arXiv subjects

Xiaoqi Qin

Publications and source records attributed to Xiaoqi Qin.

46 records · Page 3Linked to original sources

arXiv2022

Communication Beyond Transmitting Bits: Semantics-Guided Source and Channel Coding

Classical communication paradigms focus on accurately transmitting bits over a noisy channel, and Shannon theory provides a fundamental theoretical limit on the rate of reliable communications. In this approach, bits are treated equally, and the communication system is oblivious to what meaning these bits convey or how they would be used. Future communications towards intelligence and conciseness will predictably play a dominant role, and the proliferation of connected intelligent agents requires a radical rethinking of coded transmission paradigm to support the new communication morphology on the horizon. The recent concept of "semantic communications" offers a promising research direction. Injecting semantic guidance into the coded transmission design to achieve semantics-aware communications shows great potential for further breakthrough in effectiveness and reliability. This article sheds light on semantics-guided source and channel coding as a transmission paradigm of semantic communications, which exploits both data semantics diversity and wireless channel diversity together to boost the whole system performance. We present the general system architecture and key techniques, and indicate some open issues on this topic.

cs.IT↗

arXiv2022

Joint Device Selection and Power Control for Wireless Federated Learning

This paper studies the joint device selection and power control scheme for wireless federated learning (FL), considering both the downlink and uplink communications between the parameter server (PS) and the terminal devices. In each round of model training, the PS first broadcasts the global model to the terminal devices in an analog fashion, and then the terminal devices perform local training and upload the updated model parameters to the PS via over-the-air computation (AirComp). First, we propose an AirComp-based adaptive reweighing scheme for the aggregation of local updated models, where the model aggregation weights are directly determined by the uplink transmit power values of the selected devices and which enables the joint learning and communication optimization simply by the device selection and power control. Furthermore, we provide a convergence analysis for the proposed wireless FL algorithm and the upper bound on the expected optimality gap between the expected and optimal global loss values is derived. With instantaneous channel state information (CSI), we formulate the optimality gap minimization problems under both the individual and sum uplink transmit power constraints, respectively, which are shown to be solved by the semidefinite programming (SDR) technique. Numerical results reveal that our proposed wireless FL algorithm achieves close to the best performance by using the ideal FedAvg scheme with error-free model exchange and full device participation.

cs.IT↗

arXiv2022

Dynamic Clustering and Power Control for Two-Tier Wireless Federated Learning

Federated learning (FL) has been recognized as a promising distributed learning paradigm to support intelligent applications at the wireless edge, where a global model is trained iteratively through the collaboration of the edge devices without sharing their data. However, due to the relatively large communication cost between the devices and parameter server (PS), direct computing based on the information from the devices may not be resource efficient. This paper studies the joint communication and learning design for the over-the-air computation (AirComp)-based two-tier wireless FL scheme, where the lead devices first collect the local gradients from their nearby subordinate devices, and then send the merged results to the PS for the second round of aggregation. We establish a convergence result for the proposed scheme and derive the upper bound on the optimality gap between the expected and optimal global loss values. Next, based on the device distance and data importance, we propose a hierarchical clustering method to build the two-tier structure. Then, with only the instantaneous channel state information (CSI), we formulate the optimality gap minimization problem and solve it by using an efficient alternating minimization method. Numerical results show that the proposed scheme outperforms the baseline ones.

cs.IT↗

arXiv2022

Coexistence between Task- and Data-Oriented Communications: A Whittle's Index Guided Multi-Agent Reinforcement Learning Approach

We investigate the coexistence of task-oriented and data-oriented communications in a IoT system that shares a group of channels, and study the scheduling problem to jointly optimize the weighted age of incorrect information (AoII) and throughput, which are the performance metrics of the two types of communications, respectively. This problem is formulated as a Markov decision problem, which is difficult to solve due to the large discrete action space and the time-varying action constraints induced by the stochastic availability of channels. By exploiting the intrinsic properties of this problem and reformulating the reward function based on channel statistics, we first simplify the solution space, state space, and optimality criteria, and convert it to an equivalent Markov game, for which the large discrete action space issue is greatly relieved. Then, we propose a Whittle's index guided multi-agent proximal policy optimization (WI-MAPPO) algorithm to solve the considered game, where the embedded Whittle's index module further shrinks the action space, and the proposed offline training algorithm extends the training kernel of conventional MAPPO to address the issue of time-varying constraints. Finally, numerical results validate that the proposed algorithm significantly outperforms state-of-the-art age of information (AoI) based algorithms under scenarios with insufficient channel resources.

cs.IT↗

arXiv2022

Service Delay Minimization for Federated Learning over Mobile Devices

Federated learning (FL) over mobile devices has fostered numerous intriguing applications/services, many of which are delay-sensitive. In this paper, we propose a service delay efficient FL (SDEFL) scheme over mobile devices. Unlike traditional communication efficient FL, which regards wireless communications as the bottleneck, we find that under many situations, the local computing delay is comparable to the communication delay during the FL training process, given the development of high-speed wireless transmission techniques. Thus, the service delay in FL should be computing delay + communication delay over training rounds. To minimize the service delay of FL, simply reducing local computing/communication delay independently is not enough. The delay trade-off between local computing and wireless communications must be considered. Besides, we empirically study the impacts of local computing control and compression strategies (i.e., the number of local updates, weight quantization, and gradient quantization) on computing, communication and service delays. Based on those trade-off observation and empirical studies, we develop an optimization scheme to minimize the service delay of FL over heterogeneous devices. We establish testbeds and conduct extensive emulations/experiments to verify our theoretical analysis. The results show that SDEFL reduces notable service delay with a small accuracy drop compared to peer designs.

