387 research outputs found
RIS-Aided MIMO Systems with Hardware Impairments: Robust Beamforming Design and Analysis
Reconfigurable intelligent surface (RIS) has been anticipated to be a novel
cost-effective technology to improve the performance of future wireless
systems. In this paper, we investigate a practical RIS-aided
multiple-input-multiple-output (MIMO) system in the presence of transceiver
hardware impairments, RIS phase noise and imperfect channel state information
(CSI). Joint design of the MIMO transceiver and RIS reflection matrix to
minimize the total average mean-square-error (MSE) of all data streams is
particularly considered. This joint design problem is non-convex and
challenging to solve due to the newly considered practical imperfections. To
tackle the issue, we first analyze the total average MSE by incorporating the
impacts of the above system imperfections. Then, in order to handle the tightly
coupled optimization variables and non-convex NP-hard constraints, an efficient
iterative algorithm based on alternating optimization (AO) framework is
proposed with guaranteed convergence, where each subproblem admits a
closed-form optimal solution by leveraging the majorization-minimization (MM)
technique. Moreover, via exploiting the special structure of the unit-modulus
constraints, we propose a modified Riemannian gradient ascent (RGA) algorithm
for the discrete RIS phase shift optimization. Furthermore, the optimality of
the proposed algorithm is validated under line-of-sight (LoS) channel
conditions, and the irreducible MSE floor effect induced by imperfections of
both hardware and CSI is also revealed in the high signal-to-noise ratio (SNR)
regime. Numerical results show the superior MSE performance of our proposed
algorithm over the adopted benchmark schemes, and demonstrate that increasing
the number of RIS elements is not always beneficial under the above system
imperfections.Comment: 30 pages, 8 figures. This paper has been submitted to IEEE journal
for possible publicatio
Signals and Images in Sea Technologies
Life below water is the 14th Sustainable Development Goal (SDG) envisaged by the United Nations and is aimed at conserving and sustainably using the oceans, seas, and marine resources for sustainable development. It is not difficult to argue that signals and image technologies may play an essential role in achieving the foreseen targets linked to SDG 14. Besides increasing the general knowledge of ocean health by means of data analysis, methodologies based on signal and image processing can be helpful in environmental monitoring, in protecting and restoring ecosystems, in finding new sensor technologies for green routing and eco-friendly ships, in providing tools for implementing best practices for sustainable fishing, as well as in defining frameworks and intelligent systems for enforcing sea law and making the sea a safer and more secure place. Imaging is also a key element for the exploration of the underwater world for various scopes, ranging from the predictive maintenance of sub-sea pipelines and other infrastructure projects, to the discovery, documentation, and protection of sunken cultural heritage. The scope of this Special Issue encompasses investigations into techniques and ICT approaches and, in particular, the study and application of signal- and image-based methods and, in turn, exploration of the advantages of their application in the previously mentioned areas
Integrated Sensing and Communication for 6G: Ten Key Machine Learning Roles
Integrating sensing and communication is a defining theme for future wireless
systems. This is motivated by the promising performance gains, especially as
they assist each other, and by the better utilization of the wireless and
hardware resources. Realizing these gains in practice, however, is subject to
several challenges where leveraging machine learning can provide a potential
solution. This article focuses on ten key machine learning roles for joint
sensing and communication, sensing-aided communication, and communication-aided
sensing systems, explains why and how machine learning can be utilized, and
highlights important directions for future research. The article also presents
real-world results for some of these machine learning roles based on the
large-scale real-world dataset DeepSense 6G, which could be adopted in
investigating a wide range of integrated sensing and communication problems.Comment: Submitted to IEE
Efficient DSP and Circuit Architectures for Massive MIMO: State-of-the-Art and Future Directions
Massive MIMO is a compelling wireless access concept that relies on the use
of an excess number of base-station antennas, relative to the number of active
terminals. This technology is a main component of 5G New Radio (NR) and
addresses all important requirements of future wireless standards: a great
capacity increase, the support of many simultaneous users, and improvement in
energy efficiency. Massive MIMO requires the simultaneous processing of signals
from many antenna chains, and computational operations on large matrices. The
complexity of the digital processing has been viewed as a fundamental obstacle
to the feasibility of Massive MIMO in the past. Recent advances on
system-algorithm-hardware co-design have led to extremely energy-efficient
implementations. These exploit opportunities in deeply-scaled silicon
technologies and perform partly distributed processing to cope with the
bottlenecks encountered in the interconnection of many signals. For example,
prototype ASIC implementations have demonstrated zero-forcing precoding in real
time at a 55 mW power consumption (20 MHz bandwidth, 128 antennas, multiplexing
of 8 terminals). Coarse and even error-prone digital processing in the antenna
paths permits a reduction of consumption with a factor of 2 to 5. This article
summarizes the fundamental technical contributions to efficient digital signal
processing for Massive MIMO. The opportunities and constraints on operating on
low-complexity RF and analog hardware chains are clarified. It illustrates how
terminals can benefit from improved energy efficiency. The status of technology
and real-life prototypes discussed. Open challenges and directions for future
research are suggested.Comment: submitted to IEEE transactions on signal processin
- …