Multiuser Resource Allocation Research Articles

Innovative resource allocation mechanism for optimizing 5G multi user‐massive multiple input multiple output system

Abstract5G networks are essential in all locations owing to the multitude of advantages they provide. As a result, the number of users has increased dramatically. Nevertheless, these users require a variety of resources in order to function efficiently. Deep learning techniques have been created to improve the precision and dependability of resource allocation in the context of 5G networks. This research utilizes an efficient recurrent neural network (ERNN) to handle resource allocation for 5G multiuser (MU)‐massive multiple input multiple output (MIMO). In order to optimize the objective functions, the first application of the multi‐objective differential evaluation algorithm (MODEA) is used. The neural network is provided with these updated goal functions in order to allocate resources. ERNN evaluates the level of need for each individual user. By partitioning the resource at this level, it maintains a high throughput while distributing it to each user. In addition, the fairness index of the resource distribution system based on neural networks is established. The suggested method achieves a data transfer rate of 290 bits per second (bps) and a fairness index of 0.97% when used by 50 users. The findings of the proposed method exhibit superior performance compared to other existing methods in the field of 5G massive MIMO.

Cost-Aware Computation Offloading and Resource Allocation in Ultra-Dense Multi-Cell, Multi-User and Multi-Task MEC Networks

Cost-Aware Computation Offloading and Resource Allocation in Ultra-Dense Multi-Cell, Multi-User and Multi-Task MEC Networks

Framework for Optimized Resource Allocation in Multi-User, Multi-Service, Multi-Device Aerial Networks

Framework for Optimized Resource Allocation in Multi-User, Multi-Service, Multi-Device Aerial Networks

Personalized Saliency in Task-Oriented Semantic Communications: Image Transmission and Performance Analysis

Semantic communication, as a promising technology, has emerged to break through the Shannon limit, which is envisioned as the key enabler and fundamental paradigm for future 6G networks and applications, e.g., smart healthcare. In this paper, we focus on UAV image-sensing-driven task-oriented semantic communications scenarios. The majority of existing work has focused on designing advanced algorithms for high-performance semantic communication. However, the challenges, such as energy-hungry and efficiency-limited image retrieval manner, and semantic encoding without considering user personality, have not been explored yet. These challenges have hindered the widespread adoption of semantic communication. To address the above challenges, at the semantic level, we first design an energy-efficient task-oriented semantic communication framework with a triple-based scene graph for image information. We then design a new personalized semantic encoder based on user interests to meet the requirements of personalized saliency. Moreover, at the communication level, we study the effects of dynamic wireless fading channel on semantic transmission mathematically and thus design an optimal multi-user resource allocation scheme by using game theory. Numerical results based on real-world datasets clearly indicate that the proposed framework and schemes significantly enhance the personalization and anti-interference performance of semantic communication, and are also efficient to improve the communication quality of semantic communication services.

An multi-BSS multi-user full duplex MAC protocol based on AP cooperation for the next generation WLAN

Aiming at the problems of low efficiency of buffer information collection and sharp drop of system throughput caused by the aggravation of conflicts in the multi basic service set (BSS) high density deployment overlap coverage scenario of next generation wireless local access network (WLAN), an access point cooperation based multi-BSS multi-user full duplex multiple access protocol named as CMMFD is proposed. Firstly, a channel resource allocation algorithm for stations (STA) in overlapping coverage area is designed, in which, the access control (AC) equipment allocates the channel resources for all STA in response to buffer state and interference information (BI) according to the proportion of STA in the overlapped coverage area. Secondly, a BI information collection protocol process based on access point (AP) cooperation sending trigger frame is designed to collect all STA information. After receiving trigger frame, STA will access the channel and report BI information in p-probability according to the channel resources allocated by AC. Finally, a multi-BSS multi-user channel resource allocation algorithm based on full duplex is designed. AC allocates channel resources according to all BI information reported by AP, and schedules STA in the multi-BSS to carry out multi-user full duplex transmission over the sub-channel. Simulation results show that the throughput of the CMMFD protocol improves by 29.6%, compared with Mu-FuPlex protocol and EnFD-OMAX protocol.

