Pub Date : 2024-09-30DOI: 10.1109/OJCOMS.2024.3470844
Kaiqian Qu;Shuaishuai Guo;Nasir Saeed;Jia Ye
This paper explores the potential near-field beamforming (NFBF) in integrated sensing and communication (ISAC) systems with extremely large-scale arrays (XL-arrays). The large-scale antenna arrays increase the possibility of having communication users and targets of interest in the near field of the base station (BS). The paper first establishes the models of near-field spherical waves and far-field plane waves. With the models, we analyze the near-field beam focusing ability and the far-field beam steering ability by finding the gain-loss mathematical expression caused by the far-field steering vector mismatch in the near-field case. Subsequently, we analyzed the performance degradation caused by traditional far-field beamforming in the near field for both communication and sensing. We formulate the transceiver NFBF design problem as maximizing the sensing signal-to-interference-plus-noise ratio (SINR) while ensuring the required communication quality-of-service (QoS) and total power constraint. We decompose it into two subproblems and solve them using the generalized Rayleigh entropy theory and the Semi-Definite Relaxation (SDR) technique. Additionally, we prove the attainability of the optimal solution for SDR. Additionally, a low-complexity design scheme is proposed as an alternative to the SDR approach for obtaining transmit beamforming. The simulation results validate the effectiveness of the proposed NFBF scheme, demonstrating its capability to manage co-angle interference and enhance both communication and sensing performance.
{"title":"Near-Field Integrated Sensing and Communication: Performance Analysis and Beamforming Design","authors":"Kaiqian Qu;Shuaishuai Guo;Nasir Saeed;Jia Ye","doi":"10.1109/OJCOMS.2024.3470844","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3470844","url":null,"abstract":"This paper explores the potential near-field beamforming (NFBF) in integrated sensing and communication (ISAC) systems with extremely large-scale arrays (XL-arrays). The large-scale antenna arrays increase the possibility of having communication users and targets of interest in the near field of the base station (BS). The paper first establishes the models of near-field spherical waves and far-field plane waves. With the models, we analyze the near-field beam focusing ability and the far-field beam steering ability by finding the gain-loss mathematical expression caused by the far-field steering vector mismatch in the near-field case. Subsequently, we analyzed the performance degradation caused by traditional far-field beamforming in the near field for both communication and sensing. We formulate the transceiver NFBF design problem as maximizing the sensing signal-to-interference-plus-noise ratio (SINR) while ensuring the required communication quality-of-service (QoS) and total power constraint. We decompose it into two subproblems and solve them using the generalized Rayleigh entropy theory and the Semi-Definite Relaxation (SDR) technique. Additionally, we prove the attainability of the optimal solution for SDR. Additionally, a low-complexity design scheme is proposed as an alternative to the SDR approach for obtaining transmit beamforming. The simulation results validate the effectiveness of the proposed NFBF scheme, demonstrating its capability to manage co-angle interference and enhance both communication and sensing performance.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6353-6366"},"PeriodicalIF":6.3,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10700785","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-30DOI: 10.1109/OJCOMS.2024.3470689
Salem Titouni;Idris Messaoudene;Yassine Himeur;Massinissa Belazzoug;Boualem Hammache;Shadi Atalla;Wathiq Mansoor
Joint Communication Radar (JCR) systems have garnered significant attention due to their ability to simultaneously perform communication and radar sensing tasks. However, in challenging environments, JCR signals are vulnerable to multipath propagation, resulting in signal degradation, interference, and reduced system performance. This paper explores the challenges posed by multipath effects on JCR signals and proposes novel mitigation techniques to enhance their robustness and reliability. The suggested method involves employing a spectral transformation to enhance the JCR-emitted signal, resulting in a significant improvement in the overall effectiveness of JCR systems. Consequently, the numerical implementation of the JCR system integrated with the proposed technique leads to improved performance metrics, including Multipath Error Envelope (MEE), Root Mean Square Error (RMSE), and Standard Deviation (STD). By effectively mitigating the adverse impacts of multipath propagation, the proposed methodologies enhance the robustness and accuracy of JCR systems, leading to improved communication reliability and radar sensing capabilities. Notably, the proposed method achieved a minimal Root Mean Square Error (RMSE) of just 0.05, marking a substantial enhancement in performance compared to existing methods.
