552 research outputs found
Reliable indoor optical wireless communication in the presence of fixed and random blockers
The advanced innovation of smartphones has led to the exponential growth of internet users which is expected to reach 71% of the global population by the end of 2027. This in turn has given rise to the demand for wireless data and internet devices that is capable of providing energy-efficient, reliable data transmission and high-speed wireless data services. Light-fidelity (LiFi), known as one of the optical wireless communication (OWC) technology is envisioned as a promising solution to accommodate these demands. However, the indoor LiFi channel is highly environment-dependent which can be influenced by several crucial factors (e.g., presence of people, furniture, random users' device orientation and the limited field of view (FOV) of optical receivers) which may contribute to the blockage of the line-of-sight (LOS) link.
In this thesis, it is investigated whether deep learning (DL) techniques can effectively learn the distinct features of the indoor LiFi environment in order to provide superior performance compared to the conventional channel estimation techniques (e.g., minimum mean square error (MMSE) and least squares (LS)). This performance can be seen particularly when access to real-time channel state information (CSI) is restricted and is achieved with the cost of collecting large and meaningful data to train the DL neural networks and the training time which was conducted offline. Two DL-based schemes are designed for signal detection and resource allocation where it is shown that the proposed methods were able to offer close performance to the optimal conventional schemes and demonstrate substantial gain in terms of bit-error ratio (BER) and throughput especially in a more realistic or complex indoor environment.
Performance analysis of LiFi networks under the influence of fixed and random blockers is essential and efficient solutions capable of diminishing the blockage effect is required. In this thesis, a CSI acquisition technique for a reconfigurable intelligent surface (RIS)-aided LiFi network is proposed to significantly reduce the dimension of the decision variables required for RIS beamforming. Furthermore, it is shown that several RIS attributes such as shape, size, height and distribution play important roles in increasing the network performance. Finally, the performance analysis for an RIS-aided realistic indoor LiFi network are presented. The proposed RIS configuration shows outstanding performances in reducing the network outage probability under the effect of blockages, random device orientation, limited receiver's FOV, furniture and user behavior.
Establishing a LOS link that achieves uninterrupted wireless connectivity in a realistic indoor environment can be challenging. In this thesis, an analysis of link blockage is presented for an indoor LiFi system considering fixed and random blockers. In particular, novel analytical framework of the coverage probability for a single source and multi-source are derived. Using the proposed analytical framework, link blockages of the indoor LiFi network are carefully investigated and it is shown that the incorporation of multiple sources and RIS can significantly reduce the LOS coverage blockage probability in indoor LiFi systems
Synergizing Beyond Diagonal Reconfigurable Intelligent Surface and Rate-Splitting Multiple Access
This work focuses on the synergy of rate-splitting multiple access (RSMA) and beyond diagonal reconfigurable intelligent surface (BD-RIS) to enlarge the coverage, improve the performance, and save on antennas. Specifically, we employ a multi-sector BD-RIS modeled as a prism, which can achieve highly directional full-space coverage, in a multiuser multiple input single output communication system. With the multi-sector BD-RIS aided RSMA model, we jointly design the transmit precoder and BD-RIS matrix under the imperfect channel state information (CSI) conditions. The robust design is performed by solving a stochastic average sum-rate maximization problem. With sample average approximation and weighted minimum mean square error-rate relationship, the stochastic problem is transformed into a deterministic one with multiple blocks, each of which is iteratively designed. Simulation results show that multi-sector BD-RIS aided RSMA outperforms space division multiple access schemes. More importantly, synergizing multi-sector BD-RIS with RSMA is an efficient strategy to reduce the number of active antennas at the transmitter and the number of passive antennas in BD-RIS
Block-Level Interference Exploitation Precoding for MU-MISO: An ADMM Approach
We study constructive interference based block-level precoding (CI-BLP) in
the downlink of multi-user multiple-input single-output (MU-MISO) systems.
