Clustered cell-free networking paves a new way for enabling scalable joint transmission among access points (APs) by partitioning the whole network into non-overlapping sub-networks. Previous works adopted clustering algorithms, graph partitioning methods or conventional continuous optimization theories to partition a network based on the channels between all users and all APs, resulting in huge channel measurement and computational costs. This makes these methods difficult to be implemented in practical systems since the optimal network partition could vary frequently due to user mobility. In addition, existing methods were usually designed for specific clustered cell-free networking problems with different optimization algorithms employed. In this paper, we leverage deep reinforcement learning (DRL) for clustered cell-free networking so as to rapidly adapt to user movements in dynamic environments, and propose a deep deterministic policy gradient based clustered cell-free networking (DDPG-C2F) framework that can be adapted in various application scenarios. Moreover, in our framework, only one single channel needs to be estimated at each AP as the input of the neural network, which greatly reduces the channel measurement costs for clustered cell-free networking, and the training and inference costs of our framework. The proposed DDPG-C2F framework is then applied to various clustered cell-free networking problems with different objectives and constraints to demonstrate its performance. Simulation results show that our framework outperforms existing baselines in all scenarios. Moreover, we show that the proposed framework can reduce the handover cost over user mobility, and is robust to dynamic scenarios with random user joining or leaving.

With camera-equipped phones becoming essential items carried by people, utilizing multi-perspective views (MPV) captured from the surrounding environment has emerged as a promising approach to achieve pervasive localization in indoor environments. This MPV-based localization requires extensive data, necessitating the use of crowdsourcing for data collection and training. In this distributed process, multiple clients can process and share results, raising concerns about privacy breaches. To address this challenge, this paper introduces federated learning (FL) into MPV-based localization, resulting in theMPVFedLocalgorithm, which facilitates distributed learning without exchanging raw local data. However, FL-based methods often experience reduced accuracy due to data heterogeneity. To overcome this, we propose a model self-supervised federated learning framework withinMPVFedLoc. This framework integrates self-supervised learning at the model level and incorporates a model self-supervised loss into the local training objective to mitigate the bias between the global and local models. To evaluate the performance of MPV-based localization, we construct a benchmark dataset named TJF_Building and conduct extensive experiments. Additionally, we compare the performance ofMPVFedLocwith three state-of-the-art FL methods in both homogeneous and heterogeneous settings. Experimental results demonstrate the robustness and effectiveness ofMPVFedLoc, particularly in handling heterogeneous scenarios. Further experiments on two typical image classification datasets also highlight the potential ofMPVFedLocfor diverse tasks.

3D reconstruction faces challenges such as geometric warping and structural ambiguity, particularly in intricate topologies, heavy occlusions and complex backgrounds. These problems are partly attributed to excessive feature entanglement, which induces semantic confusion and spatial ambiguity. To address these limitations, we propose an occlusion sensitivity-guided semantic-disentangled Mamba-CNN network that enables human-controllable disentanglement of multi-attribute information within a bias-free semi-supervised framework. Specifically, we create various occlusion conditions and assign pseudo-labels to the augmented data within a semi-supervised framework, which enables the exploration of occlusion sensitivity of different semantic attributes for human-controllable semantic disentanglement. To reduce the bias between the augmented samples and their assigned pseudo-labels, we use linear PIoU and nonlinear MS-SSIM algorithms to calculate the confidence of pseudo-labels, which minimizes the error propagation caused by the bias. Then, we develop a disentangled multi-depth Mamba-CNN block that combines CNN’s local feature extraction capability with Mamba’s ability to capture long-range dependencies. This allows our model to effectively capture disentangled multi-attribute spatial features and semantic representations. However, critical cross-level semantic attribute connections could be lost in the disentanglement process. To tackle this, we propose a multi-attribute semantic query block to dynamically re-establish these connections and minimize cross-attribute information loss. Extensive quantitative and qualitative evaluations on object and face reconstruction demonstrate that our method outperforms existing state-of-the-art approaches. Codes and all data are publicly available at https://github.com/Ray-tju/sensitivity-guided-semantic-disentangled-Mamba-CNN.

This paper explores the challenges of user equipment (UE) association and downlink power control in scalable cell-free massive multiple-input multiple-output (MIMO) systems. Initially, we develop a scalable UE association algorithm, which ensures that each UE is connected to only a small set of access points (APs). This approach effectively reduces both fronthaul requirements and the computational load on the APs. The algorithm employs a competition-based mechanism to ensure that APs efficiently distribute workloads while satisfying the quality of service (QoS) requirements of UEs, preventing them from losing network connectivity. Second, we introduce a distributed downlink power control method based on deep neural networks (DNNs) to improve the long-term downlink spectral efficiency (SE) of the entire network. This method relies solely on locally collected large-scale fading information as DNN input, making it adaptable to dynamic scenarios involving varying numbers of associated UEs. To further enhance the training efficiency of the DNN, we design a distributed training framework that fully leverages the computational resources of distributed edge processors (EPs). Simulation results show that the proposed UE association algorithm and downlink power control method exhibit significant advantages over the benchmarks.

