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Book ; Online: Joint User Pairing and Beamforming Design of Multi-STAR-RISs-Aided NOMA in the Indoor Environment via Multi-Agent Reinforcement Learning

Park, Yu Min / Tun, Yan Kyaw / Hong, Choong Seon

2023

Abstract: The development of 6G/B5G wireless networks, which have requirements that go beyond current 5G networks, is gaining interest from academic and industrial. However, to increase 6G/B5G network quality, conventional cellular networks that rely on ... ...

Abstract	The development of 6G/B5G wireless networks, which have requirements that go beyond current 5G networks, is gaining interest from academic and industrial. However, to increase 6G/B5G network quality, conventional cellular networks that rely on terrestrial base stations are constrained geographically and economically. Meanwhile, NOMA allows multiple users to share the same resources, which improves the spectral efficiency of the system and has the advantage of supporting a larger number of users. Additionally, by intelligently manipulating the phase and amplitude of both the reflected and transmitted signals, STAR-RISs can achieve improved coverage, increased spectral efficiency, and enhanced communication reliability. However, STAR-RISs must simultaneously optimize the Amplitude and Phase-shift corresponding to reflection and transmission, which makes the existing terrestiral networks more complicated and is considered a major challenging issue. Motivated by the above, we study the joint user pairing for NOMA and beamforming design of Multi-STAR-RISs in an indoor environment. Then, we formulate the optimization problem with the objective of maximizing the total throughput of MUs by jointly optimizing the decoding order, user pairing, active beamforming, and passive beamforming. However, the formulated problem is a MINLP. To tackle this challenge, we first introduce the decoding order for NOMA networks. Next, we decompose the original problem into two subproblems namely: 1) MU pairing and 2) Beamforming optimization under the optimal decoding order. For the first subproblem, we employ correlation-based K-means clustering to solve the user pairing problem. Then, to jointly deal with beamforming vector optimizations, we propose MAPPO, which can make quick decisions in the given environment owing to its low complexity. Comment: 8 pages, 9 figures, IEEE/IFIP Network Operations and Management Symposium (NOMS) 2024 submitted
Keywords	Computer Science - Information Theory ; Computer Science - Artificial Intelligence ; Computer Science - Networking and Internet Architecture
Subject code	003
Publishing date	2023-11-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Min-max Decoding Error Probability Optimization in RIS-Aided Hybrid TDMA-NOMA Networks

Le, Tra Huong Thi / Tun, Yan Kyaw

2023

Abstract: One of the primary objectives for future wireless communication networks is to facilitate the provision of ultra-reliable and low-latency communication services while simultaneously ensuring the capability for vast connection. In order to achieve this ... ...

Abstract	One of the primary objectives for future wireless communication networks is to facilitate the provision of ultra-reliable and low-latency communication services while simultaneously ensuring the capability for vast connection. In order to achieve this objective, we examine a hybrid multi-access scheme inside the finite blocklength (FBL) regime. This system combines the benefits of non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA) schemes with the aim of fulfilling the objectives of future wireless communication networks. In addition, a reconfigurable intelligent surface (RIS) is utilized to facilitate the establishment of the uplink transmission between the base station and mobile devices in situations when impediments impede their direct communication linkages. This paper aims to minimize the worst-case decoding-error probability for all mobile users by jointly optimizing power allocation, receiving beamforming, blocklength, RIS reflection, and user pairing. To deal with the coupled variables in the formulated mixed-integer non-convex optimization problem, we decompose it into three sub-problems, namely, 1) decoding order determination problem, 2) joint power allocation, receiving beamforming, RIS reflection, and blocklength optimization problem, and 3) optimal user pairing problem. Then, we provide the sequential convex approximation (SCA) and semidefinite relaxation (SDR)-based algorithms as potential solutions for iteratively addressing the deconstructed first two sub-problems at a fixed random user pairing. In addition, the Hungarian matching approach is employed to address the challenge of optimizing user pairing. In conclusion, we undertake a comprehensive simulation, which reveals the advantageous qualities of the proposed algorithm and its superior performance compared to existing benchmark methods. Comment: 11 pages, 7 figures
Keywords	Computer Science - Networking and Internet Architecture ; Computer Science - Information Theory
Subject code	003
Publishing date	2023-10-18
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Aerial STAR-RIS Empowered MEC

Aung, Pyae Sone / Nguyen, Loc X. / Tun, Yan Kyaw / Han, Zhu / Hong, Choong Seon

A DRL Approach for Energy Minimization

2023

Abstract: Multi-access Edge Computing (MEC) addresses computational and battery limitations in devices by allowing them to offload computation tasks. To overcome the difficulties in establishing line-of-sight connections, integrating unmanned aerial vehicles (UAVs) ...

