Performance of Cell-Free Massive MIMO With Joint User Clustering and Access Point Selection

Wang, Rui; Shen, Min; Yun, He; Liu, Xiangyan

doi:10.1109/access.2021.3056051

Cited by 29 publications

(13 citation statements)

References 32 publications

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“…In this simulation, two antennas were installed in each AP, and it was assumed that there were L = 100, K = 60 in the area. In order to perform in a real environment and reduce computational complexity, the equal gain algorithm in [ 8 , 25 ] was applied as a power control algorithm. Among all cases, the CDF performance of the original cell-free massive MIMO (Full CF) outperformed the other cases.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System

Shin

Kim

et al. 2022

Sensors

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A cell-free massive multiple input multiple output (MIMO) system is an attractive network model that is in the spotlight in 5G and future communication systems. Despite numerous advantages, the cell-free massive MIMO system has a problem in that it is difficult to operate in reality due to its vast amount of calculation. The user-centric cell-free massive MIMO model has a more feasible and scalable benefit than the cell-free massive MIMO model. However, this model has the disadvantage that as the number of users in the area increases, there are users who do not receive the service. In this paper, the proposed scheme creates connections for unserved users under a user-centric scheme without additional access point (AP) installation and disconnection for existing users. A downlink user-centric cell-free massive MIMO system model in which the APs are connected to the central processing unit (CPU) and the APs and users are geographically distributed is considered. First, the downlink spectral efficiency formula is derived and applied to the user-centric cell-free massive MIMO system. Then, the proposed scheme and power control algorithm are applied to the derived formula. The simulation results show that the unserved users within the area disappear by using the proposed scheme, while the bit error rate (BER) performance and sum rate improve compared to the existing scheme. In addition, it is shown that the proposed scheme works well even with a very large number of users in the area, and a significant service performance improvement for the worst 10% of users and the overall improvement of per-user throughput for the bottom 70% of users are ensured.

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Section: Simulation Resultsmentioning

confidence: 99%

“…To clearly confirm the performance of the proposed algorithm, this paper adopts an equivalent gain power control algorithm as in [ 8 , 25 ] instead of an additional power control algorithm. In the user-centric cell-free massive MIMO model, the power control coefficient between AP l and user k can be written by

…”

Section: Proposed Scheme and Power Controlmentioning

confidence: 99%

Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System

Shin

Kim

et al. 2022

Sensors

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“…In light of that, a new fronthaul topology enabling APs to share a serial fronthaul link with a peruser limited fronthaul bandwidth was considered in [19]. Similarly a joint power allocation and AP selection problem for uplink TDD CF-mMIMO systems was tackled in [20].…”

Section: Introductionmentioning

confidence: 99%

Joint Access Configuration and Beamforming for Cell-Free Massive MIMO Systems With Dynamic TDD

et al. 2022

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We address the trade-off between system throughput and user equipment (UE) fairness in dynamic time division duplex (TDD) cell-free (CF)-massive multiple-input multiple-output (mMIMO) systems, developing to that end a joint access point (AP) access configuration, power allocation, and beamforming design scheme. Unlike state-of-the-art (SotA) methods, which mostly focus on sum-rate maximization or max-min worst case optimization, our design approach is to maximize the geometric mean of the UEs' throughput performance under a constrained transmit power, which is shown to yield an excellent compromise between system throughput and user fairness. The direct reformulation of the resulting optimization problem is, however, hard to solve due not only to the non-convexity of the objective function, but also to the binary constraint induced by the AP access configuration. We thus present an efficient (i.e., polynomial time complexity) solution for the problem via a combination of fractional programming (FP) and convex-concave procedure (CCP), assisted by a negative entropy regularizer that promotes a binary solution. Numerical simulations are offered to evaluate the throughput performance and fairness index of the proposed algorithm in comparison not only to conventional TDD-based solutions, but also to recent dynamic TDD SotA designs, which illustrate the effectiveness of the proposed approach over existing methodologies both in throughput and fairness.INDEX TERMS Cell-free massive multiple-input multiple-output, convex optimization, convex-concave procedure, dynamic time-division duplex, geometric mean, fractional programming.

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“…and access to the network with the highest utility value [9,10]. In addition, some literatures adopt mathematical models such as fuzzy logic [11][12][13], optimization calculation [14][15][16], neural network [17,18], game theory [19][20][21] to design access selection algorithms.…”

Section: Introductionmentioning

confidence: 99%

Multiattribute Access Selection Algorithm for Heterogeneous Wireless Networks Based on Fuzzy Network Attribute Values

et al. 2022

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An important feature of the wireless network scenario is that there are multi-radio access technologies in the same area, and the signal coverage of these networks overlaps each other, forming the heterogeneous wireless network area. Network selection algorithm is the key technology of heterogeneous wireless network. The common network selection algorithms are based on accurate network attribute values. However, due to the mobility of users, the interference of wireless signals and the fluctuation of network state, the network attributes obtained by the algorithms are often uncertain. To solve this problem, this paper designs a multi-attribute access selection approach based on the fuzzy network attributes. This approach calculates the network attribute values by interval hesitant fuzzy theory at first. Then, it calculates the subjective weights of network attribute values by the analytic hierarchy process and the objective weights of network attribute values by the entropy method. The integrated weights of subjective weights and objective weights are obtained by the method based on the longest geometric distance to the negative ideal solution. In the end, we calculate the scores of candidate networks by grey relational analysis based on the intuitionistic fuzzy decision matrix. The simulation shows that the algorithm proposed by this paper can select the most suitable network and reduce the number of handoffs under the environment of uncertain network attribute values.

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Performance of Cell-Free Massive MIMO With Joint User Clustering and Access Point Selection

Cited by 29 publications

References 32 publications

Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System

Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System

Joint Access Configuration and Beamforming for Cell-Free Massive MIMO Systems With Dynamic TDD

Multiattribute Access Selection Algorithm for Heterogeneous Wireless Networks Based on Fuzzy Network Attribute Values

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