85 research outputs found
NOMA-aided Joint Communication, Sensing, and Multi-tier Computing Systems
A non-orthogonal multiple access (NOMA)-aided joint communication, sensing,
and multi-tier computing (JCSMC) framework is proposed. In this framework, a
multi-functional base station (BS) carries out target sensing, while providing
edge computing services to the nearby users. To enhance the computation
efficiency, the multi-tier computing structure is exploited, where the BS can
further offload the computation tasks to a powerful Cloud server (CS). The
potential benefits of employing NOMA in the proposed JCSMC framework are
investigated, which can maximize the computation offloading capacity and
suppress the inter-function interference. Based on the proposed framework, the
transmit beamformer of the BS and computation resource allocation at the BS and
the CS are jointly optimized to maximize the computation rate subject to the
communication-computation causality and the sensing quality constraints. Both
partial and binary computation offloading modes are considered: 1) For the
partial offloading mode, a weighted minimum mean square error based alternating
optimization algorithm is proposed to solve the corresponding non-convex
optimization problem. It is proved that a KKT optimal solution can be obtained;
2) For the binary offloading mode, the resultant highly-coupled mixed-integer
optimization problem is first transformed to an equivalent but more tractable
form. Then, the reformulated problem is solved by utilizing the alternating
direction method of multipliers approach to obtain a nearly optimal solution.
Finally, numerical results verify the effectiveness of the proposed algorithms
and the proposed NOMA-aided JCSMC frameworkComment: 30 pages, 8 figure
Jointly Active and Passive Beamforming Designs for IRS-Empowered WPCN
This paper studies an intelligent reflecting surface (IRS)-empowered wireless powered communication network (WPCN) in Internet of Things (IoT) networks. In particular, a power station (PS) with multiple antennas uses energy beamforming to enable wireless charging to multiple IoT devices, in the downlink wireless energy transfer (WET) phase; then, during the uplink wireless information transfer (WIT) phase, these IoT devices utilise the harvested energy to concurrently transmit their individual information signal to a multi-antenna access point (AP), which equips with multi-user decomposition (MUD) techniques to reconstruct the IoT devices’ signal. An IRS is deployed to improve the energy collection and information transmission capabilities in the WET and WIT phases, respectively. To examine the performance of the system under study, We maximize the sum throughput with the aim of jointly designing the optimal solutions for the active PS energy beamforming, AP receive beamforming, passive IRS beamforming, and time scheduling. Due to the multiple coupled variables, the resulting formulation is non-convex, and a two-level scheme to solve the problem is proposed. At the outer level, a one-dimensional (1-D) search method is applied to find the optimal time scheduling, while at the inner level, an iterative block coordinate descent (BCD) algorithm is proposed to design the optimal receive beamforming, energy beamforming, and IRS phase shifts. In particular, the receive beamforming part is designed by considering the equivalence between sum rate maximisation and sum mean square error (MSE) minimisation, thereby deriving a closed-form solution. Furthermore, we alternately optimize the energy beamforming and IRS phase shifts using Lagrange dual transformation (LDT), quadratic transformation (QT), and alternating direction method of multipliers (ADMM) methods. Finally, numerical results are presented to showcase the performance of the proposed solution and highlight its advant..
Wireless for Machine Learning
As data generation increasingly takes place on devices without a wired
connection, Machine Learning over wireless networks becomes critical. Many
studies have shown that traditional wireless protocols are highly inefficient
or unsustainable to support Distributed Machine Learning. This is creating the
need for new wireless communication methods. In this survey, we give an
exhaustive review of the state of the art wireless methods that are
specifically designed to support Machine Learning services. Namely,
over-the-air computation and radio resource allocation optimized for Machine
Learning. In the over-the-air approach, multiple devices communicate
simultaneously over the same time slot and frequency band to exploit the
superposition property of wireless channels for gradient averaging
over-the-air. In radio resource allocation optimized for Machine Learning,
Active Learning metrics allow for data evaluation to greatly optimize the
assignment of radio resources. This paper gives a comprehensive introduction to
these methods, reviews the most important works, and highlights crucial open
problems.Comment: Corrected typo in author name. From the incorrect Maitron to the
correct Mairto
Holistic resource management in UAV-assisted wireless networks
Unmanned aerial vehicles (UAVs) are considered as a promising solution to assist terrestrial networks in future wireless networks (i.e., beyond fifth-generation (B5G) and sixth-generation (6G)).
The convergence of various technologies requires future wireless networks to provide multiple
functionalities, including communication, computing, control, and caching (4C), necessary for
applications such as connected robotics and autonomous systems. The majority of existing works
consider the developments in 4C individually, which limits the cooperation among 4C for potential
gains. UAVs have been recently introduced to supplement mobile edge computing (MEC) in terrestrial networks to reduce network latency by providing mobile resources at the network edge in
future wireless networks. However, compared to ground base stations (BSs), the limited resources
at the network edge call for holistic management of the resources, which requires joint optimization. We provide a comprehensive review of holistic resource management in UAV-assisted wireless networks. Integrated resource management considers the challenges associated with aerial
networks (such as three-dimensional (3D) placement of UAVs, trajectory planning, channel modelling, and backhaul connectivity) and terrestrial networks (such as limited bandwidth, power, and
interference). We present architectures (source-UAV-destination and UAV-destination architecture)
and 4C in UAV-assisted wireless networks. We then provide a detailed discussion on resource
management by categorizing the optimization problems into individual or combinations of two
(communication and computation) or three (communication, computation and control). Moreover,
solution approaches and performance metrics are discussed and analyzed for different objectives
and problem types. We formulate a mathematical framework for holistic resource management
to minimize the linear combination of network latency and cost for user association while guaranteeing the offloading, computing, and caching constraints. Binary decision variables are used
to allocate offloading and computing resources. Since the decision variables are binary and constraints are linear, the formulated problem is a binary linear programming problem. We propose
a heuristic algorithm based on the interior point method by exploiting the optimization structure
of the problem to get a sub-optimal solution with less complexity. Simulation results show the effectiveness of the proposed work when compared to the optimal results obtained using branch and
bound. Finally, we discuss insight into the potential future research areas to address the challenges
of holistic resource management in UAV-assisted wireless networks
Opportunistic Access Point Selection for Mobile Edge Computing Networks
In this paper, we investigate a mobile edge computing (MEC) network with two computational access points (CAPs), where the source is equipped with multiple antennas and it has some computational tasks to be accomplished by the CAPs through Nakagami-m distributed wireless links. Since the MEC network involves both communication and computation, we first define the outage probability by taking into account the joint impact of latency and energy consumption. From this new definition, we then employ receiver antenna selection (RAS) or maximal ratio combining (MRC) at the receiver, and apply selection combining (SC) or switch-and-stay combining (SSC) protocol to choose a CAP to accomplish the computational task from the source. For both protocols along with the RAS and MRC, we further analyze the network performance by deriving new and easy-to-use analytical expressions for the outage probability over Nakagami-m fading channels, and study the impact of the network parameters on the outage performance. Furthermore, we provide the asymptotic outage probability in the low regime of noise power, from which we obtain some important insights on the system design. Finally, simulations and numerical results are demonstrated to verify the effectiveness of the proposed approach. It is shown that the number of transmit antenna and Nakagami parameter can help reduce the latency and energy consumption effectively, and the SSC protocol can achieve the same performance as the SC protocol with proper switching thresholds of latency and energy consumption
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