{"title":"An Efficient Spectral Approach for JCR Narrow Band Signals in Presence of Multipath and Noise","authors":"Salem Titouni;Idris Messaoudene;Yassine Himeur;Massinissa Belazzoug;Boualem Hammache;Shadi Atalla;Wathiq Mansoor","doi":"10.1109/OJCOMS.2024.3470689","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3470689","url":null,"abstract":"Joint Communication Radar (JCR) systems have garnered significant attention due to their ability to simultaneously perform communication and radar sensing tasks. However, in challenging environments, JCR signals are vulnerable to multipath propagation, resulting in signal degradation, interference, and reduced system performance. This paper explores the challenges posed by multipath effects on JCR signals and proposes novel mitigation techniques to enhance their robustness and reliability. The suggested method involves employing a spectral transformation to enhance the JCR-emitted signal, resulting in a significant improvement in the overall effectiveness of JCR systems. Consequently, the numerical implementation of the JCR system integrated with the proposed technique leads to improved performance metrics, including Multipath Error Envelope (MEE), Root Mean Square Error (RMSE), and Standard Deviation (STD). By effectively mitigating the adverse impacts of multipath propagation, the proposed methodologies enhance the robustness and accuracy of JCR systems, leading to improved communication reliability and radar sensing capabilities. Notably, the proposed method achieved a minimal Root Mean Square Error (RMSE) of just 0.05, marking a substantial enhancement in performance compared to existing methods.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6343-6352"},"PeriodicalIF":6.3,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10700591","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1109/OJCOMS.2024.3468873
Lan Ma;Liyang Zhou;Shaoteng Liu;Xiangyu Chen;Qifu Sun
Row-diagonal parity (RDP) code is a classical $(k+2,~k)$ systematic maximum distance separable (MDS) array code with $k leq L-1$ under sub-packetization level $l = L-1$ , where L is a prime integer. When $k = L-1$ , its encoding requires $2-{}frac {2}{k}$ XORs per original data bit, which exactly achieves theoretical optimal lower bound. In this paper, we present three new constructions of $(k+2,~k)$ systematic MDS array codes. First, under sub-packetization level $l = 4$ , we novelly design a $(17,~15)$ array code ${mathcal {C}}_{1}$ , where k can reach the largest possible value to satisfy the MDS property. Moreover, when $k leq 7$ , the encoding complexity of its subcodes can exactly achieve the theoretical optimal $2-{}frac {2}{k}$ XORs per original data bit, and likewise, the decoding complexity of the subcodes with $k leq 4$ is also exactly optimal. Under sub-packetization level $l = L-1$ with certain primes L, the second construction yields an MDS array code ${mathcal {C}}_{2}$ with $k leq {}frac {L(L-1)}{2}$ , and the encoding complexity of ${mathcal {C}}_{2}$ is also exactly optimal for $k = L-1$ , $2L-3$ . Furthermore, based on bit permutation, the third MDS array code ${mathcal {C}}_{3}$ is obtained with $k leq L(L-1)$ under sub-packetization level $l = 2(L-1)$ with certain primes L. In particular, as an extension of ${mathcal {C}}_{2}$ , ${mathcal {C}}_{3}$ exactly achieves the optimal encoding complexity for $k = 2(2L-3)$ , which does not hold for other array codes in the literature.
