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Advanced Coding and Stochastic Signal Processing in Dense Communication Networks

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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Network Science".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 12075

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Special Issue Editor

Prof. Dr. Jan Sykora

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Guest Editor

Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 160 00 Prague, Czech Republic
Interests: coding and information theory; stochastic signal processing; iterative and cooperative algorithms on graphs; estimation theory; physical layer network coding

Special Issue Information

Dear Colleagues,

Advanced coding and stochastic signal processing are key enabling techniques for reliable communications in dense, highly interfering wireless communication networks. The dense networks are defined as multi-node/user wireless network scenarios where mutual signal interaction is a predominant limiting factor. This situation requires that the coding and processing fully exploits the knowledge of the interaction structure and form, together with the network topology, and uses it proactively in the distributed processing algorithms on all network nodes. Frequently, these communication networks are used as a part of some control or sensing network data-processing algorithm or physical device/machinery control loop.

This Special Issue focusses on advances in the following areas:

Coding and processing for interference-limited dense wireless networks;
Network coding, physical-layer network coding, and relaying technique;
Channel and network state estimation and sensing for multi-node dense networks;
Distributed and cooperative algorithms on graphs;
Machine learning approaches for dense network coding and processing;
Network coding aided by active and smart meta-surfaces;
Low-latency and ultra-reliable communication;
Coding and processing for Non-Orthogonal Multiple Access channel;
Coding and processing for weakly defined channel, network and node state information;
Joint design of coding/processing and control and/or data-sensing network algorithms over an unreliable dense network.

Prof. Dr. Jan Sykora
Guest Editor

Manuscript Submission Information

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Keywords

Network coding
Physical layer network coding
Channel and network state estimation
Sensing for multi-node dense networks
Distributed and cooperative algorithms on graphs
Machine learning approaches for dense network coding and processing
Non-Orthogonal Multiple Access channel
Weakly-defined channel, network and node state information
Data sensing network algorithms

Published Papers (6 papers)

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Research

11 pages, 540 KiB

Open AccessArticle

Validation of Parallel Distributed Adaptive Signal Processing (PDASP) Framework through Processing-Inefficient Low-Cost Platforms

by Hasan Raza, Ishtiaq Ahmad, Noor M. Khan, Waseem Abbasi, Muhammad Shahid Anwar, Sadique Ahmad and Mohammed A. El-Affendi

Mathematics 2022, 10(23), 4600; https://doi.org/10.3390/math10234600 - 5 Dec 2022

Viewed by 1200

Abstract

The computational complexity of the multiple-input and multiple-output (MIMO) based least square algorithm is very high and it cannot be run on processing-inefficient low-cost platforms. To overcome complexity-related problems, a parallel distributed adaptive signal processing (PDASP) architecture is proposed, which is a distributed framework used to efficiently run the adaptive filtering algorithms having high computational cost. In this paper, a communication load-balancing procedure is introduced to validate the PDASP architecture using low-cost wireless sensor nodes. The PDASP architecture with the implementation of a multiple-input multiple-output (MIMO) based Recursive Least Square (RLS) algorithm is deployed on the processing-inefficient low-cost wireless sensor nodes to validate the performance of the PDASP architecture in terms of computational cost, processing time, and memory utilization. Furthermore, the processing time and memory utilization provided by the PDASP architecture are compared with sequentially operated RLS-based MIMO channel estimator on

2 \times 2

3 \times 3

, and

4 \times 4

MIMO communication systems. The measurement results show that the sequentially operated MIMO RLS algorithm based on

3 \times 3

and

4 \times 4

MIMO communication systems is unable to work on a single unit; however, these MIMO systems can efficiently be run on the PDASP architecture with reduced memory utilization and processing time. Full article

(This article belongs to the Special Issue Advanced Coding and Stochastic Signal Processing in Dense Communication Networks)

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17 pages, 559 KiB

Open AccessArticle

Scalable Cell-Free Massive MIMO with Multiple CPUs

by Feiyang Li, Qiang Sun, Xiaodi Ji and Xiaomin Chen

Mathematics 2022, 10(11), 1900; https://doi.org/10.3390/math10111900 - 1 Jun 2022

Cited by 3 | Viewed by 2020

Abstract

In this paper, we consider the uplink of a scalable cell-free massive MIMO (CF-M-MIMO) system where user equipments (UEs) are served only by a subset of access points (APs). All APs are physically divided into predetermined “real clusters”, which are linked to different cooperative central processing units (CPUs). Based on the cooperative nature of the considered communications framework, we assume that each UE is affiliated with a “virtual cluster”, which is associated with some APs coming from different real clusters. Thanks to the degrees of cooperation among multiple CPUs, the uplink spectral efficiencies (SEs) of four different levels are analyzed. To achieve system scalability, the CF-M-MIMO system with multiple CPUs is introduced, which leads to lower SE. To this end, we design a joint combining method based on statistical channel state informations (CSIs), which not only has low complexity but also improves the SE of the system. Simulation results indicate that the average rate of our proposed method can be improved by about 30%. Full article

(This article belongs to the Special Issue Advanced Coding and Stochastic Signal Processing in Dense Communication Networks)

