620 research outputs found
Machine Learning-based Methods for Reconfigurable Antenna Mode Selection in MIMO Systems
MIMO technology has enabled spatial multiple access and has provided a higher
system spectral efficiency (SE). However, this technology has some drawbacks,
such as the high number of RF chains that increases complexity in the system.
One of the solutions to this problem can be to employ reconfigurable antennas
(RAs) that can support different radiation patterns during transmission to
provide similar performance with fewer RF chains. In this regard, the system
aims to maximize the SE with respect to optimum beamforming design and RA mode
selection. Due to the non-convexity of this problem, we propose machine
learning-based methods for RA antenna mode selection in both dynamic and static
scenarios. In the static scenario, we present how to solve the RA mode
selection problem, an integer optimization problem in nature, via deep
convolutional neural networks (DCNN). A Multi-Armed-bandit (MAB) consisting of
offline and online training is employed for the dynamic RA state selection. For
the proposed MAB, the computational complexity of the optimization problem is
reduced. Finally, the proposed methods in both dynamic and static scenarios are
compared with exhaustive search and random selection methods
Deep Learning-Based Channel Extrapolation for Pattern Reconfigurable Massive MIMO
Reconfigurable antennas that can dynamically change their operation state
exhibit excellent adaptivity and flexibility over traditional antennas, and
MIMO arrays that consist of multifunctional and reconfigurable antennas (MRAs)
are foreseen as one promising solution towards future Holographic MIMO.
Specifically, in pattern reconfigurable MIMO (PR-MIMO) communication systems,
accurate acquisition of channel state information (CSI) of all the radiation
modes is a challenging task, because using conventional pilot-based channel
estimation techniques in PR-MIMO systems incurs overwhelming pilot overheads.
In this letter, we leverage deep learning methods to design a PR neural
network, which can use the estimated CSI for one radiation mode to infer CSIs
for the other radiation modes. In order to reduce the pilot overheads, we
propose a new channel estimation method specially for PR-MIMO systems, which
divides the transmit antennas of PR-MIMO into groups and antennas in different
groups employ different radiation modes. Compared with conventional
full-connected real-valued deep neural networks (DNN), the PR neural network
which uses complex-valued coefficients can work directly in the complex domain.
Experiment results show that the proposed channel extrapolation method offers
significant performance gains in terms of extrapolation accuracy over benchmark
schemes
On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds
Fifth generation (5G) mobile communication systems have entered the stage of commercial development, providing users with new services and improved user experiences as well as offering a host of novel opportunities to various industries. However, 5G still faces many challenges. To address these challenges, international industrial, academic, and standards organizations have commenced research on sixth generation (6G) wireless communication systems. A series of white papers and survey papers have been published, which aim to define 6G in terms of requirements, application scenarios, key technologies, etc. Although ITU-R has been working on the 6G vision and it is expected to reach a consensus on what 6G will be by mid-2023, the related global discussions are still wide open and the existing literature has identified numerous open issues. This paper first provides a comprehensive portrayal of the 6G vision, technical requirements, and application scenarios, covering the current common understanding of 6G. Then, a critical appraisal of the 6G network architecture and key technologies is presented. Furthermore, existing testbeds and advanced 6G verification platforms are detailed for the first time. In addition, future research directions and open challenges are identified for stimulating the on-going global debate. Finally, lessons learned to date concerning 6G networks are discussed
Holographic MIMO Communications: Theoretical Foundations, Enabling Technologies, and Future Directions
Future wireless systems are envisioned to create an endogenously
holography-capable, intelligent, and programmable radio propagation
environment, that will offer unprecedented capabilities for high spectral and
energy efficiency, low latency, and massive connectivity. A potential and
promising technology for supporting the expected extreme requirements of the
sixth-generation (6G) communication systems is the concept of the holographic
multiple-input multiple-output (HMIMO), which will actualize holographic radios
with reasonable power consumption and fabrication cost. The HMIMO is
facilitated by ultra-thin, extremely large, and nearly continuous surfaces that
