Pub Date : 2024-08-22DOI: 10.1109/OJCOMS.2024.3447839
Muhammad Usman Khan;Enrico Testi;Marco Chiani;Enrico Paolini
Cell-free massive MIMO (CF-mMIMO) networks leverage seamless cooperation among numerous access points to serve a large number of users over the same time/frequency resources. This paper addresses the challenges of pilot and data power control, as well as pilot assignment, in the uplink of a cell-free massive MIMO (CF-mMIMO) network, where the number of users significantly exceeds that of the available orthogonal pilots. We first derive the closed-form expression of the achievable uplink rate of a user. Subsequently, harnessing the universal function approximation capability of artificial neural networks, we introduce a novel multi-task deep learning-based approach for joint power control and pilot assignment, aiming to maximize the minimum user rate. Our proposed method entails the design and unsupervised training of a deep neural network (DNN), employing a custom loss function specifically tailored to perform joint power control and pilot assignment, while simultaneously limiting the total network power usage. Extensive simulations demonstrate that our method outperforms the existing power control and pilot assignment strategies in terms of achievable network throughput, minimum user rate, and per-user energy consumption. The model versatility and adaptability are assessed by simulating two different scenarios, namely a urban macro (UMa) and an industrial one.
{"title":"Joint Power Control and Pilot Assignment in Cell-Free Massive MIMO Using Deep Learning","authors":"Muhammad Usman Khan;Enrico Testi;Marco Chiani;Enrico Paolini","doi":"10.1109/OJCOMS.2024.3447839","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3447839","url":null,"abstract":"Cell-free massive MIMO (CF-mMIMO) networks leverage seamless cooperation among numerous access points to serve a large number of users over the same time/frequency resources. This paper addresses the challenges of pilot and data power control, as well as pilot assignment, in the uplink of a cell-free massive MIMO (CF-mMIMO) network, where the number of users significantly exceeds that of the available orthogonal pilots. We first derive the closed-form expression of the achievable uplink rate of a user. Subsequently, harnessing the universal function approximation capability of artificial neural networks, we introduce a novel multi-task deep learning-based approach for joint power control and pilot assignment, aiming to maximize the minimum user rate. Our proposed method entails the design and unsupervised training of a deep neural network (DNN), employing a custom loss function specifically tailored to perform joint power control and pilot assignment, while simultaneously limiting the total network power usage. Extensive simulations demonstrate that our method outperforms the existing power control and pilot assignment strategies in terms of achievable network throughput, minimum user rate, and per-user energy consumption. The model versatility and adaptability are assessed by simulating two different scenarios, namely a urban macro (UMa) and an industrial one.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643563","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137548","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}
Recent years have witnessed the Open Radio Access Network (RAN) paradigm transforming the fundamental ways cellular systems are deployed, managed, and optimized. This shift is led by concepts such as openness, softwarization, programmability, interoperability, and intelligence of the network, which have emerged in wired networks through Software-defined Networking (SDN) but lag behind in cellular systems. The realization of the Open RAN vision into practical architectures, intelligent data-driven control loops, and efficient software implementations, however, is a multifaceted challenge, which requires (i) datasets to train Artificial Intelligence (AI) and Machine Learning (ML) models; (ii) facilities to test models without disrupting production networks; (iii) continuous and automated validation of the RAN software; and (iv) significant testing and integration efforts. This paper is a tutorial on how Colosseum—the world’s largest wireless network emulator with hardware in the loop—can provide the research infrastructure and tools to fill the gap between the Open RAN vision, and the deployment and commercialization of open and programmable networks. We describe how Colosseum implements an Open RAN digital twin through a high-fidelity Radio Frequency (RF) channel emulator and endto- end softwarized O-RAN and 5G-compliant protocol stacks, thus allowing users to reproduce and experiment upon topologies representative of real-world cellular deployments. Then, we detail the twinning infrastructure of Colosseum, as well as the automation pipelines for RF and protocol stack twinning. Finally, we showcase a broad range of Open RAN use cases implemented on Colosseum, including the real-time connection between the digital twin and real-world networks, and the development, prototyping, and testing of AI/ML solutions for Open RAN.
