Low-complexity Resource Allocation Algorithm Research Articles

Incentive Mechanism and Resource Allocation for Collaborative Task Offloading in Energy-Efficient Mobile Edge Computing

We investigate a mobile edge computing (MEC) system that supports collaborative task offloading, allowing busy users to offload their tasks to the server and idle users, or perform them locally. However, executing computing tasks consumes device energy, making idle users unwilling to perform other users' tasks due to limited battery capacity. Additionally, the components of the total energy expenditure of the MEC system supporting collaborative task offloading are complex, necessitating appropriate resource allocation and offloading strategies to minimize the system's total energy consumption. To address these challenges, we propose a computing resource sharing auction (CRSA) algorithm to motivate idle users to participate in task offloading. Then, we establish a non-convex mixed-integer nonlinear programming (MINLP) problem to minimize the total energy consumed by the system. By utilizing the McCormick and continuous relaxation (CR) approaches, we develop a low-complexity resource allocation algorithm. Finally, the numerical results demonstrate the effectiveness of the proposed mechanism and resource allocation algorithm.

Vehicle Clustering and Resource Allocation Algorithm Based on Cellular Network

<p>As a special Mobile Ad-hoc Network (MANET), Vehicular Ad-hoc Network (VANET) plays a very important role in the future intelligent transportation system. In order to solve the problems of unstable communication connection, fast network topology change and low communication resource utilization caused by high vehicle mobility in VANET, a low-complexity resource allocation algorithm based on vehicle cluster is proposed. Firstly, considering the speed, position and moving direction of the vehicles, a vehicle clustering algorithm based on movement consistency is proposed to cluster the vehicles and keep the vehicle cluster stable. Secondly, a low-complexity resource allocation algorithm is proposed to improve the utilization rate of communication resources, which is constrained by the interference caused by the vehicle clusters to the cellular users. Simulation results show that the proposed algorithm has low complexity and can better maintain the stability of vehicle clusters and improve the system capacity in the common complex Internet of Vehicles (IoV) scenarios in cities.</p> <p> </p>

Low complexity resource allocation algorithms for chunk based OFDMA multi-user networks with max–min fairness

This paper investigates the resource allocation problem in orthogonal frequency division multiple access networks with multiple users. Different from most works that perform resource allocation based on each subcarrier, we consider the subcarriers are grouped into chunks due to simplicity of implementation. The objective is to maximize the users’ minimal transmission rate by adjusting the transmission power allocation and chunk allocation while taking into account several important constraints. The problem is formulated as a mixed integer nonlinear programming problem, which is generally hard to solve. By binary relaxation, the problem is transformed to be a convex problem. By exploiting the special structure of the relaxed problem, a barrier method based fast algorithm is proposed. To achieve binary chunk allocation, a low complexity suboptimal algorithm is proposed further. When the number of chunks is less than the number of users, it is necessary to consider the joint admission control and resource allocation problem. To solve this problem, a fast algorithm based on bisection search and Hungarian method is designed. Simulations are carried out to validate the superior performance of the proposed algorithms in terms of the users’ minimal transmission rate and running time comparing with benchmark algorithms under different conditions.

Joint rate control and power allocation for low-latency reliable D2D-based relay network

Emerging 5G applications impose stringent requirements on network latency and reliability. In this work, we propose a low-latency reliable device-to-device (D2D) relay network framework to improve the cell coverage and user satisfaction. Particularly, we develop a cross-layer low-complexity resource allocation algorithm, which jointly optimizes the rate control and power allocation from a long-term perspective. The long-term optimization problem is transformed into a series of short-term subproblems by using Lyapunov optimization, and the objective function is separated into two independent subproblems related to rate control in network layer and power allocation in physical layer. Next, the Karush-Kuhn-Tucher (KKT) conditions and alternating direction method of multipliers (ADMM) algorithm are employed to solve the rate control subproblem and power allocation subproblem, respectively. Finally, simulation results demonstrate that the proposed algorithm can reach 99.9% of the optimal satisfaction of D2D pairs with lower average network delay compared to the baseline algorithm. Furthermore, the convergence time of the ADMM-based power allocation algorithm is only about 1.7% of that by using the CVX toolbox.

A Low-Complexity Resource Allocation Algorithm for Indoor Visible Light Communication Ultra-Dense Networks

In this paper, a low-complexity multi-cell resource allocation algorithm with a near-optimal system throughput is proposed to resolve the conflict between the high system throughput and low complexity of indoor visible light communication ultra-dense networks (VLC-UDNs). First, by establishing the optimal model of the resource allocation problem in each cell, we concluded that the problem is a convex optimization problem. After this, the analytic formula of the normalized scaling factor of each terminal for resource allocation is derived after reasonable approximate treatment. The resource allocation algorithm is subsequently proposed. Finally, the complexity analysis shows that the proposed algorithm has polynomial complexity, which is lower than the classical optimal inter-point method. The simulation results show that the proposed method achieves a improvement of 57% in performance in terms of the average system throughput and improvement of 67% in performance in terms of the quality of service (QoS) guarantee against the required data rate proportion allocation (RDR-PA) method.

