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

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Ioannis G. Fraimis ◽  
Stavros A. Kotsopoulos
Keyword(s):  
Resource Allocation ◽  
Complexity Analysis ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Spatial Diversity ◽  
Frequency Diversity ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Input Multiple Output ◽  
Efficient Resource

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.

scholarly journals Performance Analysis of Bandwidth Allocation Based on Dynamic User Priority for Indoor Visible Light Communication Ultra-Dense Networks

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 276
Author(s):  
Xiang-Wei Bai ◽  
Qing Li ◽  
Si-Yu Tao
Keyword(s):  
Resource Allocation ◽  
Visible Light ◽  
Bandwidth Allocation ◽  
Blocking Probability ◽  
Visible Light Communication ◽  
System Throughput ◽  
Multiple Features ◽  
Dense Networks ◽  
User Priority ◽  
Priority Model

Focusing on the high user density in visible light communication ultra-dense networks (VLC-UDNs), this paper proposes a resource allocation method based on dynamic user priority (DUP). Firstly, this paper establishes the DUP model, which realizes a multi-dimensional measurement for differences of users. Considering the variety of network environments, we dynamically select multiple features of users and achieve the calculation of DUP by fuzzy logic (FL). Secondly, the throughput-maximizing resource allocation (TMRA) scheme with user priority guarantee is proposed. Thirdly, the lower bound of the proposed DUP-TMRA is derived. Simulation results show that the proposed multi-dimensional DUP model outperforms the conventional one-dimensional DUP model and fixed priority model. In addition, the proposed TMRA scheme outperforms the conventional proportion allocation scheme. Finally, in comparisons of system throughput, the proposed DUP-TMRA achieves 4% performance improvement against the conventional required data rate proportion allocation (RPA) method. In comparisons of fairness, DUP achieves the modest performance. In comparisons of satisfaction, when the average blocking probability is higher than 0.45, the proposed DUP-TMRA improves the proportion of satisfied users against the conventional RPA method by up to 17.5%.

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 Resource Allocation Algorithm for Ultra-Dense Networks Based on Deep Reinforcement Learning

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Huashuai Zhang ◽  
Tingmei Wang ◽  
Haiwei Shen
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Data Traffic ◽  
Wireless Data ◽  
Resource Allocation Algorithm ◽  
Allocation Algorithm ◽  
Q Learning ◽  
Dense Networks ◽  
Target Network ◽  
Wireless Resource Allocation

The resource optimization of ultra-dense networks (UDNs) is critical to meet the huge demand of users for wireless data traffic. But the mainstream optimization algorithms have many problems, such as the poor optimization effect, and high computing load. This paper puts forward a wireless resource allocation algorithm based on deep reinforcement learning (DRL), which aims to maximize the total throughput of the entire network and transform the resource allocation problem into a deep Q-learning process. To effectively allocate resources in UDNs, the DRL algorithm was introduced to improve the allocation efficiency of wireless resources; the authors adopted the resource allocation strategy of the deep Q-network (DQN), and employed empirical repetition and target network to overcome the instability and divergence of the results caused by the previous network state, and to solve the overestimation of the Q value. Simulation results show that the proposed algorithm can maximize the total throughput of the network, while making the network more energy-efficient and stable. Thus, it is very meaningful to introduce the DRL to the research of UDN resource allocation.

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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