| 1. |
- Bhar, Chayan, 1988, et al.
(författare)
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Energy-and Bandwidth-Efficient, QoS-Aware Edge Caching in Fog-Enhanced Radio Access Networks
- 2021
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Ingår i: IEEE Journal on Selected Areas in Communications. - 0733-8716 .- 1558-0008. ; 39:9, s. 2762-2771
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Tidskriftsartikel (refereegranskat)abstract
- The emerging video services are associated with stringent quality-of-service (QoS) requirements and place high bandwidth demands on the core networks. Edge caching can facilitate the stringent QoS demands while easing the bandwidth requirement from core networks. However, such schemes require on-field caching equipment, in which energy consumption is a function of cache utilization. Designing opportunistic caching strategies for energy efficiency is therefore essential in such schemes. This paper studies the possibilities for achieving high energy efficiency, QoS, and low bandwidth consumption from the core network, in an optically fronthauled fog-enhanced radio access network that implements edge caching. An analytical model for such a network has been derived to measure latency, bandwidth consumption, and cache utilization. It is deduced from the results that low latency (high QoS) and bandwidth consumption can be ensured in such schemes while reducing the energy consumption by up to 93%. The derived model allows to design caching strategies for addressing the trade-off between energy efficiency, QoS, and bandwidth efficiency.
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| 2. |
- Buchberger, Andreas, 1990, et al.
(författare)
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Pruning and Quantizing Neural Belief Propagation Decoders
- 2021
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Ingår i: IEEE Journal on Selected Areas in Communications. - 0733-8716 .- 1558-0008. ; 39:7, s. 1957-1966
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Tidskriftsartikel (refereegranskat)abstract
- We consider near maximum-likelihood (ML) decoding of short linear block codes. In particular, we propose a novel decoding approach based on neural belief propagation (NBP) decoding recently introduced by Nachmani et al. in which we allow a different parity-check matrix in each iteration of the algorithm. The key idea is to consider NBP decoding over an overcomplete parity-check matrix and use the weights of NBP as a measure of the importance of the check nodes (CNs) to decoding. The unimportant CNs are then pruned. In contrast to NBP, which performs decoding on a given fixed parity-check matrix, the proposed pruning-based neural belief propagation (PB-NBP) typically results in a different parity-check matrix in each iteration. For a given complexity in terms of CN evaluations, we show that PB-NBP yields significant performance improvements with respect to NBP. We apply the proposed decoder to the decoding of a Reed-Muller code, a short low-density parity-check (LDPC) code, and a polar code. PB-NBP outperforms NBP decoding over an overcomplete parity-check matrix by 0.27–0.31 dB while reducing the number of required CN evaluations by up to 97%. For the LDPC code, PB-NBP outperforms conventional belief propagation with the same number of CN evaluations by 0.52 dB. We further extend the pruning concept to offset min-sum decoding and introduce a pruning-based neural offset min-sum (PB-NOMS) decoder, for which we jointly optimize the offsets and the quantization of the messages and offsets. We demonstrate performance 0.5 dB from ML decoding with 5-bit quantization for the Reed-Muller code.
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| 3. |
- Cai, Shangming, et al.
(författare)
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DynaComm: Accelerating Distributed CNN Training between Edges and Clouds through Dynamic Communication Scheduling
- 2022
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Ingår i: IEEE Journal on Selected Areas in Communications. - : IEEE. - 0733-8716 .- 1558-0008. ; 40:2, s. 611-625
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Tidskriftsartikel (refereegranskat)abstract
- To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic. Typically, edge devices collaboratively train a shared model using real-time generated data through the Parameter Server framework. Although all the edge devices can share the computing workloads, the distributed training processes over edge networks are still time-consuming due to the parameters and gradients transmission procedures between parameter servers and edge devices. Focusing on accelerating distributed Convolutional Neural Networks (CNNs) training at the network edge, we present DynaComm, a novel scheduler that dynamically decomposes each transmission procedure into several segments to achieve optimal layer-wise communications and computations overlapping during run-time. Through experiments, we verify that DynaComm manages to achieve optimal layer-wise scheduling for all cases compared to competing strategies while the model accuracy remains untouched.
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| 4. |
- Cao, Bin, et al.
