![]() Bhandari, Sovit ![]() ![]() ![]() Scientific Conference (2023, June 01) Next-generation multi-spot beam satellite systems open a new way to design low earth orbit (LEO) satellite communication systems with full flexibility in managing bandwidth, transmit power, and spot beam ... [more ▼] Next-generation multi-spot beam satellite systems open a new way to design low earth orbit (LEO) satellite communication systems with full flexibility in managing bandwidth, transmit power, and spot beam coverage, enabling the adoption of spatial multiplexing techniques to meet the unprecedented demand for future mobile traffic. However, conventional spatial multiplexing techniques perform poorly in satellite systems due to high correlation between the satellite channels, resulting in inefficient mitigation of inter-user interference. In this paper, we exploit the flexibility of multi-spot beam LEO satellites and consider the geographic distribution of users to improve the performance of LEO satellite-assisted edge caching systems. Our goal is to jointly optimize the beam coverage, bandwidth and transmit power and minimize the cache delivery time. In particular, the spot beam coverage is optimized by using the K-means algorithm applied to the realistic user demands, followed by a proposed successive convex approximation (SCA)-based iterative algorithm for optimizing the radio resources. Simulations shows that our optimal approach outperforms the conventional precoding-based approach and also shows a significant improvement in the minimization of the maximum content delivery time. [less ▲] Detailed reference viewed: 355 (239 UL)![]() Minardi, Mario ![]() ![]() ![]() in IEEE/IFIP Network Operations and Management Symposium (NOMS) 2023, Miami, Florida, USA, 8-12 May 2023 (2023, June) Detailed reference viewed: 56 (9 UL)![]() Mostaani, Arsham ![]() ![]() ![]() in IEEE Access (2022) Communication system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel. However, in the era ... [more ▼] Communication system design has been traditionally guided by task-agnostic principles, which aim at efficiently transmitting as many correct bits as possible through a given channel. However, in the era of cyber-physical systems, the effectiveness of communications is not dictated simply by the bit rate, but most importantly by the efficient completion of the task in hand, e.g., controlling remotely a robot, automating a production line or collaboratively sensing through a drone swarm. In parallel, it is projected that by 2023, half of the worldwide network connections will be among machines rather than humans. In this context, it is crucial to establish a new paradigm for designing communication strategies for multi-agent cyber-physical systems. This is a daunting task, since it requires a combination of principles from information, communication, control theories and computer science in order to formalize a general framework for task-oriented communication designs. In this direction, this paper reviews and structures the relevant theoretical work across a wide range of scientific communities. Subsequently, it proposes a general conceptual framework for task-oriented communication design, along with its specializations according to targeted use cases. Furthermore, it provides a survey of relevant contributions in dominant applications, such as industrial internet of things, multi-unmanned aerial vehicle (UAV) systems, autonomous vehicles, distributed learning systems, smart manufacturing plants, 5G and beyond self-organizing networks, and tactile internet. Finally, this paper also highlights the most important open research topics from both the theoretical framework and application points of view. [less ▲] Detailed reference viewed: 47 (5 UL)![]() He, Ke ![]() ![]() ![]() in IEEE GLOBECOM 2022 proceedings (2022, December) This paper investigates the massive multi-input multi-output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the ... [more ▼] This paper investigates the massive multi-input multi-output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the number of radio frequency (RF) chains is smaller than the number of antennas. The base station employs antenna selection (AS) to fully harness the spatial multiplexing gain. Conventional AS techniques require full channel state information (CSI), which is time-consuming as the antennas cannot be simultaneously connected to the RF chains during the channel estimation process. To tackle this issue, we propose a novel joint channel prediction and AS (JCPAS) framework to reduce the CSI acquisition time and improve the system performance under temporally correlated channels. Our proposed JCPAS framework is a fully probabilistic model driven by deep unsupervised learning. The proposed framework is able to predict the current full CSI, while requiring only a historical window of partial observations. Extensive simulation