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

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 « 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.

Dynamic Scheduling and Power Allocation with Random Arrival Rates in Dense User-Centric Scalable Cell-Free MIMO Networks

“…At time slot t, if H(t), estimated at the APs, and Q(t) are utilized, LM 2 UEs are scheduled in the Lyapunov optimization phase, and this set of UEs is denoted as K act,LO . If LM 2 UEs are served, it can maximize network throughput performance [13]. Among K act,LO , the set of UEs that the AP needs to serve is denoted as s ′ l , and it can be obtained as A l ∩ K act,LO .…”

“…The CPU utilizes it to compute the allocated rate to each UE. The allocated rate is calculated using the following formula [13]:…”

“…There has been some research on Lyapunov optimization [13][14][15][16][17][18]. The authors of [14] provided a comprehensive methodology for Lyapunov drift-plus-penalty optimization, aiming to stabilize the queue while minimizing the penalty function defined in the system.…”

“…Scheduling problems in CF networks have been discussed in previous papers [12,13,19,20], which conducted scheduling considering throughput fairness for each time slot in environments where the number of antennas is similar to the number of UEs. The authors of [13] considered active UEs to achieve maximum system performance. Similarly, in this paper, we adopt active UEs that can lead to maximum performance in a dense user-centric scalable cell-free environment.…”

“…In practice, due to the installation cost constraints of APs, a CF-MIMO network that exceeds the number of antennas is challenging. Additionally, it is expected that the number of UEs will grow at a faster rate than the number of antennas in the future [12,13]. Consequently, not all users can be scheduled in the same time slot.…”