Utility Maximization for Multihop Wireless Networks Employing BATS Codes

IEEE Open J. Veh. Technol.

Yeung

et al. 2023

Emerging topics such as environmental protection and energy utilization have pushed research and development of electric vehicles and hybrid electric vehicles (EVs/HEVs). In the last few decades, numerous technologies have been developed for EV/HEV importance. In this article, key research topics in the area of EVs/HEVs, namely electric machines, electrochemical energy sources, wireless charging infrastructure, and latest EV/HEV models are covered. This paper aims to consolidate the key emerging technologies in this field and provide the readers a blueprint to begin their own journeys.INDEX TERMS Electric machine, induction machine, wound field synchronous machine, reluctance machine, stator permanent-magnet machine, field modulation machine, high-speed machine, electrochemical energy source, battery, fuel cell, ultracapacitor, wireless charging infrastructure, part-and-charge, coil design, move-and-charge, electric vehicle, hybrid vehicle.

Section: B Bats Codementioning

confidence: 99%

“…This is the author's version which has not been fully edited and content may change prior to final publication. the end-to-end information rate, the communication cost on each network link should also be controlled [64]. To support the more complicated network topology for a BATS code flow, a new flow control mechanism is required.…”

Section: Volume 11mentioning

confidence: 99%

On Scalable Network Communication for Infrastructure-Vehicle Collaborative Autonomous Driving

Yang

IEEE Open J. Veh. Technol.

Yeung

et al. 2023

“…As a remark, the value of t L max can affect the stability of the packet buffer. For a general network transmission scenario with multiple transmission sessions, the value of t L max can be determined by optimizing the utility of certain local network transmissions [82,83].…”

Section: Blockwise Adaptive Recodingmentioning

confidence: 99%

BAR: Blockwise Adaptive Recoding for Batched Network Coding

Yang

Zhou

et al. 2023

Entropy

Self Cite

Multi-hop networks have become popular network topologies in various emerging Internet of Things (IoT) applications. Batched network coding (BNC) is a solution to reliable communications in such networks with packet loss. By grouping packets into small batches and restricting recoding to the packets belonging to the same batch; BNC has much smaller computational and storage requirements at intermediate nodes compared with direct application of random linear network coding. In this paper, we discuss a practical recoding scheme called blockwise adaptive recoding (BAR) which learns the latest channel knowledge from short observations so that BAR can adapt to fluctuations in channel conditions. Due to the low computational power of remote IoT devices, we focus on investigating practical concerns such as how to implement efficient BAR algorithms. We also design and investigate feedback schemes for BAR under imperfect feedback systems. Our numerical evaluations show that BAR has significant throughput gain for small batch sizes compared with existing baseline recoding schemes. More importantly, this gain is insensitive to inaccurate channel knowledge. This encouraging result suggests that BAR is suitable to be used in practice as the exact channel model and its parameters could be unknown and subject to changes from time to time.

“…Packet separation of blockwise adaptive recoding was discussed in [32]. Utility maximization with adaptive recoding was investigated in [35]. A small-sample inferred rank distribution for adaptive recoding was modelled in [36].…”

Section: Related Work On Adaptive Recodingmentioning

confidence: 99%

A Unified Adaptive Recoding Framework for Batched Network Coding

2019 IEEE International Symposium on Information Theory (ISIT)

Tang

et al. 2019

Self Cite

Batched network coding is a variation of random linear network coding which has low computational and storage costs. In order to adapt random fluctuations in the number of erasures in individual batches, it is not optimal to recode and transmit the same number of packets for all batches. Different distributed optimization models, which are called adaptive recoding schemes, were formulated for this purpose.The key component of these optimization problems is the expected value of the rank distribution of a batch at the next network node, which is also known as the expected rank. In this paper, we put forth a unified adaptive recoding framework. We show that the expected rank functions are concave when the packet loss pattern is a stationary stochastic process regardless of the field size, which covers but not limited to independent packet loss and burst packet loss. Under this concavity assumption, we show that there always exists a solution which not only can minimize the randomness on the number of recoded packets but also can tolerate rank distribution errors due to inaccurate measurements or limited precision of the machine. To obtain such an optimal solution, we propose tuning schemes that can turn any feasible solution into a desired optimal solution.