This paper investigates the mixed-timescale resource allocation problem in satellite-terrestrial integrated networks (STIN). Moreover, the multi-access edge computing (MEC) technology and millimeter wave (mmWave) with rich spectrum resource are merged into the STIN to improve the network performance. A network utility maximization problem characterized by the achievable rate and backhaul reduction is formulated under the constraints of the maximum caching capacity, transmission power of mmWave small-cell base stations (SBSs) and quality of service (QoS) for Internet of Things (IoT) devices, where the caching placement, power allocation and user-SBS association are jointly optimized. In order to tackle this mixed-integer nonlinear programming (MINLP) problem, we decompose the original problem into the long-term caching placement subproblem, and short-term power allocation and user-SBS association subproblems. Then, a multi-agent deep reinforcement learning (MADRL)-based independent proximal policy optimization (IPPO) algorithm is proposed to solve the short-term user-SBS association subproblem. Meanwhile, the linear programming (LP) is used to solve the long-term caching placement subproblem. Furthermore, we derive the closed-form solution of the short-term power allocation subproblem through the Karush-Kuhn-Tucker (KKT) conditions. Simulation results are carried out to validate the effectiveness and scalability of the proposed joint approach.

The rapid growth in mobile video services has significantly increased network traffic, posing new challenges for efficient utilization of limited network resources. In this paper, we propose an energy-efficient framework for cross-domain resource coordination tailored specifically to scalable video coding (SVC) multicast transmission. Our work jointly considers both optical resources on the wired side and wireless resources on the wireless side, where we introduce advanced techniques such as multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) to boost spectral efficiency and better accommodate high data-rate demands. By leveraging the flexible wavelength allocation capability and energy-saving advantages of time and wavelength division multiplexed passive optical network (TWDM-PON), we formulate a unified optimization problem based on a utility function that balances user quality of experience (QoE) and overall system power consumption. The optimization problem involves selecting suitable SVC enhancement layers for users, assigning wireless resources, and allocating optical wavelengths. To simplify this complex process, we divide the original problem into two subproblems: user grouping and resource allocation. The subproblem is converted into a convex form using the convex-concave procedure (CCP) and solved iteratively. Our proposed solution effectively coordinates optical and wireless resources, addressing repeated requests for identical video content alongside QoE requirements. Simulation results demonstrate that our method achieves improved resource utilization and a better balance between QoE and power efficiency compared to existing approaches.

In this paper, we propose a semantic communication framework facilitated by multi-access edge computing (MEC)assisted satellite-air-ground integrated networks (SAGIN), which comprises of LEO satellites, unmanned aerial vehicles (UAVs), and macro-cell base stations (MBSs). Considering the limited wireless resources and diversified quality of service (QoS) requirements of semantic tasks, an optimization problem with the goal of minimizing system cost in terms of the task latency and energy consumption is formulated. In order to address the mixed-integer nonlinear programming (MINLP) problem, we propose an alternating optimization algorithm that tackles UAV deployment sub-problem with the successive convex approximation (SCA) method, task offloading, semantic compression, power allocation and computation resource allocation optimization with deep reinforcement learning (DRL)-based multi-agent proximal policy optimization (MAPPO) method. Simulation results demonstrate that our proposed algorithm outperformes other reinforcement learning algorithms, i.e., about 5.12%, 23.72% and 35.64% over PPO, DDPG, and A2C, respectively.

Low-earth orbit (LEO) satellite networks has attracted a lot of attention, since it is able to provide high-quality services worldwide. With the assistance of NFV technology, the flexibility and the quality of service (QoS) of LEO satellite networks can be further improved. However, most existing researches have not considered virtual network functions (VNFs) reusing during the process of service function chain (SFC) placement in NFV-enabled satellite networks. In this paper, we investigate the problem of SFC placement, and propose a joint VNF reusing and routing algorithm. By considering both of the initialization delay and sharing of VNFs, the service deployment delay can be effectively reduced, and the network profit can be largely improved. In our setting, simulation results show that our proposed algorithm outperforms baseline algorithms in profit, with an improvement of at least 23 %.

In this paper, the problem of collaborative vehicle sensing is investigated. In the considered model, a set of cooperative vehicles provide sensing information to sensing request vehicles with limited sensing and communication resources. A base station (BS) determines the subset of sensing request vehicles that each cooperative vehicle will serve and the sub-regions that each cooperative vehicle will detect. We formulate an optimization problem aiming to maximize the number of successfully detected sub-regions of sensing request vehicles while satisfying the cooperative sensing energy requirement by jointly determining the cooperative vehicle association and the sensing sub-region selection. To solve this problem, we propose a graph attention based reinforcement learning (RL) algorithm that can generate the graph information vectors based on the correlation between each cooperative vehicle and each sensing request vehicle. Using the learned graph information, the joint cooperative vehicle association and sensing sub-region selection strategy will be determined. Simulation results show that the proposed scheme can improve the number of successfully detected sub-regions of sensing request vehicles by up to 12.5% compared to the conventional RL algorithm without using graph attention networks (GANs).

