Pub Date : 2024-06-01DOI: 10.23919/JCN.2024.000034
{"title":"Open access publishing agreement","authors":"","doi":"10.23919/JCN.2024.000034","DOIUrl":"https://doi.org/10.23919/JCN.2024.000034","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10579725","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.23919/JCN.2024.000026
Ahmet Soran;Murat Yuksel;Mehmet Hadi Gunes
Recent trends led to higher data volumes to be transferred and processed over the network. Legacy routing protocols, e.g., OSPF for intra-domain routing, send data from a source to destination on one of the shortest paths. We propose a novel approach to parallelize data transfers by leveraging the multi-core CPUs in the routers. We describe an end-to- end method to optimize data flows on multiple paths. Multicore parallel routing (MCPR) generates new virtual topology substrates from the underlying router topology and performs the shortest path routing on each substrate. Even though calculating the shortest paths could be done with well-known techniques such as OSPF's Dijkstra implementation, finding optimal substrates and setting their link weights to maximize the network throughput over multiple end-to-end paths is still an NP-hard problem. In MCPR, we focus on designing heuristics for substrate generation from a given router topology. Each substrate is a subgraph of the router topology and each link on each substrate is to be assigned a weight to steer the shortest-path routing for maximal network throughput. Heuristics' interim goal is to generate substrates in such a way that the shortest path between a source-destination pair on each substrate minimally overlaps with the shortest paths calculated by the other substrates. Once these substrates are determined, we assign each substrate to a core in the router and employ a multi-path transport protocol, similar to MPTCP, to perform end-to-end data transfers. We designed heuristics that utilize node centrality, edge centrality, or flow patterns. We evaluated the MCPR heuristics on router-level ISP topologies and compared the network throughput against single shortest-path routing under extensive simulation scenarios including heterogeneous core count across the routers and network failures. The evaluations showed that MCPR heuristics can attain network throughput speedups reaching 2.6 while incurring only polynomial control overhead.
{"title":"MCPR: Routing using parallel shortest paths","authors":"Ahmet Soran;Murat Yuksel;Mehmet Hadi Gunes","doi":"10.23919/JCN.2024.000026","DOIUrl":"https://doi.org/10.23919/JCN.2024.000026","url":null,"abstract":"Recent trends led to higher data volumes to be transferred and processed over the network. Legacy routing protocols, e.g., OSPF for intra-domain routing, send data from a source to destination on one of the shortest paths. We propose a novel approach to parallelize data transfers by leveraging the multi-core CPUs in the routers. We describe an end-to- end method to optimize data flows on multiple paths. Multicore parallel routing (MCPR) generates new virtual topology substrates from the underlying router topology and performs the shortest path routing on each substrate. Even though calculating the shortest paths could be done with well-known techniques such as OSPF's Dijkstra implementation, finding optimal substrates and setting their link weights to maximize the network throughput over multiple end-to-end paths is still an NP-hard problem. In MCPR, we focus on designing heuristics for substrate generation from a given router topology. Each substrate is a subgraph of the router topology and each link on each substrate is to be assigned a weight to steer the shortest-path routing for maximal network throughput. Heuristics' interim goal is to generate substrates in such a way that the shortest path between a source-destination pair on each substrate minimally overlaps with the shortest paths calculated by the other substrates. Once these substrates are determined, we assign each substrate to a core in the router and employ a multi-path transport protocol, similar to MPTCP, to perform end-to-end data transfers. We designed heuristics that utilize node centrality, edge centrality, or flow patterns. We evaluated the MCPR heuristics on router-level ISP topologies and compared the network throughput against single shortest-path routing under extensive simulation scenarios including heterogeneous core count across the routers and network failures. The evaluations showed that MCPR heuristics can attain network throughput speedups reaching 2.6 while incurring only polynomial control overhead.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10579713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.23919/JCN.2024.000017
Hoa Tran-Dang;Dong-Seong Kim
This paper deals with the task offloading problem in the dynamic fog computing networks (FCNs) that involves the task and resource allocations between a set of task nodes (TNs) having task computation needs and a set of helper nodes (HNs) having available computing resources. The problem is associated with the presence of selfishness and rational nodes of these nodes, in which the objective of TNs is to minimize the task completion time by offloading the tasks to the HNs while the HNs tend to maximize their monetization of task offloading resources. To tackle this problem, we use the fairness and stability principle of matching theory to assign the tasks of TNs to the resources of HNs based on their mutual preferences in a decentralized manner. However, the uncertainty of computing resource availability of HNs as well as dynamics of QoS requirements of tasks result in the lack of preferences of TN side that mainly poses a critical challenge to obtain a stable and reliable matching outcome. To address this challenge, we develop the first, to our knowledge, Thompson sampling based multi-armed bandit (MAB) learning to acquire better exploitation and exploration trade-off, therefore allowing TNs to achieve the informed preference relations of HNs quickly. Motivated by the above considerations, this paper aims at design a bandit learning based matching model (BLM) to realize the efficient decentralized task offloading algorithms in the dynamic FCNs. Extensive simulation results demonstrate the potential advantages of the TS based learning over the ε-greedy and UCB based baselines.