Clustered cell-free networking is a promising paradigm for future mobile communication systems, which dynamically partitions the whole network into multiple small subnetworks to avoid the cell-edge problem in cellular networks. To optimize network partition, previous approaches primarily relied on heuristics and relaxation techniques. Recent studies leveraged graph partitioning theory to address this problem by representing the wireless network as an undirected bipartite graph. In this paper, we focus on the load balanced clustered cell-free networking problem with the objective of maximizing the minimum sum rate among all subnetworks. In contrast to previous works, we propose a new directed graph model and equivalently transform the problem into a sequence of min-max K-cut problems. Subsequently, a streaming balanced assignment algorithm is proposed to solve min-max K-cut problems. Building upon this, we develop a sequential min-max K-cut approach with theoretical guarantees. Simulation results demonstrate that our method outperforms existing algorithms by significantly improving the minimum subnetwork sum rate, thereby effectively balancing the loads of subnetworks.

Clustered cell-free networking has appeared as a promising technique to overcome the cell-edge problem in cellular networks. This is achieved by dynamically decomposing the whole network into a number of non-overlapping weakly-interfered subnetworks and conducting joint processing among the base-stations (BSs) in the same subnetwork. For optimizing clustered cell-free networking, i.e., network decomposition, existing works usually assumed that all the BSs are involved in signal transmission/reception. However, switching a BS far away from user equipments (UEs) into sleep mode could improve the data rate due to the reduced inter- Ueinterference. In this paper, we aim to maximize the downlink sum rate of the network by optimizing clustered cell-free networking while considering dynamic BS sleeping. By decomposing the problem into a BS assignment subproblem and a UE assignment subproblem, an alternating optimization based clustered cell-free networking (AO-C2F -Net) algorithm is proposed. Simulation results corroborate that enabling BS sleeping improves the sum rate performance. In addition, the proposed AO-C2F -Net algorithm outperforms existing benchmarks in terms of optimizing both clustered cell-free networking and BS sleeping.

Clustered cell-free networking paves a new way for enabling joint transmission among access points (APs) by decomposing the whole network into non-overlapping subnetworks. Previous works adopted clustering algorithms or conventional optimization theories to group users and APs into subnetworks, which usually require high computational costs and have long running time. In this paper, we leverage deep reinforcement learning (DRL) for clustered cell-free networking so as to rapidly adapt to user movement in dynamic scenarios. To effectively reduce the joint processing complexity of each subnetwork, we aim to maximize the balance of the subnetworks in addition to the sum rate. With such an objective, we propose a novel deep deterministic policy gradient based clustered cell-free networking (DDPG-C2F) framework. It is worth mentioning that the proposed framework requires much lower channel measurement overhead and computational complexity compared to the conventional approaches. Simulation results demonstrate the superior performance of the proposed DDPG-C2F framework in various scenarios.

With high-precision location information becoming increasingly essential, the prevail of wireless localization networks has raised concerns about the growing risk of location privacy disclosure. Existing privacy-preserving strategies, such as encryption and noise addition, often suffer from drawbacks like high computational complexity and accuracy degradation. This paper explores the potential for enhancing location privacy protection through power allocation during the transmission of measurements. We begin by defining a location secrecy metric, which is dependent on transmitted power, to quantify the risk of an agent’s location being exposed, considering a general measurement model of eavesdroppers in wireless localization systems. Using this metric as a privacy constraint, we formulate an optimization problem for power allocation that aims to minimize the agent’s localization error while ensuring the required level of location privacy protection in both non-cooperative and cooperative localization scenarios. The resulting problems are challenging due to their nonlinear objective and constraint functions. To address this, we reformulate the problem into a tractable form using fractional programming and a series of problem transformations, converting it into a semidefinite programming (SDP) form. Numerical results validate the efficacy of the proposed strategy in protecting privacy and demonstrate its superiority over existing strategies in balancing localization accuracy. Additionally, the analysis provides valuable insights into the trade-off between localization accuracy and privacy protection.

For large-scale wireless networks, the optimal network decomposition has been proposed from a graph theoretical perspective to maximize the number of decomposed subnetworks with an interference constraint. When each user is associated with multiple base-stations (BSs) to form its virtual cell, however, the optimal network decomposition may lead to highly unbalanced subnetwork sizes if the key system parameters are not properly set. In this paper, an analytical framework is proposed to characterize the distribution of subnetwork size in terms of the number of users in a randomly chosen subnetwork for virtual-cell-based optimal decomposition. The analysis reveals that the distribution of subnetwork size is crucially determined by the virtual cell size, i.e., the number of BSs associated with each user, and the ratio of the number of users to the number of BSs, both of which should be carefully tuned to produce balanced subnetworks.