results show that the proposed JCPAS can significantly improve the system performance under temporally correlated channels, especially for very large-scale systems with highly correlated channels. [less ▲] Detailed reference viewed: 74 (17 UL)![]() Minardi, Mario ![]() ![]() ![]() in IEEE Transactions on Network and Service Management (2022) Detailed reference viewed: 36 (6 UL)![]() ; ; Vu, Thang Xuan ![]() in IEEE Transactions on Wireless Communications (2022), 21(11), 9582-9595 Low earth orbit (LEO) satellite-assisted communications have been considered as one of the key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space ... [more ▼] Low earth orbit (LEO) satellite-assisted communications have been considered as one of the key elements in beyond 5G systems to provide wide coverage and cost-efficient data services. Such dynamic space-terrestrial topologies impose an exponential increase in the degrees of freedom in network management. In this paper, we address two practical issues for an over-loaded LEO-terrestrial system. The first challenge is how to efficiently schedule resources to serve a massive number of connected users, such that more data and users can be delivered/served. The second challenge is how to make the algorithmic solution more resilient in adapting to dynamic wireless environments. We first propose an iterative suboptimal algorithm to provide an offline benchmark. To adapt to unforeseen variations, we propose an enhanced meta-critic learning algorithm (EMCL), where a hybrid neural network for parameterization and the Wolpertinger policy for action mapping are designed in EMCL. The results demonstrate EMCL’s effectiveness and fast-response capabilities in over-loaded systems and in adapting to dynamic environments compare to previous actor-critic and meta-learning methods. [less ▲] Detailed reference viewed: 29 (1 UL)![]() Singh, Vibhum ![]() ![]() ![]() in IEEE Wireless Communications Letters (2022), 11(12), 2655-2659 Future wireless networks pose several challenges such as high spectral efficiency, wide coverage massive connectivity, low receiver complexity, etc. To this end, this letter investigates an overlay based ... [more ▼] Future wireless networks pose several challenges such as high spectral efficiency, wide coverage massive connectivity, low receiver complexity, etc. To this end, this letter investigates an overlay based cognitive hybrid satellite-terrestrial network (CHSTN) combining non-orthogonal multiple access (NOMA) and conventional Alamouti space-time block coding (STBC) techniques. Herein, a decode-and-forward based secondary terrestrial network cooperates with a primary satellite network for dynamic spectrum access. Further, for reliable content delivery and low latency requirements, wireless caching is employed, whereby the secondary network can store the most popular contents of the primary network. Considering the relevant heterogeneous fading channel models and the NOMA-based imperfect successive interference cancellation, we examine the performance of CHSTN for the cache-free (CF) STBC-NOMA and the cache-aided (CA) STBC-NOMA schemes. We assess the outage probability expressions for primary and secondary networks and further, highlight the corresponding achievable diversity orders. Indicatively, the proposed CF/CA STBC-NOMA schemes for CHSTN perform significantly better than the benchmark standalone NOMA and OMA schemes. [less ▲] Detailed reference viewed: 81 (32 UL)![]() Mostaani, Arsham ![]() ![]() ![]() in IEEE Open Journal of the Communications Society (2022) Various applications for inter-machine communications are on the rise. Whether it is for autonomous driving vehicles or the internet of everything, machines are more connected than ever to improve their ... [more ▼] Various applications for inter-machine communications are on the rise. Whether it is for autonomous driving vehicles or the internet of everything, machines are more connected than ever to improve their performance in fulfilling a given task. While in traditional communications the goal has often been to reconstruct the underlying message, under the emerging task-oriented paradigm, the goal of communication is to enable the receiving end to make more informed decisions or more precise estimates/computations. Motivated by these recent developments, in this paper, we perform an indirect design of the communications in a multi-agent system (MAS) in which agents cooperate to maximize the averaged sum of discounted one-stage rewards of a collaborative task. Due to the bit-budgeted communications between the agents, each agent should efficiently represent its local observation and communicate an abstracted version of the observations to improve the collaborative task performance. We first show that this problem can be approximated as a form of data-quantization problem which we call task-oriented data compression (TODC). We then