The three-dimensional (3D) location optimization for reconfigurable intelligent surface (RIS) aided millimeter wave network is investigated. We first formulate the signal-tonoise ratio (SNR) maximization model by jointly optimizing the precoding vector, the RIS location and its parameter matrix in a multiple-input single-output downlink network. The optimal maximum ratio transmission precoding is applied, and the alternating direction method of multipliers is proposed for the highly nonlinear combinatorial problem. The subproblem with discrete variables has closed form solution, and the nonlinear square subproblem is solved via the Levenberg-Marquardt method. In simulations, the proposed model achieves the best SNR among all the compared models. And the RIS is suggested to be deployed near the base station, similar to the lower frequency band case.

In this paper, the problem of vehicle service mode selection (sensing, communication, or both) and vehicle connections within terahertz (THz) enabled joint sensing and communications over vehicular networks is studied. The considered network consists of several service provider vehicles (SPVs) that can provide: 1) only sensing service, 2) only communication service, and 3) both services, sensing service request vehicles, and communication service request vehicles. Based on the vehicle network topology and their service accessibility, SPVs strategically select service request vehicles to provide sensing, communication, or both services. This problem is formulated as an optimization problem, aiming to maximize the number of successfully served vehicles by jointly determining the service mode of each SPV and its associated vehicles. To solve this problem, we propose a dynamic graph neural network (GNN) model that selects appropriate graph information aggregation functions according to the vehicle network topology, thus extracting more vehicle network information compared to traditional static GNNs that use fixed aggregation functions for different vehicle network topologies. Using the extracted vehicle network information, the service mode of each SPV and its served service request vehicles will be determined. Simulation results show that the proposed dynamic GNN based scheme can improve the number of successfully served vehicles by up to 17% compared to a GNN based algorithm with a fixed neural network model.

The combination of practical and easily deployable partially-connected (PC) hybrid beamforming (HBF) architecture and low-resolution (LR) quantizers guides a new direction for future millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) green communication. To fill the research gap in this area, we aim to propose an energy-efficient transmission scheme for the multi-user (MU) multiple-input multiple-output (MIMO) system based on LR digital-to-analog converters (DAC)-aided PC-HBF architecture. For this purpose, we formulate the energy efficiency (EE) maximization problem for jointly optimizing beamforming, power allocation, and DAC resolution selection. Confronted with a non-deterministic polynomial (NP)-hard mixed integer nonlinear programming problem, the original problem is decoupled into several subproblems with respect to each optimization variable, and we design targeted solutions respectively. More specifically, the beamforming design is primarily focused on mitigating spectral efficiency (SE) bottlenecks due to quantization distortion noise and MU interference, which distinguishes our work from the existing scheme. Besides, we propose a quadratic transformation (QT)-based power allocation scheme and a coordinate ascent search (CAS) for DAC resolution selection to maximize EE. Finally, we propose an innovative two-layer nested (TLN) scheme integrating the above design for a stable solution to the original problem. Simulation results show that the proposed TLN scheme has a fast convergence rate and significantly outperforms existing schemes in terms of EE and SE. In addition, a proposed low-complexity (LC) scheme exhibits performance close to that of the TLN scheme and demonstrates good robustness.

In order to support colourful multimedia services with strict quality-of-service (QoS) requirements of user equipments (UEs), the space-air-ground integrated networks (SAGIN) can be taken as a promising approach to enhance network capacity. Among them, millimeter wave (mmWave) and edge caching promise to significantly improve the SAGIN performance due to the advantage in rich bandwidth resource and low latency, respectively. In this paper, we investigate the joint caching and resource allocation for multimedia services in SAGIN, where multimedia content requests can be simultaneously served by multiple access points (APs). Considering the delay-constraint of multimedia services, we then formulate a mixed-integer non-linear programming (MINLP) problem aiming at minimizing the service delay, which involves jointly optimizing coded caching (CC), power allocation (PA) and UEs-to-APs association (UA). We propose to find the optimal solution by employing an alternating iteration optimization framework. The optimal CC and PA problems are firstly addressed by utilizing convex optimization technology. Then, two many-to-many swap matching algorithms are developed to slove the UA subproblem effectively. Numerical results demonstrate that our proposed algorithms can substantially reduce the service delay over other benchmarks.

In this paper, the problem of vehicle service mode selection (sensing, communication, or both) and vehicle connections within terahertz (THz) enabled joint sensing and communications over vehicular networks is studied. The considered network consists of several service provider vehicles (SPVs) that can provide: 1) only sensing service, 2) only communication service, and 3) both services, sensing service request vehicles, and communication service request vehicles. Based on the vehicle network topology and their service accessibility, SPVs strategically select service request vehicles to provide sensing, communication, or both services. This problem is formulated as an optimization problem, aiming to maximize the number of successfully served vehicles by jointly determining the service mode of each SPV and its associated vehicles. To solve this problem, we propose a dynamic graph neural network (GNN) model that selects appropriate graph information aggregation functions according to the vehicle network topology, thus extracting more vehicle network information compared to traditional static GNNs that use fixed aggregation functions for different vehicle network topologies. Using the extracted vehicle network information, the service mode of each SPV and its served service request vehicles will be determined. Simulation results show that the proposed dynamic GNN based method can improve the number of successfully served vehicles by up to 17% and 28% compared to a GNN based algorithm with a fixed neural network model and a conventional optimization algorithm without using GNNs.

