1,310 research outputs found
Energy-Efficient Wireless Communications with Distributed Reconfigurable Intelligent Surfaces
This paper investigates the problem of resource allocation for a wireless
communication network with distributed reconfigurable intelligent surfaces
(RISs). In this network, multiple RISs are spatially distributed to serve
wireless users and the energy efficiency of the network is maximized by
dynamically controlling the on-off status of each RIS as well as optimizing the
reflection coefficients matrix of the RISs. This problem is posed as a joint
optimization problem of transmit beamforming and RIS control, whose goal is to
maximize the energy efficiency under minimum rate constraints of the users. To
solve this problem, two iterative algorithms are proposed for the single-user
case and multi-user case. For the single-user case, the phase optimization
problem is solved by using a successive convex approximation method, which
admits a closed-form solution at each step. Moreover, the optimal RIS on-off
status is obtained by using the dual method. For the multi-user case, a
low-complexity greedy searching method is proposed to solve the RIS on-off
optimization problem. Simulation results show that the proposed scheme achieves
up to 33\% and 68\% gains in terms of the energy efficiency in both single-user
and multi-user cases compared to the conventional RIS scheme and
amplify-and-forward relay scheme, respectively
Federated Learning for 6G: Applications, Challenges, and Opportunities
Traditional machine learning is centralized in the cloud (data centers).
Recently, the security concern and the availability of abundant data and
computation resources in wireless networks are pushing the deployment of
learning algorithms towards the network edge. This has led to the emergence of
a fast growing area, called federated learning (FL), which integrates two
originally decoupled areas: wireless communication and machine learning. In
this paper, we provide a comprehensive study on the applications of FL for
sixth generation (6G) wireless networks. First, we discuss the key requirements
in applying FL for wireless communications. Then, we focus on the motivating
application of FL for wireless communications. We identify the main problems,
challenges, and provide a comprehensive treatment of implementing FL techniques
for wireless communications
STAR-RIS Enabled Heterogeneous Networks: Ubiquitous NOMA Communication and Pervasive Federated Learning
This paper integrates non-orthogonal multiple access (NOMA) and over-the-air
federated learning (AirFL) into a unified framework using a simultaneous
transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The
STAR-RIS plays an important role in adjusting the decoding order of hybrid
users for efficient interference mitigation and omni-directional coverage
extension. To capture the impact of non-ideal wireless channels on AirFL, a
closed-form expression for the optimality gap (a.k.a. convergence upper bound)
between the actual loss and the optimal loss is derived. This analysis reveals
that the learning performance is significantly affected by active and passive
beamforming schemes as well as wireless noise. Furthermore, when the learning
rate diminishes as the training proceeds, the optimality gap is explicitly
characterized to converge with a linear rate. To accelerate convergence while
satisfying QoS requirements, a mixed-integer non-linear programming (MINLP)
problem is formulated by jointly designing the transmit power at users and the
configuration mode of STAR-RIS. Next, a trust region-based successive convex
approximation method and a penalty-based semidefinite relaxation approach is
proposed to handle the decoupled non-convex subproblems iteratively. An
alternating optimization algorithm is then developed to find a suboptimal
solution for the original MINLP problem. Extensive simulation results show that
i) the proposed framework can efficiently support NOMA and AirFL users via
concurrent uplink communications, ii) our algorithms can achieve a faster
convergence rate on the IID and non-IID settings as compared to baselines, and
iii) both the spectrum efficiency and learning performance can be significantly
improved with the aid of the well-tuned STAR-RIS.Comment: 16 pages, 8 figure
Integrating Over-the-Air Federated Learning and Non-Orthogonal Multiple Access: What Role can RIS Play?
With the aim of integrating over-the-air federated learning (AirFL) and
non-orthogonal multiple access (NOMA) into an on-demand universal framework,
this paper proposes a novel reconfigurable intelligent surface (RIS)-aided
hybrid network by leveraging the RIS to flexibly adjust the signal processing
order of heterogeneous data. The objective of this work is to maximize the
achievable hybrid rate by jointly optimizing the transmit power, controlling
the receive scalar, and designing the phase shifts. Since the concurrent
transmissions of all computation and communication signals are aided by the
discrete phase shifts at the RIS, the considered problem (P0) is a challenging
mixed integer programming problem. To tackle this intractable issue, we
decompose the original problem (P0) into a non-convex problem (P1) and a
combinatorial problem (P2), which are characterized by the continuous and
discrete variables, respectively. For the transceiver design problem (P1), the
power allocation subproblem is first solved by invoking the
difference-of-convex programming, and then the receive control subproblem is
addressed by using the successive convex approximation, where the closed-form
expressions of simplified cases are derived to obtain deep insights. For the
reflection design problem (P2), the relaxation-then-quantization method is
adopted to find a suboptimal solution for striking a trade-off between
complexity and performance. Afterwards, an alternating optimization algorithm
is developed to solve the non-linear and non-convex problem (P0) iteratively.