introduce the state-aggregation for information compression algorithm (SAIC) to solve the formulated TODC problem. It is shown that SAIC is able to achieve near-optimal performance in terms of the achieved sum of discounted rewards. The proposed algorithm is applied to a geometric consensus problem and its performance is compared with several benchmarks. Numerical experiments confirm the promise of this indirect design approach for task-oriented multi-agent communications. [less ▲] Detailed reference viewed: 59 (6 UL)![]() ; ; et al in IEEE Transactions on Wireless Communications (2022), 21(9), 7374-7390 This paper develops a novel framework to defeat a super-reactive jammer, one of the most difficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budget and is ... [more ▼] This paper develops a novel framework to defeat a super-reactive jammer, one of the most difficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budget and is equipped with the self-interference suppression capability to simultaneously attack and listen to the transmitter’s activities. Consequently, dealing with super-reactive jammers is very challenging. Thus, we introduce a smart deception mechanism to attract the jammer to continuously attack the channel and then leverage jamming signals to transmit data based on the ambient backscatter communication technology. To detect the backscattered signals, the maximum likelihood detector can be adopted. However, this method is notorious for its high computational complexity and requires the model of the current propagation environment as well as channel state information. Hence, we propose a deep learning-based detector that can dynamically adapt to any channels and noise distributions. With a Long Short-Term Memory network, our detector can learn the received signals’ dependencies to achieve a performance close to that of the optimal maximum likelihood detector. Through simulation and theoretical results, we demonstrate that with our approaches, the more power the jammer uses to attack the channel, the better bit error rate performance the transmitter can achieve. [less ▲] Detailed reference viewed: 25 (4 UL)![]() ; ; et al in IEEE Transactions on Mobile Computing (2022), 21(8), 2803-2817 In this paper, we propose a novel energy-efficient framework for an electric vehicle (EV) network using a contract theoretic-based economic model to maximize the profits of charging stations (CSs) and ... [more ▼] In this paper, we propose a novel energy-efficient framework for an electric vehicle (EV) network using a contract theoretic-based economic model to maximize the profits of charging stations (CSs) and improve the social welfare of the network. Specifically, we first introduce CS-based and CS clustering-based decentralized federated energy learning (DFEL) approaches which enable the CSs to train their own energy transactions locally to predict energy demands. In this way, each CS can exchange its learned model with other CSs to improve prediction accuracy without revealing actual datasets and reduce communication overhead among the CSs. Based on the energy demand prediction, we then design a multi-principal one-agent (MPOA) contract-based method. In particular, we formulate the CSs' utility maximization as a non-collaborative energy contract problem in which each CS maximizes its utility under common constraints from the smart grid provider (SGP) and other CSs' contracts. Then, we prove the existence of an equilibrium contract solution for all the CSs and develop an iterative algorithm at the SGP to find the equilibrium. Through simulation results using the dataset of CSs' transactions in Dundee city, the United Kingdom between 2017 and 2018, we demonstrate that our proposed method can achieve the energy demand prediction accuracy improvement up to 24.63% and lessen communication overhead by 96.3% compared with other machine learning algorithms. Furthermore, our proposed method can outperform non-contract-based economic models by 35% and 36% in terms of the CSs' utilities and social welfare of the network, respectively. [less ▲] Detailed reference viewed: 100 (4 UL)![]() Kavehmadavani, Fatemeh ![]() ![]() ![]() in Traffic Steering for eMBB and uRLLC Coexistence in Open Radio Access Networks (2022, May 16) Existing radio access network (RAN) architectures are lack of sufficient openness, flexibility, and intelligence to meet the diverse demands of emerging services in beyond 5G and 6G wireless networks ... [more ▼] Existing radio access network (RAN) architectures are lack of sufficient openness, flexibility, and intelligence to meet the diverse demands of emerging services in beyond 5G and 6G wireless networks, including enhanced mobile broadband (eMBB) and ultra-reliable and low-latency (uRLLC). Open RAN (ORAN) is a promising paradigm that allows building a virtualized and intelligent architecture. In this paper, we focus on traffic