Cell-free networks can effectively reduce interference due to diversity gain. Two key technologies, access point (AP) clustering and transceiver design, play key roles in cell-free networks, and they are implemented at different layers of the air interface. To address the issues and obtain global optimal results, this paper proposes an uplink joint AP clustering and receiver optimization algorithm, where a cross-layer optimization model is built based on graph neural networks (GNNs) with low computational complexity. Experimental results show that the proposed algorithm can activate fewer APs for each user with a small performance loss compared with conventional algorithms.

Autonomous driving is an emerging technology attracting interests from various sectors in recent years. Most of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intelligent modules. In this paper, we attempt to exploit the connectivity among vehicles and propose a systematic framework to develop autonomous driving techniques. We first introduce a general hierarchical information fusion framework for cooperative sensing to obtain global situational awareness for vehicles. Following this, a cooperative intelligence framework is proposed for autonomous driving systems. This general framework can guide the development of data collection, sharing and processing strategies to realize different intelligent functions in autonomous driving.

This paper proposes an analytical mining tool for big graph data based on MapReduce and bulk synchronous parallel (BSP) computing model. The tool is named Mapreduce and BSP based Graph-mining tool (MBGM). The core of this mining system are four sets of parallel graph-mining algorithms programmed in the BSP parallel model and one set of data extraction-transformation-loading (ETL) algorithms implemented in MapReduce. To invoke these algorithm sets, we designed a workflow engine which optimized for cloud computing. Finally, a well-designed data management function enables users to view, delete and input data in the Hadoop distributed file system (HDFS). Experiments on artificial data show that the components of graph-mining algorithm in MBGM are efficient.

A number of mobile Online Social Networking (OSN) services have appeared in the market in recent times. While most mobile systems benefit greatly from cloud services, centralized servers and communications infrastructure is not always available. Nor are location-based services offered to mobile devices without GPS. To take advantage of cloud and to address these problems, a Wi-Fi based multihop networking system called MoNet is proposed. On top of MoNET we propose a privacy-aware geosocial networking service called WiFace. Where there is no infrastructure, a distributed content sharing protocol significantly shortens the relay path, reduces conflicts, and improves data availability. Furthermore, a security mechanism is developed to protect privacy. Comprehensive experiments performed on MoNet show that the system is more than sufficient to support social networking and even audio and video applications.