{"title":"New Systematic MDS Array Codes With Two Parities","authors":"Lan Ma;Liyang Zhou;Shaoteng Liu;Xiangyu Chen;Qifu Sun","doi":"10.1109/OJCOMS.2024.3468873","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3468873","url":null,"abstract":"Row-diagonal parity (RDP) code is a classical \u0000<inline-formula> <tex-math>$(k+2,~k)$ </tex-math></inline-formula>\u0000 systematic maximum distance separable (MDS) array code with \u0000<inline-formula> <tex-math>$k leq L-1$ </tex-math></inline-formula>\u0000 under sub-packetization level \u0000<inline-formula> <tex-math>$l = L-1$ </tex-math></inline-formula>\u0000, where L is a prime integer. When \u0000<inline-formula> <tex-math>$k = L-1$ </tex-math></inline-formula>\u0000, its encoding requires \u0000<inline-formula> <tex-math>$2-{}frac {2}{k}$ </tex-math></inline-formula>\u0000 XORs per original data bit, which exactly achieves theoretical optimal lower bound. In this paper, we present three new constructions of \u0000<inline-formula> <tex-math>$(k+2,~k)$ </tex-math></inline-formula>\u0000 systematic MDS array codes. First, under sub-packetization level \u0000<inline-formula> <tex-math>$l = 4$ </tex-math></inline-formula>\u0000, we novelly design a \u0000<inline-formula> <tex-math>$(17,~15)$ </tex-math></inline-formula>\u0000 array code \u0000<inline-formula> <tex-math>${mathcal {C}}_{1}$ </tex-math></inline-formula>\u0000, where k can reach the largest possible value to satisfy the MDS property. Moreover, when \u0000<inline-formula> <tex-math>$k leq 7$ </tex-math></inline-formula>\u0000, the encoding complexity of its subcodes can exactly achieve the theoretical optimal \u0000<inline-formula> <tex-math>$2-{}frac {2}{k}$ </tex-math></inline-formula>\u0000 XORs per original data bit, and likewise, the decoding complexity of the subcodes with \u0000<inline-formula> <tex-math>$k leq 4$ </tex-math></inline-formula>\u0000 is also exactly optimal. Under sub-packetization level \u0000<inline-formula> <tex-math>$l = L-1$ </tex-math></inline-formula>\u0000 with certain primes L, the second construction yields an MDS array code \u0000<inline-formula> <tex-math>${mathcal {C}}_{2}$ </tex-math></inline-formula>\u0000 with \u0000<inline-formula> <tex-math>$k leq {}frac {L(L-1)}{2}$ </tex-math></inline-formula>\u0000, and the encoding complexity of \u0000<inline-formula> <tex-math>${mathcal {C}}_{2}$ </tex-math></inline-formula>\u0000 is also exactly optimal for \u0000<inline-formula> <tex-math>$k = L-1$ </tex-math></inline-formula>\u0000, \u0000<inline-formula> <tex-math>$2L-3$ </tex-math></inline-formula>\u0000. Furthermore, based on bit permutation, the third MDS array code \u0000<inline-formula> <tex-math>${mathcal {C}}_{3}$ </tex-math></inline-formula>\u0000 is obtained with \u0000<inline-formula> <tex-math>$k leq L(L-1)$ </tex-math></inline-formula>\u0000 under sub-packetization level \u0000<inline-formula> <tex-math>$l = 2(L-1)$ </tex-math></inline-formula>\u0000 with certain primes L. In particular, as an extension of \u0000<inline-formula> <tex-math>${mathcal {C}}_{2}$ </tex-math></inline-formula>\u0000, \u0000<inline-formula> <tex-math>${mathcal {C}}_{3}$ </tex-math></inline-formula>\u0000 exactly achieves the optimal encoding complexity for \u0000<inline-formula> <tex-math>$k = 2(2L-3)$ </tex-math></inline-formula>\u0000, which does not hold for other array codes in the literature.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6329-6342"},"PeriodicalIF":6.3,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10695780","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142408889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-25DOI: 10.1109/OJCOMS.2024.3467683
Sebastian Semper;Joël Naviliat;Jonas Gedschold;Michael Döbereiner;Steffen Schieler;Reiner S. Thomä
The recent advances in Integrated Sensing and Communications (ICAS) essentially transform mobile radio networks into a diverse, dynamic and heterogeneous sensing network. For the application of localization, the acquired sensing data needs to be processed to estimates of the state vectors of the targets in a timely manner. This paper aims at providing a roadmap for the development of a suitable computing system for ICAS. We propose to embed the signal processing into the concept of edge computing. It provides the necessary theoretical computing framework, since it alleviates the need for communication with a remote cloud. To obtain localization information in such a distributed, asynchronous and heterogeneous scenario, we study how existing maximum likelihood estimation techniques can be transformed into algorithms that can be orchestrated close to the edge. The advantage of these approaches is that they have well studied statistical properties and efficient algorithmic implementations exist. We propose to study to derive a graph that encodes these algorithms' processing by relating individual and isolated computations in terms of the input/output-behavior of so-called compute nodes. This compute graph structure can then be flexibly distributed across multiple devices and even whole processing/sensing units. Moreover, modern computing architectures leverage such graph structures to optimize the efficient use of computing hardware. Additionally, once this graph is constructed we have laid the groundwork for the possibility to exchange certain compute steps by deep learning architectures. For instance, this allows to sidestep some costly iterative part of traditional maximum likelihood estimators, which further contributes to the low-latency of the localization task. Moreover, deep learning methods bear the promise of being more robust to model mismatches in contrast to the conventional model based approaches. As a consequence, we can then study the relation between those classical methods and the new deep learning based methods and analyze the achievable performance. One key final result will be a deeper understanding of how well the maximum likelihood approach can be applied to ICAS and how much it profits from the combination with modern deep learning techniques.