Reconfigurable intelligent surface (RIS) has emerged as a highly promising technology for future wireless sensing applications, primarily due to its capability to dynamically manipulate the incoming signals through meticulous configuration of the RIS profile. This paper delves into the optimal design strategy for the RIS profile, aiming at maximizing the localization accuracy of the non-line-of-sight user within typical downlink orthogonal frequency division multiplexing (OFDM) systems. Assuming prior knowledge of the user’s location, we first derive the measurement model and corresponding position error bound (PEB) for the considered OFDM systems. Subsequently, under the total power budget constraint, we establish a closed-form solution for the optimal covariance matrix of the RIS profile by leveraging the subspace structure information of the channel states. Building upon this, the design of the optimal RIS profile is executed using the time-sharing technique. Taking into account the hardware limitations, we further formulate a more practical RIS profile design problem by incorporating the unit-modulus constraint for each RIS element, which, however, is non-convex and thus hard to tackle. To address this issue, we employ the alternating minimization technique to compute a suboptimal solution for the constrained RIS profile design problem. Simulation results demonstrate the remarkable PEB performance achieved by both the proposed optimal and constrained RIS profile design strategies. It is also illustrated that the proposed constrained RIS profile design surpasses other state-of-the-art alternatives, exhibiting a comparable performance to our devised optimal benchmark.

Clustered cell-free networking has been considered as an effective scheme to trade off between the low complexity of current cellular networks and the superior performance of fully cooperative networks. With clustered cell-free networking, the wireless network is decomposed into a number of disjoint parallel operating subnetworks with joint processing adopted inside each subnetwork independently for intra-subnetwork interference mitigation. Different from the existing works that aim to maximize the number of subnetworks without considering the limited processing capability of base-stations (BSs), this paper investigates the clustered cell-free networking problem with the objective of maximizing the sum ergodic capacity while imposing a limit on the number of user equipments (UEs) in each subnetwork to constrain the joint processing complexity. By successfully transforming the combinatorial NP-hard clustered cell-free networking problem into an integer convex programming problem, the problem is solved by the branch-and-bound method. To further reduce the computational complexity, a bisection clustered cell-free networking ($\text {B}\text {C}^{2}\text {F}$-Net) algorithm is proposed to decompose the network hierarchically. Simulation results show that compared to the branch-and-bound based scheme, the proposed$\text {B}\text {C}^{2}\text {F}$-Net algorithm significantly reduces the computational complexity yet achieves nearly the same network decomposition result. Moreover, our$\text {B}\text {C}^{2}\text {F}$-Net algorithm achieves near-optimal performance and outperforms the state-of-the-art benchmarks with up to 25% capacity gain.

Estimating air-to-ground (A2G) channel for a cellular-connected unmanned aerial vehicle (UAV) requires frequent pilot transmission due to its high mobility. To reduce the pilot overhead, researchers have attempted to predict future channels based on the historical ones by leveraging learning techniques. However, most existing works are limited to sequentially forecasting subsequent channels, suffering from the error accumulation problem that consequently hampers the prediction accuracy. To address this issue, this paper proposes a novel learning-based estimate-then-predict scheme for A2G channel tracking. In this scheme, the UAV transmits limited pilots, and the base station (BS) first performs channel estimation by exploiting the received pilots and then predicts a series of subsequent channels concurrently. Specifically, in the estimation phase, we propose a least-squares feedforward neural network (LS-FNN) to fuse the benefits of LS in high signal-to-noise ratio (SNR) regime and FNN in low SNR regime. In the prediction phase, a multi-time-interval long-short-term-memory (MTI-LSTM) network is proposed for concurrent channel prediction. A distinctive difference from prior works is that layer normalization is employed to greatly increase the prediction accuracy at no cost of additional neurons. Simulation results corroborate the superior performance of our proposed scheme over the state-of-the-art benchmarks.

Determining the network capacity, which is a crucial performance metric of wireless systems, is becoming increasingly important with the growing need for future ultra-dense networks. There have been a multitude of works applying random matrix theory (RMT) to capacity analysis. However, most of them approximate the interference as noise and rely on the selection of hyper-parameters, and thus impairs the accuracy. In this paper, we first propose a double splitting model to decompose the capacity into four parts, two of which can be analytically calculated, while the other two are significantly smaller and thus have minimal impact on the overall accuracy. This helps to avoid the approximations of previous methods, simplifying the calculation of capacity and improving the numerical stability. Second, to compute the aforementioned smaller parts, we generalize the moment passing method to more scenarios, and avoid the hyper-parameter selection that impairs the robustness. We also derive the recursive expressions of the moments of any order, enabling flexible trade-offs between efficiency and accuracy. Numerical experiments demonstrate the high efficiency and accuracy of our methods.

