Joint Sum Rate and Blocklength Optimization in RIS-Aided Short Packet URLLC Systems

Hashemi, Ramin; Ali, Samad; Mahmood, Nurul Huda

doi:10.1109/lcomm.2022.3180396

Cited by 28 publications

(14 citation statements)

References 17 publications

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“…The optimized CBLs are obtained by training an agent with significantly reduced action dimension, i.e., only K = 4 actions are generated while considering only constraint C 4 which is met by scaling of outputs as in (30). It is worth noting that the suggested optimized CBL vector via this agent is the same as employing the difference of concave optimization method as studied in [25]. Note that the ZF performs better in higher SNR regimes as the gap between curves reduces, i.e., the ZF precoder and optimized CBL and active/passive beamformers curves get closer as the total transmit power p total increases.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In [23] the unmanned aerial vehicles (UAVs) trajectory and CBL allocation in FBL regime as well as phase shift optimization in a RIS-aided network to minimize the total error probability was investigated. In [24], [25] a CBL allocation and the RIS reflective phase shift optimization problem (with user grouping in [24]) was studied in a URLLC system where a dedicated RIS assists the BS in transmitting short packets in FBL scenario. The proposed optimization problems were tackled by a semi-definite relaxation method and the user grouping problem in [24] was solved by a greedy algorithm.…”

Section: A Related Workmentioning

confidence: 99%

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Deep Reinforcement Learning for Practical Phase-Shift Optimization in RIS-Aided MISO URLLC Systems

Hashemi

Ali

Mahmood

2023

IEEE Internet Things J.

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We study the joint active/passive beamforming and channel blocklength (CBL) allocation in a non-ideal reconfigurable intelligent surface (RIS)-aided ultra-reliable and lowlatency communication (URLLC) system. The considered scenario is a finite blocklength (FBL) regime and the problem is solved by leveraging a deep reinforcement learning (DRL) algorithm named twin-delayed deep deterministic policy gradient (TD3). First, assuming an industrial automation system, the signal-to-interference-plus-noise ratio and achievable rate in the FBL regime are identified for each actuator. Next, the joint active/passive beamforming and CBL optimization problem is formulated where the objective is to maximize the total achievable FBL rate in all actuators, subject to non-linear amplitude response at the RIS elements, BS transmit power budget and total available CBL. Since the formulated problem is highly nonconvex and non-linear, we resort to employing an actor-critic policy gradient DRL algorithm based on TD3. The considered method relies on interacting RIS with the industrial automation environment by taking actions which are the phase shifts at the RIS elements, CBL variables, and BS beamforming to maximize the expected observed reward, i.e., the total FBL rate. We assess the performance loss of the system when the RIS is non-ideal, i.e., with non-linear amplitude response, and compare it with ideal RIS without impairments. The numerical results show that optimizing the RIS phase shifts, BS beamforming, and CBL variables via the TD3 method with deterministic policy outperforms conventional methods and it is highly beneficial for improving the network total FBL rate considering finite CBL size.

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Section: Numerical Resultsmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Deep Reinforcement Learning for Practical Phase-Shift Optimization in RIS-Aided MISO URLLC Systems

Hashemi

Ali

Mahmood

2023

IEEE Internet Things J.

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“…Novel technologies like the use of RIS are introducing a paradigm shift by allowing to shape the propagation characteristics favorably, and hence are poised to be an integral part of future 6G systems [25], [39]. An RIS-assisted wireless link allows the components of the received signal at a target receiver to add constructively resulting in boosting the received signal-to-noise ratio (SNR).…”

Section: Example Of Usage Of Proposed Architecturementioning

confidence: 99%

A Functional Architecture for 6G Special-Purpose Industrial IoT Networks

Mahmood

Berardinelli

Khatib³

et al. 2023

IEEE Trans. Ind. Inf.

Self Cite

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Future industrial applications will encompass compelling new use cases requiring stringent performance guarantees over multiple key performance indicators (KPI) such as reliability, dependability, latency, time synchronization, security, etc. Achieving such stringent and diverse service requirements necessitates the design of a special-purpose Industrial Internet of Things (IIoT) network comprising a multitude of specialized functionalities and technological enablers. This article proposes an innovative architecture for such a special-purpose 6G IIoT network incorporating seven functional building blocks categorized into: special-purpose functionalities and enabling technologies. The former consists of Wireless Environment Control, Traffic/Channel Prediction, Proactive Resource Management and End-to-End Optimization functions; whereas the latter includes Synchronization and Coordination, Machine Learning and Artificial Intelligence Algorithms, and Auxiliary Functions. The proposed architecture aims at providing a resource-efficient and holistic solution for the complex and dynamically challenging requirements imposed by future 6G industrial use cases. Selected test scenarios are provided and assessed to illustrate cross-functional collaboration and demonstrate the applicability of the proposed architecture in a wireless IIoT network.

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“…The advantages in utilizing RIS on the URLLC systems have recently sparkled a flurry of research activities [ 11 , 12 , 13 ]. For instance, Ref.…”

Section: Introductionmentioning

confidence: 99%

“…[ 11 ] studied the joint optimization of user grouping and the passive beamforming so as to minimize the total blocklength. A multi-objective optimization problem was studied for maximizing the achievable rate while minimizing the utilized channel blocklength in the downlink channel of the multiuser RIS-URLLC systems [ 12 ]. The effect of RIS in the device-to-device (D2D) URLLC transmissions was also studied in [ 13 ], where a robust active and passive beamforming design was proposed to maximize the number of actuators with successful decoding.…”

Section: Introductionmentioning

confidence: 99%

Delay Minimization in RIS-Assisted URLLC Systems under Reliability Constraints

Song

Zhao

et al. 2023

Entropy

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The ultra-reliable and low-latency communication (URLLC) systems are expected to support the stringent quality of service (QoS) demands in the Internet of Things (IoT) networks. In order to support the strict latency and reliability constraints, it is preferable to deploy a reconfigurable intelligent surface (RIS) in the URLLC systems to improve the link quality. In this paper, we focus on the uplink of an RIS-assisted URLLC system, and we propose to minimize the transmission latency under the reliability constraints. To solve the non-convex problem, a low-complexity algorithm is proposed by using the Alternating Direction Method of Multipliers (ADMM) technique. The RIS phase shifts optimization, which is typically non-convex, is efficiently solved by formulating as a Quadratically Constrained Quadratic Programming (QCQP) problem. Simulation results verify that our proposed ADMM-based method is able to achieve better performance than the conventional semi-definite relaxation (SDR)-based method with lower computational complexity. Our proposed RIS-assisted URLLC system is able to significantly reduce the transmission latency, which highlights the great potential in deploying RIS in the IoT networks with strict reliability requirements.

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Joint Sum Rate and Blocklength Optimization in RIS-Aided Short Packet URLLC Systems

Cited by 28 publications

References 17 publications

Deep Reinforcement Learning for Practical Phase-Shift Optimization in RIS-Aided MISO URLLC Systems

Deep Reinforcement Learning for Practical Phase-Shift Optimization in RIS-Aided MISO URLLC Systems

A Functional Architecture for 6G Special-Purpose Industrial IoT Networks

Delay Minimization in RIS-Assisted URLLC Systems under Reliability Constraints

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