Multichannel Relay assisted NOMA-ALOHA with Reinforcement Learning based Random Access

© 2023, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/VTC2023-Spring57618.2023.10200766We investigate multichannel relay assisted non-orthogonal multiple access (NOMA) in slotted ALOHA systems, where each user randomly accesses one of different channel slots and different transmit power for uplink transmissions over two-hop links, to and from the relay. By using multi-agent reinforcement learning, we propose greedy and non-greedy random access methods so that each user can learn its best strategies of random access over multiple relay slots. Random collisions and fading over the relay slots are both considered. The behaviors of relay-aided NOMA-ALOHA strategies are evaluated with the simulation. It is shown that the greedy method outperforms the non-greedy method in terms of average success rate. For deployment of relay, the greedy method benefits in improving transmission reliability under the symmetric relay channels (between the two-hop links) compared to asymmetric channels. Thus, it is interpreted that the proposed greedy method is more promising to the NOMA-ALOHA systems under a symmetric multichannel relay

Stochastic Geometry-Based Throughput Analysis of User-Specific Power-Level-Constrained GF-NOMA

Hirai T., Ueda Y., Wakamiya N.. Stochastic Geometry-Based Throughput Analysis of User-Specific Power-Level-Constrained GF-NOMA. IEEE Internet of Things Journal, (2024); https://doi.org/10.1109/JIOT.2024.3409698.This paper proposes a stochastic geometry-based analytical framework for the throughput of the grant-free power-domain non-orthogonal multiple access (GF-NOMA) with user-specific constraints of selectable power levels and analyzes the achievable throughput. Our analytical framework uses stochastic geometry to reflect selectable power levels constrained by the maximum transmission power and channel of each user to an inhomogeneous offered load per level. This key idea enables our framework to analyze the throughput bounded by the geographical user distribution and derive a suitable selection strategy of power levels under the constraint more accurately than the existing models. Our analytical results showed that our framework analyzed the throughput with only an analysis error of 0.1% compared with the Monte Carlo simulations, although the existing model overestimated 58% higher throughput. By using the proposed analytical model, our results presented decreasing the achievable throughput with increasing the coverage range. This paper also proposes a heuristic method based on our proposed analytical model to derive a suitable selection strategy of power levels. Our results highlight that the derived selection strategy on our analytical framework achieved 20 higher throughput than the baseline strategy, where each user randomly selects a power level under the power level constraint

Link-Layer Rate of Multiple Access Technologies with Short-Packet Communications for uRLLC

Mission-critical applications such as autonomous vehicles, tactile Internet, and factory automation require seamless connectivity with stringent requirements of latency and reliability. These futuristic applications are supported with the service class of ultra reliable and low-latency communications (uRLLC). In this thesis, the performance of core enablers of the uRLLC, non-orthogonal multiple access (NOMA), and NOMA-random access (NOMA-RA) in conjunction with the short-packet communications regime is investigated. More specifically, the achievable effective capacity (EC) of two-user and multi-user NOMA and conditional throughput of the NOMA-RA with short-packet communications are derived. A closed-form expressions for the EC of two-user NOMA network in finite blocklength regime (short-packet communication) is derived, while considering transmissions over Rayleigh fading channels and adopting a practical path-loss model. While considering the multi-user NOMA network, the total EC of two-user NOMA subsets is derived, which shows that the NOMA set with users having distinct channel conditions achieve maximum aggregate EC. The comparison of link-layer rate of NOMA and orthogonal multiple access (OMA) shows that OMA with short-packet communications outperformed the NOMA at low SNR (20dB). However, at high SNR region (from 20dB to 40dB), the two-user NOMA performs much better than OMA. To further investigate the impact of the channel conditions on the link-layer rate of NOMA and OMA, the simulation results with generalized fading model, i.e., Nakagami-m are also presented. The NOMA-RA with short-packet communications is also regarded as the core enabler of uRLLC. How the NOMA-RA with short-packet communications access the link-layer resources is investigated in detail. The conditional throughput of NOMA-RA is derived and compared with the conventional multiple access scheme. It is clear that NOMA-RA with optimal access probability region (from 0.05 to 0.1) shows maximum performance. Finally, the thesis is concluded with future work, and impact of this research on the industrial practice are also highlighted

