Maximizing Downlink User Connection Density in NOMA-aided NB-IoT Networks Through a Graph Matching Approach

Abstract

We develop a framework for maximizing the number of transmitted packets for devices in a Narrowband Internet of Things (NB-IoT) network using non-orthogonal multiple access (NOMA) in the downlink. The base station (BS) chooses one of the multiple available physical resource blocks (PRBs) that are well separated in frequency for a device, giving them the advantage of exploiting frequency diversity. The scheduling strategy focuses on the two-fold problem involving efficient device clustering and optimum power allocation. This problem is a mixed-integer non-convex problem. We propose a bipartite graph matching approach, termed minimum weight full matching with pruning (MWFMP), to address the problem over multiple PRBs and solve it under the quality-of-service (QoS), allowable PRB, power budget, and interference constraints. Additionally, we provide a comparison with a greedy heuristic, the multi-PRB stratified device allocation (MPSDA), where we extend our previous work for a single PRB connectivity problem. Furthermore, we compare our algorithms to orthogonal multiple access (OMA) scheduling, which is prevalent in legacy LTE networks. We show that our algorithms steadily outperform the connectivity performance offered by OMA.

Connection Throughput Maximization for Grant-Based NOMA Massive IoT with Graph Matching

“…Furthermore, CTMBM with M = 3 connects 59% more devices than the We highlight the performance of CTMBM when considering 2 different service classes with M = 2 in Figure 5. Here, the plots in red and blue are priority-based CTMBM (P-CTMBM), where edge weights are assigned using (9). The target data rate is fixed as R = 26 kbps.…”

“…Motivated by the current state-of-the-art solutions, we propose a bi-partite graph-matching framework that performs joint user-pairing and power allocation to users, utilizing NOMA to enhance the uplink connection throughput, which is the total number of devices in the system that achieve their target data rate under the power budget, transmission deadline and successive interference cancellation (SIC) constraint. In our previous work [9], we addressed the problem of connectivity maximization without any deadlines in the downlink for NB-IoT networks using a similar graph-matching approach with the key distinction of per-PRB power budget instead of the per-device power budget which is important in the uplink. We believe that the metric of user connection throughput is much more relevant for the presented system model as opposed to other metrics like sum-data rate and user access delay as studied in the literature.…”