Abstract	Multi-access Edge Computing (MEC) addresses computational and battery limitations in devices by allowing them to offload computation tasks. To overcome the difficulties in establishing line-of-sight connections, integrating unmanned aerial vehicles (UAVs) has proven beneficial, offering enhanced data exchange, rapid deployment, and mobility. The utilization of reconfigurable intelligent surfaces (RIS), specifically simultaneously transmitting and reflecting RIS (STAR-RIS) technology, further extends coverage capabilities and introduces flexibility in MEC. This study explores the integration of UAV and STAR-RIS to facilitate communication between IoT devices and an MEC server. The formulated problem aims to minimize energy consumption for IoT devices and aerial STAR-RIS by jointly optimizing task offloading, aerial STAR-RIS trajectory, amplitude and phase shift coefficients, and transmit power. Given the non-convexity of the problem and the dynamic environment, solving it directly within a polynomial time frame is challenging. Therefore, deep reinforcement learning (DRL), particularly proximal policy optimization (PPO), is introduced for its sample efficiency and stability. Simulation results illustrate the effectiveness of the proposed system compared to benchmark schemes in the literature.
Keywords	Computer Science - Networking and Internet Architecture ; Electrical Engineering and Systems Science - Signal Processing
Subject code	690
Publishing date	2023-12-14
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Satellite-based ITS Data Offloading & Computation in 6G Networks

Hassan, Sheikh Salman / Park, Yu Min / Tun, Yan Kyaw / Saad, Walid / Han, Zhu / Hong, Choong Seon

A Cooperative Multi-Agent Proximal Policy Optimization DRL with Attention Approach

2022

Abstract: The proliferation of intelligent transportation systems (ITS) has led to increasing demand for diverse network applications. However, conventional terrestrial access networks (TANs) are inadequate in accommodating various applications for remote ITS ... ...

Abstract	The proliferation of intelligent transportation systems (ITS) has led to increasing demand for diverse network applications. However, conventional terrestrial access networks (TANs) are inadequate in accommodating various applications for remote ITS nodes, i.e., airplanes and ships. In contrast, satellite access networks (SANs) offer supplementary support for TANs, in terms of coverage flexibility and availability. In this study, we propose a novel approach to ITS data offloading and computation services based on SANs. We use low-Earth orbit (LEO) and cube satellites (CubeSats) as independent mobile edge computing (MEC) servers that schedule the processing of data generated by ITS nodes. To optimize offloading task selection, computing, and bandwidth resource allocation for different satellite servers, we formulate a joint delay and rental price minimization problem that is mixed-integer non-linear programming (MINLP) and NP-hard. We propose a cooperative multi-agent proximal policy optimization (Co-MAPPO) deep reinforcement learning (DRL) approach with an attention mechanism to deal with intelligent offloading decisions. We also decompose the remaining subproblem into three independent subproblems for resource allocation and use convex optimization techniques to obtain their optimal closed-form analytical solutions. We conduct extensive simulations and compare our proposed approach to baselines, resulting in performance improvements of 9.9%, 5.2%, and 4.2%, respectively. Comment: 18 Pages, 20 Figures, Submitted to IEEE Transactions on Mobile Computing (TMC)-(Under Major Revision)
Keywords	Computer Science - Networking and Internet Architecture
Subject code	629
Publishing date	2022-12-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Energy-Efficient Communication Networks via Multiple Aerial Reconfigurable Intelligent Surfaces

Aung, Pyae Sone / Park, Yu Min / Tun, Yan Kyaw / Han, Zhu / Hong, Choong Seon

DRL and Optimization Approach

2022

Abstract: In the realm of wireless communications in 5G, 6G and beyond, deploying unmanned aerial vehicle (UAV) has been an innovative approach to extend the coverage area due to its easy deployment. Moreover, reconfigurable intelligent surface (RIS) has also ... ...