steering (TS) scheme based on multi-connectivity (MC) and network slicing (NS) techniques to efficiently allocate heterogeneous network resources in “NextG” cellular networks. We formulate the RAN resource allocation problem to simultaneously maximize the weighted sum eMBB throughput and minimize the worst-user uRLLC latency subject to QoS requirements, and orthogonality, power, and limited fronthaul constraints. Since the formulated problem is categorized as a mixed integer … [less ▲] Detailed reference viewed: 41 (15 UL)![]() Vu, Thang Xuan ![]() ![]() in IEEE International Conference on Communications (2022, May) The next generation multibeam satellites open up a new way to design satellite communication channels with the full flexibility in bandwidth, transmit power and beam coverage management. In this paper, we ... [more ▼] The next generation multibeam satellites open up a new way to design satellite communication channels with the full flexibility in bandwidth, transmit power and beam coverage management. In this paper, we exploit the flexible multibeam satellite capabilities and the geographical distribution of users to improve the performance of satellite-assisted edge caching systems. Our aim is to jointly optimize the bandwidth allocation in multibeam and caching decisions at the edge nodes to address two important problems: i) cache feeding time minimization and ii) cache hits maximization. To tackle the non-convexity of the joint optimization problem, we transform the original problem into a difference-of-convex (DC) form, which is then solved by the proposed iterative algorithm whose convergence to at least a local optimum is theoretically guaranteed. Furthermore, the effectiveness of the proposed design is evaluated under the realistic beams coverage of the satellite SES-14 and Movielens data set. Numerical results show that our proposed joint design can reduce the caching feeding time by 50% and increase the cache hit ratio (CHR) by 10% to 20% compared to existing solutions. Furthermore, we examine the impact of multispot beams and multicarrier wide-beam on the joint design and discuss potential research directions. [less ▲] Detailed reference viewed: 80 (54 UL)![]() ; ; Vu, Thang Xuan ![]() in Proceeding of IEEE ICC 2022 (2022, May) Towards the next generation networks, low earth orbit (LEO) satellites have been considered as a promising component for beyond 5G networks. In this paper, we study downlink LEO-5G communication systems ... [more ▼] Towards the next generation networks, low earth orbit (LEO) satellites have been considered as a promising component for beyond 5G networks. In this paper, we study downlink LEO-5G communication systems in a practical scenario, where the integrated LEO-terrestrial system is over-loaded by serving a number of terminals with high-volume traffic requests. Our goal is to optimize resource scheduling such that the amount of undelivered data and the number of unserved terminals can be minimized. Due to the inherent hardness of the formulated quadratic integer programming problem, the optimal algorithm requires unaffordable complexity. To solve the problem, we propose a near-optimal algorithm based on alternating direction method of multipliers (ADMM-HEU), which saves computational time by taking advantage of the distributed ADMM structure, and a low-complexity heuristic algorithm (LC-HEU), which is based on estimation and greedy methods. The results demonstrate the near-optimality of ADMM-HEU and the computational efficiency of LC-HEU compared to the benchmarks. [less ▲] Detailed reference viewed: 43 (2 UL)![]() Maity, Ilora ![]() ![]() ![]() in IEEE Wireless Communications and Networking Conference (WCNC) (2022, April 10) In this paper, we address the virtual network embedding (VNE) problem in non-terrestrial networks (NTNs) enabling dynamic changes in the virtual network function (VNF) deployment to maximize the service ... [more ▼] In this paper, we address the virtual network embedding (VNE) problem in non-terrestrial networks (NTNs) enabling dynamic changes in the virtual network function (VNF) deployment to maximize the service acceptance rate and service revenue. NTNs such as satellite networks involve highly dynamic topology and limited resources in terms of rate and power. VNE in NTNs is a challenge because a static strategy under-performs when new service requests arrive or the network topology changes unexpectedly due to failures or other events. Existing solutions do not consider the power constraint of satellites and rate limitation of inter-satellite links (ISLs) which are essential parameters for dynamic adjustment of existing VNE strategy in NTNs. In this work, we propose a dynamic VNE algorithm that selects a suitable VNE strategy for new and existing services considering the time-varying network topology. The proposed scheme, D-ViNE, increases the service acceptance ratio by 