, propose an efficient iterative beam domain power allocation algorithm with guaranteed convergence to a stationary point. In addition, we derive the deterministic equivalent of the objective in each iteration to further reduce the optimization complexity. Simulation results show that the proposed NOUM transmission can provide a significant performance gain in terms of the achievable ergodic unicast-multicast rate region over the conventional orthogonal approach.

We investigate robust multigroup multicast transmission for frame-based multi-beam satellite communication systems with full frequency reuse. To mitigate the effect of outdated channel state information (CSI), we first investigate robust multigroup multicast precoding that maximizes the minimum average signal to interference plus noise ratio (SINR) under the per-beam power constraints. We show the relationship between the robust max-min fair problem and the robust power minimization problem with partial CSI and propose a low complexity precoder for the robust power minimization problem. We then investigate user clustering which only requires partial CSI. We show that the approximated average SINR can be maximized provided that the estimated channel vectors of the users in the same cluster are linearly dependent while those in different clusters are mutually orthogonal. Motivated by this user clustering condition, we further propose a low-complexity user clustering algorithm. Simulation results demonstrate that the proposed robust approach can provide substantial performance gains over the conventional approach in multi-beam satellite communication systems.

In this paper, we propose a novel broad coverage precoder design for three-dimensional (3D) massive multi-input multi-output (MIMO) equipped with huge uniform planar arrays (UPAs). The desired two-dimensional (2D) angle power spectrum is assumed to be separable. We use the per-antenna constant power constraint and the semi-unitary constraint which are widely used in the literature. For normal broad coverage precoder design, the dimension of the optimization space is the product of the number of antennas at the base station (BS) and the number of transmit streams. With the proposed method, the design of the high-dimensional precoding matrices is reduced to that of a set of low-dimensional orthonormal vectors, and of a pair of low-dimensional vectors. The dimensions of the vectors in the set and the pair are the number of antennas per column and per row of the UPA, respectively. We then use optimization methods to generate the set of orthonormal vectors and the pair of vectors, respectively. Finally, simulation results show that the proposed broad coverage precoding matrices achieve nearly the same performance as the normal broad coverage precoder with much lower computational complexity.

In this work, we propose a satellite-aided permissionless consensus protocol for scalable space–terrestrial blockchains. We design its working principle and workflow by taking full advantage of satellites for extensive coverage and ubiquitous connectivity. Based on the proposed protocol, we demonstrate how such a space–terrestrial blockchain grows and evolves through several typical cases in the presence of adversarial nodes, user misbehavior, and transmission outage. Taking proof of work (PoW) as a benchmark, we assess the system security by considering both adversarial miners and possible colluding satellites. Then, we analyze the maximum blockchain throughput under network capacity limits and evaluate the impact of information propagation delay via a Markov model. Simulation results support that the proposed satellite-aided consensus protocol achieves higher throughput and exhibits greater scalability than PoW.

Free-space optical (FSO) communication has become a cost-effective method to provide high data rates. However, the turbulence-induced fading limits its application to short-range applications. To address this, we propose a multiuser diversity (MD) FSO scheme in which the $N$th best user is selected and the channel fluctuations can be effectively exploited to produce a selection diversity gain. More specifically, we first present the statistics analysis for the considered system over both weak and strong atmospheric turbulence channels. Based on these statistics, the outage probability, bit-error rate performance, average capacity, diversity order, and coverage are analyzed. Results show that the diversity order for the gamma–gamma fading is $N\ \min\{\alpha, \beta\}/2$, where $N$ is the number of users, and $\alpha$ and $\beta$ are the channel fading parameters related to the effective atmospheric conditions of the link.

In this paper, we investigate energy-efficient multicast precoding for massive multiple-input multiple-output (MIMO) transmission. In contrast with most previous work, where instantaneous channel state information (CSI) is exploited to facilitate energy-efficient wireless transmission design, we assume that the base station can only exploit statistical CSI of the user terminals for downlink multicast precoding. First, in terms of maximizing the system energy efficiency, the eigenvectors of the optimal energy-efficient multicast transmit covariance matrix are identified in closed form, which indicates that optimal energy-efficient multicast precoding should be performed in the beam domain in massive MIMO. Then, the large-dimensional matrix-valued precoding design is simplified into an energy-efficient power allocation problem in the beam domain with significantly reduced optimization variables. Using Dinkelbach’s transform, we further propose a sequential beam domain power allocation algorithm which is guaranteed to converge to the global optimum. In addition, we use the large-dimensional random matrix theory to derive the deterministic equivalent of the objective to reduce the computational complexity involved in sample averaging. We present numerical results to illustrate the near-optimal performance of our proposed energy-efficient multicast precoding for massive MIMO.