168,367 research outputs found
Hybrid precoding for beamspace MIMO systems with sub-connected switches: a machine learning approach
By employing lens antenna arrays, the number of radio frequency (RF) chains in millimeter-wave (mmWave) communications can be significantly reduced. However, most existing studies consider the phase shifters (PSs) as the main components of the analog beamformer, which may result in a significant loss of energy efficiency (EE). In this paper, we propose a switch selecting network to solve this issue, where the analog part of the beamspace MIMO system is realized by a sub-connected switch selecting network rather than the PS network. Based on the proposed architecture and inspired by the cross-entropy (CE) optimization developed in machine learning, an optimal hybrid cross-entropy (HCE)-based hybrid precoding scheme is designed to maximize the achievable sum rate, where the probability distribution of the hybrid precoder is updated by minimizing CE with unadjusted probabilities and smoothing constant. Simulation results show that the proposed HCE-based hybrid precoding can not only effectively achieve the satisfied sum-rate, but also outperform the PSs schemes concerning energy efficiency
Spectrally and Energy Efficient Wireless Communications: Signal and System Design, Mathematical Modelling and Optimisation
This thesis explores engineering studies and designs aiming to meeting the requirements of enhancing capacity and energy efficiency for next generation communication networks. Challenges of spectrum scarcity and energy constraints are addressed and new technologies are proposed, analytically investigated and examined.
The thesis commences by reviewing studies on spectrally and energy-efficient techniques, with a special focus on non-orthogonal multicarrier modulation, particularly spectrally efficient frequency division multiplexing (SEFDM). Rigorous theoretical and mathematical modelling studies of SEFDM are presented. Moreover, to address the potential application of SEFDM under the 5th generation new radio (5G NR) heterogeneous numerologies, simulation-based studies of SEFDM coexisting with orthogonal frequency division multiplexing (OFDM) are conducted. New signal formats and corresponding transceiver structure are designed, using a Hilbert transform filter pair for shaping pulses. Detailed modelling and numerical investigations show that the proposed signal doubles spectral efficiency without performance degradation, with studies of two signal formats; uncoded narrow-band internet of things (NB-IoT) signals and unframed turbo coded multi-carrier signals. The thesis also considers using constellation shaping techniques and SEFDM for capacity enhancement in 5G system. Probabilistic shaping for SEFDM is proposed and modelled to show both transmission energy reduction and bandwidth saving with advantageous flexibility for data rate adaptation. Expanding on constellation shaping to improve performance further, a comparative study of multidimensional modulation techniques is carried out. A four-dimensional signal, with better noise immunity is investigated, for which metaheuristic optimisation algorithms are studied, developed, and conducted to optimise bit-to-symbol mapping. Finally, a specially designed machine learning technique for signal and system design in physical layer communications is proposed, utilising the application of autoencoder-based end-to-end learning. Multidimensional signal modulation with multidimensional constellation shaping is proposed and optimised by using machine learning techniques, demonstrating significant improvement in spectral and energy efficiencies
Assessing and augmenting SCADA cyber security: a survey of techniques
SCADA systems monitor and control critical infrastructures of national importance such as power generation and distribution, water supply, transportation networks, and manufacturing facilities. The pervasiveness, miniaturisations and declining costs of internet connectivity have transformed these systems from strictly isolated to highly interconnected networks. The connectivity provides immense benefits such as reliability, scalability and remote connectivity, but at the same time exposes an otherwise isolated and secure system, to global cyber security threats. This inevitable transformation to highly connected systems thus necessitates effective security safeguards to be in place as any compromise or downtime of SCADA systems can have severe economic, safety and security ramifications. One way to ensure vital asset protection is to adopt a viewpoint similar to an attacker to determine weaknesses and loopholes in defences. Such mind sets help to identify and fix potential breaches before their exploitation. This paper surveys tools and techniques to uncover SCADA system vulnerabilities. A comprehensive review of the selected approaches is provided along with their applicability
Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