In this paper, the problem of maximizing the sum of data rates of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 28.6% gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS.

In this paper, the problem of joint sensing and communications is studied over terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles provide either communication service or sensing service to communication target vehicles or sensing target vehicles, respectively. Therefore, it is necessary to determine the service mode (i.e., providing sensing or communication service) for each service provider vehicle and the subset of target vehicles that each service provider vehicle will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of all communication target vehicles while satisfying the sensing service requirements of all sensing target vehicles by determining the service mode and the user association for each service provider vehicle. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle's graph information related to its location, connection, and communication interference. Using the extracted graph information, the joint service mode selection and user association strategy will be determined. Simulation results show that the proposed GNN-based scheme can achieve 94% of the sum rate produced by the optimal solution, and yield up to 3.95% and 36.16% improvements in sum rate, respectively, compared to a homogeneous GNN-based algorithm and the conventional optimization algorithm without using GNNs.

In this paper, the problem of joint communication and sensing is studied in the context of terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles (SPVs) provide either communication service or sensing service to target vehicles, where it is essential to determine 1) the service mode (i.e., providing either communication or sensing service) for each SPV and 2) the subset of target vehicles that each SPV will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of the communication target vehicles, while satisfying the sensing service requirements of the sensing target vehicles, by determining the service mode and the target vehicle association for each SPV. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle’s graph information related to its location, connection, and communication interference. Using this extracted graph information, a joint service mode selection and target vehicle association strategy is then determined to adapt to the dynamic vehicle topology with various vehicle types (e.g., target vehicles and service provider vehicles). Simulation results show that the proposed GNN-based scheme can achieve 93.66% of the sum rate achieved by the optimal solution, and yield up to 3.16% and 31.86% improvements in sum rate, respectively, over a homogeneous GNN-based algorithm and a conventional optimization algorithm without using GNNs.

In this paper, the problem of maximizing the sum rate of all users in an intelligent reflecting surface (IRS)-assisted millimeter wave multicast multiple-input multiple-output communication system is studied. In the considered model, one IRS is deployed to assist the communication from a multi-antenna base station (BS) to the multi-antenna users that are clustered into several groups. Our goal is to maximize the sum rate of all users by jointly optimizing the transmit beamforming matrices of the BS, the receive beamforming matrices of the users, and the phase shifts of the IRS. To solve this non-convex problem, we first use a block diagonalization method to represent the beamforming matrices of the BS and the users by the phase shifts of the IRS. Then, substituting the expressions of the beamforming matrices of the BS and the users, the original sum-rate maximization problem can be transformed into a problem that only needs to optimize the phase shifts of the IRS. To solve the transformed problem, a manifold method is used. Simulation results show that the proposed scheme can achieve up to 13.3 % gain in terms of the sum rate of all users compared to the algorithm that optimizes the hybrid beamforming matrices of the BS and the users using our proposed scheme and randomly determines the phase shifts of the IRS.

Mobile/wireless virtual reality (VR) services, especially immersive 360° VR videos, have advanced unprecedentedly in recent years. However, the high bandwidth requirement of VR services has compounded the burden on wireless networks. Multicast is a high potential technique for alleviating the bandwidth requirement of 360° VR video streaming, but the multicast capacity is still constrained by the users with poor channel conditions, and it vanishes when the number of users increases while the number of the base station (BS) antennas is fixed. To overcome the drawbacks of multicast, sidelink, which is an adaptation of the core LTE standard that allows the device-to-device (D2D) communications without going through a BS, can be utilized. In this article, two sidelink-aided multicast scenarios (i.e., independent decoding and joint decoding) are studied for multiquality tiled 360° VR video transmission. We propose a utility model for each scenario, and quality level selection, sidelink sender/receiver selection, and transmission resource allocation are optimized to maximize the total utility of all users under the bandwidth constraints as well as the quality smoothness constraints for multiquality tiles. We then develop an iterative two-stage algorithm to obtain suboptimal solutions to the formulated mixed-integer nonlinear programming (MINLP) problems. Simulation results demonstrate the advantage of the proposed solutions over several baseline schemes.

The key challenges in live video multicasting include how to properly form multicast groups, select video versions and allocate wireless resources, in order to guarantee the quality of experience (QoE) while ensuring low latency delivery. To address these challenges, in this paper, a novel multicast framework that leverages the advantages of network-assisted dynamic adaptive streaming over HTTP and cloud radio access networks is proposed, where a multicast assistant server is deployed at the edge of a mobile network. Under this architecture, a joint user grouping, version selection, and bandwidth allocation method is designed to optimize the sum of users’ utilities. In particular, a two-step scheme is proposed to solve this complex problem. The number of multicast groups is first automatically determined and a user clustering method is presented. Then, group-level version selection and spectrum assignment algorithms are performed at different time scales. Simulation results demonstrate that our proposed scheme can improve at least 7% QoE compared to baseline methods.

