No. 2 (2023)
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Published:
2023-06-29
ARTICLES FROM THIS ISSUE
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Efficient Iterative Detection Based on Conjugate Gradient and Successive Over-Relaxation Methods for Uplink Massive MIMO Systems
Abstract
Being a crucial aspect of fifth-generation (5G) mobile communications systems, massively multiple-input multiple-output (mMIMO) architectures are expected to help achieve the highest key performance indicators. However, the huge numbers of antennas used in such systems make it difficult to determine the inversion of the signal channel matrix relied upon by several detection methods, hence posing a problem with accurate estimation of the symbols sent. In this paper, conjugate gradient (CG) and successive over-relaxation (SOR) methods are selected to construct a new iterative approach that avoids the matrix inversion computation issue. This suggested approach for uplink mMIMO detection is based on a joint cascade structure of both iterative methods. The CG method is first applied and adjusted for the initial solution, followed by the SOR method in the final iterations for terminal computations, resulting in an algorithm with robust performance and low computational complexity. Furthermore, the new hybrid scheme operates based on the relaxation parameter, whose value has a great impact on error performance and, whose optimal determination is necessary. Numerical simulations reveal that the proposed scheme is capable of significantly improving signal detection accuracy with minimum complexity. The simulation results indicated that the proposed detector outperforms CG and SOR detectors, achieves close to optimal performance, requires fewer iterations, and reduces complexity.
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Performance Enhancement of Chaotic Error Correction Coding Using Consecutive Sequences
Abstract
The use of chaotic dynamics for error correction is the subject of extensive research, as the approach allows to avoid the use of redundant data. This work proposes a new technique for non-coherent chaos communications for modifying error-correction depending on chaotic dynamics. In the proposed system, there are two consecutive sequences created from a comparable chaotic map, with the second series being created as the latest value of the initial one. Generation of a sequential chaotic sequence with a comparable chaotic dynamic delivers additional information to the receiver, allowing it to appropriately recover information and, hence, facilitate the receiver’s bit-error performance. For error correction and for detecting the symbol that is transmitted, a suboptimal technique based on the nearest distance between chaotic map trajectories over the n-dimensional sequence received is utilized. Simulation results show that the proposed error correction approach improves Eb/N0 as the dimension of the trajectory map increases, indicating that the method improves overall error correction performance. With the dimension of 4, a gain of 0.8 dB in Eb/N0 is achieved compared with an approach without any error-correcting schemes, at the bit-error probability of 10−3.
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Design of a Fractional Order Low-pass Filter Using a Differential Voltage Current Conveyor
Abstract
In this paper, an active implementation of a differential voltage current conveyor (DVCC) based on a low-pass filter operating in the fractional order domain is presented. The transfer function for a fractional order system is dependent on the rational approximation of sα. Different methods used for calculating the rational approximation, including Carlson, Elkhazalil, and curve fitting, are evaluated here. Finally, to validate the theoretical results, a fractional order Butterworth filter is simulated in the Pspice environment using the 0.5 micrometer CMOS technology with an R-C network-based fractional order capacitor. Additionally, using the Monte Carlo analysis, the impact of current and voltage faults on DVCC response is investigated. It has been inferred that realization with a wider bandwidth is possible.
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Jamming Signal Cancellation by Channel Inversion Power Control for Preserving Covert Communications
Abstract
Uninformed jammers are used to facilitate covert communications between a transmitter and an intended receiver under the surveillance of a warden. In reality, the signals the uniformed jammer emits to make the warden’s decision uncertain have inadvertently interfered with the detection of the intended receiver. In this paper, we apply truncated channel inversion power control (TCIPC) to both the transmitter and the uninformed jammer. The TCIPC scheme used on the uninformed jammer may help the intended receiver remove jamming signals using the successive interference cancellation (SIC) technique. Under the assumption that the warden knows the channel coefficient between two intended transceivers and achieves the optimal detection power threshold, we form the optimization problem to maximize the effective transmission rate (ETR) under covertness and decoding constraints. With the aim of enhancing covertness-related performance, we achieve the optimal power control parameters and determine system parameter-related constraints required for the existence of these solutions. According to the simulations, the use of the TCIPC scheme on the uninformed jammer significantly improves covertness-related performance in comparison to that of random power control (RPC) and constant power control (CPC) schemes. In addition, simulation results show that, for the TCIPC scheme: 1) the maximum ETR tends to converge as the transmitter’s or the uninformed jammer’s maximum transmit power increases, and 2) there exists an optimal value of the transmitter’s predetermined transmission rate to achieve the optimal performance.
