Improving Fairness for Cell-Free Massive MIMO Through Interference-Aware Massive Access

Chen, Shuaifei; Zhang, Jiayi; Björnson, Emil; Ai, Bo

doi:10.1109/tvt.2022.3227408

Cited by 11 publications

(6 citation statements)

References 15 publications

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“…In particular, it is known that for A max sufficiently large the time-averaged service rates generated by the algorithm (i.e., 1 t ř t τ "1 µ k pτ q for large t) approximate the optimal throughput rate point solution of (8) within a gap Op1{V q, while the time-averaged sum queue backlog grows as OpV q. 6…”

Section: A Rate Allocationmentioning

confidence: 99%

“…2]. Recognizing that the placement of "more RUs than UEs" is hardly justifiable from an operator deployment cost viewpoint, more recent works have considered a more realistic RU/UE density regime L ă K ă LM with M ą 1 (e.g., see [7], [15]- [17], [19]). In these works, the K UEs are all simultaneously active and the system performance is studied in terms of the per-user ergodic rates (e.g., see [6], [7], [15]- [17]).…”

Section: A Motivationmentioning

confidence: 99%

“…We also notice that well-known problems such as pilot allocation and user-centric cluster formation have been treated in the current literature in the assumption that all users are active all the time [7], [16], [17], [19]. In contrast, in the presence of dynamic scheduling, an important question is whether pilot assignment and cluster formation should be performed at each scheduling decision (dynamic reassignment) or once for all to all users independently of the scheduling decisions (fixed assignment).…”

Section: B Novelty and Contributionsmentioning

confidence: 99%

“…1 Due to the distribution of RUs across the network area, cell-free massive MIMO is expected to serve all UEs with approximately the same quality of service. Unfortunately, this is not easy to achieve, even with efforts to make the network more fair [6]. While early works on cell-free massive MIMO assumed L ą K [4, Ch.…”

Section: Introductionmentioning

confidence: 99%

“…2] leading to overall large UE data rates, recent works considered the more realistic UE density regime K ą L, where K is comparable to M L (i.e., in the same order of magnitude) taking into account multi-antenna RUs. This regime yields a relatively unfair distribution of the UE data rates [6], [7]. 1 The set of integers from 1 to N is denoted by rN s.…”

Section: Introductionmentioning

confidence: 99%

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Fairness Scheduling in Dense User-Centric Cell-Free Massive MIMO Networks

Göttsch

Osawa

Ohseki

et al. 2022

2022 56th Asilomar Conference on Signals, Systems, and Computers

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We consider fairness scheduling in a user-centric cell-free massive MIMO network, where L remote radio units, each with M antennas, serve K « LM user equipments (UEs). Recent results show that the maximum network sum throughput is achieved where Kact « LM 2 UEs are simultaneously active in any given time-frequency slots. However, the number of users K in the network is usually much larger. This requires that users are scheduled over the time-frequency resource and achieve a certain throughput rate as an average over the slots. We impose throughput fairness among UEs with a scheduling approach aiming to maximize a concave component-wise non-decreasing network utility function of the per-user throughput rates. In cell-free user-centric networks, the pilot and cluster assignment is usually done for a given set of active users. Combined with fairness scheduling, this requires pilot and cluster reassignment at each scheduling slot, involving an enormous overhead of control signaling exchange between network entities. We propose a fixed pilot and cluster assignment scheme (independent of the scheduling decisions), which outperforms the baseline method in terms of UE throughput, while requiring much less control information exchange between network entities.

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Section: A Rate Allocationmentioning

confidence: 99%