Specifically, our aim is to extend the analysis on CI-BLP to the case where the
considered number of symbol slots is smaller than that of the users. To this
end, we mathematically prove the feasibility of using the pseudo-inverse to
obtain the optimal CI-BLP precoding matrix in a closed form. Similar to the
case when the number of users is small, we show that a quadratic programming
(QP) optimization on simplex can be constructed. We also design a
low-complexity algorithm based on the alternating direction method of
multipliers (ADMM) framework, which can efficiently solve large-scale QP
problems. We further analyze the convergence and complexity of the proposed
algorithm. Numerical results validate our analysis and the optimality of the
proposed algorithm, and further show that the proposed algorithm offers a
flexible performance-complexity tradeoff by limiting the maximum number of
iterations, which motivates the use of CI-BLP in practical wireless systems
Active RIS Assisted Rate-Splitting Multiple Access Network: Spectral and Energy Efficiency Tradeoff
With the increasing demand of high data rate and massive access in both ultra-dense and industrial Internet-of-things networks, spectral efficiency (SE) and energy efficiency (EE) are regarded as two important and inter-related performance metrics for future networks. In this paper, we investigate a novel integration of rate-splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) into cellular systems to achieve a desirable tradeoff between SE and EE. Different from the commonly used passive RIS, we adopt reflection elements with active load to improve a newly defined metric, called resource efficiency (RE), which is capable of striking a balance between SE and EE. This paper focuses on the RE optimization by jointly designing the base station (BS) transmit precoding and RIS beamforming (BF) while guaranteeing the transmit and forward power budgets of the BS and RIS, respectively. To efficiently tackle the challenges for solving the RE maximization problem due to its fractional objective function, coupled optimization variables, and discrete coefficient constraint, the formulated nonconvex problem is solved by proposing a two-stage optimization framework. For the outer stage problem, a quadratic transformation is used to recast the fractional objective into a linear form, and a closed-form solution is obtained by using auxiliary variables. For the inner stage problem, the system sum rate is approximated into a linear function. Then, an alternating optimization (AO) algorithm is proposed to optimize the BS precoding and RIS BF iteratively, by utilizing the penalty dual decomposition (PDD) method. Simulation results demonstrate the superiority of the proposed design compared to other benchmarks
Double RIS-Assisted MIMO Systems Over Spatially Correlated Rician Fading Channels and Finite Scatterers
This paper investigates double RIS-assisted MIMO communication systems over
Rician fading channels with finite scatterers, spatial correlation, and the
existence of a double-scattering link between the transceiver. First, the
statistical information is driven in closed form for the aggregated channels,
unveiling various influences of the system and environment on the average
channel power gains. Next, we study two active and passive beamforming designs
corresponding to two objectives. The first problem maximizes channel capacity
by jointly optimizing the active precoding and combining matrices at the
transceivers and passive beamforming at the double RISs subject to the
transmitting power constraint. In order to tackle the inherently non-convex
issue, we propose an efficient alternating optimization algorithm (AO) based on
the alternating direction method of multipliers (ADMM). The second problem
enhances communication reliability by jointly training the encoder and decoder
at the transceivers and the phase shifters at the RISs. Each neural network
representing a system entity in an end-to-end learning framework is proposed to
minimize the symbol error rate of the detected symbols by controlling the
transceiver and the RISs phase shifts. Numerical results verify our analysis
and demonstrate the superior improvements of phase shift designs to boost
system performance.Comment: 15 pages, 9 figures, accepted by IEEE Transactions on Communication
Discrete-Value Group and Fully Connected Architectures for Beyond Diagonal Reconfigurable Intelligent Surfaces
Reconfigurable intelligent surfaces (RISs) allow controlling the propagation
environment in wireless networks through reconfigurable elements. Recently,
beyond diagonal RISs (BD-RISs) have been proposed as novel RIS architectures
whose scattering matrix is not limited to being diagonal. However, BDRISs have
been studied assuming continuous-value scattering matrices, which are hard to
implement in practice. In this paper, we address this problem by proposing two
solutions to realize discrete-value group and fully connected RISs. First, we
propose scalar-discrete RISs, in which each entry of the RIS impedance matrix
is independently discretized. Second, we propose vector-discrete RISs, where
the entries in each group of the RIS impedance matrix are jointly discretized.