{"title":"Bandit learning based stable matching for decentralized task offloading in dynamic fog computing networks","authors":"Hoa Tran-Dang;Dong-Seong Kim","doi":"10.23919/JCN.2024.000017","DOIUrl":"https://doi.org/10.23919/JCN.2024.000017","url":null,"abstract":"This paper deals with the task offloading problem in the dynamic fog computing networks (FCNs) that involves the task and resource allocations between a set of task nodes (TNs) having task computation needs and a set of helper nodes (HNs) having available computing resources. The problem is associated with the presence of selfishness and rational nodes of these nodes, in which the objective of TNs is to minimize the task completion time by offloading the tasks to the HNs while the HNs tend to maximize their monetization of task offloading resources. To tackle this problem, we use the fairness and stability principle of matching theory to assign the tasks of TNs to the resources of HNs based on their mutual preferences in a decentralized manner. However, the uncertainty of computing resource availability of HNs as well as dynamics of QoS requirements of tasks result in the lack of preferences of TN side that mainly poses a critical challenge to obtain a stable and reliable matching outcome. To address this challenge, we develop the first, to our knowledge, Thompson sampling based multi-armed bandit (MAB) learning to acquire better exploitation and exploration trade-off, therefore allowing TNs to achieve the informed preference relations of HNs quickly. Motivated by the above considerations, this paper aims at design a bandit learning based matching model (BLM) to realize the efficient decentralized task offloading algorithms in the dynamic FCNs. Extensive simulation results demonstrate the potential advantages of the TS based learning over the ε-greedy and UCB based baselines.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10579722","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The envisioned smart hospital framework leveraging the sixth-generation (6G) technology aims to enhance healthcare services by ensuring reliable communication across various wireless channel conditions, including both line-of-sight and obstructed paths. However, the traditional orthogonal frequency division multiplexing (OFDM) approach, used in 4G and 5G, struggles with the high Doppler shifts associated with dynamic environments, presenting challenges for burgeoning smart hospital demands. To address this, orthogonal time frequency space (OTFS) modulation is proposed. The OTFS operates effectively across both stationary and highly mobile channels by manipulating delay and Doppler domains. Nevertheless, a high peak-to-average power ratio (PAPR) remains a critical challenge for OTFS implementation within 6G smart hospitals. Elevated PAPR levels can reduce power amplifier efficiency, causing them to operate outside their ideal linear range and impairing battery performance. They also contribute to signal distortion, increased interference, and suboptimal spectrum utilization, which can undermine wireless communication and data integrity. To mitigate the PAPR issue in OTFS, this work introduces a hybrid algorithm that integrates the benefits of the Riemann matrix optimal phase element-based partial transmission sequence (PTS) and selective mapping (SLM), along with A and μ-law complementary algorithms. This study compares the performance of the proposed hybrid algorithm with traditional PAPR reduction techniques by evaluating metrics such as PAPR, bit error rate (BER), and power spectrum density (PSD) within the Rician and Rayleigh fading channels. Simulation outcomes indicate that the hybrid algorithm achieves superior PAPR, BER, and PSD performance with only a marginal increase in complexity when compared with the established methods.