{"title":"Distributed Computing and Model-Based Estimation for Integrated Communications and Sensing: A Roadmap","authors":"Sebastian Semper;Joël Naviliat;Jonas Gedschold;Michael Döbereiner;Steffen Schieler;Reiner S. Thomä","doi":"10.1109/OJCOMS.2024.3467683","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3467683","url":null,"abstract":"The recent advances in Integrated Sensing and Communications (ICAS) essentially transform mobile radio networks into a diverse, dynamic and heterogeneous sensing network. For the application of localization, the acquired sensing data needs to be processed to estimates of the state vectors of the targets in a timely manner. This paper aims at providing a roadmap for the development of a suitable computing system for ICAS. We propose to embed the signal processing into the concept of edge computing. It provides the necessary theoretical computing framework, since it alleviates the need for communication with a remote cloud. To obtain localization information in such a distributed, asynchronous and heterogeneous scenario, we study how existing maximum likelihood estimation techniques can be transformed into algorithms that can be orchestrated close to the edge. The advantage of these approaches is that they have well studied statistical properties and efficient algorithmic implementations exist. We propose to study to derive a graph that encodes these algorithms' processing by relating individual and isolated computations in terms of the input/output-behavior of so-called compute nodes. This compute graph structure can then be flexibly distributed across multiple devices and even whole processing/sensing units. Moreover, modern computing architectures leverage such graph structures to optimize the efficient use of computing hardware. Additionally, once this graph is constructed we have laid the groundwork for the possibility to exchange certain compute steps by deep learning architectures. For instance, this allows to sidestep some costly iterative part of traditional maximum likelihood estimators, which further contributes to the low-latency of the localization task. Moreover, deep learning methods bear the promise of being more robust to model mismatches in contrast to the conventional model based approaches. As a consequence, we can then study the relation between those classical methods and the new deep learning based methods and analyze the achievable performance. One key final result will be a deeper understanding of how well the maximum likelihood approach can be applied to ICAS and how much it profits from the combination with modern deep learning techniques.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6279-6290"},"PeriodicalIF":6.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10693506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-25DOI: 10.1109/OJCOMS.2024.3467680
Steven Claessens;Prerna Dhull;Dominique Schreurs;Sofie Pollin
Lowering the dependency of receivers and sensors on energy supplies is a requirement for a realistic Internet of Things. This is certainly achieved when sensor nodes are powered wirelessly. Local oscillators (LOs), required to receive and transmit modern radio frequency (RF) waveforms, consume a considerable amount of the power budget. We propose a Wireless Local Oscillator (WiLO) concept to move the LO from the sensor to an external location and transmit it wirelessly to the sensor. This WiLO is modeled as a constant tone transmission. As is well known, the sensor can backscatter the constant tone, which enables uplink transmission. Our system approach allows the downconversion of any RF waveform without LO and mixer while simultaneously utilizing the same signal for power transfer. In this work, we demonstrate our approach to different types of OFDM signals, which can be considered as a general complex RF signal example to be received. Our WiLO-based technique to receive any modern communication signal without LO, in combination with harvesting energy from the tone and backscattering on that tone, results in a promising energy-efficient IoT solution. We present the performance model with design requirements for WiLO tone and amplitudes of OFDM tones for feasible reception of WiLO-OFDM. Simultaneous Wireless Information and Power Transfer (SWIPT) applications typically operate in a high SNR regime, and both energy harvesting and information transfer are equally important. The cost of 12 dB performance with an AWGN noisy channel can be acceptable by saving a significant amount of power by removing the LO.
