Combined Beam Alignment and Power Allocation for NOMA-Empowered mmWave Communications

Attaoui, Wissal; Sabir, Essaïd

doi:10.1007/978-3-030-58008-7_7

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…• Strongly directional beams applied in millimeter-wave communications lead to correlated channels, which can degrade the performance of OMA schemes, thereby the need of NOMA [52].…”

Section: E Access Methods: Oma Vs Nomamentioning

confidence: 99%

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Initial Access & Beam Alignment for mmWave and Terahertz Communications

2022

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The initial access in Millimeter wave (mmWave) 5G communications is very challenging and time consuming. In general, mmWave and terahertz communications require the use of directional antennas to seek narrow beams. However, due to this directionality, many issues can impact the beam alignment between transmitter and receiver. In this paper, we present a comprehensive survey of beam alignment and initial access in mmWave and terahertz 5G/6G systems. First, we present a detailed overview of initial access methods, techniques, and beam management procedures. Then, we classify recent works related to beam alignment based on their objective functions (i.e., latency, power allocation, QoS, energy consumption, cost). We also highlight the beam alignment in terahertz 6G, where we find that deep learning and reconfigurable intelligent surface are the two protagonists that help to achieve fast beam alignment.INDEX TERMS 5G/6G, mmWave, terahertz, beamforming, initial access, beam alignment, beam steering, reconfigurable intelligent surface.

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“…• Strongly directional beams applied in millimeter-wave communications lead to correlated channels, which can degrade the performance of OMA schemes, thereby the need of NOMA [52].…”

Section: E Access Methods: Oma Vs Nomamentioning

confidence: 99%

“…In [68] and [52], authors use exhaustive search to solve the joint beam alignment and power allocation for 2 users in downlink mmWave NOMA 5G. The issue is formulated as an optimization approach aiming to maximize the sum rate subject to power allocation constraint.…”

Section: ) Beam Alignment and Power Allocationmentioning

confidence: 99%

Initial Access & Beam Alignment for mmWave and Terahertz Communications

2022

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“…In the physical layer, RL can be employed for link preservation, channel tracking, radio access association, on-demand beamforming, energy harvesting, modulation selection, radio identification, etc. [ 8 , 13 , 16 , 17 , 18 ].…”

Section: Related Workmentioning

confidence: 99%

D2D Mobile Relaying Meets NOMA—Part II: A Reinforcement Learning Perspective

Driouech

Sabir

Ghogho

et al. 2021

Sensors

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Structureless communications such as Device-to-Device (D2D) relaying are undeniably of paramount importance to improving the performance of today’s mobile networks. Such a communication paradigm requires a certain level of intelligence at the device level, thereby allowing it to interact with the environment and make proper decisions. However, decentralizing decision-making may induce paradoxical outcomes, resulting in a drop in performance, which sustains the design of self-organizing yet efficient systems. We propose that each device decides either to directly connect to the eNodeB or get access via another device through a D2D link. In the first part of this article, we describe a biform game framework to analyze the proposed self-organized system’s performance, under pure and mixed strategies. We use two reinforcement learning (RL) algorithms, enabling devices to self-organize and learn their pure/mixed equilibrium strategies in a fully distributed fashion. Decentralized RL algorithms are shown to play an important role in allowing devices to be self-organized and reach satisfactory performance with incomplete information or even under uncertainties. We point out through a simulation the importance of D2D relaying and assess how our learning schemes perform under slow/fast channel fading.

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“…It allows allocating one frequency channel to multiple users at the same time within the same cell either in the power domain or the code domain. Moreover, NOMA offers a number of advantages, including improved spectral efficiency, enhanced resource allocation, higher cell-edge throughput, and lower latency (no scheduling request from users to base station is required) [9][10][11].…”

Section: Our Contributionsmentioning

confidence: 99%

D2D Mobile Relaying Meets NOMA—Part I: A Biform Game Analysis

Driouech

Sabir

Ghogho

et al. 2021

Sensors

Self Cite

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Structureless communications such as Device-to-Device (D2D) relaying are undeniably of paramount importance to improving the performance of today’s mobile networks. Such a communication paradigm requires implementing a certain level of intelligence at device level, allowing to interact with the environment and select proper decisions. However, decentralizing decision making sometimes may induce some paradoxical outcomes resulting, therefore, in a performance drop, which sustains the design of self-organizing, yet efficient systems. Here, each device decides either to directly connect to the eNodeB or get access via another device through a D2D link. Given the set of active devices and the channel model, we derive the outage probability for both cellular link and D2D link, and compute the system throughput. We capture the device behavior using a biform game perspective. In the first part of this article, we analyze the pure and mixed Nash equilibria of the induced game where each device seeks to maximize its own throughput. Our framework allows us to analyse and predict the system’s performance. The second part of this article is devoted to implement two Reinforcement Learning (RL) algorithms enabling devices to self-organize themselves and learn their equilibrium pure/mixed strategies, in a fully distributed fashion. Simulation results show that offloading the network by means of D2D-relaying improves per device throughput. Moreover, detailed analysis on how the network parameters affect the global performance is provided.

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Combined Beam Alignment and Power Allocation for NOMA-Empowered mmWave Communications

Cited by 4 publications

References 18 publications

Initial Access & Beam Alignment for mmWave and Terahertz Communications

Initial Access & Beam Alignment for mmWave and Terahertz Communications

D2D Mobile Relaying Meets NOMA—Part II: A Reinforcement Learning Perspective

D2D Mobile Relaying Meets NOMA—Part I: A Biform Game Analysis

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