{"title":"Hybrid approaches to PAPR, BER, and PSD optimization in 6G OTFS: Implications for healthcare","authors":"Arun Kumar;Sumit Chakravarthy;Nishant Gaur;Aziz Nanthaamornphong","doi":"10.23919/JCN.2024.000027","DOIUrl":"https://doi.org/10.23919/JCN.2024.000027","url":null,"abstract":"The envisioned smart hospital framework leveraging the sixth-generation (6G) technology aims to enhance healthcare services by ensuring reliable communication across various wireless channel conditions, including both line-of-sight and obstructed paths. However, the traditional orthogonal frequency division multiplexing (OFDM) approach, used in 4G and 5G, struggles with the high Doppler shifts associated with dynamic environments, presenting challenges for burgeoning smart hospital demands. To address this, orthogonal time frequency space (OTFS) modulation is proposed. The OTFS operates effectively across both stationary and highly mobile channels by manipulating delay and Doppler domains. Nevertheless, a high peak-to-average power ratio (PAPR) remains a critical challenge for OTFS implementation within 6G smart hospitals. Elevated PAPR levels can reduce power amplifier efficiency, causing them to operate outside their ideal linear range and impairing battery performance. They also contribute to signal distortion, increased interference, and suboptimal spectrum utilization, which can undermine wireless communication and data integrity. To mitigate the PAPR issue in OTFS, this work introduces a hybrid algorithm that integrates the benefits of the Riemann matrix optimal phase element-based partial transmission sequence (PTS) and selective mapping (SLM), along with A and μ-law complementary algorithms. This study compares the performance of the proposed hybrid algorithm with traditional PAPR reduction techniques by evaluating metrics such as PAPR, bit error rate (BER), and power spectrum density (PSD) within the Rician and Rayleigh fading channels. Simulation outcomes indicate that the hybrid algorithm achieves superior PAPR, BER, and PSD performance with only a marginal increase in complexity when compared with the established methods.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10579720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.23919/JCN.2024.000032
{"title":"Information for authors","authors":"","doi":"10.23919/JCN.2024.000032","DOIUrl":"https://doi.org/10.23919/JCN.2024.000032","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10579709","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
More and more traffic is migrating to private networks in use in various professional environments. With this, there comes a growing diversity in applications, each of them requiring different quality of service. This poses challenges to properly managing such networks, which can span both wired and wireless segments. To tackle the issue of network management and application demands in such networks, we introduce a network- and application-aware adaptive congestion control algorithm, which provides congestion-free service differentiation to the flows in wireless networks in a decentralized manner. The designed algorithm operates based on the real-time network information obtained from in-band network telemetry and aggregated flow information from intermediate nodes. The algorithm performs three times better than the existing CUBIC congestion control algorithm and twice better in a multi-flow architecture. The designed algorithm is the first step towards adaptive transport and application layer protocols which are the future of private professional networks.
{"title":"Network- and application-aware adaptive congestion control algorithm","authors":"Ramyashree Venkatesh Bhat;Jetmir Haxhibeqiri;Ingrid Moerman;Jeroen Hoebeke","doi":"10.23919/JCN.2023.000052","DOIUrl":"https://doi.org/10.23919/JCN.2023.000052","url":null,"abstract":"More and more traffic is migrating to private networks in use in various professional environments. With this, there comes a growing diversity in applications, each of them requiring different quality of service. This poses challenges to properly managing such networks, which can span both wired and wireless segments. To tackle the issue of network management and application demands in such networks, we introduce a network- and application-aware adaptive congestion control algorithm, which provides congestion-free service differentiation to the flows in wireless networks in a decentralized manner. The designed algorithm operates based on the real-time network information obtained from in-band network telemetry and aggregated flow information from intermediate nodes. The algorithm performs three times better than the existing CUBIC congestion control algorithm and twice better in a multi-flow architecture. The designed algorithm is the first step towards adaptive transport and application layer protocols which are the future of private professional networks.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10579716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.23919/JCN.2024.000018
Mariana Baracat de Mello;Luciano Leonel Mendes;Daniely Gomes Silva;Paulo Ricardo Branco da Silva;Tiago Cardoso Barbosa
In remote rural areas, it is not possible to employ massive multiple-input multiple-output (MIMO), small cells, and ultra-dense networks (UDNs) with the aim of increasing throughput. A solution is to improve the waveform spectral efficiency, integrating faster than Nyquist (FTN) signaling with generalized frequency division multiplexing (GFDM). However, this presents high self-interference in the time and frequency domains, requiring dedicated detectors for performance loss mitigation. Hard decision detection schemes primarily designed for MIMO have been adapted to detect FTN-GFDM signals without degradation of the uncoded bit error rate (BER), but these schemes are suboptimal in terms of capacity as they do not provide all the information contained in log-likelihood ratios (LLRs). We design and evaluate in this paper a soft sphere detector (SD) algorithm for FTN-GFDM that can be integrated with state-of-the-art forward error control (FEC) decoders for good BER performance over mobile channels. The SD detector is combined with polar codes, and the BER and complexity are evaluated for different channel models. The results show that FTN-GFDM can provide high spectrum efficiency gains without significant coded BER losses and with affordable complexity on the receiver side, which makes this waveform an interesting candidate for mobile networks in remote areas.
