5 research outputs found
Massive Access in Cell-Free Massive MIMO-Based Internet of Things: Cloud Computing and Edge Computing Paradigms
This paper studies massive access in cell-free massive multi-input
multi-output (MIMO) based Internet of Things and solves the challenging active
user detection (AUD) and channel estimation (CE) problems. For the uplink
transmission, we propose an advanced frame structure design to reduce the
access latency. Moreover, by considering the cooperation of all access points
(APs), we investigate two processing paradigms at the receiver for massive
access: cloud computing and edge computing. For cloud computing, all APs are
connected to a centralized processing unit (CPU), and the signals received at
all APs are centrally processed at the CPU. While for edge computing, the
central processing is offloaded to part of APs equipped with distributed
processing units, so that the AUD and CE can be performed in a distributed
processing strategy. Furthermore, by leveraging the structured sparsity of the
channel matrix, we develop a structured sparsity-based generalized approximated
message passing (SS-GAMP) algorithm for reliable joint AUD and CE, where the
quantization accuracy of the processed signals is taken into account. Based on
the SS-GAMP algorithm, a successive interference cancellation-based AUD and CE
scheme is further developed under two paradigms for reduced access latency.
Simulation results validate the superiority of the proposed approach over the
state-of-the-art baseline schemes. Besides, the results reveal that the edge
computing can achieve the similar massive access performance as the cloud
computing, and the edge computing is capable of alleviating the burden on CPU,
having a faster access response, and supporting more flexible AP cooperation.Comment: 17 pages, 16 figures. The current version has been accepted by IEEE
Journal on Selected Areas in Communications (JSAC) Special Issue on Massive
Access for 5G and Beyon
6G Network AI Architecture for Everyone-Centric Customized Services
Mobile communication standards were developed for enhancing transmission and
network performance by using more radio resources and improving spectrum and
energy efficiency. How to effectively address diverse user requirements and
guarantee everyone's Quality of Experience (QoE) remains an open problem. The
Sixth Generation (6G) mobile systems will solve this problem by utilizing
heterogenous network resources and pervasive intelligence to support
everyone-centric customized services anywhere and anytime. In this article, we
first coin the concept of Service Requirement Zone (SRZ) on the user side to
characterize and visualize the integrated service requirements and preferences
of specific tasks of individual users. On the system side, we further introduce
the concept of User Satisfaction Ratio (USR) to evaluate the system's overall
service ability of satisfying a variety of tasks with different SRZs. Then, we
propose a network Artificial Intelligence (AI) architecture with integrated
network resources and pervasive AI capabilities for supporting customized
services with guaranteed QoEs. Finally, extensive simulations show that the
proposed network AI architecture can consistently offer a higher USR
performance than the cloud AI and edge AI architectures with respect to
different task scheduling algorithms, random service requirements, and dynamic
network conditions
Throughput Maximization for RIS-Assisted UAV-Enabled WPCN
This paper investigates a reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV)-enabled wireless powered communication network (WPCN). In the system, a UAV acts as a hybrid access point (HAP) to charge usersin the downlink (DL) and receive messages in the uplink (UL). In particular, the RIS is exploited to significantly enhance the efficiency of both the DL and UL transmission. Our objective is to enhance the minimum throughput among all ground users by jointly optimizing the horizontal location of UAVs, the transmit power of users, transmission time allocation, and passive beamforming vectors at the RIS. To address this problem, we present an alternating optimization-based algorithm with low complexity to decompose the problem into four subproblemsand solve them sequentially. In particular, we derive a lower bound of the composite channel gain to tighten the constraints and employ successive convex approximation (SCA) to optimize the horizontal location of the UAV. The transmit power closedform optimum solutions are then obtained, and the problem oftime allocation is reformulated as a linear programming problem. Finally, we optimize the passive beamforming vectors by adopting semi-definite relaxation (SDR). The effectiveness of the algorithm is supported by numerical results, which also demonstrate that the RIS-assisted UAV-enabled WPCN outperforms the traditional WPCN in terms of the minimum throughput.<br/