Federated Learning (FL) is a promising distributed learning mechanism that revolutionizes our interaction with data in the IoT ecosystem. Due to the rapidly growing scale of smart devices and the limited transmission resources of networks, a simple, consistent and scalable FL framework aiming to address the communication bottleneck is urgently needed. In this work, we propose an efficient approach with Selective Aggregation of Models (SAM) to mitigate the communication overload in FL systems. The introduction of SAM enables each local client to upload its model with a certain probability, resulting in a significant reduction in costly communication expenses. We design the algorithm for SAM, analyze the convergence bound on non-convex objectives for heterogeneous data, which illustrates the impact of the selection probability as well as the set size of participating clients on the system performance, and assess the conservation for the network resource utilization by modeling queuing systems. We conduct various experiments to evaluate the performance of SAM, whose outcomes suggest that significant alleviation of the communication bottleneck can be accomplished with marginal cost of performance loss. It will also be shown that SAM is a communication-efficient method that can be freely applied to other frameworks.

The wide applications of artificial intelligence (AI) on massive Internet-of-things or smartphones raises significant concerns about privacy, heterogeneity, and resource efficiency. Correspondingly, federated learning emerges as an effective way to enable AI over massively distributed nodes without uploading the raw data. Conventional works mostly focus on learning a single unified model for one solitary task. Multi-task learning (MTL) outperforms single-task learning by training multiple models concurrently, leading to reduced model sizes and increased flexibility. However, existing federated learning efforts often face challenges in efficiently managing MTL scenarios, particularly with the presence of stragglers, without incurring prohibitive computation and communication costs. In this paper, inspired by the natural cloud-BS-terminal hierarchy of cellular networks, we provide a viable resource-aware hierarchical federated MTL (RHFedMTL) solution to meet the task heterogeneity corresponding to different non-IID (independent and identically distributed) training datasets. Specifically, a primal-dual method has been leveraged to effectively transform the coupled MTL into some local optimization sub-problems within BSs. Therefore, it enables solving different tasks within a BS and aggregating the multi-task result in the cloud without uploading the raw data. Furthermore, compared with existing methods that reduce resource costs by simply changing the aggregation frequency, we dive into the intricate relationship between resource consumption and learning accuracy, and develop a resource-aware learning strategy for adjusting the iteration number on local terminals and BSs to meet the resource budget. Extensive simulation results demonstrate the effectiveness and superiority of RHFedMTL in terms of improving the learning accuracy and boosting the convergence rate.

Multi-Agent Reinforcement Learning (MARL) has become a classic paradigm to solve diverse, intelligent control tasks like autonomous driving in Internet of Vehicles (IoV). However, the widely assumed existence of a central node to implement centralized federated learning-assisted MARL might be impractical in highly dynamic scenarios, and the excessive communication overheads possibly overwhelm the IoV system. Therefore, in this paper, we design a communication efficient cooperative MARL algorithm, named RSM-MAPPO, to reduce the communication overheads in a fully distributed architecture. In particular, RSM-MAPPO enhances the multi-agent Proximal Policy Optimization (PPO) by incorporating the idea of segment mixture and augmenting multiple model replicas from received neighboring policy segments. Afterwards, RSM-MAPPO adopts a theory-guided metric to regulate the selection of contributive replicas to guarantee the policy improvement. Finally, extensive simulations in a mixed-autonomy traffic control scenario verify the effectiveness of the RSM-MAPPO algorithm.

Federated learning (FL) has prevailed as an efficient and privacy-preserved scheme for distributed learning. In this work, we mainly focus on the optimization of computation and communication in FL from a view of pruning. By adopting layer-wise pruning in local training and federated updating, we formulate an explicit FL pruning framework, FedLP (Federated Layer-wise Pruning), which is model-agnostic and universal for different types of deep learning models. Two specific schemes of FedLP are designed for scenarios with homogeneous local models and heterogeneous ones. Both theoretical and experimental evaluations are developed to verify that FedLP relieves the system bottlenecks of communication and computation with marginal performance decay. To the best of our knowledge, FedLP is the first framework that formally introduces the layer-wise pruning into FL. Within the scope of federated learning, more variants and combinations can be further designed based on FedLP.

Multi-Agent Reinforcement Learning (MARL) has reached astonishing achievements in various fields such as the traffic control of vehicles in a wireless connected environment. In MARL, how to effectively decompose a global feedback into the relative contributions of individual agents belongs to one of the most fundamental problems. However, the volatility of the environment (e.g., the vehicle movement and wireless disturbance) could significantly shape the time-varying topological relationships among agents, thus making the Value Decomposition (VD) challenging. Therefore, in order to cope with this annoying volatility, it becomes imperative to design a dynamic VD framework. Hence, in this paper, we propose a novel Stochastic VMIX (SVMIX) methodology by embedding the dynamic topological features into the VD and incorporating the corresponding components into a multi-agent actor-critic architecture. In particular, the Stochastic Graph Neural Network (SGNN) is leveraged to effectively extract underlying dynamics embedded in topological features and improve the flexibility of VD against the environment volatility. Finally, the superiority of SVMIX is verified through extensive simulations.

