Energy Efficiency Optimization for Irregular Repetition Slotted ALOHA-Based Massive Access

Chen, Zhengchuan; Feng, Yifan; Tian, Zhong; Jia, Yunjian; Wang, Min; Quek, Tony Q. S.

doi:10.1109/lwc.2022.3151931

Cited by 10 publications

(2 citation statements)

References 13 publications

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“…For IR-SA systems, this paper focuses on the maximization of the offered traffic. The minimization of the age of information over collision channels can be found [2] [36] [37], and the maximization of device energy efficiency over 1-and 2-MPR channels has been studied [38] [39]. Interested readers are referred to these works and references cited therein for more detailed description.…”

Section: B Related Workmentioning

confidence: 99%

Irregular Repetition Slotted ALOHA Over Rayleigh Block Fading Channels: Bounds and Threshold Saturation via Spatial Coupling

Takahashi,

Song,

Kimura

et al. 2023

IEEE Access

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In irregular repetition slotted ALOHA (IR-SA) systems, a population of devices transmits their packets to an access point (AP) within a frame of slots. The AP decodes these packets by iterative processing between intra-and inter-slot successive interference cancellations. The average normalized offered traffic, as a performance metric, represents the number of packets transmitted per slot when the packet loss rate approaches zero. Such asymptotic types of traffic as the belief propagation (BP) threshold, the maximum a posteriori (MAP) threshold, and the converse bound of IR-SA systems have been analyzed over various channel models. However, over fading channels, the MAP threshold and the converse bound have not yet been investigated. This paper derives an MAP threshold and a converse bound of the systems over Rayleigh block fading channels. The derivations are based on two extrinsic information transfer (EXIT) curves, which are associated with two iterative density evolution equations to analyze the BP threshold of the IR-SA systems. First, since an open decoding tunnel exists in an EXIT chart, the sum of the two areas below two EXIT curves is smaller than the area of the entire domain. This provides the traffic's converse bound, which is tight. Second, a coincidence of the BP EXIT and MAP EXIT curves makes it possible to derive the traffic's MAP threshold. Third, a density evolution for a spatially-coupled scheme is formulated and gives a BP decoding threshold of the traffic. Numerical results show that the spatially-coupled scheme achieves a threshold saturation effect where the BP threshold approaches the MAP threshold.INDEX TERMS belief propagation (BP) threshold, extrinsic information transfer (EXIT) curve, maximum a posteriori (MAP) threshold, slotted ALOHA, spatial coupling

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Section: B Related Workmentioning

confidence: 99%

Irregular Repetition Slotted ALOHA Over Rayleigh Block Fading Channels: Bounds and Threshold Saturation via Spatial Coupling

Takahashi,

Song,

Kimura

et al. 2023

IEEE Access

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“…al. analysed the maximum reception rate and the transmit power of users [5], which helps to maximize energy efficiency. In [6], the authors proposed to reduce the number of useless replicas based on a tracking degree distribution control (TDDC) algorithm, which can reduce power waste.…”

Section: Introductionmentioning

confidence: 99%

DS-IRSA: A Deep Reinforcement Learning and Sensing Based IRSA

Hmedoush,

Gu,

Adjih

et al. 2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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One of the main difficulties to enable the future scaling of IoT networks is the issue of massive connectivity. Recently, Modern Random Access protocols have emerged as a promising solution to provide massive connections for IoT. One main protocol of this family is Irregular Repetition Slotted Aloha (IRSA), which can asymptotically reach the optimal throughput of 1 packet/slot. Despite this, the problem is not yet solved due to lower throughput in non-asymptotic cases with smaller frame sizes. In this paper, we propose a new variant of IRSA protocol named Deep-Learning and Sensing-based IRSA (DS-IRSA) to optimise the performance of IRSA in short frame IoTs, where a sensing phase is added before the transmission phase and users' actions in both phases are managed by a deep reinforcement learning (DRL) method. Our goal is to learn to interact and ultimately to learn a sensing protocol entirely through Deep Learning. In this way, active users can coordinate well with each other and the throughput of the whole system can be well improved. Simulation results show that our proposed scheme convergence quickly towards the optimal performance of almost 1 packet/slot for small frame sizes and with enough minislots and can achieve higher throughput in almost all cases.

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