近年来，开放式无线接入网（RAN）范例改变了蜂窝系统部署、管理和优化的基本方式。这一转变由开放性、软化、可编程性、互操作性和网络智能化等概念引领，这些概念已通过软件定义网络（SDN）在有线网络中出现，但在蜂窝系统中却相对滞后。然而，要将开放式 RAN 的愿景转化为实用的架构、智能数据驱动的控制回路和高效的软件实施，却是一项多方面的挑战，这需要：(i) 用于训练人工智能 (AI) 和机器学习 (ML) 模型的数据集；(ii) 在不中断生产网络的情况下测试模型的设施；(iii) RAN 软件的持续和自动验证；以及 (iv) 大量的测试和集成工作。本文将介绍 Colosseum（世界上最大的无线网络仿真器）如何提供研究基础设施和工具，以填补开放式 RAN 愿景与开放式可编程网络的部署和商业化之间的空白。我们将介绍 Colosseum 如何通过高保真射频（RF）信道仿真器和端到端软化 O-RAN 和 5G 兼容协议栈实现开放 RAN 数字孪生，从而让用户能够重现和实验代表真实世界蜂窝部署的拓扑结构。然后，我们将详细介绍 Colosseum 的孪生基础设施，以及射频和协议栈孪生的自动化管道。最后，我们将展示在 Colosseum 上实现的各种开放式 RAN 用例，包括数字孪生和真实世界网络之间的实时连接，以及开放式 RAN 的 AI/ML 解决方案的开发、原型设计和测试。
{"title":"Colosseum: The Open RAN Digital Twin","authors":"Michele Polese;Leonardo Bonati;Salvatore D'Oro;Pedram Johari;Davide Villa;Sakthivel Velumani;Rajeev Gangula;Maria Tsampazi;Clifton Paul Robinson;Gabriele Gemmi;Andrea Lacava;Stefano Maxenti;Hai Cheng;Tommaso Melodia","doi":"10.1109/OJCOMS.2024.3447472","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3447472","url":null,"abstract":"Recent years have witnessed the Open Radio Access Network (RAN) paradigm transforming the fundamental ways cellular systems are deployed, managed, and optimized. This shift is led by concepts such as openness, softwarization, programmability, interoperability, and intelligence of the network, which have emerged in wired networks through Software-defined Networking (SDN) but lag behind in cellular systems. The realization of the Open RAN vision into practical architectures, intelligent data-driven control loops, and efficient software implementations, however, is a multifaceted challenge, which requires (i) datasets to train Artificial Intelligence (AI) and Machine Learning (ML) models; (ii) facilities to test models without disrupting production networks; (iii) continuous and automated validation of the RAN software; and (iv) significant testing and integration efforts. This paper is a tutorial on how Colosseum—the world’s largest wireless network emulator with hardware in the loop—can provide the research infrastructure and tools to fill the gap between the Open RAN vision, and the deployment and commercialization of open and programmable networks. We describe how Colosseum implements an Open RAN digital twin through a high-fidelity Radio Frequency (RF) channel emulator and endto- end softwarized O-RAN and 5G-compliant protocol stacks, thus allowing users to reproduce and experiment upon topologies representative of real-world cellular deployments. Then, we detail the twinning infrastructure of Colosseum, as well as the automation pipelines for RF and protocol stack twinning. Finally, we showcase a broad range of Open RAN use cases implemented on Colosseum, including the real-time connection between the digital twin and real-world networks, and the development, prototyping, and testing of AI/ML solutions for Open RAN.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643670","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169672","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 : 2024-08-21DOI: 10.1109/OJCOMS.2024.3447042
Hyosang Ju;Jisang Park;Donghun Lee;Min Jang;Juho Lee;Sang-Hyo Kim
In this paper, a new design of concatenated polar codes is proposed. By concatenating an outer code with polar codes, the distance spectrum can be improved, leading to enhanced decoding performance of vanilla polar codes. In the 5G New Radio standard, both cyclic redundancy check precoding and systematic single-parity-check precoding schemes are adopted and this combination provides stable decoding performance over a wide range of coding parameters. We focus on the design of single-paritycheck precoded polar codes. For the special systematic pre-coding scheme, code construction depends solely on the selection of information and parity bits from the source bits. Since the conventional parity bit selection criteria can draw weaknesses for some coding parameters, we develop new criteria that enhance the protection of weak source bits under the successive cancelation decoding. The simulation results demonstrate that the proposed design consistently outperforms the conventional one across a wide range of coding parameters. The improvement is more pronounced in short-length codes.