Three-dimensional (3D) multiple input and multiple output (3D-MIMO) with massive antennas is a key technology to achieve high spectral efficiency and user experienced data rate for the fifth generation (5G) mobile communication system. To implement 3D-MIMO in 5G system, practical constraints on the product design should be considered. This paper proposes a systematic design for the 3D-MIMO product by considering the restrictions of both base band and the hardware, including cost, size, weight, and heat dissipation. The design has been implemented for 2.6-GHz time-division duplex band, and field trials have been conducted for performance validation with practical intercell interference in commercial network. The trial results show that this 3D-MIMO design can meet the spectral efficiency requirement of the 5G enhanced mobile broadband services. The performance gain of 3D-MIMO varies with the traffic load. When the traffic load is heavy, 3D-MIMO can enhance the cell throughput by 4~6.7 times. When the traffic load is low, the performance gain of this 3D-MIMO design decreases. The results from field trial also show that the performance of 3D-MIMO degrades in mobility scenarios, where further enhancement on acquiring instant channel status information are necessary to improve the robustness of 3D-MIMO to mobility.

3D-MIMO, using two dimensional antenna array at base station, demonstrates promising throughput gain over conventional antenna system during recent academic and industry studies. To realize the performance gain, accurate channel state information (CSI) feedback is one essential aspect. One effective way to obtain this CSI is taking advantage of channel reciprocity between uplink and downlink in TDD system. This paper analyzes the uplink pilot contamination problem in practical TDD LTE system using a novel pilot contamination model, besides, pilot contamination elimination methods in terms of increasing pilot power and reducing pilot collision probability are evaluated. The simulation results show that system performance degradation is less than 21% considering the practical pilot contamination, which still outperform the traditional 2D-MIMO system. Moreover, about 15% performance gain could be benefit from pilot contamination reduction approaches.

Single and Multi-layer adaptive eigen based beamforming (EBB) has been proved to be a promising method to achieve the tradeoff between diversity and spatial multiplexing. But the CQI in multi-layer mode is calculated according to the CQI in single-layer mode. Lack of accurate multi-layer CQI will induce performance loss when implementing layer adaptation at eNodeB. In this paper, aiming to reduce the complexity of the UEs, we deduce a low complexity eigen value calculation algorithm. In order to considering the effects of interference among different layers, virtual equalization (VE) algorithm is proposed. On the basis of these, a novel single- / multi-layer adaptive scheme for EBB is presented. Finally, the performance of the proposed scheme is studied using semi-dynamic system level simulator.

The virtual MIMO (V-MIMO) and coordinated multipoint transmission (CoMP) are adopted by the LTE system to extend the uplink spectral efficiency. V-MIMO makes use of spatial multiplexing to increase system throughput while CoMP by means of mitigating inter cell interference (ICI) significantly achieve the same effect. In this paper, we present an orthogonality based proportional fair scheduling (OPF) scheme for virtual MIMO and CoMP in TD-LTE uplink. To evaluate the effectiveness of the OPF algorithm, three schemes are analyzed and compared: conventional single input multiple output (SIMO), V-MIMO and CoMP. Finally, we evaluate and compare the potential gain in spectral efficiency for the SIMO, V-MIMO and CoMP using LTE semi-dynamic system level simulator.

In LTE downlink multiple-input multiple-output (MIMO), eigen based beamforming (EBB) is a promising approach for single-user scenario, while block diagonalization based beamforming (BDBF) eliminates intra-cell interferences significantly for multi-user scenario. However, both of them neglect inter-cell interferences which may lead system performance degradation. This paper presents an interference suppression based beamforming (ISBF) scheme to suppress inter-cell interferences. We first introduce interference suppression matrix (ISM) into EBB and BDBF to propose a novel beamforming matrix construction method. Then the ISM reporting scheme is discussed to support ISBF, and a precoded uplink pilot based (PUPB) reporting scheme is presented exploiting the channel reciprocity in TDD system. Finally, the simulation results are provided using dynamic system level simulator. Our results show that ISBF improves cell average and cell edge spectral efficiency significantly compared with traditional EBB and BDBF.

This paper investigates the performance of VoIP service in 3G-LTE system and IEEE 802.16m system both in TDD mode, and compares their different system characteristics in details. Thereafter semi-persistent scheduling, dynamic scheduling and link adaptation schemes involved are presented. Finally, this paper demonstrates different VoIP capacities of 3G-LTE and 802.16m under different circumstances, and explains how the different characteristics of the two systems affect the results.