降低接收器和传感器对能源供应的依赖是现实物联网的要求。当传感器节点采用无线供电时，就能实现这一目标。接收和发送现代射频（RF）波形所需的本地振荡器（LO）消耗了大量的功率预算。我们提出了一种无线本地振荡器 (WiLO) 概念，将本地振荡器从传感器移到外部位置，然后无线传输到传感器。这种 WiLO 被模拟为恒定音调传输。众所周知，传感器可以反向散射恒定音调，从而实现上行链路传输。我们的系统方法允许在没有 LO 和混频器的情况下对任何射频波形进行下变频，同时利用相同的信号进行功率传输。在这项工作中，我们针对不同类型的 OFDM 信号演示了我们的方法，这些信号可视为需要接收的一般复杂射频信号示例。我们基于 WiLO 的技术可在没有 LO 的情况下接收任何现代通信信号，结合从音调中收集能量并对该音调进行反向散射，从而产生了一种前景广阔的高能效物联网解决方案。我们介绍了性能模型，以及 WiLO 音调和 OFDM 音调振幅的设计要求，以实现 WiLO-OFDM 的可行接收。同步无线信息和功率传输（SWIPT）应用通常在高信噪比环境中运行，能量采集和信息传输同等重要。在 AWGN 噪声信道中，12 dB 性能的代价可以通过移除 LO 节省大量功率来接受。
{"title":"WiLO-OFDM Transmission Scheme for Simultaneous Wireless Information and Power Transfer","authors":"Steven Claessens;Prerna Dhull;Dominique Schreurs;Sofie Pollin","doi":"10.1109/OJCOMS.2024.3467680","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3467680","url":null,"abstract":"Lowering the dependency of receivers and sensors on energy supplies is a requirement for a realistic Internet of Things. This is certainly achieved when sensor nodes are powered wirelessly. Local oscillators (LOs), required to receive and transmit modern radio frequency (RF) waveforms, consume a considerable amount of the power budget. We propose a Wireless Local Oscillator (WiLO) concept to move the LO from the sensor to an external location and transmit it wirelessly to the sensor. This WiLO is modeled as a constant tone transmission. As is well known, the sensor can backscatter the constant tone, which enables uplink transmission. Our system approach allows the downconversion of any RF waveform without LO and mixer while simultaneously utilizing the same signal for power transfer. In this work, we demonstrate our approach to different types of OFDM signals, which can be considered as a general complex RF signal example to be received. Our WiLO-based technique to receive any modern communication signal without LO, in combination with harvesting energy from the tone and backscattering on that tone, results in a promising energy-efficient IoT solution. We present the performance model with design requirements for WiLO tone and amplitudes of OFDM tones for feasible reception of WiLO-OFDM. Simultaneous Wireless Information and Power Transfer (SWIPT) applications typically operate in a high SNR regime, and both energy harvesting and information transfer are equally important. The cost of 12 dB performance with an AWGN noisy channel can be acceptable by saving a significant amount of power by removing the LO.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6261-6278"},"PeriodicalIF":6.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10693571","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-25DOI: 10.1109/OJCOMS.2024.3467385
Hazem Barka;Md Sahabul Alam;Georges Kaddoum;Minh Au;Basile L. Agba
The rapid expansion of Internet of Things (IoT) networks has paved the way for their integration into mission-critical applications requiring secure and reliable monitoring, such as smart grid utilities. However, these advanced power grids face significant challenges in maintaining reliable wireless communication, particularly in hostile environments like high-voltage substations and power plants. These environments are characterized by intense bursts of interference, known as impulsive noise with memory. To address this problem, in this study, we introduce a two-process receiver design. The first process is a multi-step receiver parameter estimation process. The second process is a novel memory-aware log-likelihood ratio (LLR) calculation method designed to mitigate the effects of impulsive noise with memory using the parameters estimated from the first process. This method is computationally efficient, which makes it suitable for IoT devices with limited computational capabilities. Simulation results obtained show that the proposed method achieves a bit error rate (BER) similar to the corresponding BERs of the best-performing algorithms with perfect noise parameters. Furthermore, it outperforms the Viterbi algorithm amid imperfect noise parameters. Notably, it method achieves these benchmarks while substantially improving execution time.