{"title":"FTN-GFDM detection based on reduced-complexity soft sphere decoding integrated with polar codes","authors":"Mariana Baracat de Mello;Luciano Leonel Mendes;Daniely Gomes Silva;Paulo Ricardo Branco da Silva;Tiago Cardoso Barbosa","doi":"10.23919/JCN.2024.000018","DOIUrl":"https://doi.org/10.23919/JCN.2024.000018","url":null,"abstract":"In remote rural areas, it is not possible to employ massive multiple-input multiple-output (MIMO), small cells, and ultra-dense networks (UDNs) with the aim of increasing throughput. A solution is to improve the waveform spectral efficiency, integrating faster than Nyquist (FTN) signaling with generalized frequency division multiplexing (GFDM). However, this presents high self-interference in the time and frequency domains, requiring dedicated detectors for performance loss mitigation. Hard decision detection schemes primarily designed for MIMO have been adapted to detect FTN-GFDM signals without degradation of the uncoded bit error rate (BER), but these schemes are suboptimal in terms of capacity as they do not provide all the information contained in log-likelihood ratios (LLRs). We design and evaluate in this paper a soft sphere detector (SD) algorithm for FTN-GFDM that can be integrated with state-of-the-art forward error control (FEC) decoders for good BER performance over mobile channels. The SD detector is combined with polar codes, and the BER and complexity are evaluated for different channel models. The results show that FTN-GFDM can provide high spectrum efficiency gains without significant coded BER losses and with affordable complexity on the receiver side, which makes this waveform an interesting candidate for mobile networks in remote areas.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10579719","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-06-01DOI: 10.23919/JCN.2024.000015
Toan-Van Nguyen;Thien Huynh-The;Vo-Nguyen Quoc Bao
In this paper, we study wireless energy transfer full-duplex (FD) Internet-of-things (IoT) networks, where multiple FD IoT relays are deployed to assist short-packet communications between a source and a robot destination with multiple antennas in automation factories. Considering two residual interference (RSI) models for FD relays, we propose a full relay selection (FRS) scheme to maximize the e2e signal-to-noise ratio of packet transmissions. We derive the closed-form expressions for the average block error rate (BLER) and throughput of the considered system, based on which the approximation analysis is also carried out. Towards real-time configurations, we design a deep learning framework based on the FRS scheme to accurately predict the average BLER and system throughput via a short inference process. Simulation results reveal the significant effects of RSI models on the performance of FD IoT networks. Furthermore, the CNN design achieves the lowest root-mean-squared error among other schemes such as the conventional CNN and deep neural network. Furthermore, the DL framework can estimate similar BLER and throughput values as the FRS scheme, but with significantly reduced complexity and execution time, showing the potential of DL design in dealing with complex scenarios of heterogeneous IoT networks.