8.51% compared to the benchmark scheme TS-MAPSCH. [less ▲] Detailed reference viewed: 110 (28 UL)![]() Vu, Thang Xuan ![]() ![]() ![]() in IEEE Transactions on Wireless Communications (2022), 21(3), 1794-1805 Multiuser techniques play a central role in the fifth-generation (5G) and beyond 5G (B5G) wireless networks that exploit spatial diversity to serve multiple users simultaneously in the same frequency ... [more ▼] Multiuser techniques play a central role in the fifth-generation (5G) and beyond 5G (B5G) wireless networks that exploit spatial diversity to serve multiple users simultaneously in the same frequency resource. It is well known that a multi-antenna base station (BS) can efficiently serve a number of users not exceeding the number of antennas at the BS via precoding design. However, when there are more users than the number of antennas at the BS, conventional precoding design methods perform poorly because inter-user interference cannot be efficiently eliminated. In this paper, we investigate the performance of a highly-loaded multiuser system in which a BS simultaneously serves a number of users that is larger than the number of antennas. We propose a dynamic bandwidth allocation and precoding design framework and apply it to two important problems in multiuser systems: i) User fairness maximization and ii) Transmit power minimization, both subject to predefined quality of service (QoS) requirements. The premise of the proposed framework is to dynamically assign orthogonal frequency channels to different user groups and carefully design the precoding vectors within every user group. Since the formulated problems are non-convex, we propose two iterative algorithms based on successive convex approximations (SCA), whose convergence is theoretically guaranteed. Furthermore, we propose a low-complexity user grouping policy based on the singular value decomposition (SVD) to further improve the system performance. Finally, we demonstrate via numerical results that the proposed framework significantly outperforms existing designs in the literature. [less ▲] Detailed reference viewed: 85 (26 UL)![]() Tran Dinh, Hieu ![]() ![]() ![]() in IEEE Transactions on Wireless Communications (2022), 21(3), 1621-1637 Unmanned aerial vehicle (UAV) communication hasemerged as a prominent technology for emergency communi-cations (e.g., natural disaster) in the Internet of Things (IoT)networks to enhance the ability of ... [more ▼] Unmanned aerial vehicle (UAV) communication hasemerged as a prominent technology for emergency communi-cations (e.g., natural disaster) in the Internet of Things (IoT)networks to enhance the ability of disaster prediction, damageassessment, and rescue operations promptly. A UAV can bedeployed as a flying base station (BS) to collect data from time-constrained IoT devices and then transfer it to a ground gateway(GW). In general, the latency constraint at IoT devices and UAV’slimited storage capacity highly hinder practical applicationsof UAV-assisted IoT networks. In this paper, full-duplex (FD)radio is adopted at the UAV to overcome these challenges. Inaddition, half-duplex (HD) scheme for UAV-based relaying isalso considered to provide a comparative study between twomodes (viz., FD and HD). Herein, a device is considered tobe successfully served iff its data is collected by the UAV andconveyed to GW timely during flight time. In this context,we aim to maximize the number of served IoT devices byjointly optimizing bandwidth, power allocation, and the UAVtrajectory while satisfying each device’s requirement and theUAV’s limited storage capacity. The formulated optimizationproblem is troublesome to solve due to its non-convexity andcombinatorial nature. Towards appealing applications, we firstrelax binary variables into continuous ones and transform theoriginal problem into a more computationally tractable form.By leveraging inner approximation framework, we derive newlyapproximated functions for non-convex parts and then develop asimple yet efficient iterative algorithm for its solutions. Next,we attempt to maximize the total throughput subject to thenumber of served IoT devices. Finally, numerical results showthat the proposed algorithms significantly outperform benchmarkapproaches in terms of the number of served IoT devices andsystem throughput. [less ▲] Detailed reference viewed: 110 (26 UL)![]() ; ; Vu, Thang Xuan ![]() Book published by IET (2022) The latest advances in several emerging technologies such as AI, blockchain, privacy-preserving algorithms used in localization and positioning systems, cloud computing and computer vision all have great ... [more ▼] The latest advances in several emerging technologies such as AI, blockchain, privacy-preserving algorithms used in localization and positioning systems, cloud computing and computer vision all have great potential in facilitating social distancing. Benefits range from supporting