{"title":"An LLR-Based Receiver for Mitigating Bursty Impulsive Noise With Unknown Distributions","authors":"Hazem Barka;Md Sahabul Alam;Georges Kaddoum;Minh Au;Basile L. Agba","doi":"10.1109/OJCOMS.2024.3467385","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3467385","url":null,"abstract":"The rapid expansion of Internet of Things (IoT) networks has paved the way for their integration into mission-critical applications requiring secure and reliable monitoring, such as smart grid utilities. However, these advanced power grids face significant challenges in maintaining reliable wireless communication, particularly in hostile environments like high-voltage substations and power plants. These environments are characterized by intense bursts of interference, known as impulsive noise with memory. To address this problem, in this study, we introduce a two-process receiver design. The first process is a multi-step receiver parameter estimation process. The second process is a novel memory-aware log-likelihood ratio (LLR) calculation method designed to mitigate the effects of impulsive noise with memory using the parameters estimated from the first process. This method is computationally efficient, which makes it suitable for IoT devices with limited computational capabilities. Simulation results obtained show that the proposed method achieves a bit error rate (BER) similar to the corresponding BERs of the best-performing algorithms with perfect noise parameters. Furthermore, it outperforms the Viterbi algorithm amid imperfect noise parameters. Notably, it method achieves these benchmarks while substantially improving execution time.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6166-6179"},"PeriodicalIF":6.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10693527","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383396","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cell-free massive MIMO (CF-mMIMO) systems represent a deeply investigated evolution from the conventional multicell co-located massive MIMO (MC-mMIMO) network deployments. Anticipating a gradual integration of CF-mMIMO systems alongside pre-existing MC-mMIMO network elements, this paper considers a scenario where both deployments coexist, in order to serve a large number of users using a shared set of frequencies. The investigation explores the impact of this co-existence on the network’s downlink performance, considering various degrees of mutual cooperation, precoder selection, and power control strategies. Moreover, to take into account the effect of the proposed cooperation scenarios on the fronthaul links, this paper also provides a fronthaul-aware heuristic association algorithm between users and network elements, which allows the fulfillment of the front-haul requirement on each link. The research is finally completed by extensive simulations, shedding light on the performance outcomes associated with the various levels of cooperation and several solutions delineated in the paper.
{"title":"Co-Existing/Cooperating Multicell Massive MIMO and Cell-Free Massive MIMO Deployments: Heuristic Designs and Performance Analysis","authors":"Stefano Buzzi;Carmen D’Andrea;Li Wang;Ahmet Hasim Gokceoglu;Gunnar Peters","doi":"10.1109/OJCOMS.2024.3465878","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3465878","url":null,"abstract":"Cell-free massive MIMO (CF-mMIMO) systems represent a deeply investigated evolution from the conventional multicell co-located massive MIMO (MC-mMIMO) network deployments. Anticipating a gradual integration of CF-mMIMO systems alongside pre-existing MC-mMIMO network elements, this paper considers a scenario where both deployments coexist, in order to serve a large number of users using a shared set of frequencies. The investigation explores the impact of this co-existence on the network’s downlink performance, considering various degrees of mutual cooperation, precoder selection, and power control strategies. Moreover, to take into account the effect of the proposed cooperation scenarios on the fronthaul links, this paper also provides a fronthaul-aware heuristic association algorithm between users and network elements, which allows the fulfillment of the front-haul requirement on each link. The research is finally completed by extensive simulations, shedding light on the performance outcomes associated with the various levels of cooperation and several solutions delineated in the paper.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6180-6200"},"PeriodicalIF":6.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10685533","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1109/OJCOMS.2024.3466225
Farhad Rezazadeh;Sergio Barrachina-Muñoz;Hatim Chergui;Josep Mangues;Mehdi Bennis;Dusit Niyato;Houbing Song;Lingjia Liu
The move toward artificial intelligence (AI)-native sixth-generation (6G) networks has put more emphasis on the importance of explainability and trustworthiness in network management operations, especially for mission-critical use-cases. Such desired trust transcends traditional post-hoc explainable AI (XAI) methods to using contextual explanations for guiding the learning process in an in-hoc way. This paper proposes a novel graph reinforcement learning (GRL) framework named TANGO which relies on a symbolic subsystem. It consists of a Bayesian-graph neural network (GNN) Explainer, whose outputs, in terms of edge/node importance and uncertainty, are periodically translated to a logical GRL reward function. This adjustment is accomplished through defined symbolic reasoning rules within a Reasoner. Considering a real-world testbed proof-of-concept (PoC), a gNodeB (gNB) radio resource allocation problem is formulated, which aims to minimize under- and over-provisioning of physical resource blocks (PRBs) while penalizing decisions emanating from the uncertain and less important edge-nodes relations. Our findings reveal that the proposed in-hoc explainability solution significantly expedites convergence compared to standard GRL baseline and other benchmarks in the deep reinforcement learning (DRL) domain. The experiment evaluates performance in AI, complexity, energy consumption, robustness, network, scalability, and explainability metrics. Specifically, the results show that TANGO achieves a noteworthy accuracy of 96.39% in terms of optimal PRB allocation in inference phase, outperforming the baseline by $1.22times $ .