{"title":"Short-packet communications in wireless energy transfer full-duplex IoT networks with deep learning design","authors":"Toan-Van Nguyen;Thien Huynh-The;Vo-Nguyen Quoc Bao","doi":"10.23919/JCN.2024.000015","DOIUrl":"https://doi.org/10.23919/JCN.2024.000015","url":null,"abstract":"In this paper, we study wireless energy transfer full-duplex (FD) Internet-of-things (IoT) networks, where multiple FD IoT relays are deployed to assist short-packet communications between a source and a robot destination with multiple antennas in automation factories. Considering two residual interference (RSI) models for FD relays, we propose a full relay selection (FRS) scheme to maximize the e2e signal-to-noise ratio of packet transmissions. We derive the closed-form expressions for the average block error rate (BLER) and throughput of the considered system, based on which the approximation analysis is also carried out. Towards real-time configurations, we design a deep learning framework based on the FRS scheme to accurately predict the average BLER and system throughput via a short inference process. Simulation results reveal the significant effects of RSI models on the performance of FD IoT networks. Furthermore, the CNN design achieves the lowest root-mean-squared error among other schemes such as the conventional CNN and deep neural network. Furthermore, the DL framework can estimate similar BLER and throughput values as the FRS scheme, but with significantly reduced complexity and execution time, showing the potential of DL design in dealing with complex scenarios of heterogeneous IoT networks.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10579721","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141495103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.23919/JCN.2024.000002
Julius Ssimbwa;Seok-Hyun Yoon;Yeongrok Lee;Young-Chai Ko
In the pursuit of a highly reliable and low-latency-enabled 5G-advanced new radio unlicensed (NR-U) system, addressing the challenge of high error rates and high signaling overhead transmissions remains key to improving network performance. In this context, to reduce error rates, mechanisms such as retransmissions can be employed. However, performing multiple retransmissions comes at the cost of utilizing extra transmission resources, which in turn affects the spectral efficiency of the network. This would further necessitate proper scheduling to alleviate resource wastage and undesirable collisions during data transmission. In this article, we provide an overview of the design specifications of the long-term evolution-license assisted access (LTE-LAA) technology and the prospective enhancements to enable NR-U operation in bands beyond 7 GHz. Additionally, we examine the configurations of selected design features to enable NR-U scheduling. Specifically, we illustrate the benefits and the limitations of the choice of the switching pattern under the frame structure, the feedback value type under the hybrid automatic repeat request (HARQ) procedure, and the timing parameters under the radio link control (RLC) layer. Besides, we present simulation results to depict the impact of the configurations mentioned above on the performance of NR-U.
{"title":"Towards 5G-advanced NR-unlicensed systems: Physical layer design and performance","authors":"Julius Ssimbwa;Seok-Hyun Yoon;Yeongrok Lee;Young-Chai Ko","doi":"10.23919/JCN.2024.000002","DOIUrl":"https://doi.org/10.23919/JCN.2024.000002","url":null,"abstract":"In the pursuit of a highly reliable and low-latency-enabled 5G-advanced new radio unlicensed (NR-U) system, addressing the challenge of high error rates and high signaling overhead transmissions remains key to improving network performance. In this context, to reduce error rates, mechanisms such as retransmissions can be employed. However, performing multiple retransmissions comes at the cost of utilizing extra transmission resources, which in turn affects the spectral efficiency of the network. This would further necessitate proper scheduling to alleviate resource wastage and undesirable collisions during data transmission. In this article, we provide an overview of the design specifications of the long-term evolution-license assisted access (LTE-LAA) technology and the prospective enhancements to enable NR-U operation in bands beyond 7 GHz. Additionally, we examine the configurations of selected design features to enable NR-U scheduling. Specifically, we illustrate the benefits and the limitations of the choice of the switching pattern under the frame structure, the feedback value type under the hybrid automatic repeat request (HARQ) procedure, and the timing parameters under the radio link control (RLC) layer. Besides, we present simulation results to depict the impact of the configurations mentioned above on the performance of NR-U.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10522523","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140880728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-04-01DOI: 10.23919/JCN.2024.000021
{"title":"Open access publishing agreement","authors":"","doi":"10.23919/JCN.2024.000021","DOIUrl":"https://doi.org/10.23919/JCN.2024.000021","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":null,"pages":null},"PeriodicalIF":3.6,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10522520","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140880790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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