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User-Centric Clustering Under Fairness Scheduling in Cell-Free Massive MIMO
Information Theory · Submitted 15 May 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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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We consider fairness scheduling in a user-centric cell-free massive MIMO network, where $L$ remote radio units, each with $M$ antennas, serve $K_{\rm tot} \approx LM$ user equipments (UEs). Recent results show that the maximum network sum throughput is achieved where $K_{\rm act} \approx \frac{LM}{2}$ UEs are simultaneously active in any given time-frequency slots. However, the number of users $K_{\rm tot}$ 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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | We consider fairness scheduling in a user-centric cell-free massive MIMO network, where $L$ remote radio units, each with $M$ antennas, serve $K_{\rm tot} \approx LM$ user equipments (UEs). | background 0.96 | 0.20 | 0.02 | asserted 0.91 |
| s1 | Recent results show that the maximum network sum throughput is achieved where $K_{\rm act} \approx \frac{LM}{2}$ UEs are simultaneously active in any given time-frequency slots. | background 0.96 | 0.06 | 0.05 | asserted 1.00 |
| s2 | However, the number of users $K_{\rm tot}$ in the network is usually much larger. | background 0.88 | 0.11 | 0.02 | asserted 0.94 |
| s3 | This requires that users are scheduled over the time-frequency resource and achieve a certain throughput rate as an average over the slots. | background 0.90 | 0.10 | 0.03 | asserted 0.96 |
| s4 | 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. | method 0.67 | 0.78 | 0.08 | asserted 0.96 |
| s5 | In cell-free user-centric networks, the pilot and cluster assignment is usually done for a given set of active users. | background 0.99 | 0.07 | 0.02 | asserted 0.95 |
| s6 | 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. | background 0.66 | 0.15 | 0.05 | asserted 0.99 |
| s7 | 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. | contribution 0.97 | 0.93 | 0.87 | asserted 1.00 |
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The paper
Original abstract
We consider fairness scheduling in a user-centric cell-free massive MIMO network, where $L$ remote radio units, each with $M$ antennas, serve $K_{\rm tot} \approx LM$ user equipments (UEs). Recent results show that the maximum network sum throughput is achieved where $K_{\rm act} \approx \frac{LM}{2}$ UEs are simultaneously active in any given time-frequency slots. However, the number of users $K_{\rm tot}$ 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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What it cites · 6 sentences without a confident label
Builds on 1 paper
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We build on the system proposed in [ref] , which considers $>$ UEs, and sets $$ as the number of active users that (approximately) maximizes the network total SE.
Fairness Scheduling in Dense User-Centric Cell-Free Massive MIMO Networks · Related Literature · 0.85
Uses the method or tool of 2 papers
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Given the selected set $(t)$, UL pilots and user-centric clusters are assigned to the active UEs following the semi-overloaded pilot assignment method from [ref] , where an RU may assign the same pilot sequence to multiple UEs provided that the channel subspaces of the UEs are nearly mutually orthogonal, such that accurate channel estimation is possible (negligible mutual pilot contamination, using the decontamination method of [ref] ).
Overloaded Pilot Assignment with Pilot Decontamination for Cell-Free Systems · Pilot and Cluster Reallocation Scheme · 1.00
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Hence, the optimization of $r_k$ is immediate and yields [ref] [equation] Since, as said before, the statistics of $_k( _k (t), (t) )$ for a UE $k$ are generally extremely hard to obtain and depend on the scheduling policy itself, here we consider the localized adaptive scheme proposed in [ref] , where each user $k$ collects a sliding window of $N$ past samples of the instantaneous mutual information and optimizes its transmission rate $r_k$ using the empirical complementary CDF based on these samples.[As in [ref] , the allocated rates are initialized by a “start-up” phase consisting of $N_init$ time slots.
Fairness Scheduling in Dense User-Centric Cell-Free Massive MIMO Networks · Rate Allocation · 0.97
Uses data or values from 1 paper
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Since we consider a network like in [ref] , we choose $τ_p = 20$ and $=40$, the approximate optimal parameters according to the results in [ref] .
Fairness Scheduling in Dense User-Centric Cell-Free Massive MIMO Networks · Numerical Evaluations and Outlook · 0.92
Compares with 1 paper
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We proposed a fixed pilot and cluster assignment scheme under scheduling, which greatly reduces the communication overhead between UEs and RUs, while also achieving better performance compared to the scheme in [ref] .
Fairness Scheduling in Dense User-Centric Cell-Free Massive MIMO Networks · Concluding Remarks · 1.00
Cites as support 1 paper
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We focus on UL results, since by duality, the UL and downlink data rates and system performance are almost identical [ref] . =-1
UL-DL duality for cell-free massive MIMO with per-AP power and information constraints · Uplink Decoding with Partial Channel State Information · 0.96
Cites as background 1 paper
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Scheduling in cell-free massive MIMO has been considered in relatively few works [ref] in comparison with the very large number of papers considering the ergodic rate of a fixed set of “always active” users (see, e.g., [ref] and references therein).
Fairness Scheduling in Dense User-Centric Cell-Free Massive MIMO Networks · Related Literature · 0.88
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