User Scheduling and Antenna Topology in Dense Massive MIMO Networks: An Experimental Study

Abstract

A massive MIMO network can serve ten's of users simultaneously. However, in dense scenarios the users are potentially closely-spaced, potentially resulting in substantial inter-user interference. Scheduling can overcome this by selecting the users that lead to the highest combined spectral efficiency. As scheduling comes with a significant pilot overhead, an alternative strategy could minimize user correlation by distributing the antenna elements in space. In this paper, we propose a comprehensive system study including antenna topology and distribution, user scheduling and pilot overhead reduction. Our user scheduling and pilot reduction algorithms are evaluated using system level simulations relying on indoor line-of-sight channel measurements from a 64 antenna base station at 2.61GHz. To have a thorough evaluation of the proposed algorithm, we consider four different antenna topologies, including co-located and distributed placement of the base station arrays. Our evaluation shows that in a conference room with 64 densely deployed users, our proposed low complexity algorithm can improve the spectral efficiency by at least 14% compared to random user selection with the best antenna distribution strategy. Finally, our results show that by relying on channel hardening, we reduce the pilot overhead by 3.2x.

Low-Complexity Distance-Based Scheduling for Multi-User XL-MIMO Systems

“…III. USER SCHEDULING WITH IMPERFECT CSI It is clear from (6) that the system performance strongly depends on the determination of the user set S. While the consideration of instantaneous CSI fully available at the BS is a typical assumption in the literature [10,11,15,16], channel uncertainties due to channel aging and imperfect estimation also impact on this choice [8]. Besides, the cost of channel training reduces the achievable SE as a pre-log factor in (5), an issue usually neglected in the related literature.…”

“…Similarly, the complexity of the precoding design and the selection of the optimal set of users for transmission grows with the problem dimension (i.e., antennas and users) [5,6]. Besides, precise channel characterization beyond simplified line-of-sight (LoS) and one-ring approaches conventionally assumed in this context [2,7] are required to realistically predict network performance [8]. Finally, although the assumption of perfect Channel State Information (CSI) availability is of widespread use in the XL-MIMO literature Manuscript received Feb. XX, 2023; revised XX, 2023.…”

Fading in Reflective and Heavily Shadowed Industrial Environments With Large Antenna Arrays

“…To combat shadowing, a distributed deployment of the many antennas provides an interesting potential, resulting in that not all antennas are blocked simultaneously. Various distributed configurations (ranging from semi-distributed to fully distributed) have been investigated in different indoor scenarios [20]- [23] and their performance has been compared to the co-located equivalent [20], [21].…”

A Deep Reinforcement Learning-Based Resource Scheduler for Massive MIMO Networks

“…The large complexity associated with solving an ILP, when the number of users and resource blocks is large, prohibits designing optimal yet computationally feasible schedulers that can work in the time-stringent 5G and beyond standards. There is a large body of work [2]- [5] that design heuristics or approximation algorithms with low complexity to optimize the spectral efficiency of the networks. However, they either do not evaluate fairness at all or demonstrate poor fairness.…”