Multi-MEC server multi-user resource allocation in heterogeneous network

Aiming at the coexistence of large and small cells in heterogeneous cellular networks, the difference of computing resources between mobile equipment and base stations, and the diversity of service quality demands of users, a resource allocation algorithm based on matching game theory was proposed. We assume that some users have computing needs and can perform MEC offloading or D2D offloading. We define cost performance as the ratio of the data rate per user to its price cost, and resource allocation as the cost performance maximization problem. Considering that the formulation problem is a mixed integer nonlinear problem and it is difficult to obtain the optimal solution, we design an improved Gale-Shapley algorithm based on matching game theory to obtain the feasible solution of the problem. Simulation results show that the proposed multi-MEC server multi-user resource allocation strategy can effectively reduce user service delay and improve system performance in heterogeneous cellular networks.

Resource Allocation Algorithm for Physical Layer of Broadband Power Line Carrier Communication Applied to Electric Internet of Things

Broadband power line carrier communication adopts adaptive orthogonal frequency division multiplexing OFDM technology to effectively increase the communication rate, and provides ample communication resource guarantee for the realization of power multiservice applications. Aiming at the problem of broadband power line carrier communication resource allocation, this paper proposes a low-complexity physical layer resource allocation algorithm. First, based on the equal power allocation method, the set of subcarriers required by each user to meet their minimum rate requirements is determined, and the multi-user resource allocation problem is reduced to a single-user optimal power allocation problem, and then the Lagrange multiplier method is used to optimize the power of the sub-carrier set used by each user to further improve the system throughput. Experiments show that the algorithm in this paper has higher throughput and can meet the minimum rate requirements of more users.

The Efficiency of Resource Allocation Mechanisms for Budget-Constrained Users

We study the efficiency of mechanisms for allocating a divisible resource. Given scalar signals submitted by all users, such a mechanism decides the fraction of the resource that each user will receive and a payment that will be collected from her. Users are self-interested and aim to maximize their utility (defined as their value for the resource fraction they receive minus their payment). Starting with the seminal work of Johari and Tsitsiklis, a long list of papers studied the price of anarchy (in terms of the social welfare—the total users’ value) of resource allocation mechanisms for a variety of allocation and payment rules. Here, we further assume that each user has a budget constraint that invalidates strategies that yield a payment that is higher than the user’s budget. This subtle assumption, which is arguably more realistic, constitutes the traditional price of anarchy analysis meaningless as the set of equilibria may change drastically and their social welfare can be arbitrarily far from optimal. Instead, we study the price of anarchy using the liquid welfare benchmark that measures efficiency taking budget constraints into account. We show a tight bound of 2 on the liquid price of anarchy of the well-known Kelly mechanism and prove that this result is essentially best possible among all multiuser resource allocation mechanisms. This comes in sharp contrast to the no-budget setting where there are mechanisms that considerably outperform Kelly in terms of social welfare and even achieve full efficiency. In our proofs, we exploit the particular structure of worst-case games and equilibria, which also allows us to design (nearly) optimal two-player mechanisms by solving simple differential equations.

Theoretical Derivation and Optimization Verification of BER for Indoor SWIPT Environments

Symmetrical antenna array is useful for omni bearing beamforming adjustment with multiple receivers. Beam-forming techniques using evolution algorithms have been studied for multi-user resource allocation in simultaneous wireless information and power transfer (SWIPT) systems. In a high-capacity broadband communication system there are many users with wearable devices. A transmitter provides simultaneous wireless information and power to a particular receiver, and the other receivers harvest energy from the radio frequency while being idle. In addition, the ray bounce tracking method is used to estimate the multi-path channel, and the Fourier method is used to perform the time domain conversion. A simple method for reducing the frequency selective effort of the multiple channels using the feed line length instead of the digital phase shifts is proposed. The feed line length and excitation current of the transmitting antennas are adjusted to maximize the energy harvest efficiency under the bit error rate (BER) constraint. We use the time-domain multipath signal to calculate the BER, which includes the inter symbol interference for the wideband system. In addition, we use multi-objective function for optimization. To the best of our knowledge, resource allocation algorithms for this problem have not been reported in the literature. The optimal radiation patterns are synthesized by the asynchronous particle swarm optimization (APSO) and self-adaptive dynamic differential evolution (SADDE) algorithms. Both APSO and SADDE can form good patterns for the receiver for energy harvesting. However, APSO has a faster convergence speed than SADDE.