(författare)
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Mobility-Aware Multiobjective Task Offloading for Vehicular Edge Computing in Digital Twin Environment
- 2023
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Ingår i: IEEE Journal on Selected Areas in Communications. - : Institute of Electrical and Electronics Engineers (IEEE). - 0733-8716 .- 1558-0008. ; 41:10, s. 3046-3055
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Tidskriftsartikel (refereegranskat)abstract
- In vehicular edge computing (VEC), vehicle users (VUs) can offload their computation-intensive tasks to edge server (ES) that provides additional computation resources. Due to the edge server being closer to VUs, the propagation delay between the ESs and the VUs is lower compared to cloud computing. Applying digital twin to VEC allows for low-cost trial in task offloading. In real-word, the mobility of VUs cannot be ignored and the downlink delay in receiving process results from ES is related to the mobility of VUs. Therefore, a five-objective optimization model including downlink delay, computation delay, energy consumption, load balancing, and user satisfaction of the VUs is constructed. To solve the above model, an improved CMA-ES algorithm based on the guiding point (GP-CMA-ES) is proposed. When the number of VUs increases, the dimension of variables also increases. Therefore, a convergence-related variable grouping strategy based on the relationship detection between variables and objectives is proposed. The performance of algorithm GP-CMA-ES is compared with five algorithms in the digital twin environment.
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| 5. |
- Cao, Yuan, et al.
(författare)
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Hybrid Trusted/Untrusted Relay Based Quantum Key Distribution over Optical Backbone Networks
- 2021
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Ingår i: IEEE Journal on Selected Areas in Communications. - 0733-8716 .- 1558-0008. ; 39:9, s. 2701-2718
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Tidskriftsartikel (refereegranskat)abstract
- Quantum key distribution (QKD) has demonstrated a great potential to provide future-proofed security, especially for 5G and beyond communications. As the critical infrastructure for 5G and beyond communications, optical networks can offer a cost-effective solution to QKD deployment utilizing the existing fiber resources. In particular, measurement-device-independent QKD shows its ability to extend the secure distance with the aid of an untrusted relay. Compared to the trusted relay, the untrusted relay has obviously better security, since it does not rely on any assumption on measurement and even allows to be accessed by an eavesdropper. However, it cannot extend QKD to an arbitrary distance like the trusted relay, such that it is expected to be combined with the trusted relay for large-scale QKD deployment. In this work, we study the hybrid trusted/untrusted relay based QKD deployment over optical backbone networks and focus on cost optimization during the deployment phase. A new network architecture of hybrid trusted/untrusted relay based QKD over optical backbone networks is described, where the node structures of the trusted relay and untrusted relay are elaborated. The corresponding network, cost, and security models are formulated. To optimize the deployment cost, an integer linear programming model and a heuristic algorithm are designed. Numerical simulations verify that the cost-optimized design can significantly outperform the benchmark algorithm in terms of deployment cost and security level. Up to 25% cost saving can be achieved by deploying QKD with the hybrid trusted/untrusted relay scheme while keeping much higher security level relative to the conventional point-to-point QKD protocols that are only with the trusted relays.
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| 6. |
- Castaneda, Oscar, et al.
(författare)
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Finite-alphabet MMSE equalization for all-digital massive MU-MIMO mmWave communications
- 2020
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Ingår i: IEEE Journal on Selected Areas in Communications. - 0733-8716 .- 1558-0008. ; 38:9, s. 2128 -2141
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Tidskriftsartikel (refereegranskat)abstract
- We propose finite-alphabet equalization, a new paradigm that restricts the entries of the spatial equalization matrix to low-resolution numbers, enabling high-throughput, low-power, and low-cost hardware equalizers. To minimize the performance loss of this paradigm, we introduce FAME, short for finite-alphabet minimum mean-square error (MMSE) equalization, which is able to significantly outperform a naïve quantization of the linear MMSE matrix. We develop efficient algorithms to approximately solve the NP-hard FAME problem and showcase that near-optimal performance can be achieved with equalization coefficients quantized to only 1-3 bits for massive multi-user multiple-input multiple-output (MU-MIMO) millimeter-wave (mmWave) systems. We provide very-large scale integration (VLSI) results that demonstrate a reduction in equalization power and area by at least a factor of 3.9× and 5.8×, respectively.
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| 7. |
- Champati, Jaya Prakash, et al.
