Efficient Wireless Sensor Network for Radiation Detection in Nuclear Sites

Due to the severe damages of nuclear accidents, there is still an urgent need to develop efficient radiation detection wireless sensor networks (RDWSNs) that precisely monitor irregular radioactivity. It should take actions that mitigate the severe costs of accidental radiation leakage, especially around nuclear sites that are the main sources of electric power and a lot of health and industrial applications. Recently, leveraging machine learning (ML) algorithms to RDWSNs is a promising solution due to its several pros, such as online learning and self-decision making. This paper addresses a novel and efficient ML-based RDWSNs that utilizes millimeter waves (mmWaves) to cope with future networks requirements. Specifically, we leverage an online learning multi-armed bandit (MAB) algorithm called Thomson sampling (TS) to a 5G enabled RDWSN to efficiently forward the measured radiation levels of the distributed radiation sensors within the monitoring area. The utilized sensor nodes are lightweight smart radiation sensors that are mounted on mobile devices and measure radiations using software applications installed in these mobiles. Moreover, a battery aware TS (BA-TS) algorithm is proposed to forward the sensed radiation levels to fusion decision center efficiently. BA-TS reflects the remaining battery of each mobile device to prolong the network lifetime. Simulation results ensure the efficiency of the proposed BA-TS algorithm regards throughput and network lifetime over TS and exhaustive search method

Efficient Wireless Sensor Network for Radiation Detection in Nuclear Sites

Due to the severe damages of nuclear accidents, there is still an urgent need to develop efficient radiation detection wireless sensor networks (RDWSNs) that precisely monitor irregular radioactivity. It should take actions that mitigate the severe costs of accidental radiation leakage, especially around nuclear sites that are the main sources of electric power and a lot of health and industrial applications. Recently, leveraging machine learning (ML) algorithms to RDWSNs is a promising solution due to its several pros, such as online learning and self-decision making. This paper addresses a novel and efficient ML-based RDWSNs that utilizes millimeter waves (mmWaves) to cope with future networks requirements. Specifically, we leverage an online learning multi-armed bandit (MAB) algorithm called Thomson sampling (TS) to a 5G enabled RDWSN to efficiently forward the measured radiation levels of the distributed radiation sensors within the monitoring area. The utilized sensor nodes are lightweight smart radiation sensors that are mounted on mobile devices and measure radiations using software applications installed in these mobiles. Moreover, a battery aware TS (BA-TS) algorithm is proposed to forward the sensed radiation levels to fusion decision center efficiently. BA-TS reflects the remaining battery of each mobile device to prolong the network lifetime. Simulation results ensure the efficiency of the proposed BA-TS algorithm regards throughput and network lifetime over TS and exhaustive search method

Reconfigurable Intelligent Surface-Aided Millimetre Wave Communications Utilizing Two-Phase Minimax Optimal Stochastic Strategy Bandit

Millimetre wave (mm Wave) communications, that is, 30 to 300 GHz, have intermittent short-range transmissions, so the use of reconfigurable intelligent surface (RIS) seems to be a promising solution to extend its coverage. However, optimizing phase shifts (PSs) of both mm Wave base station (BS) and RIS to maximize the received spectral efficiency at the intended receiver seems challenging due to massive antenna elements usage. In this paper, an online learning approach is proposed to address this problem, where it is considered a two-phase multi-armed bandit (MAB) game. In the first phase, the PS vector of the mm Wave BS is adjusted, and based on it, the PS vector of the RIS is calibrated in the second phase and vice versa over the time horizon. The minimax optimal stochastic strategy(MOSS) MAB algorithm is utilized to implement the proposed two-phase MAB approach efficiently. Furthermore, to relax the problem of estimating the channel state information(CSI) of both mm Wave BS and RIS, codebook-based PSs are considered. Finally, numerical analysis confirms the superior performance of the proposed scheme against the optimal performance under different scenarios

Budgeted Thompson Sampling for IRS Enabled WiGig Relaying

Intelligent reconfigurable surface (IRS) is a competitive relaying technology to widen the WiGig coverage range, as it offers an effective means of addressing blocking issues. However, selecting the optimal IRS relay for maximum attainable data rate is a time-consuming process, as it requires WiGig beamforming training (BT) to tune the phase shifts (PSs) for WiGig base station (WGBS) and IRS relays. This paper proposes a self-learning-based budgeted Thomson sampling approach for IRS relay probing (BTS-IRS) to address this challenge. The BT time cost of probing the IRS relay is incorporated into the main BTS formula, where both payoff and cost posterior distributions are sampled separately, their ratio is estimated, and the arm/IRS relay with the highest ratio is decided. This enables the IRS relay to be chosen with the lowest BT time cost. Numerical results demonstrate the improved performance of the BTS-IRS relaying technique regarding BT time consumption/cost, spectral efficiency, and attainable data rate when compared to other benchmarks

Two-Stage Multiarmed Bandit for Reconfigurable Intelligent Surface Aided Millimeter Wave Communications

