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

2006 ◽  
Author(s):  
Maduranga Liyanage ◽  
Iwao Sasase
Keyword(s):  
Resource Allocation ◽  
Low Complexity ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Multiple Attribute

Low-Complexity Resource Allocation Algorithm for Multicell OFDMA System

2013 ◽  
Vol E96.B (5) ◽  
pp. 1218-1221 ◽  
Author(s):  
Qingli ZHAO ◽  
Fangjiong CHEN ◽  
Sujuan XIONG ◽  
Gang WEI
Keyword(s):  
Resource Allocation ◽  
Low Complexity ◽  
Ofdma System ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm

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

2019 ◽  
Vol 9 (7) ◽  
pp. 1391 ◽  
Author(s):  
Xiangwei Bai ◽  
Qing Li ◽  
Yanqun Tang
Keyword(s):  
Resource Allocation ◽  
Visible Light ◽  
Complexity Analysis ◽  
Visible Light Communication ◽  
Low Complexity ◽  
System Throughput ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Dense Networks ◽  
Average System

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.

scholarly journals An Efficient Resource Allocation Algorithm for Device-To-Device Communications

2019 ◽  
Vol 9 (18) ◽  
pp. 3816 ◽  
Author(s):  
Saraereh ◽  
Mohammed ◽  
Khan ◽  
Rabie ◽  
Affess
Keyword(s):  
Resource Allocation ◽  
Optimal Algorithm ◽  
Search Algorithm ◽  
Low Complexity ◽  
System Capacity ◽  
Total System ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Device To Device ◽  
Total Capacity

In order to solve the problem of interference and spectrum optimization caused by D2D (device-to-device) communication multiplexing uplink channel of heterogeneous cellular networks, the allocation algorithm based on the many-to-one Gale-Shapley (M21GS) algorithm proposed in this paper can effectively solve the resource allocation problem of D2D users multiplexed cellular user channels in heterogeneous cellular network environments. In order to improve the utilization of the wireless spectrum, the algorithm allows multiple D2D users to share the channel resources of one cellular user and maintains the communication service quality of the cellular users and D2D users by setting the signal to interference and noise ratio (SINR) threshold. A D2D user and channel preference list are established based on the implemented system’s capacity to maximize the system total capacity objective function. Finally, we use the Kuhn–Munkres (KM) algorithm to achieve the optimal matching between D2D clusters and cellular channel to maximize the total capacity of D2D users. The MATLAB simulation is used to compare and analyze the total system capacity of the proposed algorithm, the resource allocation algorithm based on the delay acceptance algorithm, the random resource allocation algorithm and the optimal exhaustive search algorithm, and the maximum allowable access for D2D users. The simulation results show that the proposed algorithm has fast convergence and low complexity, and the total capacity is close to the optimal algorithm.

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