cs.LG↗

arXiv2022

Energy-efficient Caching and Task offloading for Timely Status Updates in UAV-assisted VANETs

Intelligent edge network is maturing to enable smart and efficient transportation systems. In this letter, we consider unmanned aerial vehicle (UAV)-assisted vehicular networks where UAVs provide caching and computing services in complement with base station (BS). One major challenge is that vehicles need to obtain timely situational awareness via orchestration of ubiquitous caching and computing resources. Note that cached data for vehicles' perception tasks contains time-varying context information, thus freshness of cached data should be considered in conjunction with task execution to guarantee timeliness of obtained status updates. To this end, we propose a two-stage performance metric to quantify the impact of cache refreshing and computation offloading decisions on the age of status updates. We formulate an energy minimization problem by jointly considering cache refreshing, computation offloading and aging of status updates. To facilitate online decision making, we propose a deep deterministic policy gradient(DDPG)-based solution procedure and incorporate differentiated experience replay mechanism to accelerate convergence. Simulation results show that the performance of proposed solution is competitive in terms of energy consumption for obtaining fresh status updates.

cs.NI↗

arXiv2022

Towards Semantic Communications: A Paradigm Shift

The last seventy years have witnessed the transition of communication from Shannon's theoretical concept to current high-efficient practical systems. Classical communication systems address the capability-deficiency issue mainly by module-stacking and technique-densification with ever-increasing complexity. In such a traditional viewpoint, classical source coding only uses explicit probabilistic models to compress data, regardless of the meaning of transmitted source messages. Also, channel coded transmission does not identify the source content. In this sense, state-of-the-art communication systems work merely at the technical level as summarized by Weaver. Unlike the traditional system design philosophy, this article proposes a new route to boost the system capabilities towards intelligence-endogenous and primitive-concise communications. The communication paradigm upgrades to the semantic level, which is radically different since all the key techniques imply the use of meanings of transmitted data, thus deeply changing the design of the communication system. This paradigm shifting unveils a promising direction due to its ability to offer an identical quality of service with much lower data transmission requirement. Different from other similar works, this article constitutes a brief tutorial on the framework of semantic communications, its gain analyzed from the information theory perspective, a method to calculate the semantic compression bound, and an exemplary use case of semantic communications.

cs.IT↗

arXiv2020

Differentially Private ADMM for Distributed Medical Machine Learning

Due to massive amounts of data distributed across multiple locations, distributed machine learning has attracted a lot of research interests. Alternating Direction Method of Multipliers (ADMM) is a powerful method of designing distributed machine learning algorithm, whereby each agent computes over local datasets and exchanges computation results with its neighbor agents in an iterative procedure. There exists significant privacy leakage during this iterative process if the local data is sensitive. In this paper, we propose a differentially private ADMM algorithm (P-ADMM) to provide dynamic zero-concentrated differential privacy (dynamic zCDP), by inserting Gaussian noise with linearly decaying variance. We prove that P-ADMM has the same convergence rate compared to the non-private counterpart, i.e., $\mathcal{O}(1/K)$ with $K$ being the number of iterations and linear convergence for general convex and strongly convex problems while providing differentially private guarantee. Moreover, through our experiments performed on real-world datasets, we empirically show that P-ADMM has the best-known performance among the existing differentially private ADMM based algorithms.

cs.LG↗

arXiv2019

Age Based Task Scheduling and Computation Offloading in Mobile-Edge Computing Systems

To support emerging real-time monitoring and control applications, the timeliness of computation results is of critical importance to mobile-edge computing (MEC) systems. We propose a performance metric called age of task (AoT) based on the concept of age of information (AoI), to evaluate the temporal value of computation tasks. In this paper, we consider a system consisting of a single MEC server and one mobile device running several applications. We study an age minimization problem by jointly considering task scheduling, computation offloading and energy consumption. To solve the problem efficiently, we propose a light-weight task scheduling and computation offloading algorithm. Through performance evaluation, we show that our proposed age-based solution is competitive when compared with traditional strategies.

eess.SP↗

arXiv2018

Distributed Layered Grant-Free Non-Orthogonal Multiple Access for Massive MTC

Grant-free transmission is considered as a promising technology to support sporadic data transmission in massive machine-type communications (mMTC). Due to the distributed manner, high collision probability is an inherent drawback of grant-free access techniques. Non-orthogonal multiple access (NOMA) is expected to be used in uplink grant-free transmission, multiplying connection opportunities by exploiting power domain resources. However, it is usually applied for coordinated transmissions where the base station performs coordination with full channel state information, which is not suitable for grant-free techniques. In this paper, we propose a novel distributed layered grant-free NOMA framework. Under this framework, we divide the cell into different layers based on predetermined inter-layer received power difference. A distributed layered grant-free NOMA based hybrid transmission scheme is proposed to reduce collision probability. Moreover, we derive the closed-form expression of connection throughput. A joint access control and NOMA layer selection (JACNLS) algorithm is proposed to solve the connection throughput optimization problem. The numerical and simulation results reveal that, when the system is overloaded, our proposed scheme outperforms the grant-free-only scheme by three orders of magnitude in terms of expected connection throughput and outperforms coordinated OMA transmission schemes by 31.25\% with only 0.0189\% signaling overhead of the latter.

cs.IT↗