people to work from home to monitoring micro- and macro- movements such as contact tracing apps using Bluetooth, tracking the movement and transportation level of a city and wireless positioning systems to help people keep a safe distance by alerting them when they are too close to each other or to avoid congestion. However, implementing such technologies in practical scenarios still faces various challenges. This book aims to lay the foundations of how these technologies could be adopted to realize and facilitate social distancing to better manage pandemics and future outbreaks. Starting with basic concepts, models and practical technology-based social distancing scenarios, the authors present enabling wireless technologies and solutions which could be widely adopted to encourage social distancing. They include symptom prediction, detection and monitoring of quarantined people and contact tracing. In the future, smart infrastructures for next-generation wireless systems should incorporate a pandemic mode in their standard architecture and design. [less ▲] Detailed reference viewed: 46 (6 UL)![]() ; Vu, Thang Xuan ![]() in Enabling Technologies for Social Distancing: Fundamentals, concepts and solutions (2022) Vaccination is considered as the most effective solution to fight against the COVID-19 epidemic as well as other contagious and infectious diseases to bring the world to a "new normal" lifestyle. This ... [more ▼] Vaccination is considered as the most effective solution to fight against the COVID-19 epidemic as well as other contagious and infectious diseases to bring the world to a "new normal" lifestyle. This lifestyle is defined as a new way of living our work, routines, and interactions with other people to adapt with COVID-19. With the ambition to open up the economy, many countries such as United Arab Emirates, Portugal, and Singapore have achieved the coverage rate of COVID-19 vaccines for the 2nd dose above 80%. However, when the vaccine has not been evenly distributed to all countries worldwide, it means that COVID-19 cannot be ended. This is because fully vaccinated people can still be positive with COVID-19, and the effectiveness of the vaccine also decreases significantly after 6 months. Therefore, protective measures like social distancing, wearing mask, and frequent handwashing must also be practiced simultaneously to enable the "new normal" lifestyle. In this chapter, we discuss the open issues of social distancing implementation such as pandemic mode, hybrid technology solutions, security and privacy concerns, social distancing encouragement, real-time scheduling, and negative effects. Furthermore, potential solutions to these issues are also discussed. [less ▲] Detailed reference viewed: 27 (0 UL)![]() Minardi, Mario ![]() ![]() ![]() Scientific Conference (2021, September) Detailed reference viewed: 83 (24 UL)![]() Yuan, Yaxiong ![]() ![]() ![]() in IEEE Transactions on Vehicular Technology (2021) In unmanned aerial vehicle (UAV) applications, the UAV's limited energy supply and storage have triggered the development of intelligent energy-conserving scheduling solutions. In this paper, we ... [more ▼] In unmanned aerial vehicle (UAV) applications, the UAV's limited energy supply and storage have triggered the development of intelligent energy-conserving scheduling solutions. In this paper, we investigate energy minimization for UAV-aided communication networks by jointly optimizing data-transmission scheduling and UAV hovering time. The formulated problem is combinatorial and non-convex with bilinear constraints. To tackle the problem, firstly, we provide an optimal relax-and-approximate solution and develop a near-optimal algorithm. Both the proposed solutions are served as offline performance benchmarks but might not be suitable for online operation. To this end, we develop a solution from a deep reinforcement learning (DRL) aspect. The conventional RL/DRL, e.g., deep Q-learning, however, is limited in dealing with two main issues in constrained combinatorial optimization, i.e., exponentially increasing action space and infeasible actions. The novelty of solution development lies in handling these two issues. To address the former, we propose an actor-critic-based deep stochastic online scheduling (AC-DSOS) algorithm and develop a set of approaches to confine the action space. For the latter, we design a tailored reward function to guarantee the solution feasibility. Numerical results show that, by consuming equal magnitude of time, AC-DSOS is able to provide feasible solutions and saves 29.94% energy compared with a conventional deep actor-critic method. Compared to the developed near-optimal algorithm, AC-DSOS consumes around 10% higher energy but reduces the computational time from minute-level to millisecond-level. [less ▲] Detailed reference viewed: 116 (28 UL) |
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