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Pub Date : 2024-09-20DOI: 10.1109/OJCOMS.2024.3465028
Xingguang Li;Xingle Xue;Chen Zhou;Jianshe Ma;Ping Su
As an important branch of free-space optical (FSO) communication technology, ultraviolet (UV) communication is mainly applied to mobile communication platforms represented by unmanned aerial vehicle (UAV). With the development of LEDs and UV detector devices, UAV UV communication technology has shown great potential in related fields. But at the same time, it also faces some challenges. As the communication distance increases, the path loss of the UV communication system can reach 0.12dB/m, and the variation in bit error rate (BER) can rapidly deteriorate from the order of $10^{-8}$ to $10^{-1}$ . Additionally, the UV communication system mounted on a UAV platform can emit radiation into the environment, which may have negative effects on human health when the radiation intensity exceeds $0.5{mu }$ W/cm2. These issues can be summarized as the availability, stability, and effectiveness of UAV-based UV communication technology. This paper aims to comprehensively address both UAVs and UV communication, providing a detailed introduction to the challenges and solutions facing UAV-based UV communication technology. Focusing on the specific aspects of these three issues, the paper first introduces the research background, value, and challenges of UAV-based UV communication technology, and investigates the current research status of UV communication channel models and positioning techniques. In order to solve the problem of UV environmental radiation, the article goes on to introduce beamforming and power control in UV optical communication technology. To solve the problem of reducing signal attenuation and increasing the communication range, the article introduces diversity technology and networking technology. In order to balance the communication quality and communication rate during UAV movement, the article introduces adaptive modulation and adaptive coding technology. Finally, the future development direction of UAV UV communication technology is summarised.
{"title":"A Review of UV Communications for UAVs","authors":"Xingguang Li;Xingle Xue;Chen Zhou;Jianshe Ma;Ping Su","doi":"10.1109/OJCOMS.2024.3465028","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3465028","url":null,"abstract":"As an important branch of free-space optical (FSO) communication technology, ultraviolet (UV) communication is mainly applied to mobile communication platforms represented by unmanned aerial vehicle (UAV). With the development of LEDs and UV detector devices, UAV UV communication technology has shown great potential in related fields. But at the same time, it also faces some challenges. As the communication distance increases, the path loss of the UV communication system can reach 0.12dB/m, and the variation in bit error rate (BER) can rapidly deteriorate from the order of \u0000<inline-formula> <tex-math>$10^{-8}$ </tex-math></inline-formula>\u0000 to \u0000<inline-formula> <tex-math>$10^{-1}$ </tex-math></inline-formula>\u0000. Additionally, the UV communication system mounted on a UAV platform can emit radiation into the environment, which may have negative effects on human health when the radiation intensity exceeds \u0000<inline-formula> <tex-math>$0.5{mu }$ </tex-math></inline-formula>\u0000W/cm2. These issues can be summarized as the availability, stability, and effectiveness of UAV-based UV communication technology. This paper aims to comprehensively address both UAVs and UV communication, providing a detailed introduction to the challenges and solutions facing UAV-based UV communication technology. Focusing on the specific aspects of these three issues, the paper first introduces the research background, value, and challenges of UAV-based UV communication technology, and investigates the current research status of UV communication channel models and positioning techniques. In order to solve the problem of UV environmental radiation, the article goes on to introduce beamforming and power control in UV optical communication technology. To solve the problem of reducing signal attenuation and increasing the communication range, the article introduces diversity technology and networking technology. In order to balance the communication quality and communication rate during UAV movement, the article introduces adaptive modulation and adaptive coding technology. Finally, the future development direction of UAV UV communication technology is summarised.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6495-6534"},"PeriodicalIF":6.3,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684827","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