Radio map estimation is important for fingerprint-based localization, robot path planning, aerial base station placement, etc. Existing studies mainly focused on 2D radio map estimation by ignoring the impact of the heights of transmitters, receivers and obstacles, which, however, must be considered in indoor scenarios. Although the existing 2D radio map estimation methods can be extended to estimate 3D radio maps by replacing 2D operations with 3D operations, it significantly increases the computational cost. To devise an efficient method for 3D radio map estimation, we propose to represent the heights of physical objects by pixel values and the radio maps at different heights by output channels in this paper, based on which, a 2D deep residual learning method is proposed to estimate 3D indoor radio maps. The proposed method has good generalization, and can better extract features according to the pathloss characteristics. Experimental results show that our method outperforms the state-of-the-art approaches in terms of accuracy, inference speed, computational cost and memory storage. In addition, due to the lack of public datasets, a 3D indoor radio map dataset (3DiRM3200) of 3, 200 samples for 200 buildings is created, which took more than 1, 000 labour hours. The dataset and our code will be available at https://github.com/lighttime2023/3DiRM3200.git.

Clustered cell-free networking is a promising networking scheme for future mobile communications systems where the base-stations (BSs) are densely deployed. Despite its great importance, finding the optimal networking strategy aiming at maximizing the sum rate of users in the network is a non-convex combinatorial optimization problem. Previous work relaxed the clustered cell-free networking problem into a graph min$K$-cut problem to solve it suboptimally. In this paper, we leverage optimization techniques to equivalently transform the original problem into a series of graph min$K$-cut problems with theoretical guarantee. It is worth mentioning that our approach is highly general, as it is applicable to various constraints, offering adaptability and flexibility to diverse practical networking scenarios. We apply this approach to three typical clustered cellfree networking problems. Simulation results show a consistent improvement of our approach compared to existing algorithms.

Beam-level clustered cell-free networking that partitions beams from multiple base-stations (BSs) and users into non-overlapping subnetworks can enable cooperative transmission among BSs, while avoiding coordinating all the beams at a BS. Previous work adopted spectral clustering to group beams and users into subnetworks, which, however, has cubic complexity and cannot control the number of activated beams. In this paper, a tunable weighted kernel k-means algorithm along with a novel initialization approach and two tuning hyper-parameters are proposed. Simulation results show that the proposed algorithm can successfully accelerate clustered cell-free networking by avoiding eigenvalue decomposition as well as control the number of activated beams by carefully fine-tuning hyper-parameters.

The prevalence of location-based services has made positional information indispensable to everyday life, which has sparked growing concerns about the security of location privacy. In this paper, we propose to protect location privacy in wireless lo-calization from the perspective of power allocation. A closed-form expression of location secrecy metric (LSM) is first established to quantify the degree of risk that the location can be inferred by the eavesdropper. Then, a transmit power optimization problem constrained by the LSM lower bound is formulated. Through problem transformation and fractional programming, the optimal power allocation is ultimately obtained. Simulation results show that compared with the other existing strategies, the proposed power allocation strategy can effectively protect location privacy at the cost of smaller nosltioning accuracy loss.

Clustered cell-free networking has been recently proposed to trade off between the low complexity of current cellular networks and the superior performance of the full cooperative networks where all the base-stations (BSs) cooperatively serve all the user equipments (UEs). Specifically, with clustered cell-free networking, the wireless network is decomposed into a number of disjoint parallel operating subnetworks with joint processing adopted inside each subnetwork independently for intra-subnetwork interference mitigation. This paper investigates the clustered cell-free networking problem with the objective of maximizing the sum ergodic capacity while imposing a limit on the number of UEs in each subnetwork to constrain the joint processing complexity. By transforming the combinatorial NP-hard clustered cell-free networking problem into a convex programming problem, a clustered cell-free networking scheme is proposed based on the branch-and-bound method. Simulation results demonstrate the effectiveness of the proposed scheme and its superior performance with around 22% capacity gain achieved over the state-of-the-art benchmark.

By moving to millimeter wave (mmWave) frequencies, base stations (BSs) will be densely deployed to provide seamless coverage in sixth generation (6G) mobile communication systems, which, unfortunately, leads to severe cell-edge problem. In addition, with massive multiple-input-multiple-output (MIMO) antenna arrays employed at BSs, the beamspace channel is sparse for each user, and thus there is no need to serve all the users in a cell by all the beams therein jointly. Therefore, it is of paramount importance to develop a flexible clustered cell-free networking scheme that can decompose the whole network into a number of weakly interfered small subnetworks operating independently and in parallel. Given a per-user rate constraint for service quality guarantee, this paper aims to maximize the number of decomposed subnetworks so as to reduce the signaling overhead and system complexity as much as possible. By formulating it as a bipartite graph partitioning problem, a rate-constrained network decomposition (RC-NetDecomp) algorithm is proposed, which can smoothly tune the network structure from the current cellular network with simple beam allocation to a fully cooperative network by increasing the required per-user rate. Simulation results demonstrate that the proposed RC-NetDecomp algorithm outperforms existing baselines in terms of average per-user rate, fairness among users and energy efficiency.

