SDN-based Testbed for Emerging Use Cases in Beyond 5G NTN-Terrestrial Networks

in IEEE/IFIP Network Operations and Management Symposium (NOMS) 2023, Miami, Florida, USA, 8-12 May 2023 (2023, June)

Learning-Based Joint Channel Prediction and Antenna Selection for Massive MIMO with Partial CSI

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 ▲]

Adapting to Dynamic LEO-B5G Systems: Meta-Critic Learning Based Efficient Resource Scheduling

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 ▲]

Task-Oriented Data Compression for Multi-Agent Communications Over Bit-Budgeted Channels

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 ▲]

Federated Learning Meets Contract Theory: Economic-Efficiency Framework for Electric Vehicle Networks

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 ▲]

Dynamic Bandwidth Allocation and Edge Caching Optimization for Nonlinear Content Delivery through Flexible Multibeam Satellites

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 ▲]

D-ViNE: Dynamic Virtual Network Embedding in Non-Terrestrial Networks

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 ▲]

UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization

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 ▲]

Chapter 10: Open issues and future research directions

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 ▲]

Energy Minimization in UAV-Aided Networks: Actor-Critic Learning for Constrained Scheduling Optimization

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 ▲]