820 research outputs found
Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication
The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink
multi-user communication from a multi-antenna base station is investigated in
this paper. We develop energy-efficient designs for both the transmit power
allocation and the phase shifts of the surface reflecting elements, subject to
individual link budget guarantees for the mobile users. This leads to
non-convex design optimization problems for which to tackle we propose two
computationally affordable approaches, capitalizing on alternating
maximization, gradient descent search, and sequential fractional programming.
Specifically, one algorithm employs gradient descent for obtaining the RIS
phase coefficients, and fractional programming for optimal transmit power
allocation. Instead, the second algorithm employs sequential fractional
programming for the optimization of the RIS phase shifts. In addition, a
realistic power consumption model for RIS-based systems is presented, and the
performance of the proposed methods is analyzed in a realistic outdoor
environment. In particular, our results show that the proposed RIS-based
resource allocation methods are able to provide up to higher energy
efficiency, in comparison with the use of regular multi-antenna
amplify-and-forward relaying.Comment: Accepted by IEEE TWC; additional materials on the topic are included
in the 2018 conference publications at ICASSP
(https://ieeexplore.ieee.org/abstract/document/8461496) and GLOBECOM 2018
(arXiv:1809.05397
Reconfigurable Intelligent Surface Assisted High-Speed Train Communications: Coverage Performance Analysis and Placement Optimization
Reconfigurable intelligent surface (RIS) emerges as an efficient and
promising technology for the next wireless generation networks and has
attracted a lot of attention owing to the capability of extending wireless
coverage by reflecting signals toward targeted receivers. In this paper, we
consider a RIS-assisted high-speed train (HST) communication system to enhance
wireless coverage and improve coverage probability. First, coverage performance
of the downlink single-input-single-output system is investigated, and the
closed-form expression of coverage probability is derived. Moreover, travel
distance maximization problem is formulated to facilitate RIS discrete phase
design and RIS placement optimization, which is subject to coverage probability
constraint. Simulation results validate that better coverage performance and
higher travel distance can be achieved with deployment of RIS. The impacts of
some key system parameters including transmission power, signal-to-noise ratio
threshold, number of RIS elements, number of RIS quantization bits, horizontal
distance between base station and RIS, and speed of HST on system performance
are investigated. In addition, it is found that RIS can well improve coverage
probability with limited power consumption for HST communications.Comment: 14 figures, accepted by IEEE Transactions on Vehicular Technolog
Intelligent Reflective Surface Deployment in 6G: A Comprehensive Survey
Intelligent reflecting surfaces (IRSs) are considered a promising technology
that can smartly reconfigure the wireless environment to enhance the
performance of future wireless networks. However, the deployment of IRSs still
faces challenges due to highly dynamic and mobile unmanned aerial vehicle (UAV)
enabled wireless environments to achieve higher capacity. This paper sheds
light on the different deployment strategies for IRSs in future terrestrial and
non-terrestrial networks. Specifically, in this paper, we introduce key
theoretical concepts underlying the IRS paradigm and discuss the design aspects
related to the deployment of IRSs in 6G networks. We also explore
optimization-based IRS deployment techniques to improve system performance in
terrestrial and aerial IRSs. Furthermore, we survey model-free reinforcement
learning (RL) techniques from the deployment aspect to address the challenges
of achieving higher capacity in complex and mobile IRS-assisted UAV wireless
systems. Finally, we highlight challenges and future research directions from
the deployment aspect of IRSs for improving system performance for the future
6G network.Comment: 16 pages, 3 Figures, 7 table
Autonomous reconfigurable intelligent surfaces through wireless energy harvesting
In this paper, we examine the potential for a reconfigurable intelligent surface (RIS) to be powered by energy harvested from information signals. This feature might be key to reap the benefits of RIS technology's lower power consumption compared to active relays. We first identify the main RIS power-consuming components and then propose an energy harvesting and power consumption model. Furthermore, we formulate and solve the problem of the optimal RIS placement together with the amplitude and phase response adjustment of its elements in order to maximize the signal-to-noise ratio (SNR) while harvesting sufficient energy for its operation. Finally, numerical results validate the autonomous operation potential and reveal the range of power consumption values that enables it.This work was supported by the European Commission’s Horizon 2020 research and innovation programme ARIADNE (No. 871464), the Luxembourg National Research Fund (FNR) under the CORE project RISOTTI (ref. 14773976), and the Digital Futures Center.Peer ReviewedPostprint (author's final draft