{"title":"On Improving the Design of Parity-Check Polar Codes","authors":"Hyosang Ju;Jisang Park;Donghun Lee;Min Jang;Juho Lee;Sang-Hyo Kim","doi":"10.1109/OJCOMS.2024.3447042","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3447042","url":null,"abstract":"In this paper, a new design of concatenated polar codes is proposed. By concatenating an outer code with polar codes, the distance spectrum can be improved, leading to enhanced decoding performance of vanilla polar codes. In the 5G New Radio standard, both cyclic redundancy check precoding and systematic single-parity-check precoding schemes are adopted and this combination provides stable decoding performance over a wide range of coding parameters. We focus on the design of single-paritycheck precoded polar codes. For the special systematic pre-coding scheme, code construction depends solely on the selection of information and parity bits from the source bits. Since the conventional parity bit selection criteria can draw weaknesses for some coding parameters, we develop new criteria that enhance the protection of weak source bits under the successive cancelation decoding. The simulation results demonstrate that the proposed design consistently outperforms the conventional one across a wide range of coding parameters. The improvement is more pronounced in short-length codes.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643177","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169593","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 : 2024-08-21DOI: 10.1109/OJCOMS.2024.3447152
Syed Asad Ullah;Aamir Mahmood;Ali Arshad Nasir;Mikael Gidlund;Syed Ali Hassan
Given the rising demand for low-power sensing, integrating additional devices into an existing wireless infrastructure calls for innovative energy- and spectrum-efficient wireless connectivity strategies. In this respect, wireless-powered or energy-harvesting symbiotic radio (EHSR) is gaining attention for establishing the secondary relationship with the primary wireless systems in terms of RF EH and opportunistically sharing the spectrum or schedule. In this paper, assuming the commensalistic relationship with the primary system, we consider the energy-efficient optimization of such an EHSR by intelligently making EH and transmission decisions under the inherent nonlinearity of the EH circuitry and dynamics of pre-scheduled primary devices. We present a state-of-the-art deep reinforcement learning (DRL)-engineered, energy-efficient transmission strategy, which intelligently orchestrates EHSR’s uplink transmissions, leveraging the cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) scheme. We first formulate the energy efficiency (EE) optimization metric for EHSR considering the nonlinear EH model, and then we decompose the inherently complex, non-convex problem into two optimization layers. The strategy first derives the optimal transmit power and time-sharing coefficient parameters, using convex optimization. Subsequently, these inferred parameters are substituted in the subsequent layer, where the optimization problem with continuous action space is addressed via a DRL framework, named modified deep deterministic policy gradient (MDDPG). Simulation results reveal that, compared to the baseline DDPG algorithm, our proposed solution provides a 6% EE gain with the linear EH model and approximately a 7% EE gain with the non-linear EH model.