Base-stations (BSs) will be densely deployed to provide seamless coverage in sixth generation (6G) mobile communication systems, which, unfortunately, leads to severe cell-edge problem. A flexible clustered cell-free networking scheme to replace the cellular network is studied in this paper, which decomposes the whole network into a number of subnetworks operating independently. In order to reduce signaling overhead and system complexity as far as possible, we aim to maximize the number of decomposed subnetworks with a per-user rate constraint for service quality guarantee. In addition, subnetworks with BSs only are allowed to enable BS sleep mode operation. A rate-constrained network decomposition (RC-NetDecomp) algorithm is proposed, which can smoothly tune the network structure from the current cellular network to the fully cooperative network by varying the required per-user rate. Simulation results demonstrate that it outperforms the existing baselines in terms of both average per-user rate and fairness among users.

Optical coherence tomography (OCT) is widely used in high-resolution imaging of biological tissues, which can help diagnose coronary heart disease by segmenting the vessel lumen at the pixel-level. However, the lumen shape geometry is not well used in the state-of-the-art techniques for OCT image segmentation, especially the data-driven methods, leaving much room for performance improvement if some geometric features could be exploited to provide prior information. Thanks to the star shape geometry of vessel lumen, in this paper, a new Star Shape Prior based Regularizer (SSP-Regularizer) is proposed to improve segmentation performance. To validate its effectiveness, the proposed SSP-Regularizer is applied to improve the optimization scheme used in Mask-RCNN for vessel lumen segmentation. Experimental results show that superior performance is achieved with SSP-Regularizer, indicating its potentials in OCT imagery and optimization schemes.

Clustered cell-free networking that dynamically decomposes the whole network into a number of subnetworks operating in parallel could help avoid the severe cell-edge problem. In this paper, the clustered cell-free networking problem is investigated with access point (AP) selection considered for power saving. A novel user-centric ratio-fixed AP-selection based clustering (UCR-ApSel) algorithm is proposed. Simulation results show that the proposed UCR-ApSel algorithm improves not only the rate performance but also user fairness. The average per-user rate achieved with the proposed UCR-ApSel algorithm is further analyzed and a closed-form upper-bound is derived. Simulation results show that the gap between the derived upper-bound and the average per-user rate remains almost a constant as the system parameters vary, indicating that the closed-form upper-bound could provide direct guidance to practical system design and performance optimization.

This paper addresses the energy minimization problem in Device-to-Device (D2D) assisted Mobile Edge Computing (MEC) networks under the latency constraint of each individual task and the computing resource constraint of each computing entity. The energy minimization problem is formed as a two-stage optimization problem. Specifically, in the first stage, an initial feasibility problem is formed to maximize the number of executed tasks and the global energy minimization problem is tackled in the second stage while maintaining the maximum number of executed tasks. Both of the optimization problems in two stages are NP-hard, therefore a low-complexity algorithm is developed for the initial feasibility problem with a supplementary algorithm further proposed for energy minimization. Simulation results demonstrate the near-optimal performance of the proposed algorithms and the fact that with the assistance of D2D communication, the number of executed tasks is greatly increased and the energy consumption per executed task is significantly reduced in MEC networks, especially in dense user scenario.

Moving to millimeter wave (mmWave) frequencies and deploying massive multiple input multiple output (MIMO) antenna arrays have shown great potential of supporting high-data-rate communications in the fifth-generation (5G) and beyond wireless networks, thanks to the availability of huge amounts of mmWave frequency bandwidth and massive numbers of narrow and high gain beams. A number of massive MIMO beamforming techniques have been proposed, among which the fixed-beam scheme has attracted considerable interests from both academia and industry due to its simplicity and requirement of a small number of radio frequency (RF) chains compared to the number of base-station (BS) antennas. Moreover, a beam allocation based pure analog fixed-beam system requires much lower complexity and less signalling overhead than the hybrid beamforming based fixed-beam system, which can therefore be easily implemented in the practical systems. In this paper, the sum data rate of beam allocation based multi-user massive MIMO systems is studied where a near-optimal low complexity beam allocation algorithm is adopted. Simulation results show that our derived average sum data rate serves as a good approximation of the simulation results.