incorporate reconfigurable and sub-wavelength-spaced antennas and/or
metamaterials. Such surfaces comprising dense electromagnetic (EM) excited
elements are capable of recording and manipulating impinging fields with utmost
flexibility and precision, as well as with reduced cost and power consumption,
thereby shaping arbitrary-intended EM waves with high energy efficiency. The
powerful EM processing capability of HMIMO opens up the possibility of wireless
communications of holographic imaging level, paving the way for signal
processing techniques realized in the EM-domain, possibly in conjunction with
their digital-domain counterparts. However, in spite of the significant
potential, the studies on HMIMO communications are still at an initial stage,
its fundamental limits remain to be unveiled, and a certain number of critical
technical challenges need to be addressed. In this survey, we present a
comprehensive overview of the latest advances in the HMIMO communications
paradigm, with a special focus on their physical aspects, their theoretical
foundations, as well as the enabling technologies for HMIMO systems. We also
compare the HMIMO with existing multi-antenna technologies, especially the
massive MIMO, present various...Comment: double column, 58 page
Reconfigurable Intelligent Surface-Aided Millimetre Wave Communications Utilizing Two-Phase Minimax Optimal Stochastic Strategy Bandit
Millimetre wave (mm Wave) communications, that is, 30 to 300 GHz, have intermittent short-range transmissions, so the use of reconfigurable intelligent surface (RIS) seems to be a promising solution to extend its coverage. However, optimizing phase shifts (PSs) of both mm Wave base station (BS) and RIS to maximize the received spectral efficiency at the intended receiver seems challenging due to massive antenna elements usage. In this paper, an online learning approach is proposed to address this problem, where it is considered a two-phase multi-armed bandit (MAB) game. In the first phase, the PS vector of the mm Wave BS is adjusted, and based on it, the PS vector of the RIS is calibrated in the second phase and vice versa over the time horizon. The minimax optimal stochastic strategy(MOSS) MAB algorithm is utilized to implement the proposed two-phase MAB approach efficiently. Furthermore, to relax the problem of estimating the channel state information(CSI) of both mm Wave BS and RIS, codebook-based PSs are considered. Finally, numerical analysis confirms the superior performance of the proposed scheme against the optimal performance under different scenarios
mmWave Beam Alignment using Hierarchical Codebooks and Successive Subtree Elimination
We propose a best arm identification multi-armed bandit algorithm in the
fixed-confidence setting for mmWave beam alignment initial access called
\ac{SSE}. The algorithm performance approaches that of state-of-the-art
Bayesian algorithms at a fraction of the complexity and without requiring
channel state information. The algorithm simultaneously exploits the benefits
of hierarchical codebooks and the approximate unimodality of rewards to achieve
fast beam steering, in a sense that we precisely define to provide fair
comparison with existing algorithms. We derive a closed-form sample complexity,
which enables tuning of design parameters. We also perform extensive
simulations over slow fading channels to demonstrate the appealing performance
versus complexity trade-off struck by the algorithm across a wide range of
channel condition
Design and Development of Smart Brain-Machine-Brain Interface (SBMIBI) for Deep Brain Stimulation and Other Biomedical Applications
Machine collaboration with the biological body/brain by sending electrical information back and forth is one of the leading research areas in neuro-engineering during the twenty-first century. Hence, Brain-Machine-Brain Interface (BMBI) is a powerful tool for achieving such machine-brain/body collaboration. BMBI generally is a smart device (usually invasive) that can record, store, and analyze neural activities, and generate corresponding responses in the form of electrical pulses to stimulate specific brain regions. The Smart Brain-Machine-Brain-Interface (SBMBI) is a step forward with compared to the traditional BMBI by including smart functions, such as in-electrode local computing capabilities, and availability of cloud connectivity in the system to take the advantage of powerful cloud computation in decision making.
In this dissertation work, we designed and developed an innovative form of Smart Brain-Machine-Brain Interface (SBMBI) and studied its feasibility in different biomedical applications. With respect to power management, the SBMBI is a semi-passive platform. The communication module is fully passiveâpowered by RF harvested energy; whereas, the signal processing core is battery-assisted. The efficiency of the implemented RF energy harvester was measured to be 0.005%.
