A Framework for Radio Resource Allocation and SDMA Grouping in Massive MIMO Systems

Abstract

This work proposes a framework for multiuser massive Multiple Input Multiple Output (MIMO) systems which is composed of three partsclustering, grouping, and scheduling -and aims at maximizing the total system data rate considering Quality of Service (QoS) constraints. We firstly use a clustering algorithm to create clusters of spatially correlated Mobile Stations (MSs). Secondly, in the grouping part, we select a set of Space-Division Multiple Access (SDMA) groups from each cluster. These groups are used as candidate groups to receive Scheduling Unit (SU) in the scheduling part. In order to compose a group, we employ a metric that takes into account the trade-off between the spatial channel correlation and channel gain of MSs. In this context, it is proposed a suboptimal solution to avoid the high complexity required by the optimal solution. Thirdly and finally, we use the candidate SDMA groups from the grouping part to solve the data rate maximization problem considering QoS requirements. The scheduling part can be solved by our proposed optimal solution based on Branch and Bound (BB). However, since it has high computational complexity, we propose a suboptimal scheduling algorithm that presents a reduced complexity. In the simulation results, we evaluate the performance of both optimal and suboptimal solutions, as well as an adaptation of the Joint Satisfaction Maximization (JSM) scheduler to a massive MIMO scenario. Although the suboptimal solution presents a performance loss compared to the optimal one, it is more suitable for practical settings as it is able to obtain a good performance-complexity trade-off. Furthermore, we show that the choice of a suitable trade-off between the spatial channel correlation and channel gain improves the system performance. Finally, for a low number of available SDMA groups, the suboptimal solution presents near optimal outage and a throughput loss of only 10% in comparison to the high-complexity optimal solution while it outperforms the JSM solution in terms of outage and system throughput.

Reinforcement Learning-Based UAVs Resource Allocation for Integrated Sensing and Communication (ISAC) System

“…With the development of artificial intelligence in the fields of communication and radar, reinforcement learning algorithms can be introduced. In the fields of radar and communication, modulation identification, signal detection, radar target identification [20][21][22][23][24], cluster cooperative control [25][26][27][28][29][30] and resource allocation [31][32][33] are included. Additionally, policy-based reinforcement learning and action-critical deep deterministic policy gradient algorithms are used to allocate the energy resources of cellular network reasonably [34], and the resource allocation method for radar detection [35] is given.…”

IRDB: Towards enjoying spatial, rate, and beam‐channel diversity in wireless networks for multimedia applications

“…On the other hand, the advent of directional antenna technology has led to the space division multiple access (SDMA), which increase the capacity of IEEE 802.11-based networks with spatially separating the interfering transmissions. 10,11 The transmission power of a directional antenna is restricted to a specific angle. Therefore, the interference of each transmission is effectively reduced to the main-lobe of its antenna.…”

“…Thus, this technology leads to an increase in the number of simultaneous transmissions, which occupies a smaller portion of the scheduling frame. On the other hand, the advent of directional antenna technology has led to the space division multiple access (SDMA), which increase the capacity of IEEE 802.11‐based networks with spatially separating the interfering transmissions 10,11 . The transmission power of a directional antenna is restricted to a specific angle.…”

“…Suppose DT(10, 1, 48) has already been added to the routing tree. In other words, the red links (10,20) (1,48 Mbps) and (10, 18) (1,48 Mbps) are considered as the links of the current tree. IRDB greedily updates the CND set as follows: the transmission of node 10 on channels 5 and 6, the transmission of node 18 on channels 1, 2, and 4, and the transmission of node 20 on channels 1, 3, and 4 at different data rates.…”

“…The best direction of the available beam-channels for nodes 10, 18, and 20 at different data rates are shown in Figures 4 to 6, respectively. For example, in Figure 4A, DT (10,5,6) and DT (10,5,9) are configured in such a way that cover nodes 2, 8, 9, 14, and 15. However, they cover fewer nodes in other directions.…”