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30 pages, 1970 KiB

Open AccessArticle

Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry

by Reza Aghazadeh Ayoubi and Umberto Spagnolini

Mathematics 2022, 10(7), 1156; https://doi.org/10.3390/math10071156 - 2 Apr 2022

Cited by 6 | Viewed by 2716

Abstract

Device density in cellular networks is expected to increase considerably in the near future. Accordingly, the access point (AP) will be equipped with massive multiple-input multiple-output (mMIMO) antennas, using collimated millimeter-wave (mmW) and sub-THz communications, and increasing the bandwidth to accommodate the growing data rate demands. In this scenario, interference plays a critical role and, if not characterized and mitigated properly, might limit the performances of the network. In this context, this paper derives the statistical properties of the aggregated interference power for a cellular network equipping a mMIMO cylindrical array. The proposed statistical model considers the link blockage and other network parameters such as antenna configuration and device density. The findings show that the characteristic function (CF) of the aggregated interference power can be regarded as a weighted mixture of two alpha-stable distributions. Furthermore, by analyzing the service probability, it is found that there is an optimal configuration of the array depending on the AP height and device density. The proposed statistical model can be part of the design of dense networks providing valuable insights for optimal network deployment and resource management and scheduling. Full article

(This article belongs to the Special Issue Advanced Coding and Stochastic Signal Processing in Dense Communication Networks)

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16 pages, 1025 KiB

Open AccessArticle

Exact Analytical H-BER for Ad Hoc XOR H-Map Detector for Two Differentially Modulated BPSK Sources in H-MAC Channel

by Jozef Lukac and Jan Sykora

Mathematics 2022, 10(6), 903; https://doi.org/10.3390/math10060903 - 11 Mar 2022

Viewed by 1369

Abstract

In the article, we present an ad hoc (AH) detector for two differentially encoded BPSK sources in the Hierarchical MAC (H-MAC), i.e., for the case when the receiver sees the superposition of non-orthogonal signals from individual sources. (Prefix “H-” means Hierarchical, it emphasizes that the entity is related to the many-to-one principle.) The AH detector decodes the XOR H-map of the two BPSK streams—in other words, it decides whether the transmitted symbols from the two sources are the same or opposite. The BER of the detection in H-MAC is denoted as H-BER. The H-BER is compared with the other two differential detectors, with the coherent (Coh) detector, and with an approximate coherent (ApC) detector. The exact analytical H-BER formula is derived for the ad hoc and coherent detectors. The proposed ad hoc detector is very simple for evaluation, does not require the estimation of subchannel phases, does not depend on noise variance, and it is uniformly only roughly 3.5 dB worse than the coherent one. Full article

(This article belongs to the Special Issue Advanced Coding and Stochastic Signal Processing in Dense Communication Networks)

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20 pages, 6748 KiB

Open AccessArticle

Splitting Sequences for Coding and Hybrid Incremental ARQ with Fragment Retransmission

by Dragana Bajić, Goran Dimić and Nikola Zogović

Mathematics 2021, 9(20), 2620; https://doi.org/10.3390/math9202620 - 17 Oct 2021

Viewed by 1631

Abstract

This paper proposes a code defined on a finite ring

ℤ_{p_{M}}

, where

p_{M}

2^{m} - 1

is a Mersenne prime, and m is a binary size of ring elements. The code is based on a splitting sequence [...] Read more.

This paper proposes a code defined on a finite ring

ℤ_{p_{M}}

, where

p_{M}

2^{m} - 1

is a Mersenne prime, and m is a binary size of ring elements. The code is based on a splitting sequence (splitting set)

S

, defined for the given multiplier set

E = \{\pm 2^{0}, \pm 2^{1}, \dots, \pm 2^{m - 1}\}

. The elements of

E

correspond to the weights of binary error patterns that can be corrected, with the bidirectional single-bit error being the representative that occurs the most. The splitting set splits the code-word into sub-words, which inspired the name splitting code. Each sub-word, provided with auxiliary control symbols that are a byproduct of the coding procedure, corrects a single symbol error. The code can be defined, with some constraints, for general Mersenne numbers as well, while the multiplier set can be adjusted for adjacent binary errors correction. The application proposed for this code is a hybrid three-stage incremental ARQ procedure that transmits the code-word in the first stage, auxiliary control symbols in the second stage, and retransmits the sub-words detected as incorrect in the third stage. At each stage, error correction can be turned on or off, keeping both the retransmission rate and residual error rate at a low level. Full article

(This article belongs to the Special Issue Advanced Coding and Stochastic Signal Processing in Dense Communication Networks)

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19 pages, 1711 KiB

Open AccessArticle

Spectral Properties of Clipping Noise

by Alexander Frömming, Lars Häring and Andreas Czylwik

Mathematics 2021, 9(20), 2592; https://doi.org/10.3390/math9202592 - 15 Oct 2021

Cited by 3 | Viewed by 1667

Abstract

One serious disadvantage of any multicarrier-modulation technique such as orthogonal frequency division multiplexing (OFDM) is its high peak-to-average-power ratio (PAPR) which might lead to signal clipping in several scenarios. To maximize the transmit data rate, it is important to take this non-linear distortion into account. The most common approach is based on the Bussgang theorem, which splits the distortion in a correlated part, represented by a linear damping factor, and uncorrelated additive noise. However, there are two aspects that are not correctly considered by the Bussgang theorem. Firstly, clipping noise shows a frequency-dependent power spectrum which depends on the clipping probability. Secondly, some of the clipping noise power is located outside of the transmission bandwidth, so that it does not influence the transmission quality. In this work, the Bussgang theorem is reviewed in detail and the exact power spectral density of the uncorrelated clipping noise is approximated to determine the signal-to-noise power ratio on every subcarrier separately. Although it is shown that the frequency dependence within the transmission bandwidth is relatively small, at least 36% of the uncorrelated noise power, depending on the clipping level, lays outside of the transmission band. Monte Carlo simulations validate that a simple expression for the power spectral density allows to calculate the symbol error probability of an OFDM transmission system that suffers from clipping. Furthermore, the newly found result can be used to optimize bit allocation tables in bit loading algorithms or to calculate the channel capacity. Full article

(This article belongs to the Special Issue Advanced Coding and Stochastic Signal Processing in Dense Communication Networks)

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