In this paper, we develop novel precoders and combiners for multi-user hybrid digital to analog (D-A) beamforming for uplink massive multiple-input multiple-output millimeter wave systems. Considering the possibility that uplink transmissions from different users can go through the paths sharing the same physical scatters, some transmission paths of different users may have overlapped angle of arrivals (AoAs) at the base station. Therefore, the intrinsic focus is on substantially maximizing the desired signal while reducing the system interference. The analog precoders and combiners are designed by using the eigenvalue decomposition and the Riemannian optimization method based on Stiefel manifold algorithms respectively. The digital combiner adapts the minimum mean-square error by judiciously exploiting the effective uplink analog channel gains. Ultimately, simulation results show that the proposed algorithms outperform the existing algorithms in hybrid D-A paradigms in terms of the achievable uplink rate.

Resource allocation of cellular-assisted device-to-device (D2D) communication is very challenging when frequency reuse is considered among multiple D2D pairs within a cell, as intense inter D2D interference is difficult to tackle and generally causes extremely large signaling overhead for channel state information (CSI) acquisition. In this paper, a novel resource allocation framework for cellular-assisted D2D communication is developed with low signaling overhead while maintaining high system capacity. By utilizing the spatial dispersion property of the D2D pairs, a geography-based sub-cell division strategy is proposed to divide the cell into multiple sub-cells and the D2D pairs within one sub-cell are formed into one group. Then, sub-cell resource allocation is performed independently among the sub-cells without the need of any prior knowledge of inter D2D interference. Under the proposed resource allocation framework, a tractable approximation for the inter D2D interference modeling is obtained and a computationally efficient expression for the average ergodic sum capacity of the cell is derived. The expression further allows us to obtain the optimal number of sub-cells, which is an important parameter for maximizing the average ergodic sum capacity of the cell. It is shown that with small CSI feedback, the system capacity can be improved significantly by adopting the proposed resource allocation framework, especially in dense D2D deployed systems.

Massive multiple-input-multiple-output (MIMO) is a promising technique to provide high-data-rate communication in fifth-generation (5G) mobile systems, thanks to its ability to form narrow and high-gain beams. Among various massive MIMO beamforming techniques, the fixed-beam scheme has attracted considerable attention due to its simplicity. In this paper, we focus on a fixed- beam based multiuser massive MIMO system where each user is served by a beam allocated to it. As the directions of fixed beams are predetermined and the users are randomly distributed, there could be some ``worst-case'' users, located at the edge of its serving beam, suffering from strong inter-beam interference and thus experiencing low data rate. To improve the individual data rates of the worst-case users while maintaining the sum data rate, an adaptive frequency reuse scheme is proposed. Simulation results corroborate that our proposed adaptive frequency reuse strategy can greatly improve the worst-case users' data rates without sacrificing the sum data rate.

Massive multiple-input-multiple-output (MIMO) has become a promising technique to provide high-data-rate communication in fifth-generation mobile systems, thanks to its ability to form narrow and high-gain beams. Among various massive MIMO beamforming techniques, the fixed-beam scheme has attracted considerable attention due to its simplicity. In this paper, we focus on a fixed-beam based multiuser massive MIMO system, where each user is served by a beam allocated to it. To maximize the sum data rate, a greedy beam allocation algorithm is proposed under the practical condition that the number of radio frequency chains is smaller than the number of users. Simulation results show that our proposed greedy algorithm achieves nearly optimal sum data rate. As only the sum data rate is optimized, there are some “worst-case” users, who could suffer from strong inter-beam interference and thus experience low data rate. To improve the individual data rates of the worst-case users while maintaining the sum data rate, an adaptive frequency reuse scheme is proposed. Simulation results corroborate that our proposed adaptive frequency reuse strategy can greatly improve the worst-case users' data rates and the max-min fairness among served users without sacrificing the sum data rate.

High-speed train (HST) has drawn considerable attention and become one of the most preferable conveyance mechanisms. Every year the manufacture corporations achieve a higher speed record and expect to attain 1000 km/h by 2021 using hyperloop one technology. Moving at such a high speed results in a high handover rate which makes it challenging for high speed railway (HSR) mobile wireless communication to preserve steady link performance. Employing distributed antenna systems (DASs) along with the two-hop architecture, this paper proposes a fast predictive handover algorithm. In this strategy, the serving cell starts the handover preparation phase in advance by inferring the train's current location. Issuing the handover preparation phase in advance reduces the handover latency and handover command failure probability as well. Lower handover command failure probability means lower handover failure probability which could greatly improve the end-users' quality of services (QoS). The analytical results show that the proposed scheme outperforms the conventional handover scheme.