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Energy Consumption in Wireless Systems Equipped with RES, UAVs, and IRSs
Abstract
This paper investigates energy budget characteristics of mobile base stations (BSs) having the form of unmanned aerial vehicles (UAVs) equipped with radio frequency (RF) transceivers, intelligent reconfigurable surfaces (IRSs), and renewable energy sources (RES). The results obtained highlight the benefits and challenges related to using the aforementioned mobile BS, from the energy-related point of view. The specific cases researched involved two types of UAV devices, i.e. multirotor and fixed-wing (airplane-like) aircraft.
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Why Is White Noise Not Enough? Using Radio Front-End Models While Designing 6G PHY
Abstract
Each subsequent generation of wireless standards imposes stricter spectral and energy efficiency demands. So far, layered wireless transceiver architectures have been used, allowing for instance to separate channel decoding algorithms from the front-end design. Such an approach may need to be reconsidered in the upcoming 6G era. Especially hardware-originated distortions have to be taken into account while designing other layer algorithms, as high throughput and energy efficiency requirements will push these devices to their limits, revealing their non-linear characteristics. In such a context, this paper will shed some light on the new degrees of freedom enjoyed while cross-layer designing as well as controlling multicarrier and multiantenna transceivers in 6G systems.
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Improving Quality of Watermarked Medical Images Using Symmetric Dilated Convolution Neural Networks
Abstract
Rapid development of online medical technologies raises questions about the security of the patient’s medical data.
When patient records are encrypted and labeled with a watermark, they may be exchanged securely online. In order to avoid geometrical attacks aiming to steal the information, image quality must be maintained and patient data must be appropriately extracted from the encoded image. To ensure that watermarked images are more resistant to attacks (e.g. additive noise or geometric attacks), different watermarking methods have been invented in the past. Additive noise causes visual distortion and render the potentially harmful diseases more difficult to diagnose and analyze. Consequently, denoising is an important pre-processing method for obtaining superior outcomes in terms of clarity and noise reduction and allows to improve the quality of damaged medical images. Therefore, various publications have been studied to understand the denoising methods used to improve image quality. The findings indicate that deep learning and neural networks have recently contributed considerably to the advancement of image processing techniques. Consequently, a system has been created that makes use of machine learning to enhance the quality of damaged images and to facilitate the process of identifying specific diseases. Images, damaged in the course of an assault, are denoised using the suggested technique relying on a symmetric dilated convolution neural network. This improves the system’s resilience and establishes a secure environment for the exchange of data while maintaining secrecy. -
An Overview of Mobility Management Mechanisms and the Related Challenges in 5G Networks and Beyond
Abstract
Ensuring a seamless connection with various types of mobile user equipment (UE) items is one of the more significant challenges facing different generations of wireless systems. However, enabling the high-band spectrum – such as the millimeter wave (mmWave) band – is also one of the important factors of 5G networks, as it enables them to deal with increasing demand and ensures high coverage. Therefore, the deployment of new (small) cells with a short range and operating within the mmWave band is required in order to assist the macro cells which are responsible for operating long-range radio connections. The deployment of small cells results in a new network structure, known as heterogeneous networks (HetNets). As a result, the number of passthrough cells using the handover (HO) process will be dramatically increased. Mobility management (MM) in such a massive network will become crucial, especially when it comes to mobile users traveling at very high speeds. Current MM solutions will be ineffective, as they will not be able to provide the required reliability, flexibility, and scalability.