Recently, space-air-ground integrated network (SAGIN) has been widely concerned which satisfies the emerging requirement of ubiquitous high-rate wireless communication services. Due to the high mobility of satellites and aircraft in SAGIN, user handover occurs frequently. Fast and secure handover is essential to ensure the global seamless coverage of SAGIN. Existing studies mainly focus on improving the authentication security during handover process and reducing the signaling and computation overhead. However, no one optimizes the handover delay with a given security requirement in SAGIN. In this paper, we propose a new handover scheme based on pre-authentication and security context transfer, and optimize the selection of satellite nodes to ensure fast and secure handoffs. Simulation results show that our scheme algorithm reduces the handover delay by 16.92% compared with baseline algorithm.

Codebook-based hybrid analog and digital beam-forming (HBF) plays a key role in practical millimeter wave (mmWave) massive MIMO systems. Most existing work focuses on the design of analog beams, without considering the coupled relationship between beam selection and user scheduling. In this paper, we consider a downlink HBF system and address the aforementioned problem. By exploiting the orthogonality of the Discrete Fourier Transform (DFT) codebook, we propose two low complexity approaches named as E-DFT and PF-DFT according to different requirements. Specifically, E-DFT aims to maximize the sum achievable rate of the HBF system, and PF-DFT considers both the system throughput and user fairness. Simulation results show that our proposed E-DFT scheme outperforms the baseline methods in the literature, and achieves near optimal sum rate. Meanwhile, the PF-DFT scheme performs better in balancing the achievable rate and scheduling fairness.

To alleviate the spectrum scarcity, the unlicensed 60 GHz band has raised increasing concerns due to its continuous large bandwidth. Considering IEEE 802.11ad/ay has already been deployed in this millimetre‐wave band, the coexistence issues with new radio‐based access to unlicensed spectrum should be weighed when deploying fifth generation (5G) network. Fortunately, the directional transmission on beams is able to reduce interference significantly, so beam selection can be combined with power control. To maximise spectrum efficiency (SE) of the 5G network while ensuring a friendly coexistence, the authors formulate an optimisation problem by jointly considering beam selection and resource allocation. More specifically, they design a spectrum planning mechanism to reduce the interference between 5G and WiFi, and then a block coordinate descent method is used to determine the user association, beam selection and power control for 5G users, while limiting the interferences to WiFi devices. Simulation results verify the effectiveness of the proposed algorithm in terms of complexity, convergence and SE.

With the development of virtual reality (VR) technology, the future of VR systems is evolving from single-user wired connections to multi-user wireless connections. However, wireless online rendering and transmission incur extra processing and transmission latency, as well as higher bandwidth requirements. To meet the requirements of wireless VR applications and enhance the quality of the VR user experience, this paper designs a view synthesis-based 360° VR caching system over Cloud Radio Access Network (C-RAN), where both mobile edge computing (MEC) and hierarchical caching are supported. In the system, an MEC-Cache Server is deployed in the pooled Base band Units (BBU pool) and used for view synthesis and caching. In addition, the remote radio heads (RRHs) can also cache some video contents. If the requested content of a specific view is cached in the BBU pool or RRHs, or can be synthesized with the aid of the cached adjacent views, it is unnecessary to request the content from the remote VR video source server. Therefore, the transmission latency and backhaul traffic load for VR services can be decreased. We formulate a hierarchical collaborative caching problem aiming to minimize the transmission latency, which is proved NP-hard. To address the impractical expenses of the offline optimal method, an online MaxMinDistance caching algorithm with low complexity is proposed. Numerical simulation results demonstrate that the proposed caching strategy provides significantly improved cache hit rate, backhaul traffic load, transmission latency, and Quality of Experience (QoE) performances relative to conventional caching strategies.

Content caching (CC) plays a crucial role in improving quality of service (QoS) and mitigating the congestion of backhaul links for the Heterogenous Networks (HetNets). The accessibility of users to the base stations (BSs) seriously depends on the wireless channel capacities which can be improved by designing an efficient content delivery (CD) policy. Thereupon, a nature idea is to jointly design the CC and CD strategy to effectively utilize the limited radio resource and cache capacity of BSs. In this paper, we focus on the joint coded caching, user association (UA) and bandwidth allocation (BA) optimization problem, aiming at minimizing the overall power consumption. Particularly, we analyze energy consumption in both backhaul and access links under coded caching strategy. Then we utilize an alternative optimization algorithm to decompose the original problem into the CC and CD problems, which are solved via convex optimization, matching and bisection algorithm. Numerical results show that the proposed scheme significantly reduces power consumption compared to the benchmarks.