{"title":"DRL-Driven Optimization of a Wireless Powered Symbiotic Radio With Nonlinear EH Model","authors":"Syed Asad Ullah;Aamir Mahmood;Ali Arshad Nasir;Mikael Gidlund;Syed Ali Hassan","doi":"10.1109/OJCOMS.2024.3447152","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3447152","url":null,"abstract":"Given the rising demand for low-power sensing, integrating additional devices into an existing wireless infrastructure calls for innovative energy- and spectrum-efficient wireless connectivity strategies. In this respect, wireless-powered or energy-harvesting symbiotic radio (EHSR) is gaining attention for establishing the secondary relationship with the primary wireless systems in terms of RF EH and opportunistically sharing the spectrum or schedule. In this paper, assuming the commensalistic relationship with the primary system, we consider the energy-efficient optimization of such an EHSR by intelligently making EH and transmission decisions under the inherent nonlinearity of the EH circuitry and dynamics of pre-scheduled primary devices. We present a state-of-the-art deep reinforcement learning (DRL)-engineered, energy-efficient transmission strategy, which intelligently orchestrates EHSR’s uplink transmissions, leveraging the cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) scheme. We first formulate the energy efficiency (EE) optimization metric for EHSR considering the nonlinear EH model, and then we decompose the inherently complex, non-convex problem into two optimization layers. The strategy first derives the optimal transmit power and time-sharing coefficient parameters, using convex optimization. Subsequently, these inferred parameters are substituted in the subsequent layer, where the optimization problem with continuous action space is addressed via a DRL framework, named modified deep deterministic policy gradient (MDDPG). Simulation results reveal that, compared to the baseline DDPG algorithm, our proposed solution provides a 6% EE gain with the linear EH model and approximately a 7% EE gain with the non-linear EH model.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142137547","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 : 2024-08-21DOI: 10.1109/OJCOMS.2024.3447157
Ravindra S. Tomar;Mandar R. Nalavade;Gaurav S. Kasbekar
In dense millimeter wave (mmWave) networks, user association, i.e., the task of selecting the access point (AP) that each arriving user should join, significantly impacts the network performance. We consider a dense mmWave network in which each AP has multiple channels and can simultaneously serve different users using different channels. The different channels of an AP are susceptible to both blockage, which is common to all the channels of an AP, and frequency-selective fading, which is, in general, different for different channels. In each time slot, a user arrives with some probability. Our objective is to design a user association scheme for selecting the AP that each arriving user should join, so as to minimize the long-term total average holding cost incurred within the system, and thereby achieve low average delays experienced by users. This problem is an instance of the restless multi-armed bandit problem, and is provably hard to solve. We prove that the problem is Whittle indexable and present a method for calculating the Whittle indices corresponding to the different APs by solving linear systems of equations. We propose a user association policy under which, when a user arrives, it associates with the AP that has the lowest Whittle index in that time slot. Our extensive simulation results demonstrate that our proposed Whittle index-based policy outperforms user association policies proposed in prior research in terms of the average delay, average cost, as well as Jain’s fairness index (JFI).
{"title":"User Association in Dense Millimeter Wave Networks With Multi-Channel Access Points Using the Whittle Index","authors":"Ravindra S. Tomar;Mandar R. Nalavade;Gaurav S. Kasbekar","doi":"10.1109/OJCOMS.2024.3447157","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3447157","url":null,"abstract":"In dense millimeter wave (mmWave) networks, user association, i.e., the task of selecting the access point (AP) that each arriving user should join, significantly impacts the network performance. We consider a dense mmWave network in which each AP has multiple channels and can simultaneously serve different users using different channels. The different channels of an AP are susceptible to both blockage, which is common to all the channels of an AP, and frequency-selective fading, which is, in general, different for different channels. In each time slot, a user arrives with some probability. Our objective is to design a user association scheme for selecting the AP that each arriving user should join, so as to minimize the long-term total average holding cost incurred within the system, and thereby achieve low average delays experienced by users. This problem is an instance of the restless multi-armed bandit problem, and is provably hard to solve. We prove that the problem is Whittle indexable and present a method for calculating the Whittle indices corresponding to the different APs by solving linear systems of equations. We propose a user association policy under which, when a user arrives, it associates with the AP that has the lowest Whittle index in that time slot. Our extensive simulation results demonstrate that our proposed Whittle index-based policy outperforms user association policies proposed in prior research in terms of the average delay, average cost, as well as Jain’s fairness index (JFI).","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117825","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 : 2024-08-20DOI: 10.1109/OJCOMS.2024.3446457
Manjuladevi Vasudevan;Murat Yuksel
With recent advancements in the telecommunication industry and the deployment of 5G networks, radio propagation modeling is considered a fundamental task in planning and optimization. Accurate and efficient models of radio propagation enable the estimation of Path Loss (PL) or Received Signal Strength (RSS), which is used in a variety of practical applications including the construction of radio coverage maps and localization. Traditional PL models use fundamental physics laws and regression-based models, which can be guided with measurements. In general, these methods have small computational complexity and have been highly successful in attaining accurate models for settings with trivial environmental complexity (e.g., clear weather or no clutter). However, attaining high accuracy in radio propagation modeling at complex settings (e.g., an urban setting with many buildings and obstacles) has required ray tracing, which computationally complex. Recently, the wireless community has been studying Machine Learning (ML)-based modeling algorithms to find a middle-ground. ML algorithms have become faster to execute and, more importantly, more radio data measurements have become available with the increased deployment of wireless devices. In this survey, we explore the recent advancements in the use of ML for modeling and predicting radio coverage and PL.