Previously, a scheme in [1] was proposed for the outdated channel state information (CSI) problem, for data transmission in time division duplex (TDD) systems. In user movement environment, the actual channel of data transmission at downlink time slot is different from the estimated channel due to channel variation. In this paper, the effect of different user mobility on TDD downlink multiuser distributed antenna system is investigated. An efficient autocorrelation based feedback interval technique is proposed and updates CSI at less the cost of the downlink time slots. In the proposed technique, the frequency of CSI feedback for different users is proportional to their speed. Cooperative clusters are formed to maximize sum rate where channel gain based antenna selection and user clustering based on signal-to- interference plus noise ratio (SINR) threshold is applied to reduce computational complexity. Numerical results show that sum rate superiority of the proposed scheme over the user mobility.

Hybrid digital-to-analog (D-A) precoding is a promising technology to reduce the number of radio frequency (RF) chains in a millimetre-wave (mm-Wave) multiple-input multiple-output (MIMO) system. To reap the full scale benefits of the hybrid D-A precoding, in this paper, two algorithms are proposed to maximise the capacity of the hybrid D-A mm-Wave MIMO system. The first algorithm is based on the principle of manifold optimisation (MO). The second algorithm operates on particle swarm optimisation (PSO). These two algorithms are compared with three existing hybrid D-A precoding algorithms in the literature. The simulation results show that the proposed algorithms achieve higher capacity than the existing hybrid D-A precoding algorithms with lower computational complexity.

Massive multiple input multiple output communication is now possible using millimeter wave frequency band. In this paper a transmit antenna selection algorithm is developed which satisfies a quality of service (QoS) for a given user. In order to achieve a particular level of QoS, the number of transmit antennas required is determined by remodeling it as a Knapsack Problem (KP). The smallest subset of antenna elements is found at the transmitter side to achieve the desired level of QoS using KP. Furthermore, we have compared our algorithm with the sorted and unsorted sequential selection algorithm (SSA). Our algorithm achieves similar performance as compared to SSA but with lower computational complexity than the sorted SSA. Moreover, the energy efficiency of our algorithm is similar to that of the sorted SSA but superior to unsorted SSA, as it is not sensitive to the arrangement of the antenna gains.

High-speed railway (HSR) has witnessed a huge growth globally, and now is reaching a maximum speed of 575 km/h. This record of speed makes mobile communications difficult for HSR since the handover (HO) frequency increases which results in a high loss of connectivity. Based on distributed antenna systems (DASs), this paper utilizes the two-hop network architecture for HSR broadband wireless communication systems. With the target of achieving high system capacity, superior transmission reliability, and consequently high-quality broadband wireless communication service for passengers in HSR. Moreover, a Frequency Switch (FSW) scheme is proposed for the two-hop network architecture to alleviate the frequent HO issue in traditional HSR wireless communication systems where HO generally happens between the successive remote antenna units (RAUs) connecting to the same central unit (CU) control. The FSW scheme provides mobility robustness signalling process that guarantees a successful frequency switching instead of HO, and reduces the probability of radio link failure (RLF) compared to HO process in traditional HSR systems, where the HO failure (HOF) rate is about 21%. The analytical results show that the proposed scheme outperforms traditional HO schemes.

High-speed railway (HSR) has witnessed a huge growth all over the globe reaching a maximum speed of 575 km/h. This record of speed makes mobile communications difficult for HSR since the handover (HO) frequency increases which frequently results in a loss of connectivity. This paper proposes a specialized network architecture based on distributed antenna systems (DAS) for HSR broadband wireless communication systems along with the two- hop architecture. Further, a frequency switch (FSW) scheme is proposed aiming to eliminate the need for HO between the successive small coverage remote antenna units (RAUs) that fall under the same central unit (CU) control. The analytical results show that the proposed scheme outperforms traditional HO schemes and can support the application with high quality of services (QoS) requirement.

High-speed trains have become one of the most leading transportation means, where each year the manufacture companies reach a new speed record. This progressive speed records increase the handover (HO) rate which makes it very difficult for high speed railway (HSR) mobile communication to sustain a reliable communication link. Utilizing distributed antenna systems (DASs) along with the two-hop architecture, this paper analyzes the conventional handover scheme based on this architecture and proposes a faster HO strategy. The proposed scheme reduces the HO latency and failure probability which could greatly improve the end-users quality of services (QoS). The analytical results show that the proposed scheme performs better than the conventional HO scheme.

Supporting high user density and improving millimeter- wave (mm-Wave) spectral-efficiency (SE) is imperative in 5G systems. Current hybrid digital-to-analog beamforming (D-A BF) base stations (BS) can only support a particular user per radio frequency (RF) chain, which severely restricts mm-Wave SE. In this paper a novel low-complexity selection combining (LC- SC) is proposed for supporting high user density for mm-Wave BS. When compared with the current state of the art hybrid D-A BF, simulations show that LC-SC can support high user density and attain higher SE.