In this study, we consider zero-forcing (ZF) precoding under uncorrelated Rayleigh channels with both perfect channel state information (CSI) and pilot-based CSI in a multi-user multiple-input single-output (MU-MISO) system. Using perfect CSI, we first demonstrate that the needed number of the transmit antennas for ZF precoder to achieve a certain percentage of the maximal achievable sum-rate monotonically decreases with the increase of the transmit signal-to-noise ratio (SNR). In addition, the analysis of the needed transmit antennas shows that an upper bound of the required number of antennas for easy calculation only relates to the number of users and the target percentage. For completeness of the research, the pilot-based case is analyzed to examine that how pilots will affect the aforementioned results. Finally, the concept of massive MIMO for practical deployment is discussed via simulation results.

In order to obtain the worthwhile gain in coverage and the capacity required by future mobile services, heterogeneous networks (HetNets) are being considered as one of the most promising solutions. However, the ultra-dense deployment of small base stations (ud-SBSs) would significantly increase energy consumption (EC), which becomes particularly severe when meeting explosive growth of multimedia services. Proper user association (UA) and power allocation (PA) are both crucial to achieve desirable energy-saving performance in HetNets. In view of these, this paper investigates the joint UA and PA optimization problem for multimedia services in cache-enabled HetNets. The aim is to minimize the total power consumption under certain quality-of-service (QoS) requirement and maximum power limit. A non-convex mixed integer programming optimization problem is formulated. To solve the problem, a heuristic algorithm based on matching game is proposed. Numerical results demonstrate that the proposed algorithm yields a performance improvement in terms of the power consumption.

60G Hz links are highly susceptible to propagation and penetration loss. When blockages occur, an efficient approach to maintain the network connectivity is to switch the beam to the candidate path provided by a relay or a physical reflector. In this study, we propose to deploy mirrors in typical indoor environment as the virtual relay to improve the quality and robustness of the 60GHz links. By investigating the link outage probability through simulations in a variety of mirror layouts, we draw three criteria for the deployment of mirrors, i.e. shortest path, multi-mirror and on walls. Furthermore, a method for indoor mirror deployment is proposed based on shortest path first. Accordingly, the optimal mirror layout can be obtained as long as the expected coverage is given. Simulation results show that the proposed scheme is remarkably effective in improving the robustness of link connectivity at 60GHz.

Due to the short wavelength of millimeter wave (mmWave) and high directional beamforming, the 60GHz massive MIMO systems are highly vulnerable to link blockage. Beam switching to unblocked direction is an effective solution to overcome blockage. To this end, a set of backup beam pairs for beam switching must be identified at initial beam training. In this work, a low complexity beam training scheme with support for backup beam identification is proposed. Considering sparsity and clustered characteristics of mmWave channels and high probability for adjacent beams to be simultaneously blocked, we propose a new criterion for the selection of backup beams based on peak beam grouping, and design two beam grouping algorithms. The detailed procedure for beam switching when a blockage occurs is also given. Simulation results show that the proposed beam training scheme achieves near-optimal performance at initial beam training stage. Furthermore, the new method for identifying backup beam pairs is more effective to improve the spectral efficiencies of systems under blockage environments.

The large scale multiple-input multiple-output (MIMO) system with hybrid beamforming (HBF) is a promising communications technology due to its excellent tradeoff between hardware complexity and system performance. Assuming perfect channel state information is acquired, we consider a single cell downlink multi-user massive MIMO system working in a generic channel model with a hybrid structure that supports multiple streams per UE. We aim to find an analog and digital precoder/combiner that maximizes the sum-rate of the communication system. Unlike the traditional two-stage design criterion, which separately designs the analog and digital stages, our proposed criterion jointly designs two stages by trying to avoid the loss of information at each stage. When double the least number of radio frequency (RF) chains are available, we provide an asymptotically optimal solution in a massive MIMO regimen, i.e., the sum-rate of such an HBF solution could approach the channel capacity under large base station (BS) antenna arrays. A corresponding solution using the fewest RF chains is then derived. Finally, the simulation results are shown to validate the proposed schemes. Specifically, the solution with the fewest RF chains is shown to outperform the state of the art for HBF systems, even when the number of BS antennas is not very large. It should be noted that the schemes proposed in this paper have low complexity owing to their closed-form solutions.

In this paper, we present a cross-layer design for a family of OFDM-based video communications by jointly considering application layer information and the wireless channel conditions. Compared with traditional cross-layer designs, our proposed method targets to efficiently transmit video data generated with some emerging techniques for better wireless transmissions, where the video data are divided into multiple chunks and each chunk contributes independent distortion to the entire video quality. To minimize the end-to-end distortion, we formulate a generalized optimization problem and derive a joint optimal carrier matching and power allocation scheme. Rather than depending on a specific video encoding method as done in the conventional work, we intend our design to be applicable to a general family of new video schemes. We apply our proposed method to two applications, the enhanced analog coding and the uncompressed video transmission over OFDM. In both applications, the performance can be improved by adopting our scheme. Simulation results validate the effectiveness of our approach in achieving significantly better PSNR and visual quality compared to reference schemes.