Abstract	In the realm of wireless communications in 5G, 6G and beyond, deploying unmanned aerial vehicle (UAV) has been an innovative approach to extend the coverage area due to its easy deployment. Moreover, reconfigurable intelligent surface (RIS) has also emerged as a new paradigm with the goals of enhancing the average sum-rate as well as energy efficiency. By combining these attractive features, an energy-efficient RIS-mounted multiple UAVs (aerial RISs: ARISs) assisted downlink communication system is studied. Due to the obstruction, user equipments (UEs) can have a poor line of sight to communicate with the base station (BS). To solve this, multiple ARISs are implemented to assist the communication between the BS and UEs. Then, the joint optimization problem of deployment of ARIS, ARIS reflective elements on/off states, phase shift, and power control of the multiple ARISs-assisted communication system is formulated. The problem is challenging to solve since it is mixed-integer, non-convex, and NP-hard. To overcome this, it is decomposed into three sub-problems. Afterwards, successive convex approximation (SCA), actor-critic proximal policy optimization (AC-PPO), and whale optimization algorithm (WOA) are employed to solve these sub-problems alternatively. Finally, extensive simulation results have been generated to illustrate the efficacy of our proposed algorithms. Comment: 31 pages, 10 figures
Keywords	Computer Science - Networking and Internet Architecture
Subject code	000
Publishing date	2022-07-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: An Efficient Federated Learning Framework for Training Semantic Communication System

Nguyen, Loc X. / Le, Huy Q. / Tun, Ye Lin / Aung, Pyae Sone / Tun, Yan Kyaw / Han, Zhu / Hong, Choong Seon

2023

Abstract: Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy. Most semantic communication systems are built upon advanced deep learning models whose training ... ...

Abstract	Semantic communication has emerged as a pillar for the next generation of communication systems due to its capabilities in alleviating data redundancy. Most semantic communication systems are built upon advanced deep learning models whose training performance heavily relies on data availability. Existing studies often make unrealistic assumptions of a readily accessible data source, where in practice, data is mainly created on the client side. Due to privacy and security concerns, the transmission of data is restricted, which is necessary for conventional centralized training schemes. To address this challenge, we explore semantic communication in a federated learning (FL) setting that utilizes client data without leaking privacy. Additionally, we design our system to tackle the communication overhead by reducing the quantity of information delivered in each global round. In this way, we can save significant bandwidth for resource-limited devices and reduce overall network traffic. Finally, we introduce a mechanism to aggregate the global model from clients, called FedLol. Extensive simulation results demonstrate the effectiveness of our proposed technique compared to baseline methods. Comment: 5 pages, 3 figures
Keywords	Computer Science - Machine Learning
Subject code	006
Publishing date	2023-10-19
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Swin Transformer-Based Dynamic Semantic Communication for Multi-User with Different Computing Capacity

Nguyen, Loc X. / Tun, Ye Lin / Tun, Yan Kyaw / Nguyen, Minh N. H. / Zhang, Chaoning / Han, Zhu / Hong, Choong Seon

2023

Abstract: Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, ... ...

Abstract	Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, little literature has studied its effectiveness in multi-user scenarios, particularly when there are variations in the model architectures used by users and their computing capacities. To address this issue, we explore a semantic communication system that caters to multiple users with different model architectures by using a multi-purpose transmitter at the base station (BS). Specifically, the BS in the proposed framework employs semantic and channel encoders to encode the image for transmission, while the receiver utilizes its local channel and semantic decoder to reconstruct the original image. Our joint source-channel encoder at the BS can effectively extract and compress semantic features for specific users by considering the signal-to-noise ratio (SNR) and computing capacity of the user. Based on the network status, the joint source-channel encoder at the BS can adaptively adjust the length of the transmitted signal. A longer signal ensures more information for high-quality image reconstruction for the user, while a shorter signal helps avoid network congestion. In addition, we propose a hybrid loss function for training, which enhances the perceptual details of reconstructed images. Finally, we conduct a series of extensive evaluations and ablation studies to validate the effectiveness of the proposed system. Comment: 14 pages, 10 figures
Keywords	Computer Science - Information Theory
Subject code	003
Publishing date	2023-07-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Ruin Theory for User Association and Energy Optimization in Multi-access Edge Computing

Kim, Do Hyeon / Manzoor, Aunas / Alsenwi, Madyan / Tun, Yan Kyaw / Saad, Walid / Hong, Choong Seon

2021

Abstract: In this correspondence, a novel framework is proposed for analyzing data offloading in a multi-access edge computing system. Specifically, a two-phase algorithm, is proposed, including two key phases: 1) user association phase and 2) task offloading ... ...

