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

Machine Learning in Communication Systems and Networks

© 2024 The authors. Licensee MDPI, Basel, Switzerland. This is an open access Editorial distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Peer reviewe

The Rhetoric of Ecological Food: Environmental and Technological God Terms in Blue Apron, Soylent, and Slow Food

The rhetoric and language surrounding technologically and environmentally oriented food systems illustrate that what and how we eat shapes the way we think about food, ecology, and the world. Analyzing the rhetoric of current food trends protects against the risk of reproducing unproductive dualisms between ecologically oriented technologists and environmentalists. To break this dualism, in this paper I will examine the rhetoric of ecological food underlying three cases of food innovation. I first examine Blue Apron to investigate the ways technology and environment intersect. I then examine Soylent to critique rhetorics of efficiency in food discourse. Lastly, I examine Slow Food to explore how rhetorics of nostalgia shape conceptions of environment and technology. Although Blue Apron and Soylent are companies with profit motivation, and Slow Food is a social movement, all reflect the reality that the term “sustainability” has been captured by various approaches to food that reproduce nature and technology as separate. The resulting effect is the reproduction of limited approaches and understandings of ecology and ecological food. The latter reflect the reality that older versions of nature as separate from technology no longer exist. Rather than focus on nature, the goal is ecology which does not ignore or distance itself from the presence of technology. Rather than a materialist analysis of food, which is a future goal, this paper instead analyzes the rhetoric behind food as a practical and potential starting point for recognizing and tracing the chaotic consequences of using terms such as “sustainable” in the pursuit of ecological food discourse

Nodes Number Estimation based on ML for Multi-operator Unlicensed Band Sharing to Extend Indoor Connectivity

© 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/wcnc55385.2023.10118807Due to ever-increasing data and resource-hungry applications, the needs of new spectrum by mobile networks keep increasing. Unlicensed spectrum is still expected to play a crucial part in meeting the capacity demand for future mobile networks. But if this will be a reality, fair coexistence attained via practical and efficient channel access procedures would be necessary. In designing such channel access schemes, awareness of the number of nodes contending for the channel resource can be strategic. This paper investigates a node number estimation approach using machine learning (ML) techniques. When multiple nodes access the same unlicensed channel, varying idle-time can be associated to a statistical distribution. In this paper, a statistical distribution of the Idle-time slots over the channel are used to characterise and analyse the channel contention based on the number of nodes. Three ML model based approaches are evaluated and the results confirm that the proposed solution’s viability but also reveal the best performing ML technique for the task of node number estimations

Contextual Multi-Armed Bandit based Beam Allocation in mmWave V2X Communication under Blockage

© 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-Spring57618.2023.10200248Due to its low latency and high data rates support, mmWave communication has been an important player for vehicular communication. However, this carries some disadvantages such as lower transmission distances and inability to transmit through obstacles. This work presents a Contextual Multi-Armed Bandit Algorithm based beam selection to improve connection stability in next generation communications for vehicular networks. The algorithm, through machine learning (ML), learns about the mobility contexts of the vehicles (location and route) and helps the base station make decisions on which of its beam sectors will provide connection to a vehicle. In addition, the proposed algorithm also smartly extends, via relay vehicles, beam coverage to outage vehicles which are either in NLOS condition due to blockages or not served any available beam. Through a set of experiments on the city map, the effectiveness of the algorithm is demonstrated, and the best possible solution is presented

Practical Spectrum Aggregation for Secondary Networks with Imperfect Sensing

We investigate a collision-sensitive secondary network that intends to opportunistically aggregate and utilize spectrum of a primary network to achieve higher data rates. In opportunistic spectrum access with imperfect sensing of idle primary spectrum, secondary transmission can collide with primary transmission. When the secondary network aggregates more channels in the presence of the imperfect sensing, collisions could occur more often, limiting the performance obtained by spectrum aggregation. In this context, we aim to address a fundamental query, that is, how much spectrum aggregation is worthy with imperfect sensing. For collision occurrence, we focus on two different types of collision: one is imposed by asynchronous transmission; and the other by imperfect spectrum sensing. The collision probability expression has been derived in closed-form with various secondary network parameters: primary traffic load, secondary user transmission parameters, spectrum sensing errors, and the number of aggregated sub-channels. In addition, the impact of spectrum aggregation on data rate is analysed under the constraint of collision probability. Then, we solve an optimal spectrum aggregation problem and propose the dynamic spectrum aggregation approach to increase the data rate subject to practical collision constraints. Our simulation results show clearly that the proposed approach outperforms the benchmark that passively aggregates sub-channels with lack of collision awareness

Practical Spectrum Aggregation for Secondary Networks with Imperfect Sensing

We investigate a collision-sensitive secondary network that intends to opportunistically aggregate and utilize spectrum of a primary network to achieve higher data rates. In opportunistic spectrum access with imperfect sensing of idle primary spectrum, secondary transmission can collide with primary transmission. When the secondary network aggregates more channels in the presence of the imperfect sensing, collisions could occur more often, limiting the performance obtained by spectrum aggregation. In this context, we aim to address a fundamental query, that is, how much spectrum aggregation is worthy with imperfect sensing. For collision occurrence, we focus on two different types of collision: one is imposed by asynchronous transmission; and the other by imperfect spectrum sensing. The collision probability expression has been derived in closed-form with various secondary network parameters: primary traffic load, secondary user transmission parameters, spectrum sensing errors, and the number of aggregated sub-channels. In addition, the impact of spectrum aggregation on data rate is analysed under the constraint of collision probability. Then, we solve an optimal spectrum aggregation problem and propose the dynamic spectrum aggregation approach to increase the data rate subject to practical collision constraints. Our simulation results show clearly that the proposed approach outperforms the benchmark that passively aggregates sub-channels with lack of collision awareness

Multi-Agent Context Learning Strategy for Interference-Aware Beam Allocation in mmWave Vehicular Communications

© 2024, 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/TITS.2024.3351488Millimeter wave (mmWave) has been recognized as one of key technologies for 5G and beyond networks due to its potential to enhance channel bandwidth and network capacity. The use of mmWave for various applications including vehicular communications has been extensively discussed. However, applying mmWave to vehicular communications faces challenges of high mobility nodes and narrow coverage along the mmWave beams. Due to high mobility in dense networks, overlapping beams can cause strong interference which leads to performance degradation. As a remedy, beam switching capability in mmWave can be utilized. Then, frequent beam switching and cell change become inevitable to manage interference, which increase computational and signalling complexity. In order to deal with the complexity in interference control, we develop a new strategy called Multi-Agent Context Learning (MACOL), which utilizes Contextual Bandit to manage interference while allocating mmWave beams to serve vehicles in the network. Our approach demonstrates that by leveraging knowledge of neighbouring beam status, the machine learning agent can identify and avoid potential interfering transmissions to other ongoing transmissions. Furthermore, we show that even under heavy traffic loads, our proposed MACOL strategy is able to maintain low interference levels at around 10%.Peer reviewe