Reconfigurable intelligent surface (RIS) and integrated sensing and communication (ISAC) are considered promising technologies for the sixth generation (6G) wireless communication. The deployment of RIS within the mmWave ISAC system can achieve better communication performance and sensing accuracy. The mmWave band signals can be utilized to enhance transmission rates and available bandwidth significantly. However, the increased size of the RIS array and bandwidth introduces the beam-squint effect, which impacts the performance of RIS-aided communication and sensing. In this paper, we analyze the beam-squint and beam-split effects on a uniform planar array of RIS. Moreover, we derive controllable beam-squint and beam-split ranges based on true-time-delay (TTD) lines and propose RIS-aided sensing schemes with beam-squint and beam-split for a mmWave ISAC system. The proposed schemes can utilize both time-domain and frequency-domain resources for beam scanning, which reduces the time overhead compared to traditional beam scanning schemes. Simulation results illustrate the effectiveness of the proposed RIS-aided user sensing schemes.

As mobile devices become increasingly popular and the need for indoor localization services grows, the localization of indoor mobile users is becoming more and more popular. However, the instability of received signal strength in the actual environment will have a detrimental influence on indoor localization, and the large multi-story buildings will also create new challenges. In this paper, we put forward a localization model with the graph-based location mapping network. The connection mode of the access points is used to construct a graph describing the location of reference points and users. Also, the graph neural networks are used to extract graph-level representation. This model can effectively capture the misaligned features. Moreover, the proposed approach is assessed on two public datasets, i.e., UJIIndoorLoc and UTSIndoorLoc, and the performance is evaluated against several leading-edge methods. Experimental results demonstrate that the proposed model outperforms existing solutions.

With the popularity of mobile devices and the increasing demand for indoor localization services, the localization of indoor mobile users is becoming more and more popular. However, many existing methods of building the radio map require collecting the received signal strength (RSS) of a large number of access points (APs), which causes high-hardware costs and large storage. Additionally, the instability of RSS in the actual environment will have a detrimental influence on indoor localization, and the large multistory buildings will also create new challenges. In this article, we propose a localization model with the combination of the AP selection network and the graph-based location mapping network. This model selects the optimal APs through the AP selection network and reduces the number of required APs. Then, the connection mode of the selected APs is used to construct a graph describing the location of reference points and users. Besides, the graph neural networks are used to extract graph-level representation, effectively capturing the misaligned features. Moreover, evaluated on the UJIIndoorLoc and UTSIndoorLoc data sets, the proposed method could not only reduce the number of required APs while ensuring localization performance but also outperform several state-of-the-art methods.

Beam training is one of the kernel problems in Millimeter-Wave(mmWave) massive multiple-input multiple-output(MIMO) systems. The beam direction explicitly relies on user location and is implicitly related to channel state information(CSI). Based on this fact, we propose a deep neural network-based novel downlink beam prediction framework to reduce the beam training overhead while achieving higher reliability. Considering that the user location and CSI are two completely different types and dimensions of information, the proposed neural network adopts adjustable feature fusion learning(AFFL) to fuse the two kinds of information. To reduce the beam training overhead, only the user location and the CSI of a minimal number of antennas are taken as the network’s inputs. In addition, when fusing, the signal-to-noise ratio(SNR) is used to adaptively adjust the weights of the two inputs on beam prediction output. Finally, simulation results corroborate that the proposed AFFL-based framework can achieve superior performance and robustness than the strategy which solely uses CSI, especially under low SNR conditions.

Reconfigurable intelligent surface (RIS) is a revolutionary technology for achieving high rate and large coverage in future wireless networks by smartly reflecting the signals with adjustable phase shifts. To design the reflection beamforming, accurate individual channel state information is required at the RIS, which is a challenge task due to the lack of signal processing ability in passive mode. In this paper, we add signal processing units for a few antennas at the RIS to partially acquire the channels and extrapolate them to the full channels, in which the active antenna selection is a key point but has not been addressed yet. We construct an active antenna selection network that utilizes the probabilistic sampling theory to select the optimal locations of these active antennas. With this active antenna selection network, we further design two deep learning-based schemes, i.e., the channel extrapolation scheme and the beam searching scheme. The former utilizes the selection network and a convolutional neural network to extrapolate the full channels from the partial channels, while the latter adopts a fully-connected neural network to achieve the direct mapping from the partial channels to the optimal beamforming vector with maximal transmission rate. Simulation results show that the proposed optimal antenna selection outperforms the trivial uniform antenna selection, and the performance of beam searching is more stable than that of channel extrapolation with fewer active antennas.

