66 research outputs found
Resource Allocation for Near-Field Communications: Fundamentals, Tools, and Outlooks
Extremely large-scale multiple-input-multiple output (XL-MIMO) is a promising
technology to achieve high spectral efficiency (SE) and energy efficiency (EE)
in future wireless systems. The larger array aperture of XL-MIMO makes
communication scenarios closer to the near-field region. Therefore, near-field
resource allocation is essential in realizing the above key performance
indicators (KPIs). Moreover, the overall performance of XL-MIMO systems heavily
depends on the channel characteristics of the selected users, eliminating
interference between users through beamforming, power control, etc. The above
resource allocation issue constitutes a complex joint multi-objective
optimization problem since many variables and parameters must be optimized,
including the spatial degree of freedom, rate, power allocation, and
transmission technique. In this article, we review the basic properties of
near-field communications and focus on the corresponding "resource allocation"
problems. First, we identify available resources in near-field communication
systems and highlight their distinctions from far-field communications. Then,
we summarize optimization tools, such as numerical techniques and machine
learning methods, for addressing near-field resource allocation, emphasizing
their strengths and limitations. Finally, several important research directions
of near-field communications are pointed out for further investigation
Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities
Recently there has been a flurry of research on the use of reconfigurable
intelligent surfaces (RIS) in wireless networks to create smart radio
environments. In a smart radio environment, surfaces are capable of
manipulating the propagation of incident electromagnetic waves in a
programmable manner to actively alter the channel realization, which turns the
wireless channel into a controllable system block that can be optimized to
improve overall system performance. In this article, we provide a tutorial
overview of reconfigurable intelligent surfaces (RIS) for wireless
communications. We describe the working principles of reconfigurable
intelligent surfaces (RIS) and elaborate on different candidate implementations
using metasurfaces and reflectarrays. We discuss the channel models suitable
for both implementations and examine the feasibility of obtaining accurate
channel estimates. Furthermore, we discuss the aspects that differentiate RIS
optimization from precoding for traditional MIMO arrays highlighting both the
arising challenges and the potential opportunities associated with this
emerging technology. Finally, we present numerical results to illustrate the
power of an RIS in shaping the key properties of a MIMO channel.Comment: to appear in the IEEE Transactions on Cognitive Communications and
Networking (TCCN
Robust Symbol-Level Precoding for Massive MIMO Communication Under Channel Aging
This paper investigates the robust design of symbol-level precoding (SLP) for
multiuser multiple-input multiple-output (MIMO) downlink transmission with
imperfect channel state information (CSI) caused by channel aging. By utilizing
the a posteriori channel model based on the widely adopted jointly correlated
channel model, the imperfect CSI is modeled as the statistical CSI
incorporating the channel mean and channel variance information with spatial
correlation. With the signal model in the presence of channel aging, we
formulate the signal-to-noise-plus-interference ratio (SINR) balancing and
minimum mean square error (MMSE) problems for robust SLP design. The former
targets to maximize the minimum SINR across users, while the latter minimizes
the mean square error between the received signal and the target constellation
point. When it comes to massive MIMO scenarios, the increment in the number of
antennas poses a computational complexity challenge, limiting the deployment of
SLP schemes. To address such a challenge, we simplify the objective function of
the SINR balancing problem and further derive a closed-form SLP scheme.
Besides, by approximating the matrix involved in the computation, we modify the
proposed algorithm and develop an MMSE-based SLP scheme with lower computation
complexity. Simulation results confirm the superiority of the proposed schemes
over the state-of-the-art SLP schemes
Power and Beam Optimization for Uplink Millimeter-Wave Hotspot Communication Systems
We propose an effective interference management and beamforming mechanism for
uplink communication systems that yields fair allocation of rates. In
particular, we consider a hotspot area of a millimeter-wave (mmWave) access
network consisting of multiple user equipment (UE) in the uplink and multiple
access points (APs) with directional antennas and adjustable beam widths and
directions (beam configurations). This network suffers tremendously from
multi-beam multi-user interference, and, to improve the uplink transmission
performance, we propose a centralized scheme that optimizes the power, the beam
width, the beam direction of the APs, and the UE - AP assignments. This problem
involves both continuous and discrete variables, and it has the following
structure. If we fix all discrete variables, except for those related to the
UE-AP assignment, the resulting optimization problem can be solved optimally.
This property enables us to propose a heuristic based on simulated annealing
(SA) to address the intractable joint optimization problem with all discrete
variables. In more detail, for a fixed configuration of beams, we formulate a
weighted rate allocation problem where each user gets the same portion of its
maximum achievable rate that it would have under non-interfered conditions. We
solve this problem with an iterative fixed point algorithm that optimizes the
power of UEs and the UE - AP assignment in the uplink. This fixed point
algorithm is combined with SA to improve the beam configurations. Theoretical
and numerical results show that the proposed method improves both the UE rates
in the lower percentiles and the overall fairness in the network
Massive MIMO is a Reality -- What is Next? Five Promising Research Directions for Antenna Arrays
Massive MIMO (multiple-input multiple-output) is no longer a "wild" or
"promising" concept for future cellular networks - in 2018 it became a reality.