A reconfigurable intelligent surface (RIS) is a promising technology that can extend short-range millimeter wave (mmWave) communications coverage. However, phase shifts (PSs) of both mmWave transmitter (TX) and RIS antenna elements need to be optimally adjusted to effectively cover a mmWave user. This paper proposes codebook-based phase shifters for mmWave TX and RIS to overcome the difficulty of estimating their mmWave channel state information (CSI). Moreover, to adjust the PSs of both, an online learning approach in the form of a multiarmed bandit (MAB) game is suggested, where a nested two-stage stochastic MAB strategy is proposed. In the proposed strategy, the PS vector of the mmWave TX is adjusted in the first MAB stage. Based on it, the PS vector of the RIS is calibrated in the second stage and vice versa over the time horizon. Hence, we leverage and implement two standard MAB algorithms, namely Thompson sampling (TS) and upper confidence bound (UCB). Simulation results confirm the superior performance of the proposed nested two-stage MAB strategy; in particular, the nested two-stage TS nearly matches the optimal performance

Joint User Association and Power Control in UAV Network: A Graph Theoretic Approach

Unmanned aerial vehicles (UAVs) have recently been widely employed as effective wireless platforms for aiding users in various situations, particularly in hard-to-reach scenarios like post-disaster relief efforts. This study employs multiple UAVs to cover users in overlapping locations, necessitating the optimization of UAV-user association to maximize the spectral and energy efficiency of the UAV network. Hence, a connected bipartite graph is formed between UAVs and users using graph theory to accomplish this goal. Then, a maximum weighted matching-based maximum flow (MwMaxFlow) optimization approach is proposed to achieve the maximum data rate given users’ demands and the UAVs’ maximum capacities. Additionally, power control is applied using the M-matrix theory to optimize users’ transmit powers and improve their energy efficiency. The proposed strategy is evaluated and compared with other benchmark schemes through numerical simulations. The simulation outcomes indicate that the proposed approach balances spectral efficiency and energy consumption, rendering it suitable for various UAV wireless applications, including emergency response, surveillance, and post-disaster management

Distribution of Multi MmWave UAV Mounted RIS Using Budget Constraint Multi-Player MAB

Millimeter wave (mmWave), reconfigurable intelligent surface (RIS), and unmanned aerial vehicles (UAVs) are considered vital technologies of future six-generation (6G) communication networks. In this paper, various UAV mounted RIS are distributed to support mmWave coverage over several hotspots where numerous users exist in harsh blockage environment. UAVs should be spread among the hotspots to maximize their average achievable data rates while minimizing their hovering and flying energy consumptions. To efficiently address this non-polynomial time (NP) problem, it will be formulated as a centralized budget constraint multi-player multi-armed bandit (BCMP-MAB) game. In this formulation, UAVs will act as the players, the hotspots as the arms, and the achievable sum rates of the hotspots as the profit of the MAB game. This formulated MAB problem is different from the traditional one due to the added constraints of the limited budget of UAVs batteries as well as collision avoidance among UAVs, i.e., a hotspot should be covered by only one UAV at a time. Numerical analysis of different scenarios confirm the superior performance of the proposed BCMP-MAB algorithm over other benchmark schemes in terms of average sum rate and energy efficiency with comparable computational complexity and convergence rate

Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks

Device-to-device (D2D) communication is a promising paradigm for the fifth generation (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot of technical challenges such as network architecture, and neighbor discovery, etc. The complexity of configuring D2D links and managing their interference, especially when using millimeter-wave (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address these problems towards boosting the performance of D2D networks. In this paper, a comprehensive survey about recent research activities on D2D networks will be explored with putting more emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions accompanied with their existing conventional solutions, we will show how different ML techniques can be applied to enhance the D2D networks performance over using conventional ways. Then, still open research directions in ML applications on D2D networks will be investigated including their essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks will be presented. This case study will put emphasis on the high potency of using ML solutions over using the conventional non-ML based methods for highly improving the average throughput performance of mmWave NDS

Gateway Selection in Millimeter Wave UAV Wireless Networks Using Multi-Player Multi-Armed Bandit

Recently, unmanned aerial vehicle (UAV)-based communications gained a lot of attention due to their numerous applications, especially in rescue services in post-disaster areas where the terrestrial network is wholly malfunctioned. Multiple access/gateway UAVs are distributed to fully cover the post-disaster area as flying base stations to provide communication coverage, collect valuable information, disseminate essential instructions, etc. The access UAVs after gathering/broadcasting the necessary information should select and fly towards one of the surrounding gateways for relaying their information. In this paper, the gateway UAV selection problem is addressed. The main aim is to maximize the long-term average data rates of the UAVs relays while minimizing the flights’ battery cost, where millimeter wave links, i.e., using 30~300 GHz band, employing antenna beamforming, are used for backhauling. A tool of machine learning (ML) is exploited to address the problem as a budget-constrained multi-player multi-armed bandit (MAB) problem. In this setup, access UAVs act as the players, and the arms are the gateway UAVs, while the rewards are the average data rates of the constructed relays constrained by the battery cost of the access UAV flights. In this decentralized setting, where information is neither prior available nor exchanged among UAVs, a selfish and concurrent multi-player MAB strategy is suggested. Towards this end, three battery-aware MAB (BA-MAB) algorithms, namely upper confidence bound (UCB), Thompson sampling (TS), and the exponential weight algorithm for exploration and exploitation (EXP3), are proposed to realize gateways selection efficiently. The proposed BA-MAB-based gateway UAV selection algorithms show superior performance over approaches based on near and random selections in terms of total system rate and energy efficiency