The high density and heterogeneous mobility of users in many applications pose challenges for the channel acquisition in massive multiple-input-multiple-output (MIMO) systems. For such scenarios, we propose an intermittent channel estimation (ICE) scheme to save pilot resources, which utilizes the aged channel state information (CSI) based on the temporal correlations of user channels. The optimal CSI update pattern to maximize the achievable sum rate is obtained by solving a formulated multichain Markov decision process (MDP), which is denoted by ICE-MDP. Furthermore, to reduce the computational complexity of the MDP, we relax the constraint of the CSI update pattern design problem and convert it into a convex optimization problem, whose solution is denoted by ICE-CVX. The simulations validate the close-to-optimal performance and the computational efficiency of ICE-CVX and show that the ICE scheme can significantly outperform a conventional scheme which persistently updates the CSI of all users.

Beamforming structures with fixed beam codebooks provide economical solutions for millimeter wave (mmWave) communications due to the low hardware cost. However, the training overhead to search for the optimal beamforming configuration is proportional to the codebook size. To improve the efficiency of beam tracking, we propose a beam tracking scheme based on the channel fingerprint database, which comprises mappings between statistical beamforming gains and user locations. The scheme tracks user movement by utilizing the trained beam configurations and estimating the gains of beam configurations that are not trained. Simulations show that the proposed scheme achieves significant beamforming performance gains over existing beam tracking schemes.

Timely and accurate knowledge of channel state information (CSI) is necessary to support scheduling operations at both physical and network layers. In order to support pilot-free channel estimation in cell sleeping scenarios, we propose to adopt a channel database that stores the CSI as a function of geographic locations. Such a channel database is generated from historical user records, which usually can not cover all the locations in the cell. Therefore, we develop a two-step interpolation method to infer the channels at the uncovered locations. The method firstly applies the K-nearest-neighbor method to form a coarse database and then refines it with a deep convolutional neural network. When applied to the channel data generated by ray tracing software, our method shows a great advantage in performance over the conventional interpolation methods.

Channel state information (CSI) plays an important role in next-generation cellular systems with massive multiple-input multiple-output (MIMO) technology as the indicator of wireless channels.In hypercellular networks (HCNs),traffic base stations (TBSs) improve energy efficiency by dynamical sleeping.However,conventional pilot-based CSI acquisition methods cannot be applied to sleeping cells.We propose a novel CSI scheme based on channel learning to address this problem.Unlike location-aided CSI acquisition schemes,the proposed method utilizes CSI at the control base station (CBS) as input to avoid errors caused by positioning.We validate our scheme in an HCN generated by the geometry-based stochastic channel model (GSCM).The prediction accuracy of the proposed scheme is better than the K-nearest neighbor (KNN) method and close to the location-aided CSI acquisition scheme,which requires the knowledge on user position.

Massive multiple-input multiple-output (MIMO) systems need to support massive connectivity for the application of the Internet of things (IoT). The overhead of channel state information (CSI) acquisition becomes a bottleneck in the system performance due to the increasing number of users. An intermittent estimation scheme is proposed to ease the burden of channel estimation and maximize the sum capacity. In the scheme, we exploit the temporal correlation of MIMO channels and analyze the influence of the age of CSI on the downlink transmission rate using linear precoders. We show the CSI updating interval should follow a quasi-periodic distribution and reach a trade-off between the accuracy of CSI estimation and the overhead of CSI acquisition by optimizing the CSI updating frequency of each user. Numerical results show that the proposed intermittent scheme provides significant capacity gains over the conventional continuous estimation scheme.

In this letter, we propose a superposition signaling of pilots and data scheme (SPD) for beam-based frequency-division-duplex massive multiple-input multiple-output systems, which allows pilots and data to be transmitted simultaneously in the downlink. The proposed SPD scheme leverages spatial channel correlations to reduce the dimensionality loss, and more importantly, addresses the problem of uneven user channel correlations by superposition signaling of pilots and information bearing data symbols. Essentially, users with smaller dimensionality loss entail less pilots based on the SPD scheme. Simulations results reveal significant throughput gain by the SPD scheme over the state-of-the-art pilot-based approaches.

With the growing need for Internet of things (IoT) applications, wireless communications are facing the challenge of providing massive connectivity. However, as the number of users increases, the capacity of massive MIMO systems will be severely affected due to the huge channel estimation overhead. Therefore it is necessary to introduce non-orthogonal pilots to massive MIMO systems in order to ease the burden on the resources for channel estimation. In this paper, we propose a KKT-based iterative non-orthogonal pilot design algorithm which maximizes the mutual information between the received signal and the channel for MMSE channel estimation. For the typical antenna deployment of uniform linear array (ULA), we also provide scalable estimation schemes with low complexity based on channel angular representation as alternatives. The simulation results show that our schemes significantly save the estimation overhead at the cost of slight decrease in the MSE performance of the channel estimation, and the saving is even more prominent when the system accommodates more users.

Wireless communication networks rely heavily on channel state information (CSI) to make informed decision for signal processing and network operations. However, the traditional CSI acquisition methods is facing many difficulties: pilot-aided channel training consumes a great deal of channel resources and reduces the opportunities for energy saving, while location-aided channel estimation suffers from inaccurate and insufficient location information. In this paper, we propose a novel channel learning framework, which can tackle these difficulties by inferring unobservable CSI from the observable one. We formulate this framework theoretically and illustrate a special case in which the learnability of the unobservable CSI can be guaranteed. Possible applications of channel learning are then described, including cell selection in multi- tier networks, device discovery for device-to-device (D2D) communications, as well as end-to-end user association for load balancing. We also propose a neuron-network-based algorithm for the cell selection problem in multi-tier networks. The performance of this algorithm is evaluated using geometry-based stochastic channel model (GSCM). In settings with 5 small cells, the average cell-selection accuracy is 73% - only an 3.9% loss compared with a location-aided algorithm which requires genuine location information.

This paper presents the design and implementation of signaling splitting scheme in hyper-cellular network on a software defined radio platform. Hyper-cellular network is a novel architecture of future mobile communication systems in which signaling and data are decoupled at the air interface to mitigate the signaling overhead and allow energy efficient operation of base stations. On an open source software defined radio platform, OpenBTS, we investigate the feasibility of signaling splitting for GSM protocol and implement a novel system which can prove the proposed concept. Standard GSM handsets can camp on the network with the help of signaling base station, and data base station will be appointed to handle phone calls on demand. Our work initiates the systematic approach to study hyper-cellular concept in real wireless environment with both software and hardware implementations.