Base stations (BSs) with 64 fully digital transceiver chains were commercially
deployed in several countries, the key ingredients of Massive MIMO have made it
into the 5G standard, the signal processing methods required to achieve
unprecedented spectral efficiency have been developed, and the limitation due
to pilot contamination has been resolved. Even the development of fully digital
Massive MIMO arrays for mmWave frequencies - once viewed prohibitively
complicated and costly - is well underway. In a few years, Massive MIMO with
fully digital transceivers will be a mainstream feature at both sub-6 GHz and
mmWave frequencies. In this paper, we explain how the first chapter of the
Massive MIMO research saga has come to an end, while the story has just begun.
The coming wide-scale deployment of BSs with massive antenna arrays opens the
door to a brand new world where spatial processing capabilities are
omnipresent. In addition to mobile broadband services, the antennas can be used
for other communication applications, such as low-power machine-type or
ultra-reliable communications, as well as non-communication applications such
as radar, sensing and positioning. We outline five new Massive MIMO related
research directions: Extremely large aperture arrays, Holographic Massive MIMO,
Six-dimensional positioning, Large-scale MIMO radar, and Intelligent Massive
MIMO.Comment: 20 pages, 9 figures, submitted to Digital Signal Processin
Application of evolutionary computation techniques in emerging optimization problems in 5G and beyond wireless systems
Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2021.Os sistemas comunicação sem fio 5G e além (B5G, do inglês Beyong 5G) permitirão a plena implantação de aplicações existentes, como carros autônomos, redes de sensores massivas e casas inteligentes. Para tornar essas aplicações possíveis, requisitos rigorosos, como alta eficiência espectral e ultra baixa latência de comunicação, devem ser atendidos. Para atender a esses requisitos, diferentes tecnologias-chave estão em desenvolvimento, como comunicações de Ondas Milimétricas (mmWave, do inglês Millimeter Wave) e Superfícies Refletivas Inteligentes (IRS, do inglês Intelligent Reflecting Surfaces). As comunicações mmWave têm atraído grande interesse devido ao abundante espectro de frequência disponível, ao contrário das bandas congestionadas adotadas nas redes 4G. No entanto, as bandas mmWave apresentam características de propagação desfavoráveis. Para superar tais problemas de propagação, o uso de beamforming altamente direcional é uma solução eficaz. Além disso, recentemente, uma tecnologia de baixo custo e alta eficiência energética denominada IRS, uma meta-superfície equipada com um grande número de elementos passivos de baixo custo, capaz de refletir o sinal incidente com uma dada mudança de fase/amplitude, foi desenvolvida para otimizar a capacidade da rede. Implantando densamente IRSs em redes de comunicação sem fio e coordenando seus elementos de maneira inteligente, os canais sem fio entre o transmissor e o receptor podem ser intencional e deterministicamente controlados para melhorar a qualidade do sinal no receptor. Embora essas tecnologias tenham inúmeros benefícios para o desempenho do sistema, elas apresentam muitos desafios em sua implantação. Mais especificamente, embora as bandas mmWave permitam considerar o uso de beamforming altamente direcional tanto na BS quanto no UE, isto pode representar um desafio para o processo de Acesso Inicial (IA, do inglês Initial Access) pois, uma vez que a transmissão omnidirecional não pode ser aplicada, devido ao seu baixo ganho de potência e SNR recebido, a duração geral do IA pode ser muito longa. O atraso causado pela busca direcional deve ser pequeno para atender a alguns dos requisitos das redes B5G como baixa latência de ponta-a-ponta. Além disso, apesar da capacidade das IRSs de controlar os canais sem fio, o projeto do beamforming na BS e na IRS é um problema desafiador devido à necessidade de estimar a informação de estado do canal (CSI, do inglês Channel State Information) de todos os links do sistema. No entanto, para estimar o CSI entre a IRS e a BS ou entre a IRS e o UE, cada elemento da IRS precisa ser equipado com uma cadeia de radiofrequência (RF, do inglês Radio Frequency), o que aumenta consideravelmente o custo e o consumo de energia do sistema e vai contra algumas das principais vantagens de utilizar IRSs em sistemas de comunicação sem fio. Portanto, motivados pelos problemas emergentes acima, nesta tese, pretendemos desenvolver novos métodos baseados em técnicas de Computação Evolutiva tais como, Algoritmos Genéticos (GA, do inglês Genetic Algorithm) e Otimização por Enxame de Partículas (PSO, do inglês Particle Swarm Optimization), visando resolver o problema de IA e realizar o projeto do beamforming na BS e IRS sem conhecimento prévio do CSI na BS. Os resultados obtidos nesta tese mostram que os métodos desenvolvidos podem reduzir consideravelmente o atraso e alcançar um desempenho próximo ao ótimo no problema de projeto do beamforming na BS e IRS com sobrecarga de treinamento reduzida.Abstract: Beyond 5G (B5G) wireless systems will enable the deployment of demanding applications such as autonomous cars, massive sensor networks, and smart homes. To make these applications possible, stringent requirements such as improved spectrum efficiency and low communication latency