Access Point Selection for Spectral Efficiency and Load Balancing Optimization in Radio Stripes

被引:5
作者
Conceicao, Filipe [1 ,2 ]
Martins, Lucia [2 ,3 ]
Gomes, Marco [1 ,2 ]
Silva, Vitor [1 ,2 ]
Dinis, Rui [1 ,4 ]
机构
[1] Inst Telecomunicacoes IT, P-3030290 Coimbra, Portugal
[2] Univ Coimbra, Dept Elect & Comp Engn DEEC, P-3030290 Coimbra, Portugal
[3] Inst Syst Engn & Comp Coimbra INESC Coimbra, P-3030290 Coimbra, Portugal
[4] Univ Nova Lisboa UNL, Fac Ciencias & Tecnol FCT, P-2829516 Caparica, Portugal
关键词
Genetic algorithms; Antennas; Load management; Linear programming; Central Processing Unit; Signal to noise ratio; Interference; Massive MIMO (mMIMO); radio stripe (RS); AP selection (APS); bi-objective; maximum ratio combining (MRC); genetic algorithm (GA); FREE MASSIVE MIMO; POWER ALLOCATION;
D O I
10.1109/LCOMM.2023.3300358
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
This work considers the uplink (UL) of a radio stripe (RS) network with two sequential equalization schemes: the optimal sequence linear processing (OSLP) and the sequential maximum ratio combining (MRC). An access point selection (APS) scheme is employed to provide an efficient AP-to-user equipment (UE) association that aims to enhance the network spectral efficiency (SE) and supply a balanced distribution of the APs' load (load balance). A bi-objective optimization approach is developed to provide these trade-off solutions and is based on a meta-heuristic (MH) known as genetic algorithm (GA). It can be concluded that the bi-objective approach can provide APS solutions where the SE is counterbalanced with the corresponding APs' load, leading to acceptable solutions in terms of SE with much lower APs' involvement. Additionally, the APS scheme can be complemented with the low-complexity MRC since it exhibits low SE degradation when compared to the case with OSLP.
引用
收藏
页码:2383 / 2387
页数:5
相关论文
共 19 条
[1]  
[Anonymous], 1991, Handbook of Genetic Algorithms
[2]  
Bento P., 2016, P IEEE 84 VEH TECHN, P1
[3]  
Biswas S, 2021, 2021 SIXTH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET), P158, DOI [10.1109/WiSPNET51692.2021.9419450, 10.1109/WISPNET51692.2021.9419450]
[4]   Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation [J].
Bjornson, Emil ;
Sanguinetti, Luca .
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2020, 19 (01) :77-90
[5]   Bi-Objective Power Optimization of Radio Stripe Uplink Communications [J].
Conceicao, Filipe ;
Gomes, Marco ;
Silva, Vitor ;
Dinis, Rui ;
Antunes, Carlos Henggeler .
ELECTRONICS, 2022, 11 (06)
[6]   Max-Min Fairness Optimization in Uplink Cell-Free Massive MIMO Using Meta-Heuristics [J].
Conceicao, Filipe ;
Antunes, Carlos Henggeler ;
Gomes, Marco ;
Silva, Vitor ;
Dinis, Rui .
IEEE TRANSACTIONS ON COMMUNICATIONS, 2022, 70 (03) :1792-1807
[7]  
Doan T., 2018, EAI Endorsed Trans. Ind. Netw. Intell. Syst.
[8]   On the Total Energy Efficiency of Cell-Free Massive MIMO [J].
Hien Quoc Ngo ;
Le-Nam Tran ;
Duong, Trung Q. ;
Matthaiou, Michail ;
Larsson, Erik G. .
IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING, 2018, 2 (01) :25-39
[9]   Effective Channel Gain-Based Access Point Selection in Cell-Free Massive MIMO Systems [J].
Hieu Trong Dao ;
Kim, Sunghwan .
IEEE ACCESS, 2020, 8 :108127-108132
[10]   Ubiquitous cell-free Massive MIMO communications [J].
Interdonato, Giovanni ;
Bjornson, Emil ;
Hien Quoc Ngo ;
Frenger, Pal ;
Larsson, Erik G. .
EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2019, 2019 (1)