Deep Reinforcement Learning for Performance-Aware Adaptive Resource Allocation in Mobile Edge Computing

Mobile edge computing (MEC) enables to provide relatively rich computing resources in close proximity to mobile users, which enables resource-limited mobile devices to offload workloads to nearby edge servers, and thereby greatly reducing the processing delay of various mobile applications and the energy consumption of mobile devices. Despite its advantages, when a large number of mobile users simultaneously offloads their computation tasks to an edge server, due to the limited computation and communication resources of edge server, inefficiency resource allocation will not make full use of the limited resource and cause waste of resource, resulting in low system performance (the weighted sum of the number of processed tasks, the number of punished tasks, and the number of dropped tasks). Therefore, it is a challenging problem to effectively allocate the computing and communication resources to multiple mobile users. To cope with this problem, we propose a performance-aware resource allocation (PARA) scheme, the goal of which is to maximize the long-term system performance. More specifically, we first build the multiuser resource allocation architecture for computing workloads and transmitting result data to mobile devices. Then, we formulate the multiuser resource allocation problem as a Markova Decision Process (MDP). To achieve this problem, a performance-aware resource allocation (PARA) scheme based on a deep deterministic policy gradient (DDPG) is adopted to derive optimal resource allocation policy. Finally, extensive simulation experiments demonstrate the effectiveness of the PARA scheme.

Toward the energy efficiency of resource allocation algorithms for OFDMA downlink MIMO systems

The problem of the simultaneous multi-user resource allocation algorithm in orthogonal frequency division multiple access (OFDMA) based systems has recently attracted significant interest. However, most studies focus on maximizing the system throughput and spectral efficiency. As the green radio is essential in 5G and future networks, the energy efficiency becomes the major concern. In this paper, we develop four resource allocation schemes in the downlink OFDMA network and the main focus is on analyzing the energy efficiency of these schemes. Specifically, we employ the advanced multi-antenna technology in a multiple input-multiple output (MIMO) system. The first scheme is based on transmit spatial diversity (TSD), in which the vector channel with the highest gain between the base station (BTS) and specific antenna at the remote terminal (RT) is chosen for transmission. The second scheme further employs spatial multiplexing on the MIMO system to enhance the throughput. The space-division multiple-access (SDMA) scheme assigns single subcarrier simultaneously to RTs with pairwise “nearly orthogonal” spatial signatures. In the fourth scheme, we propose to design the transmit beamformers based on the zero-forcing (ZF) criterion such that the multi-user interference (MUI) is completely removed. We analyze the tradeoff between the throughput and power consumption and compare the performance of these schemes in terms of the energy efficiency.

The research on multiuser resource allocation optimization in cognitive radio

Spectrum resource allocation optimization is still a research hotspot in the field of cognitive radio. The optimization framework based on Bandwidth-Power Product (BPP) proposed in recent years provides a new direction for this field. Compared with the traditional waterfilling algorithm, the new optimization framework has effectively improved the utilization of spectrum resources. However, there are few further studies on BPP in recent years. Based on the work of predecessors, this paper improves the criterion of BPP by taking into account the impact of the multiplexing of spectrum resource in different spatial locations on the spectrum utilization. The simulation results show that the improved BPP framework can further improve the utilization of spectrum resource; besides, the framework is more in line with the actual cognitive radio environment.

