Latency Minimization in Intelligent Reflecting Surface Assisted D2D Offloading Systems

Liu, Yanzhen; Hu, Qiyu; Juntti, Markku

doi:10.1109/lcomm.2021.3093165

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…IRS-aided D2D communication: IRS is expected to assist D2D communication by improving the communication links between devices by providing a robust virtual link in case of blockage. However, when offloading using D2D devices latency [ 112 , 113 ], sum-rate maximization [ 114 ] and secrecy-rate maximization [ 115 ] will be key issues. Developing and designing D2D algorithms to solve these abovementioned issues will be an important area of research.…”

Section: Conclusion Future Work and Limitationmentioning

confidence: 99%

Design and Application of Intelligent Reflecting Surface (IRS) for Beyond 5G Wireless Networks: A Review

Okogbaa

Ahmed

Khan

et al. 2022

Sensors

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The existing sub-6 GHz band is insufficient to support the bandwidth requirement of emerging data-rate-hungry applications and Internet of Things devices, requiring ultrareliable low latency communication (URLLC), thus making the migration to millimeter-wave (mmWave) bands inevitable. A notable disadvantage of a mmWave band is the significant losses suffered at higher frequencies that may not be overcome by novel optimization algorithms at the transmitter and receiver and thus result in a performance degradation. To address this, Intelligent Reflecting Surface (IRS) is a new technology capable of transforming the wireless channel from a highly probabilistic to a highly deterministic channel and as a result, overcome the significant losses experienced in the mmWave band. This paper aims to survey the design and applications of an IRS, a 2-dimensional (2D) passive metasurface with the ability to control the wireless propagation channel and thus achieve better spectral efficiency (SE) and energy efficiency (EE) to aid the fifth and beyond generation to deliver the required data rate to support current and emerging technologies. It is imperative that the future wireless technology evolves toward an intelligent software paradigm, and the IRS is expected to be a key enabler in achieving this task. This work provides a detailed survey of the IRS technology, limitations in the current research, and the related research opportunities and possible solutions.

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Section: Conclusion Future Work and Limitationmentioning

confidence: 99%

Design and Application of Intelligent Reflecting Surface (IRS) for Beyond 5G Wireless Networks: A Review

Okogbaa

Ahmed

Khan

et al. 2022

Sensors

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“…Here, the convex optimization theory and semidefinite relaxation method are utilized to provide the optimal solution. On the other hand, an IRS-aided D2D offloading system is studied in [17], where an IRS is employed to assist in the offloading of computations from a group of intensive users to a group of idle users. A mixed-integer stochastic successive convex approximation scheme is proposed to tackle this problem.…”

Section: Related Workmentioning

confidence: 99%

“…. , s 0 M } observed by all D2D agents for each time slot do Actions a(t) are selected using the ε-greedy scheme (17) and then executed by all D2D agents ; Perform observation of the rewards r(t) and the new state s ; Update Q(s(t), a(t)) using ( 16); Update π(s(t), a(t));…”

Section: Algorithm 1: Q-learning Based Solution Schemementioning

confidence: 99%

Intelligent Reflecting Surface-Aided Device-to-Device Communication: A Deep Reinforcement Learning Approach

Sultana

Fernando

2022

Future Internet

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Recently, the growing demand of various emerging applications in the realms of sixth-generation (6G) wireless networks has made the term internet of Things (IoT) very popular. Device-to-device (D2D) communication has emerged as one of the significant enablers for the 6G-based IoT network. Recently, the intelligent reflecting surface (IRS) has been considered as a hardware-efficient innovative scheme for future wireless networks due to its ability to mitigate propagation-induced impairments and to realize a smart radio environment. Such an IRS-assisted D2D underlay cellular network is investigated in this paper. Our aim is to maximize the network’s spectrum efficiency (SE) by jointly optimizing the transmit power of both the cellular users (CUs) and the D2D pairs, the resource reuse indicators, and the IRS reflection coefficients. Instead of using traditional optimization solution schemes to solve this mixed integer nonlinear optimization problem, a reinforcement learning (RL) approach is used in this paper. The IRS-assisted D2D communication network is structured by the Markov Decision Process (MDP) in the RL framework. First, a Q-learning-based solution is studied. Then, to make a scalable solution with large dimension state and action spaces, a deep Q-learning-based solution scheme using experience replay is proposed. Lastly, an actor-critic framework based on the deep deterministic policy gradient (DDPG) scheme is proposed to learn the optimal policy of the constructed optimization problem considering continuous-valued state and action spaces. Simulation outcomes reveal that the proposed RL-based solution schemes can provide significant SE enhancements compared to the existing optimization schemes.

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“…The authors of [17] adopted deep learning to solve the prediction problem of D2D channel gain. The authors of [18] aimed at minimizing latency in intelligent reflecting surface (IRS) assisted D2D offloading systems by jointly [19] optimizing IRS phase shift and resource allocation algorithm. In [19], the authors adopted an iterative algorithm to realize the optimal power distribution to maximize the energy efficiency of D2D networks.…”

Section: Introductionmentioning

confidence: 99%

Outage Constrained Design in NOMA-Based D2D Offloading Systems

Chen

Zhang

et al. 2022

Electronics

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Non-orthogonal multiple access (NOMA) is a new multiple access method that has been considered in 5G cellular communications in recent years, and can provide better throughput than traditional orthogonal multiple access (OMA) to save communication bandwidth. Device-to-device (D2D) communication, as a key technology of 5G, can reuse network resources to improve the spectrum utilization of the entire communication network. Combining NOMA technology with D2D is an effective solution to improve mobile edge computing (MEC) communication throughput and user access density. Considering the estimation error of channel, we investigate the power of the transmit nodes optimization problem of NOMA-based D2D networks under the rates outage probability (OP) constraints of all single users. Specifically, under the channel statistical error model, the total system transmit power is minimized with the rate OP constraint of a single device. Unfortunately, the problem presented is thorny and non-convex. After equivalent transformation of the rate OP constraints by the Bernstein inequality, an algorithm based on semi-definite relaxation (SDR) can efficiently solve this challenging non-convex problem. Numerical results show that the channel estimation error increases the power consumption of the system. We also compare NOMA with the OMA mode, and the numerical results show that the D2D offloading systems based on NOMA are superior to OMA.

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Latency Minimization in Intelligent Reflecting Surface Assisted D2D Offloading Systems

Cited by 8 publications

References 15 publications

Design and Application of Intelligent Reflecting Surface (IRS) for Beyond 5G Wireless Networks: A Review

Design and Application of Intelligent Reflecting Surface (IRS) for Beyond 5G Wireless Networks: A Review

Intelligent Reflecting Surface-Aided Device-to-Device Communication: A Deep Reinforcement Learning Approach

Outage Constrained Design in NOMA-Based D2D Offloading Systems

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