Cellular-based machine-to-machine (M2M) communication is expected to facilitate services for the Internet of Things (IoT). However, because cellular networks are designed for human users, they have some limitations. Random access channel (RACH) congestion caused by massive access from M2M devices is one of the biggest factors hindering cellular-based M2M services because the RACH congestion causes random access (RA) throughput degradation and connection failures to the devices. In this paper, we show the possibility exploiting the capture effects, which have been known to have a positive impact on the wireless network system, on RA procedure for improving the RA performance of M2M devices. For this purpose, we analyze an RA procedure using a capture model. Through this analysis, we examine the effects of capture on RA performance and propose an Msg3 power-ramping (Msg3 PR) scheme to increase the capture probability (thereby increasing the RA success probability) even when severe RACH congestion problem occurs. The proposed analysis models are validated using simulations. The results show that the proposed scheme, with proper parameters, further improves the RA throughput and reduces the connection failure probability, by slightly increasing the energy consumption. Finally, we demonstrate the effects of coexistence with other RA-related schemes through simulation results
A Machine Learning based Framework for KPI Maximization in Emerging Networks using Mobility Parameters
Current LTE network is faced with a plethora of Configuration and
Optimization Parameters (COPs), both hard and soft, that are adjusted manually
to manage the network and provide better Quality of Experience (QoE). With 5G
in view, the number of these COPs are expected to reach 2000 per site, making
their manual tuning for finding the optimal combination of these parameters, an
impossible fleet. Alongside these thousands of COPs is the anticipated network
densification in emerging networks which exacerbates the burden of the network
operators in managing and optimizing the network. Hence, we propose a machine
learning-based framework combined with a heuristic technique to discover the
optimal combination of two pertinent COPs used in mobility, Cell Individual
Offset (CIO) and Handover Margin (HOM), that maximizes a specific Key
Performance Indicator (KPI) such as mean Signal to Interference and Noise Ratio
(SINR) of all the connected users. The first part of the framework leverages
the power of machine learning to predict the KPI of interest given several
different combinations of CIO and HOM. The resulting predictions are then fed
into Genetic Algorithm (GA) which searches for the best combination of the two
mentioned parameters that yield the maximum mean SINR for all users.
Performance of the framework is also evaluated using several machine learning
techniques, with CatBoost algorithm yielding the best prediction performance.
Meanwhile, GA is able to reveal the optimal parameter setting combination more
efficiently and with three orders of magnitude faster convergence time in
comparison to brute force approach
Towards Data-driven Simulation of End-to-end Network Performance Indicators
Novel vehicular communication methods are mostly analyzed simulatively or
analytically as real world performance tests are highly time-consuming and
cost-intense. Moreover, the high number of uncontrollable effects makes it
practically impossible to reevaluate different approaches under the exact same
conditions. However, as these methods massively simplify the effects of the
radio environment and various cross-layer interdependencies, the results of
end-to-end indicators (e.g., the resulting data rate) often differ
significantly from real world measurements. In this paper, we present a
data-driven approach that exploits a combination of multiple machine learning
methods for modeling the end-to-end behavior of network performance indicators
within vehicular networks. The proposed approach can be exploited for fast and
close to reality evaluation and optimization of new methods in a controllable
environment as it implicitly considers cross-layer dependencies between
measurable features. Within an example case study for opportunistic vehicular
data transfer, the proposed approach is validated against real world
measurements and a classical system-level network simulation setup. Although
the proposed method does only require a fraction of the computation time of the
latter, it achieves a significantly better match with the real world
evaluations
Architecture and Design of Medical Processor Units for Medical Networks
This paper introduces analogical and deductive methodologies for the design
medical processor units (MPUs). From the study of evolution of numerous earlier
processors, we derive the basis for the architecture of MPUs. These specialized
processors perform unique medical functions encoded as medical operational
codes (mopcs). From a pragmatic perspective, MPUs function very close to CPUs.
Both processors have unique operation codes that command the hardware to
perform a distinct chain of subprocesses upon operands and generate a specific
result unique to the opcode and the operand(s). In medical environments, MPU
decodes the mopcs and executes a series of medical sub-processes and sends out
secondary commands to the medical machine. Whereas operands in a typical
computer system are numerical and logical entities, the operands in medical
machine are objects such as such as patients, blood samples, tissues, operating
rooms, medical staff, medical bills, patient payments, etc. We follow the
functional overlap between the two processes and evolve the design of medical
computer systems and networks.Comment: 17 page
- …