Caching at the wireless edge is a promising way to alleviate the heavy burden of the backhaul links and reduce the latency of transmission and handover. Although some effective caching schemes have been introduced to Cloud based Radio Access Network (C-RAN), most of them were designed without consideration of users' mobility. In this paper, we investigate the mobility property in cache-enabled C-RAN, and propose a mobility aware proactive caching strategy. We aim to design a novel controller, which is able to utilize the computation and storage resources in C-RAN, and responsible for making cache decision for both the baseband unit (BBU) pool and Remote Radio Head (RRH). A transmission delay model is introduced to formulate the cache placement optimization problem. To solve the problem, we propose an algorithm named MAPCaching. Numerical simulation results show that MAPCaching significantly outperforms the Greedy and EPC caching strategies in terms of average content access delay and cache hit rate by 30% and 20%, respectively.

In this paper, we propose a non-orthogonal multiple access(NOMA) based hybrid beamforming design in 5G mmWave systems. The proposed design depends on the known array geometry and incurs a low training and feedback overhead. Our model assumes that each user employs analog-only beamforming while the BS performs hybrid analog and digital beamforming. To achieve a higher sum capacity, multiple users are allowed to share the same beam based on NOMA mechanism. However, the use of analog beamformer from different localized users means that the BS's digital beamformers are not perfectly aligned with the users' baseband effective channels and multiple users may be assigned similar or even identical beamformers. In the meantime, a user pairing and power allocation algorithm is proposed to mitigate interference from other beams so as to maximize the sum capacity. Simulation results verify the significant advantage of the proposed scheme in sum capacity.

Dynamic Adaptive Streaming over HTTP (DASH), as one of the main video transmission technologies, has been widely concerned. However, due to the unstability of wireless environment and the restrictions of mobile networks, it is challenging to deliver DASH-based videos in LTE systems and enhance the performance of Quality of Experience (QoE). In this paper, a problem is formulated to maximize the sum of weighted rates of all DASH users constrained by resource blocks (RB) and transmit power. To solve this problem, we propose ulti-Cell DASH-based Video Transmission (MDVT) scheme, containing two phases: RBs allocation with fixed transmit power and power allocation with fixed RBs assignment. Simulation results reveal that the performance of MDVT outperforms the state-of-art schemes.

Hybrid beamforming architecture that combines analog beamforming and digital beamforming has been widely accepted in the emerging millimeter-wave (mmWave) systems with large antenna arrays. However, the design of codebooks and beam training procedures at the analog stage is problematic in mmWave system. In this study, we propose a feasible two-stage 3D codebook design consisting of a primary codebook and an auxiliary codebook. The primary codebook generates a basic directional beam, and the number of phase shifts is quite limited in order to keep low hardware complexity. The small-size auxiliary codebook creates the finer beams that are centered on each primary beam. Furthermore, a beam search scheme designed for the two- stage codebook is proposed. We use a binary tree search method with antenna selection for the primary beam search, and a directed beam scan for the auxiliary beam search. Simulation results verify the performance advantages of our proposed codebook in beamforming gain and spectral efficiency for multi-user multiple-input multiple-output (MU-MIMO) systems. Moreover, training complexity comparisons indicate that our specially designed two-stage beam search scheme significantly outperforms other existing solutions.

With the exponential growth of the mobile video traffic and the dramatic diversity of wireless channels among users, maintaining a tradeoff between resource consumption and perceived experience of users is of overwhelming challenges. Dynamic Adaptive Streaming over HTTP(DASH), as a promising technique to improve the video transmission efficiency, achieves bitrate adaption at users' ends to accommodate to the unstability of channel conditions. However, as the thriving of live video services, multicast is a widely used transmission mode which gains huge economization of spectrum resource. This paper investigates a user grouping scheme for DASH multicast service over LTE downlink systems. The objective of the research is to maximize the users' throughput and quality of experience(QoE). First, we present a method to automatically determine the number of groups in DASH multicast scenario. Second, a cluster grouping algorithm is used to assign users into several groups. Numerical simulation results show that the proposed grouping scheme obtains favorable performance.

In recent decade, wireless networks face a large challenge of delivering HTTP adaptive streaming (HAS) due to the dramatic increase of traffic. Content caching is an effective method to relieve the burden of networks and has attracted considerable attention. Although some schemes have been investigated for content caching in cloud-based radio access networks (C-RAN), most of them were not designed for HAS. In this paper, we address the caching problem for SVC- based HAS over C-RAN. We propose a caching strategy with considering the layered property of video, the hierarchical caching architecture of C-RAN and the distribution of downloading rates. To our knowledge, no one has designed a caching scheme from such a perspective. A 0-1 programming problem is formulated to minimize the amount of backhaul traffic. Base on theoretical analysis and problem solving, a layered hierarchical caching algorithm is proposed which can provide an approximate ratio of 1#x002F;2. Simulation results are illustrated to show the effectiveness of our proposed method.

The maximal ratio combining (MRC) receiver has excellent performance in the uplink of massive multiple-input-multiple-output (MIMO) systems under favorable propagation, but the performance degrades very much in a harsh channel because of interference. Theoretical analysis showing this phenomenon is given for a particular class of fading channel. A novel receiver, exploiting preemphasis/deemphasis enhances the performance of MRC in the harsh channel by suppressing interference. MRC is shown to exhibit performance ceilings in a particular harsh massive MIMO channel whereas the novel receiver does not. Analytical lower bounds of the uplink achievable rate of the two methods and simulation results show the effectiveness of the proposed receiver. Monte Carlo simulation shows that the new receiver has comparable performance to minimum meansquare error (MMSE)/zero-forcing (ZF) while having much lower implementation complexity.