One of potential applications of SBMBI is to configure a Smart Deep-Brain-Stimulator (SDBS) based on the general SBMBI platform. The SDBS consists of brain-implantable smart electrodes and a wireless-connected external controller. The SDBS electrodes operate as completely autonomous electronic implants that are capable of sensing and recording neural activities in real time, performing local processing, and generating arbitrary waveforms for neuro-stimulation. A bidirectional, secure, fully-passive wireless communication backbone was designed and integrated into this smart electrode to maintain contact between the smart electrodes and the controller. The standard EPC-Global protocol has been modified and adopted as the communication protocol in this design. The proposed SDBS, by using a SBMBI platform, was demonstrated and tested through a hardware prototype. Additionally the SBMBI was employed to develop a low-power wireless ECG data acquisition device. This device captures cardiac pulses through a non-invasive magnetic resonance electrode, processes the signal and sends it to the backend computer through the SBMBI interface. Analysis was performed to verify the integrity of received ECG data
Antennas and Propagation Aspects for Emerging Wireless Communication Technologies
The increasing demand for high data rate applications and the delivery of zero-latency multimedia content drives technological evolutions towards the design and implementation of next-generation broadband wireless networks. In this context, various novel technologies have been introduced, such as millimeter wave (mmWave) transmission, massive multiple input multiple output (MIMO) systems, and non-orthogonal multiple access (NOMA) schemes in order to support the vision of fifth generation (5G) wireless cellular networks. The introduction of these technologies, however, is inextricably connected with a holistic redesign of the current transceiver structures, as well as the network architecture reconfiguration. To this end, ultra-dense network deployment along with distributed massive MIMO technologies and intermediate relay nodes have been proposed, among others, in order to ensure an improved quality of services to all mobile users. In the same framework, the design and evaluation of novel antenna configurations able to support wideband applications is of utmost importance for 5G context support. Furthermore, in order to design reliable 5G systems, the channel characterization in these frequencies and in the complex propagation environments cannot be ignored because it plays a significant role. In this Special Issue, fourteen papers are published, covering various aspects of novel antenna designs for broadband applications, propagation models at mmWave bands, the deployment of NOMA techniques, radio network planning for 5G networks, and multi-beam antenna technologies for 5G wireless communications
Uplink Beam Management for Millimeter Wave Cellular MIMO Systems with Hybrid Beamforming
Hybrid analog and digital BeamForming (HBF) is one of the enabling
transceiver technologies for millimeter Wave (mmWave) Multiple Input Multiple
Output (MIMO) systems. This technology offers highly directional communication,
which is able to confront the intrinsic characteristics of mmWave signal
propagation. However, the small coherence time in mmWave systems, especially
under mobility conditions, renders efficient Beam Management (BM) in standalone
mmWave communication a very difficult task. In this paper, we consider HBF
transceivers with planar antenna panels and design a multi-level beam codebook
for the analog beamformer comprising flat top beams with variable widths. These
beams exhibit an almost constant array gain for the whole desired angle width,
thereby facilitating efficient hierarchical BM. Focusing on the uplink
communication, we present a novel beam training algorithm with dynamic beam
ordering, which is suitable for the stringent latency requirements of the
latest mmWave standard discussions. Our simulation results showcase the latency
performance improvement and received signal-to-noise ratio with different
variations of the proposed scheme over the optimum beam training scheme based
on exhaustive narrow beam search.Comment: 7 pages; 6 figures; accepted to an IEEE conferenc
Fixed Rank Kriging for Cellular Coverage Analysis
Coverage planning and optimization is one of the most crucial tasks for a
radio network operator. Efficient coverage optimization requires accurate
coverage estimation. This estimation relies on geo-located field measurements
which are gathered today during highly expensive drive tests (DT); and will be
reported in the near future by users' mobile devices thanks to the 3GPP
Minimizing Drive Tests (MDT) feature~\cite{3GPPproposal}. This feature consists
in an automatic reporting of the radio measurements associated with the
geographic location of the user's mobile device. Such a solution is still
costly in terms of battery consumption and signaling overhead. Therefore,
predicting the coverage on a location where no measurements are available
remains a key and challenging task. This paper describes a powerful tool that
gives an accurate coverage prediction on the whole area of interest: it builds
a coverage map by spatially interpolating geo-located measurements using the
Kriging technique. The paper focuses on the reduction of the computational
complexity of the Kriging algorithm by applying Fixed Rank Kriging (FRK). The
performance evaluation of the FRK algorithm both on simulated measurements and
real field measurements shows a good trade-off between prediction efficiency
and computational complexity. In order to go a step further towards the
operational application of the proposed algorithm, a multicellular use-case is
studied. Simulation results show a good performance in terms of coverage
prediction and detection of the best serving cell
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