Finally, simulation results reveal that 1) the proposed RIS-aided hybrid
network can support the on-demand communication and computation efficiently, 2)
the performance gains can be improved by properly selecting the location of the
RIS, and 3) the designed algorithms are also applicable to conventional
networks with only AirFL or NOMA users
Performance-Oriented Design for Intelligent Reflecting Surface Assisted Federated Learning
To efficiently exploit the massive amounts of raw data that are increasingly
being generated in mobile edge networks, federated learning (FL) has emerged as
a promising distributed learning technique. By collaboratively training a
shared learning model on edge devices, raw data transmission and storage are
replaced by the exchange of the local computed parameters/gradients in FL,
which thus helps address latency and privacy issues. However, the number of
resource blocks when using traditional orthogonal transmission strategies for
FL linearly scales with the number of participating devices, which conflicts
with the scarcity of communication resources. To tackle this issue,
over-the-air computation (AirComp) has emerged recently which leverages the
inherent superposition property of wireless channels to perform one-shot model
aggregation. However, the aggregation accuracy in AirComp suffers from the
unfavorable wireless propagation environment. In this paper, we consider the
use of intelligent reflecting surfaces (IRSs) to mitigate this problem and
improve FL performance with AirComp. Specifically, a performance-oriented
design scheme that directly minimizes the optimality gap of the loss function
is proposed to accelerate the convergence of AirComp-based FL. We first analyze
the convergence behavior of the FL procedure with the absence of channel fading
and noise. Based on the obtained optimality gap which characterizes the impact
of channel fading and noise in different communication rounds on the ultimate
performance of FL, we propose both online and offline approaches to tackle the
resulting design problem. Simulation results demonstrate that such a
performance-oriented design strategy can achieve higher test accuracy than the
conventional isolated mean square error (MSE) minimization approach in FL.Comment: This work has been submitted to the IEEE for possible publicatio
A Comprehensive Overview on 5G-and-Beyond Networks with UAVs: From Communications to Sensing and Intelligence
Due to the advancements in cellular technologies and the dense deployment of
cellular infrastructure, integrating unmanned aerial vehicles (UAVs) into the
fifth-generation (5G) and beyond cellular networks is a promising solution to
achieve safe UAV operation as well as enabling diversified applications with
mission-specific payload data delivery. In particular, 5G networks need to
support three typical usage scenarios, namely, enhanced mobile broadband
(eMBB), ultra-reliable low-latency communications (URLLC), and massive
machine-type communications (mMTC). On the one hand, UAVs can be leveraged as
cost-effective aerial platforms to provide ground users with enhanced
communication services by exploiting their high cruising altitude and
controllable maneuverability in three-dimensional (3D) space. On the other
hand, providing such communication services simultaneously for both UAV and
ground users poses new challenges due to the need for ubiquitous 3D signal
coverage as well as the strong air-ground network interference. Besides the
requirement of high-performance wireless communications, the ability to support
effective and efficient sensing as well as network intelligence is also
essential for 5G-and-beyond 3D heterogeneous wireless networks with coexisting
aerial and ground users. In this paper, we provide a comprehensive overview of
the latest research efforts on integrating UAVs into cellular networks, with an
emphasis on how to exploit advanced techniques (e.g., intelligent reflecting
surface, short packet transmission, energy harvesting, joint communication and
radar sensing, and edge intelligence) to meet the diversified service
requirements of next-generation wireless systems. Moreover, we highlight
important directions for further investigation in future work.Comment: Accepted by IEEE JSA
6G wireless systems : a vision, architectural elements, and future directions
Internet of everything (IoE)-based smart services are expected to gain immense popularity in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G) networks can support various IoE services, they might not be able to completely fulfill the requirements of novel applications. Sixth-generation (6G) wireless systems are envisioned to overcome 5G network limitations. In this article, we explore recent advances made toward enabling 6G systems. We devise a taxonomy based on key enabling technologies, use cases, emerging machine learning schemes, communication technologies, networking technologies, and computing technologies. Furthermore, we identify and discuss open research challenges, such as artificial-intelligence-based adaptive transceivers, intelligent wireless energy harvesting, decentralized and secure business models, intelligent cell-less architecture, and distributed security models. We propose practical guidelines including deep Q-learning and federated learning-based transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-based authentication schemes to cope with these challenges. Finally, we outline and recommend several future directions. © 2013 IEEE
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