Resource allocation and computation offloading with data security for mobile edge computing

Abstract With the considerable growth of mobile users (MUs) and IoT devices, complex applications and multimedia services are rapidly increasing, thereby requiring additional computations and high data communication. However, these devices are still resource-constrained with limited computation power and energy. Furthermore, security is considered a critical issue for sensitive information communication. This study presents a multiuser resource allocation and computation offloading model with data security to address the limitations of such devices. First, the computation and radio resources are jointly considered for multiuser scenarios to guarantee the efficient utilization of shared resources. In addition, an AES cryptographic technique is introduced as a security layer to protect sensitive information from cyber-attacks. Furthermore, an integrated model, which jointly considers security, computation offloading, and resource allocation, is formulated to minimize time and energy consumption of the entire system. Finally, an offloading algorithm is developed with detailed processes to determine the optimal computation offloading decision for MUs. Simulation results show that our model and algorithm can significantly improve the performance of the entire system compared with local execution and full offloading schemes.

Dynamic Multiple Access Configuration in Intelligent Lifi Attocellular Access Points

The exponential growth in the global demand for wireless connectivity calls for efficient and reliable management of the available wireless resources. Light fidelity (LiFi) harnesses the vast untapped wireless transmission resources in the infrared spectrum and visible light spectrum to create ultra-dense wireless networks which support user mobility, multiuser access, and handover. Various multiuser access (MA) protocols have been developed to meet the varying system requirements, including orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) schemes. While NOMA, on the one hand, allows for significant enhancement in the achievable data rates, its performance may be severely degraded under particular conditions such as a large number of connected users or users existing in highly symmetrical locations. OMA, on the other hand, provides better link reliability in such scenarios but at the expense of decreased spectral efficiency. Therefore, there is a need to enable a degree of intelligence in the LiFi access point (AP) to facilitate real-time configuration of the MA protocol. To this end, this paper develops a novel cross-layer design framework for dynamic multiple access selections (DMAS) in intelligent LiFi APs. The developed framework runs at LiFi attocell system level and can be configured to cater for various system requirements in terms of sum data rate, average outage probability, and fairness. The obtained results show that DMAS introduces an effective solution for multiuser resource allocation by achieving better satisfaction of the system requirements compared to the static configuration of a single MA scheme.

Optimal Network-Assisted Multiuser DASH Video Streaming

Streaming video is becoming the predominant type of traffic over the Internet with reports forecasting the video content to account for 82% of all traffic by 2021. With significant investment on Internet backbone, the main bottleneck remains at the edge servers (e.g., WiFi access points, small cells, etc.). In this paper, we obtain and prove the optimality of a multiuser resource allocation mechanism operating at the edge server that minimizes the probability of stalling of video streams due to buffer under-flows. Our derived policy utilizes media presentation description files of clients that are sent in compliant to dynamic adaptive streaming over HTTP protocol to be cognizant of the deadlines of each of the media file to be displayed by the clients. Our policy allocates the available channel resources to the users, in a time division manner, in the order of their deadlines. After establishing the optimality of this policy to minimize the stalling probability for a network with links associated with fixed loss rates, the utility of the algorithm is verified under realistic network conditions with detailed NS-3 simulations.

Cognitive Multihoming System for Energy and Cost Aware Video Transmission

To alleviate the spectrum scarcity problem in the licensed cellular networks (LCNs), we introduce a new paradigm called cognitive multihoming (CM), where a cognitive radio (CR)-enabled base station transmits to the users simultaneously over the licensed cellular bands and primary user bands in CR networks (CRNs). The CR aspect incurs lower cost, however, at the expense of higher energy consumption due to intermittent channel sensing. On the other hand, transmission via LCN is expensive because of its licensing premium. To minimize the transmission cost while meeting the users energy and received video quality constraints, sensing duration and transmission rate over CRN, transmission rate over LCN, and network selection for retransmission of lost packets are adjusted. Solution to the multiuser resource allocation optimization problem is obtained by solving the cost minimization problem of a single-user system. The problem is nonconvex which is solved using convex-concave procedure. The proposed scheme is compared with the cases where a user operates over a single network, either LCN or CRN. The system performance results indicate that the proposed CM strategy significantly decreases the cost to the users as well as serves a higher number of users while maintaining the desired video quality and energy consumption constraints.

