Network coding allows distributed information sources such as sensors to efficiently compress and transmit data to distributed receivers across a bandwidth-limited network. Classical network coding is largely task-agnostic -- the coding schemes mainly aim to faithfully reconstruct data at the receivers, regardless of what ultimate task the received data is used for. In this paper, we analyze a new task-driven network coding problem, where distributed receivers pass transmitted data through machine learning (ML) tasks, which provides an opportunity to improve efficiency by transmitting salient task-relevant data representations. Specifically, we formulate a task-aware network coding problem over a butterfly network in real-coordinate space, where lossy analog compression through principal component analysis (PCA) can be applied. A lower bound for the total loss function for the formulated problem is given, and necessary and sufficient conditions for achieving this lower bound are also provided. We introduce ML algorithms to solve the problem in the general case, and our evaluation demonstrates the effectiveness of task-aware network coding.
Successive relaying can improve the transmission rate by allowing the source and relays to transmit messages simultaneously, but it may cause severe inter-relay interference (IRI). IRI cancellation schemes have been proposed to mitigate IRI. However, interference cancellation methods have a high risk of error propagation, resulting in a severe transmission rate loss in finite blocklength regimes. Thus, jointly decoding for successive relaying with finite-blocklength coding (FBC) remains a challenge. In this paper, we present an optimized channel-aware ordered successive relaying protocol with …
We consider a communication system in which the destination receives status updates from an information source that observes a physical process. The transmitter performs semantics-empowered filtering as a means to send only the most "important" samples to the receiver in a timely manner. As a first step, we explore a simple policy where the transmitter selects to encode only a fraction of the least frequent realizations of the observed random phenomenon, treating the remaining ones as not informative. For this timely source coding problem, we derive the optimal codeword lengths in the sense of…
Network coding (NC), when combined with multipath routing, enables a linear programming (LP) formulation for a multi-source multicast with intra-session network coding (MISNC) problem. However, it is still hard to solve using conventional methods due to the enormous scale of variables or constraints. In this paper, we try to solve this problem in terms of throughput maximization from an algorithmic perspective. We propose a novel formulation based on the extereme flow decomposition technique, which facilitates the design and analysis of approximation and online algorithms. For the offline scen…
The device-to-device communication-aided fog radio access network, referred to as \textit{D2D-aided F-RAN}, takes advantage of caching at enhanced remote radio heads (eRRHs) and D2D proximity for improved system performance. For D2D-aided F-RAN, we develop a framework that exploits the cached contents at eRRHs, their transmission rates/powers, and previously received contents by different users to deliver the requesting contents to users with a minimum completion time. Given the intractability of the completion time minimization problem, we formulate it at each transmission by approximating th…
We analyze a wireless line network employing wireless network coding. The two end nodes exchange their packets through relays. While a packet at a relay might not find its coding pair upon arrival, a transmission cost can be reduced by waiting for coding with a packet from the other side. To strike a balance between the reduced transmission cost and the cost incurred by the delay, a scheduling algorithm determining either to transmit an uncoded packet or to wait for coding is needed. Because of highly uncertain traffic injections, scheduling with no assumption of the traffic is critical. This …
A fog-radio access network (F-RAN) architecture is studied for an Internet-of-Things (IoT) system in which wireless sensors monitor a number of multi-valued events and transmit in the uplink using grant-free random access to multiple edge nodes (ENs). Each EN is connected to a central processor (CP) via a finite-capacity fronthaul link. In contrast to conventional information-agnostic protocols based on separate source-channel (SSC) coding, where each device uses a separate codebook, this paper considers an information-centric approach based on joint source-channel (JSC) coding via a non-ortho…
Universality Sacrifices Reliability in Classical-Quantum Channel Coding
The University of Tokyo（东京大学）；RIKEN Center for Quantum Computing (RQC)（理化学研究所量子计算中心）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；International Quantum Academy（国际量子学院）；Nagoya University（名古屋大学）；National Taiwan University（台湾大学）；National Center for Theoretical Sciences, Taiwan（台湾理论科学中心）；Hon Hai (Foxconn) Quantum Computing Center（鸿海（富士康）量子计算中心）
SA-RA-JSCC: SNR-Adaptive and Semantic-Rate-Aware Joint Source-Channel Coding
Pengcheng Laboratory（鹏城实验室）；School of Intelligent Systems, Sun Yat-sen University（中山大学智能学院）；Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（南方海洋科学与工程广东省实验室（珠海））；Guangdong Provincial Key Laboratory of Fire Science and Technology（广东省火灾科学与Technology重点实验室）；Beijing University of Posts and Telecommunications（北京邮电大学）
Context Video Semantic Transmission with Variable Length and Rate Coding over MIMO Channels
Department of Electronic Engineering, Shanghai Jiao Tong University（上海交通大学电子工程系）；School of Electrical Engineering and Telecommunications, University of New South Wales（新南威尔士大学电气与电信学院）；KU 6G Research Center, Khalifa University of Science and Technology（卡里玛科学技术大学KU 6G研究中心）；CentraleSupelec, University Paris-Saclay（巴黎萨克雷大学CentraleSupelec）