Spatial sharing (SPSH) is one of the most important merits in 60 GHz millimeter wave (mmWave) wireless personal area networks. Multihop architecture exhibits significant potential for concurrent transmission. In this study, we investigate the SPSH problem for single-hop and multihop architecture under the physical interference model. The minimum length schedule problem of satisfying the required traffic demand with the shortest time is formulated as the optimization problem. Concurrent scheduling and routing are jointly considered in the multihop case. To reduce the complexity of the computation, the column generation algorithm is employed to derive the optimal solution. The simulation results show that multihop architecture can enhance the network throughput by 20% to 30% compared with single-hop architecture.

Uncompressed video transmission has recently attracted research attentions because it ensures low end-to-end latency, high video quality and low complexity. In this paper, we propose a real-time uncompressed video transmission system, where unequal power allocation (UPA) strategies are adopted to minimize the end-to-end mean square error (MSE). Specifically, the relationship between the bit error rate (BER) and the MSE is established. Based on the relationship, an optimization problem is formulated, where both the power budget and the peak-to-average power ratio (PAPR) constraint are satisfied. By solving the problem, an optimal iterative UPA algorithm and a suboptimal simplified UPA algorithm are proposed. Simulation results show that the peak signal-to-noise ratio (PSNR) of the reconstructed video is improved largely.

Spatial sharing is one of the most important merits in 60 GHz millimeter wave (mmWave) wireless personal area networks (WPANs). In this paper, we address the optimal scheduling problem under physical interference model with power control and rate adaption to exploit spatial sharing in mmWave WPANs. The optimization problem is formulated as the minimum length scheduling problem with the constraint of the minimum traffic demand. To obtain the optimal solution with low computational complexity, column generation (CG) algorithm is employed to derive the optimal schedule. Simulation results demonstrate that the scheduled length for all transmission links can be significantly reduced by using rate adaption and power control schemes, and be further reduced by configuring directional antennas at both transmitter and receiver sides.

We consider designing a distributed scheduling algorithm for multi-homing users in heterogeneous cache-enabled small cell networks. The aim is to minimize the total operation cost on condition that all users' QoS requirements are satisfied. The proposed algorithm is based on alternating direction method of multipliers (ADMM) and can be carried out in each small cell base station (SCBS) independently. Simulation results show that the operation cost converges to the global optimum after a few iterations and the proposed algorithm outperforms a conventional distributed method largely.

This paper studies the opportunistic spectrum access (OSA) of secondary users in large-scale overlay cognitive radio (CR) networks. Particularly, a two-phase carrier sensing based protocol, namely the primary receiver assisted carrier sensing (PRA-CS) protocol, is investigated. Under the PRA-CS protocol, a secondary transmitter (ST) is allowed to transmit only if it satisfies the interference constraint at all the active primary receivers (PRs) and has the minimum back-off timer among its secondary contenders. It is worth noting that under the PRA-CS protocol, due to the fact that the activation of STs relies on the spatial realizations of both the primary and secondary networks, even the first order moment measure (average density) of the point process formed by the active STs can not be exactly characterized. To tackle this new difficulty, approximations are made on the conditional distributions of the eligible STs as well as the active STs given a typical primary/secondary receiver (PR/SR) activated at the origin. Based on such approximations, the coverage (transmission non-outage) performance of the primary/secondary network under the proposed PRA-CS protocol is characterized. Simulations are provided to validate our analysis.

This paper studies the opportunistic spectrum access (OSA) of the secondary users in a large-scale overlay cognitive radio (CR) network. Two threshold-based OSA schemes, namely the primary receiver assisted (PRA) protocol and the primary transmitter assisted (PTA) protocol, are investigated. Under the PRA/PTA protocols, a secondary transmitter (ST) is allowed to access the spectrum only when the maximum signal power of the received beacons/pilots sent from the active primary receivers/transmitters (PRs/PTs) is lower than a certain threshold. To measure the resulting transmission opportunity for the secondary users by the proposed OSA protocols, the concept of spatial opportunity, which is defined as the probability that an arbitrary location in the primary network is detected as a spatial spectrum hole, is introduced and then evaluated by applying tools from stochastic geometry. Based on spatial opportunity, the coverage (non-outage transmission) performance in the overlay CR network is analyzed. With the obtained results of spatial opportunity and coverage probability, we finally characterize the spatial throughput, which is defined as the average spatial density of successful transmissions in the primary/secondary network, under the PRA and PTA protocols, respectively.