Optimal Joint User Association and Multi-Pattern Resource Allocation in Heterogeneous Networks

This paper studies the joint user association and resource allocation in heterogeneous networks (HetNets) from a novel perspective, motivated by and generalizing the idea of fractional frequency reuse. By treating the multi-cell multi-user resource allocation as resource partitioning among multiple reuse patterns, we propose a unified framework to analyze and compare a wide range of user association and resource allocation strategies for HetNets, and provide an optimal benchmark for network performance. The enabling mechanisms are a novel formulation to consider all possible interference patterns or any pre-defined subset of patterns, and efficient sparsity-pursuit algorithms to find the solution. A notable feature of this formulation is that the patterns remain fixed during the resource optimization process. This creates a favorable opportunity for convex formulations while still considering interference coupling. More important, in view of the fact that multi-cell resource allocation is very computational demanding, our framework provides a systematic way to trade off performance for the reduction of computational complexity by restricting the candidate patterns to a small number of feature patterns. Relying on the sparsity-pursuit capability of the proposed algorithms, we develop a practical guideline to identify the feature patterns. Numerical results show that the identified feature patterns can significantly improve the existing strategies, and jointly optimizing the user association and resource allocation indeed brings considerable gain.

On Multiuser Resource Allocation in Relay-Based Wireless-Powered Uplink Cellular Networks

We propose relay-based wireless-powered uplink cellular networks in which users first harvest energy from RF transmissions of base station/relay nodes and then use that energy for uplink transmission. Given the limited total transmission time and available energy at the relay node, we propose different resource allocation frameworks for the proposed relay-based networks considering two different relay-based harvest-then-transmit scenarios. We first propose iterative algorithm to determine time and relay node power allocation (for downlink wireless charging and uplink data transmission/relaying) for both scenarios. We then perform joint optimal time and power allocation for one scenario. Resource allocation results show that most of the available resources (transmission time and relay node energy) are allocated for wireless energy harvesting which is similar to that observed for downlink simultaneous wireless information and power transfer (SWIPT) in the existing literature. Simulation results on comparison of different relay-based scenarios reveal interesting insights and demonstrate remarkable improvement of different performance metrics of wireless-powered cellular networks in the presence of relay node.

Resource Allocation for Multiuser Improved AF Cooperative Communication Scheme

Joint power and bandwidth allocation of an improved amplify and forward (AF) cooperative communication scheme is proposed to utilize the spectrum efficiently. Each user adapts to a mixed strategy transmission using frequency division multiplexing (FDM), where part of the data is transmitted using AF relaying with diversity, and the other part is transmitted using direct transmission without diversity. The resource allocation problem aimed at maximizing the sum rate of a multi-user up-link scenario using the improved scheme is formulated. Both flat and frequency-selective fading channels are addressed. The resource allocation problem of the power and bandwidth profiles is non-convex and difficult to solve. In this sense, a two-step iterative algorithm is proposed, which alternates between solving either power allocation sub-problem for a given bandwidth profile or a bandwidth allocation sub-problem for a given power profile. The convergence and optimality of the proposed algorithm are discussed using game theory framework. Numerical simulations show the merits of the proposed improved AF cooperative communication scheme and the proposed iterative algorithm.

Multiuser Resource Allocation for OFDM Based on AFSA with User Bandwidth Limitation

According to the issue that user bandwidth limitations is always ignored within the resources allocation algorithm of OFDM downlink, a multiuser OFDM adaptive resource allocation algorithm based on AFSA under the condition of limited users bandwidth is proposed in this paper. Under the condition of user bandwidth limitations, the maximum gain of channels is obtained and the bandwidth of each user is optimized by seeking optimal behaviors of AFSA, iteration and convergence. As a result, the resources for OFDM downlink link can be allocated more efficiently. The results of simulation experiments show that the proposed resource allocation strategy based on swarm intelligence, compared with similar algorithms, can carry out the global optimal convergence. Simultaneously, better system performance can be achieved in the given three different cases.