Power-Level-Design-Aware Scalable Framework for Throughput Analysis of GF-NOMA in mMTC

Hirai T., Oda R., Wakamiya N.. Power-Level-Design-Aware Scalable Framework for Throughput Analysis of GF-NOMA in mMTC. IEEE Internet of Things Journal , (2024); https://doi.org/10.1109/JIOT.2024.3400996.This paper proposes a scalable framework to analyze the throughput of the grant-free power-domain nonorthogonal multiple access (GF-NOMA) and presents the achievable performance in the optimized offered load at each power level (called per-level offered load) by using our framework. Our analytical model reflects packet errors caused by power collisions, characterized by GF-NOMA, based on the power level design guaranteeing the required signal-to-interference-and-noise ratio (SINR). This key idea enables analyzing the throughput of a large-scale GF-NOMA system more accurately than the existing analytical models. Also, this key idea enables optimizing the per-level offered load rather than a uniform one in typical optimization problems related to the throughput: the throughput maximization or energy minimization problem with a throughput condition. Our analytical results highlight some key insights into designing future access control methods in GF-NOMA. First, our analytical model achieves an approximation error of only 0.4% for the exact throughput obtained by the exhaustive search at five power levels; the existing analytical model provides an approximation error of 25%. Next, our proposed framework highlights that the optimal per-level offered load restrictively improves the throughput above the optimally uniform per-level offered load. Finally, our proposed framework discovers a 27 more energy-efficient per-level offered load than the existing framework at five power levels while providing higher throughput than the optimally uniform per-level offered load

Dynamics Spectrum Sharing Environment Using Deep Learning Techniques

The recent fast expansion of mobile communication services has resulted in a scarcity of spectrum resources. The challenge of multidimensional resource allocation in cognitive radio systems is addressed in this work. Complicated and dynamic Spectrum Sharing SS systems might be vulnerable to a variety of possible security and privacy vulnerabilities, necessitating protection techniques that are adaptable, dependable, and scalable. Methods based on machine learning (ML) have repeatedly been proposed to overcome these challenges. We present a complete assessment of the current progress of ML-based SS approaches, the most crucial security challenges, and the accompanying protection mechanisms in this paper. We develop cutting-edge methodologies for improving the performance of SS communication systems in a variety of critical areas, such as ML-based cognitive radio networks (CRNs), ML-based database assisted SS networks, ML-based LTE-U networks, ML-based ambient backscatter networks, and other ML-based SS solutions. The results of the simulation trials show that the suggested strategy may successfully boost the user's incentive while reducing collisions. In terms of reward, the suggested strategy beats opportunistic multichannel ALOHA by around 10% and 30%, respectively, for the single SU and multi-SU scenarios.&nbsp

SINR-Aware Deep Reinforcement Learning for Distributed Dynamic Channel Allocation in Cognitive Interference Networks

We consider the problem of dynamic channel allocation (DCA) in cognitive communication networks with the goal of maximizing a global signal-to-interference-plus-noise ratio (SINR) measure under a specified target quality of service (QoS)-SINR for each network. The shared bandwidth is partitioned into K channels with frequency separation. In contrast to the majority of existing studies that assume perfect orthogonality or a one- to-one user-channel allocation mapping, this paper focuses on real-world systems experiencing inter-carrier interference (ICI) and channel reuse by multiple large-scale networks. This realistic scenario significantly increases the problem dimension, rendering existing algorithms inefficient. We propose a novel multi-agent reinforcement learning (RL) framework for distributed DCA, named Channel Allocation RL To Overlapped Networks (CARLTON). The CARLTON framework is based on the Centralized Training with Decentralized Execution (CTDE) paradigm, utilizing the DeepMellow value-based RL algorithm. To ensure robust performance in the interference-laden environment we address, CARLTON employs a low-dimensional representation of observations, generating a QoS-type measure while maximizing a global SINR measure and ensuring the target QoS-SINR for each network. Our results demonstrate exceptional performance and robust generalization, showcasing superior efficiency compared to alternative state-of-the-art methods, while achieving a marginally diminished performance relative to a fully centralized approach