WiFi sensing is an emerging technology that utilizes wireless signals for various sensing applications. However, the reliance on supervised learning and the scarcity of labelled data and the incomprehensible channel state information (CSI) data pose significant challenges. These issues affect deep learning models’ performance and generalization across different environments. Consequently, self-supervised learning (SSL) is emerging as a promising strategy to extract meaningful data representations with minimal reliance on labelled samples. In this paper, we introduce a novel SSL framework called Context-Aware Predictive Coding (CAPC), which effectively learns from unlabelled data and adapts to diverse environments. CAPC integrates elements of Contrastive Predictive Coding (CPC) and the augmentation-based SSL method, Barlow Twins, promoting temporal and contextual consistency in data representations. This hybrid approach captures essential temporal information in CSI, crucial for tasks like human activity recognition (HAR), and ensures robustness against data distortions. Additionally, we propose a unique augmentation, employing both uplink and downlink CSI to isolate free space propagation effects and minimize the impact of electronic distortions of the transceiver. Our evaluations demonstrate that CAPC not only outperforms other SSL methods and supervised approaches, but also achieves superior generalization capabilities. Specifically, CAPC requires fewer labelled samples while significantly outperforming supervised learning by an average margin of 30.53% and surpassing SSL baselines by 6.5% on average in low-labelled data scenarios. Furthermore, our transfer learning studies on an unseen dataset with a different HAR task and environment showcase an accuracy improvement of 1.8% over other SSL baselines and 24.7% over supervised learning, emphasizing its exceptional cross-domain adaptability. These results mark a significant breakthrough in SSL applications for WiFi sensing, highlighting CAPC’s environmental adaptability and reduced dependency on labelled data.
{"title":"Context-Aware Predictive Coding: A Representation Learning Framework for WiFi Sensing","authors":"Borna Barahimi;Hina Tabassum;Mohammad Omer;Omer Waqar","doi":"10.1109/OJCOMS.2024.3465216","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3465216","url":null,"abstract":"WiFi sensing is an emerging technology that utilizes wireless signals for various sensing applications. However, the reliance on supervised learning and the scarcity of labelled data and the incomprehensible channel state information (CSI) data pose significant challenges. These issues affect deep learning models’ performance and generalization across different environments. Consequently, self-supervised learning (SSL) is emerging as a promising strategy to extract meaningful data representations with minimal reliance on labelled samples. In this paper, we introduce a novel SSL framework called Context-Aware Predictive Coding (CAPC), which effectively learns from unlabelled data and adapts to diverse environments. CAPC integrates elements of Contrastive Predictive Coding (CPC) and the augmentation-based SSL method, Barlow Twins, promoting temporal and contextual consistency in data representations. This hybrid approach captures essential temporal information in CSI, crucial for tasks like human activity recognition (HAR), and ensures robustness against data distortions. Additionally, we propose a unique augmentation, employing both uplink and downlink CSI to isolate free space propagation effects and minimize the impact of electronic distortions of the transceiver. Our evaluations demonstrate that CAPC not only outperforms other SSL methods and supervised approaches, but also achieves superior generalization capabilities. Specifically, CAPC requires fewer labelled samples while significantly outperforming supervised learning by an average margin of 30.53% and surpassing SSL baselines by 6.5% on average in low-labelled data scenarios. Furthermore, our transfer learning studies on an unseen dataset with a different HAR task and environment showcase an accuracy improvement of 1.8% over other SSL baselines and 24.7% over supervised learning, emphasizing its exceptional cross-domain adaptability. These results mark a significant breakthrough in SSL applications for WiFi sensing, highlighting CAPC’s environmental adaptability and reduced dependency on labelled data.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":"5 ","pages":"6119-6134"},"PeriodicalIF":6.3,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684811","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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