This paper studies the opportunistic spectrum access (OSA) of secondary users in a large-scale overlay cognitive radio network. Particularly, a threshold-based protocol is investigated, where the secondary transmitter is allowed to access the spectrum only if the maximum signal power of the received beacons transmitted by the primary receivers is lower than a certain threshold. To measure the resulting transmission opportunity for the secondary users by the proposed OSA protocol, the concept of spatial opportunity is introduced and evaluated by applying tools from stochastic geometry. Due to the dependency between the realizations of the active primary and secondary users, an exact calculation of the coverage probabilities of the primary and secondary networks is infeasible. To tackle this difficulty, approximation is made on the conditional distribution of the active secondary transmitters given a typical primary/secondary receiver activated at the origin. Based on this approximation, the coverage performance of the primary/secondary network under the proposed OSA protocol is characterized. To our best knowledge, this paper is the first attempt of using stochastic geometry to evaluate the performance of the threshold-based opportunistic spectrum access in large-scale cognitive radio networks.

60GHz millimeter wave communication systems usually use directional beamforming to compensate high propagation loss in this band. To fully exploit its potential of spatial reuse owing to directivity, an interference alignment scheme based on subarray partition is proposed in this paper. In this scheme, the whole antenna array is partitioned into M subarrays, where beamforming is performed within each subarray, and interference alignment is implemented among M subarray pairs. Simulation results have shown that the proposed scheme can further mitigate the interference on the basis of inner-subarray beamforming, and significantly improve the sum-capacity of the network in high transmit SNR region.

As the date rates and bandwidths of communication systems scale up, low-precision (e.g., 1-4 bits) analog-to-digital converter (ADC) is one approach to reduce the cost and power consumption for sampling rates on the order of gigahertz. Because of the significant quantization error of the low-precision ADCs, the conventional digital automatic gain control (AGC) is not applicable any more. In this work, we investigate the problem of AGC for pulse amplitude modulation (PAM) signaling over the AWGN channel. First, the optimal thresholds are derivated by maximizing the Fisher information. Then a particle filter based estimator is proposed. We divide the training sequence into several slots. In each slot, particles are generated to approximate the distribution of the input signal amplitude, which is updated basing on the quantizer outputs in previous slots. We adjust the thresholds according to the amplitude particles of the input training signal and finally obtain a maximum a posteriori probability (MAP) estimate. We obtain notable performance in terms of uncode bit error rate (BER), compared with one-shot maximum likelihood (ML) estimator with the same training length. The performance is closed to that of ideal AGC over different input amplitude values.

Relay stations (RSs) are usually used to enhance the signal strength of the mobile stations (MSs) close to the cell boundary. However, the introduction of RSs to the cellular networks increases the interference to the MS served by the base station (BS) under spatial multiplexing mode. In this paper, we propose a method which can cancel the interference through a novel use of dirty paper coding (DPC). Then, under a practical spatial multiplexing frame structure, we evaluate the cellularrelay system performance under three scheduling algorithms, Round Robin (RR) algorithm, Maximum Signal to Interference and Noise Ratio (MaxSINR) algorithm and Proportional Fair (PF) algorithm. Simulation and analytical results show that the spectral efficiency (SE) of cellular-relay system can be dramatically increased by 30% by the proposed interference cancellation method, and RR algorithm achieves the highest gain, while the gain of MaxSINR algorithm is limited gain in our scheme.

This paper discusses on the fair resource allocation in OFDMA two-hop cooperative relaying cellular networks which consist of a single base station (BS), dedicated fixed relay stations (RSs) and user stations (USs). By assuming the relay strategy employed on each subcarrier is adaptively selected among direct transmission, amplify-and-forward (AF) and decode-and-forward (DF) according to different channel conditions, we formulate a fair subcarrier assignment, relay station and relay strategy selection problem with QoS constraints in terms of user's minimum rate requirement. The formulated problem is a binary integer programming (BIP) problem which is NP-complete, so a simple suboptimal algorithm is proposed to manage the network resources. Simulations demonstrate the proposed algorithm obtains near optimal solution with low complexity and achieves a good tradeoff between the overall system performance and the fairness among users.

In this paper, a cooperative spatial multiplexing scheme is considered for dynamic relay networks. We assume that each relay node is equipped with a single antenna. To obtain spatial multiplexing gains, the distributed multiple-input multiple-output (MIMO) scheme, in which cooperating nodes act as elements of a multi antenna system, has been proposed in many papers. Since the nodes need to know their specific stream and pilot index, either internode communication or a central control unit is required. Our design objective is to obtain spatial multiplexing gains while eliminating the need for stream and pilot allocation. We introduce a novel randomized strategy that decentralize the transmission of multi streams from a set of distributed relay nodes. We analyze the constraints on the random scheme, and propose a limited feedback scheme to further improve its performance.

In this paper, we consider decentralized estimation of a noise-corrupted deterministic parameter in wireless sensor networks with the aid of relay. We propose a new relay-aided decentralized estimation scheme by which relay collects the overheard messages from sensors, computes a local message, and then sends it to a destination. Besides, we develop the sensor selection policies to select the most appropriate sensor for observation transmission, and then propose the opportunistic scheduling protocol for decentralized estimation to allow relay to perform cooperation transmission opportunistically. Numerical simulation shows the proposed strategies can provide better estimation performance without any bandwidth loss.