Although it is often used in the orthogonal frequency division multiplexing (OFDM) systems, application of massive multiple-input multiple-output (MIMO) over the orthogonal time frequency space (OTFS) modulation could suffer from enormous training overhead in high mobility scenarios. In this paper, we propose one uplink-aided high mobility downlink channel estimation scheme for the massive MIMO-OTFS networks. Specifically, we firstly formulate the time domain massive MIMO-OTFS signal model along the uplink and adopt the expectation maximization based variational Bayesian (EM-VB) framework to recover the uplink channel parameters including the angle, the delay, the Doppler frequency, and the channel gain for each physical scattering path. Correspondingly, with the help of the fast Bayesian inference, one low complex approach is constructed to overcome the bottleneck of the EM-VB. Then, we fully exploit the angle, delay and Doppler reciprocity between the uplink and the downlink and reconstruct the angles, the delays, and the Doppler frequencies for the downlink massive channels at the base station. Furthermore, we examine the downlink massive MIMO channel estimation over the delay-Doppler-angle domain. The channel dispersion of the OTFS over the delay-Doppler domain is carefully analyzed and is utilized to associate one given path with one specific delay-Doppler grid if different paths of any user have distinguished delay-Doppler signatures. Moreover, when all the paths of any user could be perfectly separated over the angle domain, we design the effective path scheduling algorithm to map different users' data into the orthogonal delay-Doppler-angle domain resource and achieve the parallel and low complex downlink 3D channel estimation. For the general case, we adopt the least square estimator with reduced dimension to capture the downlink delay-Doppler-angle channels. Various numerical examples are presented to confirm the validity and robustness of the proposed scheme.

This paper proposes a Bayesian downlink channel estimation framework for time-varying massive MIMO networks. In particular, the quantization effects at the receiver are considered. In order to fully exploit the sparsity and time correlations of channels, we formulate the time-varying massive MIMO channel as the simultaneously sparse signal model. Then, we propose a sparse Bayesian learning (SBL) framework to estimate the model parameters of the sparse virtual channel. The expectation maximization (EM) algorithm is employed to reduce complexity. Specifically, the factor graph and the general approximate message passing (GAMP) algorithms are used to compute the desired posterior statistics in the expectation step, so that high-dimensional integrals over the marginal distributions can be avoided. The non-zero supporting vector of the virtual channel is then obtained from channel statistics by a k-means clustering algorithm. After that, the reduced dimensional GAMP-based scheme is designed to make the full use of the channel temporal correlation so as to enhance the virtual channel tracking accuracy. Finally, the efficacy of the proposed framework is demonstrated through simulations.

Constructing energy-efficient data center networks (DCNs) is becoming increasingly significant. In the hybrid DCNs with both wired and wireless links, reconfigurable wireless links can effectively reduce the routing path length and the usage of the switch, thereby greatly reduce the energy consumption DCNs. In this paper, we propose an energy-efficient hybrid flow routing and antenna scheduling scheme for tree-based hybrid DCNs. Firstly, the original problem of hybrid routing and scheduling is decomposed into two subproblems by taking advantage of the wireless energy-saving features. Then, a novel weight-relaxing-rounding algorithm is developed to solve the first subproblem. Specifically, the topology characteristics of the tree-based DCNs is used to perform weight transformation and link remapping, and then to find energy-efficient wired subnet. After that, the relax-and-rounding technology is adopted to obtain energy-efficient wireless links scheduling solution. Finally, the numerical results show that the proposed scheme can achieve a near optimal solution when the network scale is small. As the network scale increases, the proposed scheme is still able to save more energy than the existing algorithm.

In this paper, we propose an angle-domain NOMA transmission scheme over multicell massive MIMO systems, where multiple users' signal can be superposed to be served by the same spatially angle-domain beams. Then, we carefully consider the performance degradation resulting from the severe inter-cell interference, and formulate an optimization problem in terms of jointly optimizing precoders and decoders (JOPD) to seek an optimal transmission policy with quality of service (QoS) requirements of both cell-edge users and cell-center users, which consequently is a maximization of a nonconvex function with nonconvex constraints. To solve this challenging problem, we invoke the constrained concave convex procedure (CCCP) method to optimize precoders with fixed decoders, while the decoders can be readily optimized with obtained precoders. Consequently, we propose an alternating optimization algorithm based on CCCP (AoCCCP) to jointly optimize precoders and decoders and then obtain a suboptimal solution of the prime problem. Simulation results verify that the proposed scheme exhibits significant performance gain in terms of sum rate as well as QoS guarantee.

This paper proposes a novel scheme for learning the channel statistics of the time- varying massive MIMO network. In particular, the effects of the quantization at the receiver are considered. Firstly, we formulate the massive MIMO channel as a simultaneously time-varying sparse signal model through virtual channel representation (VCR) and first order auto regressive (AR) model. Then, we propose a sparse Bayesian learning (SBL) framework to learn the model parameters of the sparse virtual channel. To avoid the unacceptable complexity, we apply the expectation maximization (EM) algorithm to achieve the approximate solution. Specifically, the factor graph and the general approximate message propagation (GAMP)-based message passing algorithms are used to compute our wanted posterior statistics in the expectation step. After that, the non-zero supporting vector of virtual channel is obtained from channel statistics by a k-means clustering algorithm. Finally, we demonstrate the efficacy of the proposed schemes through simulations.