must be fulfilled. In order to meet these requirements, different key technologies are in development such as millimeter Wave (mmWave) communications and Intelligent Reflecting Surfaces (IRS). The mmWave communications have attracted great interest due to the abundant available spectrum, unlike the congested bands adopted in the 4G networks. However, the mmWave bands present poor propagation characteristics. To overcome these propagation issues, the use of highly directional beamforming is an effective solution. In addition, recently, an energy-efficient and low-cost technology named IRS, which is a meta-surface equipped with a large number of low-cost passive elements, capable of reflecting the incident signal with a given phase/amplitude shift, was developed to increase the network capacity. By densely deploying IRSs in wireless communication networks and intelligently coordinating their elements, the wireless channels between the transmitter and receiver can be intentionally and deterministically controlled to improve the signal quality at the receiver. Although these technologies have uncountable benefits for the system performance, they present many challenges in their deployment. More specifically, although the mmWave bands allow to consider highly directional beamforming at the BS and UE, this can be challenging for the Initial Access (IA) process. Since omnidirectional transmission may not be applied, due to its low power gain and received SNR, the overall duration of IA can be very long. The delay caused by directional search must be small to meet some of the B5G requirements for low end-to-end latency. Moreover, despite the capacity of controlling the wireless channels of the IRSs, designing the beamforming at the BS and at the IRS is a challenging problem due to the necessity of estimating the channel state information (CSI) of all system links. However, to estimate the CSI between IRS and BS or between IRS and UE, each element of the IRS needs to be equipped with one radio-frequency (RF) chain which greatly increases the cost and energy consumption of the system and goes against some of the original advantages of using an IRS. Therefore, motivated by the above emerging problems, in this thesis, we intend to develop new methods based on Evolutionary Computation techniques, i.e., Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to solve the IA problem and to design the beamforming at the BS and IRS without CSI. Results show that the developed methods can reduce the IA delay and achieve a close-to-optimal performance in the IRS beamforming problem with reduced training overhead
A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks
Reconfigurable intelligent surfaces (RISs) have received considerable
attention as a key enabler for envisioned 6G networks, for the purpose of
improving the network capacity, coverage, efficiency, and security with low
energy consumption and low hardware cost. However, integrating RISs into the
existing infrastructure greatly increases the network management complexity,
especially for controlling a significant number of RIS elements. To unleash the
full potential of RISs, efficient optimization approaches are of great
importance. This work provides a comprehensive survey on optimization
techniques for RIS-aided wireless communications, including model-based,
heuristic, and machine learning (ML) algorithms. In particular, we first
summarize the problem formulations in the literature with diverse objectives
and constraints, e.g., sum-rate maximization, power minimization, and imperfect
channel state information constraints. Then, we introduce model-based
algorithms that have been used in the literature, such as alternating
optimization, the majorization-minimization method, and successive convex
approximation. Next, heuristic optimization is discussed, which applies
heuristic rules for obtaining low-complexity solutions. Moreover, we present
state-of-the-art ML algorithms and applications towards RISs, i.e., supervised
and unsupervised learning, reinforcement learning, federated learning, graph
learning, transfer learning, and hierarchical learning-based approaches.
Model-based, heuristic, and ML approaches are compared in terms of stability,
robustness, optimality and so on, providing a systematic understanding of these
techniques. Finally, we highlight RIS-aided applications towards 6G networks
and identify future challenges.Comment: This paper has been accepted by IEEE Communications Surveys and
Tutorial
Role of Reconfigurable Intelligent Surfaces in 6G Radio Localization: Recent Developments, Opportunities, Challenges, and Applications
Reconfigurable intelligent surfaces (RISs) are seen as a key enabler low-cost
and energy-efficient technology for 6G radio communication and localization. In
this paper, we aim to provide a comprehensive overview of the current research
progress on the RIS technology in radio localization for 6G. Particularly, we
discuss the RIS-assisted radio localization taxonomy and review the studies of
RIS-assisted radio localization for different network scenarios, bands of
transmission, deployment environments, as well as near-field operations. Based
on this review, we highlight the future research directions, associated
technical challenges, real-world applications, and limitations of RIS-assisted
radio localization
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