In both solutions, the codebook is designed offline such as to minimize the
distortion caused in the RIS impedance matrix by the discretization operation.
Numerical results show that vector-discrete RISs achieve higher performance
than scalar discrete RISs at the cost of increased optimization complexity.
Furthermore, fewer resolution bits per impedance are necessary to achieve the
performance upper bound as the group size of the group connected architecture
increases. In particular, only a single resolution bit is sufficient in fully
connected RISs to approximately achieve the performance upper bound.Comment: Accepted by IEEE for publicatio
A survey on reconfigurable intelligent surfaces: wireless communication perspective
Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS-assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS-assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi-users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed
Rate-splitting multiple access for non-terrestrial communication and sensing networks
Rate-splitting multiple access (RSMA) has emerged as a powerful and flexible
non-orthogonal transmission, multiple access (MA) and interference management
scheme for future wireless networks. This thesis is concerned with the application of
RSMA to non-terrestrial communication and sensing networks. Various scenarios
and algorithms are presented and evaluated.
First, we investigate a novel multigroup/multibeam multicast beamforming strategy
based on RSMA in both terrestrial multigroup multicast and multibeam satellite
systems with imperfect channel state information at the transmitter (CSIT). The
max-min fairness (MMF)-degree of freedom (DoF) of RSMA is derived and shown
to provide gains compared with the conventional strategy. The MMF beamforming
optimization problem is formulated and solved using the weighted minimum mean
square error (WMMSE) algorithm. Physical layer design and link-level simulations
are also investigated. RSMA is demonstrated to be very promising for multigroup
multicast and multibeam satellite systems taking into account CSIT uncertainty
and practical challenges in multibeam satellite systems.
Next, we extend the scope of research from multibeam satellite systems to satellite-
terrestrial integrated networks (STINs). Two RSMA-based STIN schemes are
investigated, namely the coordinated scheme relying on CSI sharing and the co-
operative scheme relying on CSI and data sharing. Joint beamforming algorithms
are proposed based on the successive convex approximation (SCA) approach to
optimize the beamforming to achieve MMF amongst all users. The effectiveness and
robustness of the proposed RSMA schemes for STINs are demonstrated.
Finally, we consider RSMA for a multi-antenna integrated sensing and communications (ISAC) system, which simultaneously serves multiple communication users
and estimates the parameters of a moving target. Simulation results demonstrate
that RSMA is beneficial to both terrestrial and multibeam satellite ISAC systems by
evaluating the trade-off between communication MMF rate and sensing Cramer-Rao
bound (CRB).Open Acces
Energy-Efficient Design of STAR-RIS Aided MIMO-NOMA Networks
Simultaneous transmission and reflection-reconfigurable intelligent surface
(STAR-RIS) can provide expanded coverage compared with the conventional
reflection-only RIS. This paper exploits the energy efficient potential of
STAR-RIS in a multiple-input and multiple-output (MIMO) enabled non-orthogonal
multiple access (NOMA) system. Specifically, we mainly focus on
energy-efficient resource allocation with MIMO technology in the STAR-RIS
assisted NOMA network. To maximize the system energy efficiency, we propose an
algorithm to optimize the transmit beamforming and the phases of the low-cost
passive elements on the STAR-RIS alternatively until the convergence.
Specifically, we first decompose the formulated energy efficiency problem into
beamforming and phase shift optimization problems. To efficiently address the
non-convex beamforming optimization problem, we exploit signal alignment and
zero-forcing precoding methods in each user pair to decompose MIMO-NOMA
channels into single-antenna NOMA channels. Then, the Dinkelbach approach and
dual decomposition are utilized to optimize the beamforming vectors. In order
to solve non-convex phase shift optimization problem, we propose a successive
convex approximation (SCA) based method to efficiently obtain the optimized
phase shift of STAR-RIS. Simulation results demonstrate that the proposed
algorithm with NOMA technology can yield superior energy efficiency performance
over the orthogonal multiple access (OMA) scheme and the random phase shift
scheme
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