低复杂度室内超密集可见光通信网络频谱资源分配算法

低复杂度室内超密集可见光通信网络频谱资源分配算法

Spectrum Efficient Resource Management for Multi-Carrier-Based NOMA Networks: A Graph-Based Method

This letter investigates the spectrum efficient resource management problem (SERMP) for non-orthogonal multiple access-based uplink networks. Specifically, we take the decoding threshold of successive interference cancellation into account and formulate the SERMP as a sum-rate maximization problem by jointly considering the user pairing, channel assignment, and power control. For tackling this hard problem, we first analytically get the optimal transmit power for each user pair with given channel assignments. Afterward, we tactfully transform the primal problem into a maximum weighted independent set issue in graph theory, based on which we devise a low-complexity resource allocation algorithm. Finally, simulation results exhibit the performance improvement of our algorithm against other schemes in terms of data rate and number of supported users.

Distributed resource allocation in D2D communication networks with energy harvesting relays using stable matching

Fifth Generation (5G) cellular networks are expected to provide high data rates by using emerging technologies such as multi-tier heterogeneous networks, Device to Device (D2D) communication and densification of small base stations. D2D uses direct transmission between two cellular devices to increase the system throughput. Relays are used to reduce the loss in user data rate when the D2D users are far from each other and the relay nodes are used to serve the cellular users when the channel is not good enough. To get good performance from D2D relay assisted network, suitable, robust and low complexity resource allocation algorithm must be used. In this paper, an algorithm for user association, resource blocks allocation and power control when considering the energy harvesting relays in heterogeneous multi-tier network is presented. This paper introduces a centralized solution using time sharing strategy and a distributed low complexity solution using stable matching theory.

On the Low-Complexity Resource Allocation for Self-Healing With Reduced Message Passing in Indoor Wireless Communication Systems

Recently, self-healing has been actively investigated for mitigating an unforeseen network failure. In particular, to enable self-healing operations in indoor wireless communications systems, an autonomous mechanism to resolve unforeseen network failure problems should be considered. Therefore, we here address the issue of autonomous self-healing, in which continuous connectivity can be provided to users by resolving unexpected network failures. To overcome this problem, we propose a low-complexity resource allocation algorithm based on an optimization approach with reduced message passing. In the proposed algorithm, normal base stations perform subchannel and power allocations with a minimum amount of information sharing ( $N_{H}(M-1)$ ) to provide reliable service to users in faulty cells autonomously. We also show that the proposed algorithm converges to a unique fixed point in the low-interference region by using a contraction mapping technique. Through simulation results, we demonstrate that the proposed algorithm achieves good performances with respect to the average cell capacity, user fairness, and outage probability while reducing the message passing overhead and computational complexity.

Adaptive Resource Allocation Algorithms for Multi-user MIMO-OFDM Systems

Radio resource allocation to the system users is the key challenge issue in multi-user orthogonal frequency division multiplexing (MU-OFDM) systems. In this paper, an efficient and low-complexity proportional rate-adaptive radio resource (sub-carrier and power) allocation algorithm for MU-OFDM is proposed to maximize the sum-rate capacity of the system and achieve acceptable users' rates fairness. Three-dimensional (spatial, frequency, and multi-user) greedy power allocation (GPA) algorithms for the MU multi-input multi-output OFDM (MU MIMO-OFDM) systems are proposed. The proposed algorithms start with spatial sub-carrier allocation followed by an optimal two-dimensional spatial-frequency GPA (SFGPA) step, which exploits both the spatial and frequency diversities. Therefore, spatial, frequency, and multi-user diversities are exploited by the proposed three-dimensional GPA algorithms. Several experiments are carried out to test the performance of the proposed three-dimensional GPA algorithms in terms of sum-rate capacity. The optimal solution is achieved by the three-dimensional dynamic sub-carrier-SFGPA with average gain (DS-SFGPA-AG) algorithm, which achieves the best sum-rate capacity performance compared with the other power allocation algorithms. The performance of the DS-SFGPA-AG algorithm is also analyzed and compared for the four OFDM-based systems; single-user (SU) OFDM, SU MIMO-OFDM, MU OFDM, and MU MIMO-OFDM. The MU MIMO-OFDM system with adaptive sub-carrier and power allocation outperforms the other systems.

A Low-Complexity Resource Allocation Algorithm for Multicast Service Delivery in OFDMA Networks

Allocating and managing radio resources to multicast transmissions in orthogonal frequency-division multiple access (OFDMA) systems is the challenging research issue addressed by this paper. A subgrouping technique, which divides the subscribers into subgroups according to the experienced channel quality, is considered to overcome the throughput limitations of conventional multicast data delivery schemes. A low complexity algorithm, designed to work with different resource allocation strategies, is also proposed to reduce the computational complexity of the subgroup formation problem. Simulation results, carried out by considering the Long Term Evolution system based on OFDMA, testify the effectiveness of the proposed solution, which achieves a near-optimal performance with a limited computational load for the system.