(författare)
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Minimum Achievable Peak Age of Information Under Service Preemptions and Request Delay
- 2021
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Ingår i: IEEE Journal on Selected Areas in Communications. - : IEEE Communications Society. - 0733-8716 .- 1558-0008. ; 39:5, s. 1365-1379
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Tidskriftsartikel (refereegranskat)abstract
- There is a growing interest in analysing freshness of data in networked systems. Age of Information (AoI) has emerged as a relevant metric to quantify this freshness at a receiver, and minimizing this metric for different system models has received significant research attention. However, a fundamental question remains: what is the minimum achievable AoI in any single-server-single-source queuing system for a given service-time distribution? We address this question for the average peak AoI (PAoI) statistic by considering generate-at-will source model, service preemptions, and request delays. Our main result is on the characterization of the minimum achievable average PAoI, and we show that it is achieved by a fixed-threshold policy among the set of all causal policies. We use the characterization to provide necessary and sufficient condition for preemptions to be beneficial for a given service-time distribution. Our numerical results, obtained using well-known distributions, demonstrate that the heavier the tail of a distribution the higher the performance gains of using preemptions.
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| 8. |
- Chemouil, Prosper, et al.
(författare)
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Special Issue on Advances in Artificial Intelligence and Machine Learning for Networking
- 2020
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Ingår i: IEEE Journal on Selected Areas in Communications. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0733-8716 .- 1558-0008. ; 38:10, s. 2229-2233
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Artificial Intelligence (AI) and Machine Learning (ML) approaches have emerged in the networking domain with great expectation. They can be broadly divided into AI/ML techniques for network engineering and management, network designs for AI/ML applications, and system concepts. AI/ML techniques for networking and management improve the way we address networking. They support efficient, rapid, and trustworthy engineering, operations, and management. As such, they meet the current interest in softwarization and network programmability that fuels the need for improved network automation in agile infrastructures, including edge and fog environments. Network design and optimization for AI/ML applications addresses the complementary topic of supporting AI/ML-based systems through novel networking techniques, including new architectures and algorithms. The third topic area is system implementation and open-source software development.
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| 9. |
- Chen, Shuaifei, et al.
(författare)
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Structured Massive Access for Scalable Cell-Free Massive MIMO Systems
- 2021
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Ingår i: IEEE Journal on Selected Areas in Communications. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0733-8716 .- 1558-0008. ; 39:4, s. 1086-1100
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Tidskriftsartikel (refereegranskat)abstract
- How to meet the demand for increasing number of users, higher data rates, and stringent quality-of-service (QoS) in the beyond fifth-generation (B5G) networks? Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising solution, in which many wireless access points cooperate to jointly serve the users by exploiting coherent signal processing. However, there are still many unsolved practical issues in cell-free massive MIMO systems, whereof scalable massive access implementation is one of the most vital. In this paper, we propose a new framework for structured massive access in cell-free massive MIMO systems, which comprises one initial access algorithm, a partial large-scale fading decoding (P-LSFD) strategy, two pilot assignment schemes, and one fractional power control policy. New closed-form spectral efficiency (SE) expressions with maximum ratio (MR) combining are derived. The simulation results show that our proposed framework provides high SE when using local partial minimum mean-square error (LP-MMSE) and MR combining. Specifically, the proposed initial access algorithm and pilot assignment schemes outperform their corresponding benchmarks, P-LSFD achieves scalability with a negligible performance loss compared to the conventional optimal large-scale fading decoding (LSFD), and scalable fractional power control provides a controllable trade-off between user fairness and the average SE.
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| 10. |
- Chen, Xiaoming, et al.
(författare)
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Guest Editorial Massive Access for 5G and Beyond-Part I
- 2021
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Ingår i: IEEE Journal on Selected Areas in Communications. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0733-8716 .- 1558-0008. ; 39:3, s. 611-614
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Massive access, also known as massive connectivity or massive machine-type communication (mMTC), is one of the main use cases of the fifth-generation (5G) and beyond 5G (B5G) wireless networks. In the past few years, it has received considerable attention in academia and industry. This Special Issue (SI) of the IEEE Journal on Selected Areas in Communications (JSAC) on Massive Access for 5G and Beyond contains the latest results of researchers, industry practitioners, and individuals working on related research problems. Due to the extremely high response to the Call for Papers, this SI is split into two parts. The first part includes a guest editor-authored survey paper and 17 technical papers focusing on access models and access protocols, while the second part contains 18 papers focusing on access techniques and coverage enhancement approaches. We sincerely thank the authors, reviewers, JSAC staffs, and the Senior Editor, Prof. Wayne Stark, for their effort and time in preparing this SI.
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