随着电信行业的最新进展和 5G 网络的部署，无线电传播建模被认为是规划和优化的一项基本任务。准确、高效的无线电传播模型可用于估算路径损耗（PL）或接收信号强度（RSS），而路径损耗或接收信号强度可用于各种实际应用，包括构建无线电覆盖图和定位。传统的路径损耗模型使用基本物理定律和基于回归的模型，可通过测量结果进行指导。一般来说，这些方法的计算复杂度较小，在环境复杂度较低（如天气晴朗或无杂波）的情况下，能非常成功地获得精确的模型。然而，要在复杂环境（如有许多建筑物和障碍物的城市环境）中获得高精度的无线电传播模型，就需要进行光线追踪，而光线追踪的计算复杂度很高。最近，无线界一直在研究基于机器学习（ML）的建模算法，以寻找中间地带。ML 算法的执行速度越来越快，更重要的是，随着无线设备部署的增加，无线电数据测量也越来越多。在本调查中，我们将探讨使用 ML 对无线电覆盖和 PL 进行建模和预测的最新进展。
{"title":"Machine Learning for Radio Propagation Modeling: A Comprehensive Survey","authors":"Manjuladevi Vasudevan;Murat Yuksel","doi":"10.1109/OJCOMS.2024.3446457","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3446457","url":null,"abstract":"With recent advancements in the telecommunication industry and the deployment of 5G networks, radio propagation modeling is considered a fundamental task in planning and optimization. Accurate and efficient models of radio propagation enable the estimation of Path Loss (PL) or Received Signal Strength (RSS), which is used in a variety of practical applications including the construction of radio coverage maps and localization. Traditional PL models use fundamental physics laws and regression-based models, which can be guided with measurements. In general, these methods have small computational complexity and have been highly successful in attaining accurate models for settings with trivial environmental complexity (e.g., clear weather or no clutter). However, attaining high accuracy in radio propagation modeling at complex settings (e.g., an urban setting with many buildings and obstacles) has required ray tracing, which computationally complex. Recently, the wireless community has been studying Machine Learning (ML)-based modeling algorithms to find a middle-ground. ML algorithms have become faster to execute and, more importantly, more radio data measurements have become available with the increased deployment of wireless devices. In this survey, we explore the recent advancements in the use of ML for modeling and predicting radio coverage and PL.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117831","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 : 2024-08-20DOI: 10.1109/OJCOMS.2024.3445990
Guillem Femenias;Felip Riera-Palou
Cell-free massive MIMO (CF-mMIMO) emerges as a pivotal technology in the landscape of beyond-5G and 6G wireless networks, addressing the ever-increasing demand for seamless connectivity and unprecedented data throughput. This paper undertakes a comprehensive exploration of scalable usercentric (UC) CF-mMIMO systems, focusing on critical aspects of downlink (DL) channel state information (CSI) acquisition and its intricate interactions with both distributed and centralized precoding strategies. The paper delves into the crucial role of DL CSI acquisition, particularly in scenarios of weak channel hardening arising from sparse subsets of access points (APs) serving specific mobile stations (MS) in UC strategies, and transmission over spatially correlated multiple keyhole Ricean fading channels. The main contributions of this research work include in-depth analyses of different detection schemes under varying precoding scenarios, offering valuable insights for practical deployment. The pivotal role of DL CSI acquisition in optimizing the performance of UC CF-mMIMO networks is fully assessed, dismissing the use of DL pilot-based detection approaches and advocating for either centralized precoding architectures with statistical CSI-based decoding strategies at the MSs or distributed precoding schemes with DL blind channel estimation-based decoders at the MSs.