Abstract	In this correspondence, a novel framework is proposed for analyzing data offloading in a multi-access edge computing system. Specifically, a two-phase algorithm, is proposed, including two key phases: 1) user association phase and 2) task offloading phase. In the first phase, a ruin theory-based approach is developed to obtain the users association considering the users' transmission reliability and resource utilization efficiency. Meanwhile, in the second phase, an optimization-based algorithm is used to optimize the data offloading process. In particular, ruin theory is used to manage the user association phase, and a ruin probability-based preference profile is considered to control the priority of proposing users. Here, ruin probability is derived by the surplus buffer space of each edge node at each time slot. Giving the association results, an optimization problem is formulated to optimize the amount of offloaded data aiming at minimizing the energy consumption of users. Simulation results show that the developed solutions guarantee system reliability, association efficiency under a tolerable value of surplus buffer size, and minimize the total energy consumption of all users. Comment: Accepted Article By IEEE Transactions on Vehicular Technology, DOI: https://doi.org/10.1109/TVT.2023.3269427 (In Press)
Keywords	Computer Science - Information Theory ; Computer Science - Computer Science and Game Theory
Subject code	006
Publishing date	2021-07-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Collaboration in the Sky

Tun, Yan Kyaw / Dang, Tri Nguyen / Kim, Kitae / Anselwi, Madyan / Saad, Walid / Hong, Choong Seon

A Distributed Framework for Task Offloading and Resource Allocation in Multi-Access Edge Computing

2021

Abstract: Recently, unmanned aerial vehicles (UAVs) assisted multi-access edge computing (MEC) systems emerged as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure coverage. As each UAV operates ... ...

Abstract	Recently, unmanned aerial vehicles (UAVs) assisted multi-access edge computing (MEC) systems emerged as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure coverage. As each UAV operates independently, however, it is challenging to meet the computation demands of the mobile users due to the limited computing capacity at the UAV's MEC server as well as the UAV's energy constraint. Therefore, collaboration among UAVs is needed. In this paper, a collaborative multi-UAV-assisted MEC system integrated with a MEC-enabled terrestrial base station (BS) is proposed. Then, the problem of minimizing the total latency experienced by the mobile users in the proposed system is studied by optimizing the offloading decision as well as the allocation of communication and computing resources while satisfying the energy constraints of both mobile users and UAVs. The proposed problem is shown to be a non-convex, mixed-integer nonlinear problem (MINLP) that is intractable. Therefore, the formulated problem is decomposed into three subproblems: i) users tasks offloading decision problem, ii) communication resource allocation problem and iii) UAV-assisted MEC decision problem. Then, the Lagrangian relaxation and alternating direction method of multipliers (ADMM) methods are applied to solve the decomposed problems, alternatively. Simulation results show that the proposed approach reduces the average latency by up to 40.7\% and 4.3\% compared to the greedy and exhaustive search methods. Comment: Submitted to IEEE Internet of Things Journals
Keywords	Computer Science - Networking and Internet Architecture ; Electrical Engineering and Systems Science - Signal Processing
Subject code	006
Publishing date	2021-07-30
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Dependency Tasks Offloading and Communication Resource Allocation in Collaborative UAVs Networks

Nguyen, Loc X. / Tun, Yan Kyaw / Dang, Tri Nguyen / Park, Yu Min / Han, Zhu / Hong, Choong Seon

A Meta-Heuristic Approach

2022

Abstract: In recent years, unmanned aerial vehicles (UAVs) assisted mobile edge computing systems have been exploited by researchers as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure coverage. However, ...

Abstract	In recent years, unmanned aerial vehicles (UAVs) assisted mobile edge computing systems have been exploited by researchers as a promising solution for providing computation services to mobile users outside of terrestrial infrastructure coverage. However, it remains challenging for the standalone MEC-enabled UAVs in order to meet the computation requirement of numerous mobile users due to the limited computation capacity of their onboard servers and battery lives. Therefore, we propose a collaborative scheme among UAVs so that UAVs can share the workload with idle UAVs. Moreover, current task offloading strategies frequently overlook task topology, which may result in poor performance or even system failure. To address the problem, we consider offloading tasks consisting of a set of sub-tasks, and each sub-task has dependencies on other sub-tasks, which is practical in the real world. Sub-tasks with dependencies need to wait for the resulting signal from preceding sub-tasks before being executed. This mechanism has serious effects on the offloading strategy. Then, we formulate an optimization problem to minimize the average latency experienced by users by jointly controlling the offloading decision for dependent tasks and allocating the communication resources of UAVs. The formulated problem appears to be NP-hard and cannot be solved in polynomial time. Therefore, we divide the problem into two sub-problems: the offloading decision problem and the communication resource allocation problem. Then a meta-heuristic method is proposed to find the sub-optimal solution of the task offloading problem, while the communication resource allocation problem is solved by using convex optimization. Finally, we perform substantial simulation experiments, and the result shows that the proposed offloading technique effectively minimizes the average latency of users, compared with other benchmark schemes. Comment: 14 pages, 9 figures
Keywords	Computer Science - Networking and Internet Architecture ; Electrical Engineering and Systems Science - Signal Processing
Subject code	000
Publishing date	2022-08-16
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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