Spectrum Slicing for Multiple Access Channels with Heterogeneous Services

Abstract

Wireless mobile networks from the fifth generation (5G) and beyond serve as platforms for flexible support of heterogeneous traffic types with diverse performance requirements. In particular, the broadband services aim for the traditional rate optimization, while the time-sensitive services aim for the optimization of latency and reliability, and some novel metrics such as Age of Information (AoI). In such settings, the key question is the one of spectrum slicing: how these services share the same chunk of available spectrum while meeting the heterogeneous requirements. In this work we investigated the two canonical frameworks for spectrum sharing, Orthogonal Multiple Access (OMA) and Non-Orthogonal Multiple Access (NOMA), in a simple, but insightful setup with a single time-slotted shared frequency channel, involving one broadband user, aiming to maximize throughput and using packet-level coding to protect its transmissions from noise and interference, and several intermittent users, aiming to either to improve their latency-reliability performance or to minimize their AoI. We analytically assessed the performances of Time Division Multiple Access (TDMA) and ALOHA-based schemes in both OMA and NOMA frameworks by deriving their Pareto regions and the corresponding optimal values of their parameters. Our results show that NOMA can outperform traditional OMA in latency-reliability oriented systems in most conditions, but OMA performs slightly better in age-oriented systems.

RAN Slicing Performance Tradeoffs: Timing Versus Throughput Requirements

“…The success probability for user 1 is equal to that in TDMA, given by (15). The throughput of user 1 can be computed by setting T int → ∞ in (16), which gives:…”

“…The same goal can be accomplished by controlling the rate of eMBB users, lowering their decoding threshold depending on the channel of the coupled mMTC user [15]. To the best of our knowledge, this and our own previous works [16], [17] are the first to consider coding, exploiting redundant information to decode collided packets instead of just the properties of the wireless channel.…”

“…However, the apparent trade-off between efficiency and timeliness of the slicing schemes is particularly relevant in this scenario. In our previous work, we derived the performance trade-offs with heterogeneous services with TDMA and NOMA with packetlevel coding in a simplified collision channel model, which provides conservative results for NOMA [16], [17]. Some results with capture, obtained by simulation, were provided in [17], which served as one of the main motivations for this study, as these illustrated the potential gains of NOMA.…”

“…• We analyze the joint use of OMA or NOMA with packet-level coding, which can significantly improve the performance of SIC by allowing the receiver to recover undecipherable packets after decoding a data block; • We analyze the operating regions and trade-offs of OMA and NOMA with a realistic channel model that includes the capture effect; • We show that the inclusion of capture gives rise to fundamental differences in performance in comparison to the erasure channel-based analyses from our previous work [16], [17];…”