{"title":"Unveiling New Frontiers of Downlink Training in User-Centric Cell-Free Massive MIMO","authors":"Guillem Femenias;Felip Riera-Palou","doi":"10.1109/OJCOMS.2024.3445990","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3445990","url":null,"abstract":"Cell-free massive MIMO (CF-mMIMO) emerges as a pivotal technology in the landscape of beyond-5G and 6G wireless networks, addressing the ever-increasing demand for seamless connectivity and unprecedented data throughput. This paper undertakes a comprehensive exploration of scalable usercentric (UC) CF-mMIMO systems, focusing on critical aspects of downlink (DL) channel state information (CSI) acquisition and its intricate interactions with both distributed and centralized precoding strategies. The paper delves into the crucial role of DL CSI acquisition, particularly in scenarios of weak channel hardening arising from sparse subsets of access points (APs) serving specific mobile stations (MS) in UC strategies, and transmission over spatially correlated multiple keyhole Ricean fading channels. The main contributions of this research work include in-depth analyses of different detection schemes under varying precoding scenarios, offering valuable insights for practical deployment. The pivotal role of DL CSI acquisition in optimizing the performance of UC CF-mMIMO networks is fully assessed, dismissing the use of DL pilot-based detection approaches and advocating for either centralized precoding architectures with statistical CSI-based decoding strategies at the MSs or distributed precoding schemes with DL blind channel estimation-based decoders at the MSs.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640135","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117892","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}
The objective of our work is to provide an in-depth analysis and compilation of device-level strategies for enhancing the energy efficiency of Machine-Type Communication (MTC). The necessity for such strategies stems from the growing demand for sustainable and energy-efficient communication systems in various industries. We begin by presenting a comprehensive background on MTC, detailing its essential characteristics, the architecture of machine-type devices (MTDs), and their diverse applications. Next, we explore a range of energy-efficient techniques designed to optimize key subsystems of MTDs. These subsystems include the radio for communication efficiency, processing power for computational efficiency, and sensing subsystems for data acquisition efficiency. Each technique is evaluated for its potential impact on overall energy consumption and the trade-offs and limitations associated with these techniques are also assessed. In concluding, the paper highlights potential future research directions in this domain, outlining the ongoing need for innovative solutions to meet the escalating demands of energy efficiency in MTC.
{"title":"Device-Level Energy Efficient Strategies in Machine Type Communications: Power, Processing, Sensing, and RF Perspectives","authors":"Unalido Ntabeni;Bokamoso Basutli;Hirley Alves;Joseph Chuma","doi":"10.1109/OJCOMS.2024.3443920","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3443920","url":null,"abstract":"The objective of our work is to provide an in-depth analysis and compilation of device-level strategies for enhancing the energy efficiency of Machine-Type Communication (MTC). The necessity for such strategies stems from the growing demand for sustainable and energy-efficient communication systems in various industries. We begin by presenting a comprehensive background on MTC, detailing its essential characteristics, the architecture of machine-type devices (MTDs), and their diverse applications. Next, we explore a range of energy-efficient techniques designed to optimize key subsystems of MTDs. These subsystems include the radio for communication efficiency, processing power for computational efficiency, and sensing subsystems for data acquisition efficiency. Each technique is evaluated for its potential impact on overall energy consumption and the trade-offs and limitations associated with these techniques are also assessed. In concluding, the paper highlights potential future research directions in this domain, outlining the ongoing need for innovative solutions to meet the escalating demands of energy efficiency in MTC.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084494","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 : 2024-08-14DOI: 10.1109/OJCOMS.2024.3443514
Annisa Sarah;Gianfranco Nencioni;Md Muhidul Islam Khan
Multi-access Edge Computing (MEC) allows a mobile user to access a service on a computing device called MEC Host (MEH), enabling lower latency by running the service closer to the users. When the user moves away from the serving MEH, the latency increases, which may cause a disruption of the user experience and of the service continuity. Moreover, the serving MEH may also fail, making the service unavailable. We propose a solution to a service migration problem that maximizes the MEC service availability by jointly deciding (i) migration timing and (ii) target MEH based on latency constraint, resource constraint, and availability status of a MEH. We solve the problem by using Deep Reinforcement Learning (DRL). The experiment shows that our proposed solution can successfully maintain a high service availability (more than 94%) in the presence of different failure probabilities, while another algorithm gives unstable service availability.