Cluster Index Modulation for Reconfigurable Intelligent Surface-Assisted mmWave Massive MIMO
In this paper, we propose a transmission mechanism for a reconfigurable
intelligent surface (RIS)-assisted millimeter wave (mmWave) system based on
cluster index modulation (CIM), named best-gain optimized cluster selection CIM
(BGCS-CIM). The proposed BGCS-CIM scheme considers effective cluster power gain
and spatial diversity gain obtained by the additional paths within the indexed
cluster to construct an efficient codebook. We also integrate the proposed
scheme into a practical system model to create a virtual path between
transmitter and receiver where the direct link has been blocked. Thanks to the
designed whitening filter, a closed-form expression for the upper bound on the
average bit error rate (ABER) is derived and used to validate the simulation
results. It has been shown that the proposed BGCS-CIM scheme outperforms the
existing benchmarks thanks to its higher effective cluster gain, spatial
diversity of indexed clusters, and lower inter-cluster interference.Comment: Submitted in IEE
Boosting 5G mm-Wave IAB Reliability with Reconfigurable Intelligent Surfaces
The introduction of the mm-Wave spectrum into 5G NR promises to bring about
unprecedented data throughput to future mobile wireless networks but comes with
several challenges. Network densification has been proposed as a viable
solution to increase RAN resilience, and the newly introduced IAB is considered
a key enabling technology with compelling cost-reducing opportunities for such
dense deployments. Reconfigurable Intelligent Surfaces (RIS) have recently
gained extreme popularity as they can create Smart Radio Environments by EM
wave manipulation and behave as inexpensive passive relays. However, it is not
yet clear what role this technology can play in a large RAN deployment. With
the scope of filling this gap, we study the blockage resilience of realistic
mm-Wave RAN deployments that use IAB and RIS. The RAN layouts have been
optimised by means of a novel mm-Wave planning tool based on MILP formulation.
Numerical results show how adding RISs to IAB deployments can provide high
blockage resistance levels while significantly reducing the overall network
planning cost
Wireless energy harvesting for autonomous reconfigurable intelligent surfaces
In the current contribution, we examine the feasibility of fully-energy-autonomous operation of reconfigurable intelligent surfaces (RIS) through wireless energy harvesting (EH) from incident information signals. Towards this, we first identify the main RIS energy-consuming components and present a suitable and accurate energy-consumption model that is based on the recently proposed integrated controller architecture and includes the energy consumption needed for channel estimation. Building on this model, we introduce a novel RIS architecture that enables EH through RIS unit-cell (UC) splitting. Subsequently, we introduce an EH policy, where a subset of the UCs is used for beamsteering, while the remaining UCs absorb energy. In particular, we formulate a subset allocation optimization problem that aims at maximizing the signal-to-noise ratio (SNR) at the receiver without violating the RIS’s energy consumption demands. As a problem solution, we present low-complexity heuristic algorithms. The presented numerical results reveal the feasibility of the proposed architecture and the efficiency of the presented algorithms with respect to both the optimal and very high-complexity brute-force approach and the one corresponding to random subset selection. Furthermore, the results reveal how important the placement of the RIS as close to the transmitter as possible is, for increasing the harvesting effectiveness.This work was supported by the Luxembourg National Research Fund (FNR) under the CORE project RISOTTI (ref. 14773976), the European Commission’s Horizon 2020 research and innovation programme (ARIADNE) under grant agreement No. 871464, and the Digital Futures center.Peer ReviewedPostprint (published version
Intelligent Reflecting Surfaces Positioning in 6G Networks
The work analyzed the positioning of IRS over the coverage region of micro
cell to derive optimal placement location to support cell-edge Internet of
Things (IoT) devices with a favorable signal-to-interference plus noise ratio
(SINR). Moreover, the work derived that the implementation of IRS significantly
enhances energy efficiency notably reducing the transmit power of the micro
cell base station
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