Despite substantial rate gains achieved by joint transmission from a massive amount of geographically distributed antennas, the resulting computational cost and channel measurement overhead could be unaffordable for a large-scale distributed antenna system (DAS). A scalable signal processing framework is therefore highly desirable, which could be established based on the concept of virtual cell. In a virtual-cell based DAS, each user chooses a few neighboring base-station (BS) antennas to form its virtual cell, i.e, its own serving BS antenna set. In this paper, we focus on a downlink DAS with a large number of users and BS antennas uniformly distributed in a certain area, and aim to study the effect of the virtual cell size on the average user rate. Specifically, by assuming that maximum ratio transmission (MRT) is adopted in each user's virtual cell, the achievable ergodic rate of each user is derived as an explicit function of the large-scale fading coefficients from all the users to their virtual cells, and an upper-bound of the average user rate is established, based on which a rule of thumb is developed for determining the optimal virtual cell size to maximize the average user rate. The analysis is further extended to consider multiple users grouped together and jointly served by their virtual cells using zero-forcing beamforming (ZFBF). In contrast to the no-grouping case where a small virtual cell size is preferred, it is shown that by grouping users with overlapped virtual cells, the average user rate can be significantly improved by increasing the virtual cell size, though at the cost of a higher signal processing complexity.

This paper addresses the beam allocation problem in a switched-beam based massive multiple-input-multiple-output (MIMO) system working at the millimeter wave frequency band, with the target of maximizing the sum data rate. This beam allocation problem can be formulated as a combinatorial optimization problem under two constraints that each user uses at most one beam for its data transmission and each beam serves at most one user. The brute-force search is a straightforward method to solve this optimization problem. However, for a massive MIMO system with a large number of beams N, the brute-force search results in intractable complexity O(NK), where K is the number of users. In this paper, in order to solve the beam allocation problem with affordable complexity, a suboptimal low-complexity beam allocation (LBA) algorithm is developed based on submodular optimization theory, which has been shown to be a powerful tool for solving combinatorial optimization problems. Simulation results show that our proposed LBA algorithm achieves nearly optimal sum data rate with complexity O(K log N). Furthermore, the average service ratio, i.e., the ratio of the number of users being served to the total number of users, is theoretically analyzed and derived as an explicit function of the ratio N/K.

This paper focuses on the beam allocation problem with the target of maximizing the sum rate in a switched-beam based massive multiple input multiple output (MIMO) system working at the millimeter wave (mmWave) frequency band. A simple suboptimal beam allocation algorithm is developed, whose average sum rate performance is shown to be nearly optimal while the complexity is greatly reduced. Different from conventional switched-beam systems, with a large number of beams in the massive MIMO systems, the inter-beam interference closely depends on the beam allocation result. By adopting the suboptimal beam allocation algorithm, the effect of the inter-beam interference is investigated through simulations. The results further show that the average sum rate increases with both the number of BS antenna elements and the number of users even though the inter-beam interference is enlarged with an increasing number of users.

A great deal of efforts have been made on the performance evaluation of distributed antenna systems (DASs). Most of them assume a regular base-station (BS) antenna layout where the number of BS antennas is usually small. With the growing interest in cellular systems with large antenna arrays at BSs, it becomes increasingly important to study how the BS antenna layout affects the rate performance when a vast number of BS antennas are employed. This paper presents a comparative study of the asymptotic rate performance of downlink multi-user systems with multiple BS antennas either co-located or uniformly distributed within a circular cell. Two representative linear precoding schemes, maximum ratio transmission (MRT), and zero-forcing beamforming (ZFBF), are considered, with which the effect of BS antenna layout on the rate performance is characterized. The analysis shows that as the number of BS antennas L and the number of users K grow infinitely while L/K → v, the asymptotic average user rates with the co-located antenna (CA) layout for both MRT and ZFBF are logarithmic functions of the ratio u. With the distributed antenna (DA) layout, in contrast, the scaling behavior of the average user rate closely depends on the precoding schemes. With ZFBF, for instance, the average user rate grows unboundedly as L, K → ∞ and L/K → v > 1, which indicates that substantial rate gains over the CA layout can be achieved when the number of BS antennas L is large. The gain, nevertheless, becomes marginal when MRT is adopted.

This paper presents an asymptotic rate analysis for downlink multi-user systems with L base-station (BS) antennas either co-located or uniformly distributed within a circular cell. A representative non-orthogonal linear precoding scheme, maximum ratio transmission (MRT), is considered, based on which the effect of BS antenna layout on the intra-cell interference is characterized. The analysis reveals that the ratio σ of the number of BS antennas L and the number of users K is a key parameter that determines the rate performance of non-orthogonal downlink multi-user systems. Ergodic rates in the colocated antenna (CA) layout and the distributed antenna (DA) layout both logarithmically grow with σ, yet a higher rate is achieved in the DA case thanks to enhanced signal-to-interference ratio.