{"title":"DRL-Based Availability-Aware Migration of a MEC Service","authors":"Annisa Sarah;Gianfranco Nencioni;Md Muhidul Islam Khan","doi":"10.1109/OJCOMS.2024.3443514","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3443514","url":null,"abstract":"Multi-access Edge Computing (MEC) allows a mobile user to access a service on a computing device called MEC Host (MEH), enabling lower latency by running the service closer to the users. When the user moves away from the serving MEH, the latency increases, which may cause a disruption of the user experience and of the service continuity. Moreover, the serving MEH may also fail, making the service unavailable. We propose a solution to a service migration problem that maximizes the MEC service availability by jointly deciding (i) migration timing and (ii) target MEH based on latency constraint, resource constraint, and availability status of a MEH. We solve the problem by using Deep Reinforcement Learning (DRL). The experiment shows that our proposed solution can successfully maintain a high service availability (more than 94%) in the presence of different failure probabilities, while another algorithm gives unstable service availability.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636784","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142084523","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 : 2024-08-14DOI: 10.1109/OJCOMS.2024.3444719
Mohamed A. ElMossallamy;Miao Pan;Riku Jäntti;Karim G. Seddik;Geoffrey Ye Li;Zhu Han
In this paper, we investigate frequency shift keying (FSK) over ambient orthogonal frequency division multiplexed (OFDM) signals. By cycling through a sequence of antenna loads providing different phase shifts at the tag, we are able to unidirectionally shift the ambient OFDM spectrum either up or down in frequency to disjoint subsets of the subcarriers allowing the implementation of FSK. We exploit the guard bands and the orthogonality of the OFDM subcarriers to avoid both direct-link and adjacent channel interference. Different from energy detection based techniques that suffer from asymmetric error probabilities and rely on signal-to-noise ratio (SNR) dependent detection thresholds, the proposed scheme has symmetric error probabilities and allows simple detection without the need for a threshold. We present both binary and four-ary schemes, and analyze the error performance of the optimal noncoherent detectors. For the binary scheme, we obtain exact expressions for the average probability of error, while for the four-ary scheme, a union bound is used to characterize the error performance. Single and multi-antenna receivers are considered, and their performance is analyzed. Finally, we present simulation results to corroborate our analysis and investigate the effects of multiple system parameters. The results show that the proposed scheme outperforms the baseline energy detection based schemes available in the literature in various scenarios by up to 5 dB.
{"title":"Noncoherent Frequency-Shift Keying for Ambient Backscatter Over OFDM Signals","authors":"Mohamed A. ElMossallamy;Miao Pan;Riku Jäntti;Karim G. Seddik;Geoffrey Ye Li;Zhu Han","doi":"10.1109/OJCOMS.2024.3444719","DOIUrl":"https://doi.org/10.1109/OJCOMS.2024.3444719","url":null,"abstract":"In this paper, we investigate frequency shift keying (FSK) over ambient orthogonal frequency division multiplexed (OFDM) signals. By cycling through a sequence of antenna loads providing different phase shifts at the tag, we are able to unidirectionally shift the ambient OFDM spectrum either up or down in frequency to disjoint subsets of the subcarriers allowing the implementation of FSK. We exploit the guard bands and the orthogonality of the OFDM subcarriers to avoid both direct-link and adjacent channel interference. Different from energy detection based techniques that suffer from asymmetric error probabilities and rely on signal-to-noise ratio (SNR) dependent detection thresholds, the proposed scheme has symmetric error probabilities and allows simple detection without the need for a threshold. We present both binary and four-ary schemes, and analyze the error performance of the optimal noncoherent detectors. For the binary scheme, we obtain exact expressions for the average probability of error, while for the four-ary scheme, a union bound is used to characterize the error performance. Single and multi-antenna receivers are considered, and their performance is analyzed. Finally, we present simulation results to corroborate our analysis and investigate the effects of multiple system parameters. The results show that the proposed scheme outperforms the baseline energy detection based schemes available in the literature in various scenarios by up to 5 dB.","PeriodicalId":33803,"journal":{"name":"IEEE Open Journal of the Communications Society","volume":null,"pages":null},"PeriodicalIF":6.3,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10637269","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117891","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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