Thus, smart algorithms and techniques are required in future networks. Also, machine learning (ML) techniques are perfectly capable of supporting the latest 5G technologies that are expected to deliver high data rates to upcoming use cases and services, such as massive machine type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). This paper aims to review the MM approaches used in 5G HetNets and describes the deployment of AI mechanisms and techniques in ″connected mode″ MM schemes. Furthermore, this paper addresses the related challenges and suggests potential solutions for 5G networks and beyond. -
RIS-aided Multi-hop Backhauling for 5G/6G UAV-assisted Access Points
Abstract
Drones are considered to be an important part of future 6G telecommunication systems. Thanks to their quick deployment potential, they provide additional connectivity options in the form of a flying hotspot. However, in such use cases, they typically require a wireless backhaul link to facilitate their proper operation, which might be a challenging task in dense urban environments. One of the potential methods that may be relied upon to connect such nodes is the integrated access and backhaul (IAB) approach, where part of the spectrum allocated to users accessing the base station is used for wireless backhauling. Thus, in this work, we consider the problem of establishing a multi-hop wireless backhaul link following the IAB concept, with the aid of drone relay stations (DRSs) and reconfigurable intelligent surfaces (RISs). We formulate the problem of coverage improvement with a fixed number of relays, assuming certain throughput requirements for the backhaul link. The simulations show that the use of RISs offers a coverage improvement in such a scenario or a reduction in the number of nodes involved in ensuring the required backhaul performance.
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Performance Enhancement of Cooperative MIMO-NOMA Systems Over Sub-6 GHz and mmWave Bands
Abstract
In this paper, two radio links with different frequency bands are considered for base stations (BS) serving users via decode-and-forward (DF) cooperative relays. Backhaul and access links are proposed with sub-6 GHz and millimeter wave (mmWave) bands, respectively. Non-orthogonal multiple access (NOMA) is employed in the backhaul link to simultaneously transmit a superposed signal in the power domain, using the same band. The superposed signals, containing two signals that differ in terms of power allocation factors (PAFs), are designed for two selected DF relays in the BS. The two relays are chosen from several relays to be serviced by the BS based on a pairing algorithm that depends on different users’ circumstances. The furthest DF relay detects the incoming NOMA signal directly, while the nearest one applies successive interference cancellation (SIC) before extracting its signal. Each DF relay forwards the detected signals toward their intended users over mmWave channels. Three performance metrics are utilized to evaluate the system’s performance: outage probability, achievable throughput, and bit error rate. Comparisons between two mmWave bands in the access link (28 and 73 GHz) are made to demonstrate the superiority of the 28 GHz band in terms of the three performance-related metrics.
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Design of a Microstrip Filtering Antenna for 4G and 5G Wireless Networks
Abstract
The filtering antenna provides both radiation and filtering features and is an important component for the RF front-end of wireless devices. The main function of a filtering antenna is to reject out-of-band signals, thus reducing the interference from adjacent channels. The aim of the present work is to design a 2.6 GHz microstrip filtering antenna for 4G and 5G global mobile services. The filtering antenna is designed using a hairpin bandpass filter integrated with an elliptical microstrip aerial. Good impedance matching is obtained by using appropriate dimensions of the hairpin bandpass filter. The 10 dB return loss bandwidth of the filtering antenna is approx. 5.7%, with the maximum gain for the elliptical filtering antenna of approx. 2.2 dB. Good agreements between the measured and simulated results are obtained for the proposed filtering antenna and the bandwidth covers almost the entire 2.6 GHz band.
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A Novel Kernel Algorithm for Finite Impulse Response Channel Identification
Abstract
Over the last few years, kernel adaptive filters have gained in importance as the kernel trick started to be used in classic linear adaptive filters in order to address various regression and time-series prediction issues in nonlinear environments.In this paper, we study a recursive method for identifying finite impulse response (FIR) nonlinear systems based on binary-value observation systems. We also apply the kernel trick to the recursive projection (RP) algorithm, yielding a novel recursive algorithm based on a positive definite kernel. For purposes, our approach is compared with the recursive projection (RP) algorithm in the process of identifying the parameters of two channels, with the first of them being a frequency-selective fading channel, called a broadband radio access network (BRAN B) channel, and the other being a a theoretical frequency-selective channel, known as the Macchi channel. Monte Carlo simulation results are presented to show the performance of the proposed algorithm.