Low-Complexity Energy-Efficient Resource Allocation for the Downlink of Cellular Systems

Energy efficiency (EE) is undoubtedly an important criterion for designing power-limited systems, and yet in a context of global energy saving, its relevance for power-unlimited systems is steadily growing. Equally, resource allocation is a well-known method for improving the performance of cellular systems. In this paper, we propose an EE optimization framework for the downlink of planar cellular systems over frequency-selective channels. Relying on this framework, we design two novel low-complexity resource allocation algorithms for the single-cell and coordinated multi-cell scenarios, which are EE-optimal and EE-suboptimal, respectively. We then utilize our algorithms for comparing the EE performance of the classic non-coordinated, orthogonal and coordinated multi-cell approaches in realistic power and system settings. Our results show that coordination can be a simple and effective method for improving the EE of cellular systems, especially for medium to large cell sizes. Indeed, by using a coordinated rather than a non-coordinated resource allocation approach, the per-sector energy consumption and transmit power can be reduced by up to 15% and more than 90%, respectively.

Low-Complexity Resource Allocation Algorithm for Multicell OFDMA System

Low-Complexity Resource Allocation Algorithm for Multicell OFDMA System

A Low-Complexity Resource Allocation Algorithm for MIMO-OFDMA Multicast Systems with Spectrum-Guarantee Provisioning

We study the important problem of resource allocation for the downlink of Multiple-Input Multiple output (MIMO) Multicast Wireless Systems operating over frequency-selective channels and we propose a low-complexity but efficient resource allocation algorithm for MIMO-enabled OFDMA systems. The proposed solution guarantees a minimum spectrum share for each user while also takes advantage of the multicast transmission mode. The presence of multiple antennas in both transmitter and receiver offers spatial diversity to the system along with the frequency diversity added by the OFDMA access scheme. The computational complexity is reduced from exponential to linear and validation of the proposed solution is achieved through various simulation scenarios in comparison with other multicast and unicast reference schemes used in MIMO-OFDMA systems. Numerical results and complexity analysis demonstrate the feasibility of the proposed algorithm.

A Low Complexity Resource Allocation Algorithm for OFDMA Cooperative Relay Networks with Fairness and QoS Guaranteed

This paper proposes a new resource allocation algorithm for uplink OFDMA-based cooperative relay networks, assuming multiple user nodes, multiple relay nodes and a single destination. The aim is to maximise the total sum of the users' data rates, while guaranteeing fairness among them with different QoS requirements. Assuming perfect channel state information (CSI) at the resource allocation controller, the optimisation problem is formulated such that each user is assigned a weight factor based on its QoS requirements. The ones with higher weights are given higher priorities to select their resources (relay stations and subcarriers) first. Once the required QoS is achieved for all users, the weight factor for all users is reduced to a small uniform value. The remaining resources are then allocated to the users with higher instantaneous rates in order of magnitude. The results show that the proposed algorithm outperforms the greedy and static algorithms in terms of outage probability and fairness, and at the same time outperforms Jeong's algorithm by 58% in terms of total sum rate, with an average 74% reduction in system complexity.

Low complexity resource allocation and scheduling algorithms for downlink SDMA systems

The downlink zero-forcing beamforming strategy in the case of random packet arrivals is investigated. Under this setting, the relevant fairness criterion is the stabilization of all buffer queues which guarantees a bounded average delay for all users. It has been shown that allocating resources to maximize a queue-length-weighted sum of the rates is a stabilizing policy. However, the high complexity of user selection and the feasible rates determination for optimal scheme may prevent the real-time scheduling operation. Two low complexity algorithms are provided taking the channel state, queue state and orthogonality into account. In particular, the authors pick the first user with the largest product between channel gain and queuing length, and select the remaining users to construct candidate user set based on the greedy user selection method or channel orthogonal user selection method. Then, the power and rate allocation for the selected users are implemented based on the modified water-filling method. The complexity of the proposed algorithms is analyzed. The average delay and average throughput are studied in homogeneous scenarios and heterogeneous scenarios, respectively. Simulation results show that the proposed algorithms can take full advantage of the multi-user diversity gain and provide average delay (or throughput) and fairness improvement compared with channel-aware-only schemes.

Low Complexity Resource Allocation Algorithm by Multiple Attribute Weighing and User Ranking for OFDMA Systems

We propose an effective subcarrier allocation scheme for multiuser orthogonal frequency division multiple access (OFDMA) system in the downlink transmission with low computational complexity. In the proposed scheme, by taking multiple attributes of a user's channel, such as carrier gain decrease rate and variation from the mean channel gain of the system, to determine a rank for the user, subcarriers are then allocated depending on the individual user's rank. Different channel characteristics are used to better understand a user's need for subcarriers and hence determine a priority for the user. We also adopt an attribute weighing scheme to enhance the performance of the proposed scheme. The scheme is computationally efficient, since it avoids using iterations for the algorithm convergence and also common water-filling calculations that become more complex with increasing system parameters. Low complexity is achieved by allocating subcarriers to users depending on their determined rank. Our proposed scheme is simulated in comparison with other mathematically efficient subcarrier allocation schemes as well as with a conventional greedy allocation scheme. It is shown that the proposed method demonstrates competitive results with the simulated schemes.