The low-rank property of the channel covariances can be adopted to reduce the overhead of the channel training in massive MIMO systems. In this paper, with the help of the virtual channel representation, we apply such property to both time-division duplex and frequency-division duplex systems, where the time-varying channel scenarios are considered. First, we formulate the dynamic massive MIMO channel as one sparse signal model. Then, an expectation maximization-based sparse Bayesian learning framework is developed to learn the model parameters of the sparse virtual channel. Specifically, the Kalman filter (KF) and the Rauch-Tung-Striebel smoother are utilized to track the model parameters of the uplink (UL) spatial sparse channel in the expectation step. During the maximization step, a fixed-point theorem-based algorithm and a low-complex searching method are constructed to recover the temporal varying characteristics and the spatial signatures, respectively. With the angle reciprocity, we recover the downlink (DL) model parameters from the UL ones. After that, the KF with the reduced dimension is adopt to fully exploit the channel temporal correlations to enhance the DL/UL virtual channel tracking accuracy. A monitoring scheme is also designed to detect the change of model parameters and trigger the relearning process. Finally, we demonstrate the efficacy of the proposed schemes through the numerical simulations.

In this paper, we investigate a spatially sparse code multiplexing (SCM) transmission scheme for the massive multiple-input multiple-output (MIMO) networks to enhance the access connectivity. We construct a non- orthogonal transmission policy over both power and angle domains to fully utilize the limited angle-domain degree of freedom (DoF). Firstly, the mapping structure in the angle domain and the detection method are presented. Then, we formulate an optimization problem to seek an optimal transmission policy for the proposed SCM framework, where both the design of the mapping matrix and the power allocation are concerned. To simplify the non-convex problem, we solve the problem with three steps. During the first step, we allocate different angle-domain beams for users to obtain a sparse mapping matrix; during the second step, the prime optimization is transformed as a convex power allocation problem. Finally, we pursue a suboptimal transmission strategy for the multiple clusters with iterative power allocation. Simulation results verify that the SCM scheme exhibits significant performance gain in terms of sum rate.

As a revolutionary wireless transmission strategy, interference alignment (IA) can improve the capacity of cell-edge users. However, the acquisition of the global channel state information for IA leads to unacceptable overhead in the massive MIMO systems. To tackle this problem, in this paper, we propose an IA and soft-space-reuse (IA-SSR)-based cooperative transmission scheme under the two-stage precoding framework. Specifically, the cell-center and the cell-edge users are separately treated to fully exploit the spatial degrees of freedoms. Then, the optimal power allocation policy is developed to maximize the sum-capacity of the network. Next, a low-cost channel estimator is designed for the proposed IA-SSR framework. Some practical issues in IA-SSR implementation are also discussed. Finally, plenty of numerical results are presented to show the efficiency of the proposed algorithm.

The low-rank property of the channel covariances can be adopted to reduce the overhead of the channel training in massive MIMO system. In this paper, we exploit such low-rank property through virtual channel representation (VCR) under the time-varying channel scenario. Firstly, we reformulate the dynamic massive MIMO channel as one sparse signal model through VCR. Then, an expectation maximization (EM) based sparse Bayesian learning (SBL) framework is developed to estimate the statistical parameters of the sparse virtual channel. Specifically, the Kalman filter (KF) and the Rauch-Tung-Striebel smoother (RTSS) are applied to track the posterior statistics of the angle domain sparse channel in the expectation step, while a fixed-point theorem based algorithm and a low-complexity searching algorithm are separately developed to recover the temporal varying characteristics and the spatial signatures in the maximization step. Finally, we demonstrate the efficacy of the proposed schemes through simulations.

In this paper, we consider a disruption tolerant network (DTN) which enables data transmission with intermittent connectivity and in which instantaneous end-to-end path between a source and destination may not exist. We explore routing problems in such networks in consideration of limited storage for each intermediate node. A graph model called the storage enhanced time-varying graph (STVG) is presented, which fits the dynamic topology characteristics and time- varying parameters of DTN. We transform the nodes with finite buffer in DTN into links with limited bandwidth in STVG. Then, a Shortest Path Computation with Flow Guaranteed (SPFG) algorithm is proposed to select a path with minimum overall cost (delay), meanwhile, to guarantee the transfer of a certain flow. Simulation results show that the proposed algorithm based on the STVG model can obtain better performance in delay and delivery ratio (which can be improved to more than 90%) as compared with existing routing algorithms without considering the buffer constraints.

In this study, the authors study the downlink resource allocation optimisation in femtocell networks, to maximise the number of guaranteed users whose data rate requirements are fully met. The spectral access of femtocell networks is based on orthogonal frequency division multiple access. In their work, two challenges are solved. The first is the intractable inter‐cell interference coordination brought about by the transmission delay in backhaul connections, and the second is the incorporation of physical interference model into problem formulations. To solve these problems, the authors propose a novel two‐level resource allocation scheme, implemented in both radio resource management controller and femtocell base stations, based on the maximisation of the number of guaranteed users. Notably, their proposed scheme is efficient and requires low overhead. Simulation results show that the proposed scheme offers significant performance improvement in both the percentage of guaranteed users and spectrum spatial reuse over existing methods proposed in the literature.