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Distributed machine learning at the network edge has emerged as a promising new paradigm. Various machine learning (ML) technologies will distill Artificial Intelligence (AI) from enormous mobile data to automate future wireless networking and a wide range of Internet-of-Things (IoT) applications. In distributed edge learning, multiple edge devices train a common learning model collaboratively without sending their raw data to a central server, which not only helps to preserve data privacy but also reduces network traffic. However, distributed edge training and edge inference typically still require extensive communications among devices and servers connected by wireless links. As a result, the salient features of wireless networks, including interference and channels’ heterogeneity, time-variability, and unreliability, have significant impacts on the learning performance.

In cooperative massive multiple-input multiple-output (MIMO) networks, channel statistics based adaptive feedback can considerably reduce feedback overhead for channel state information (CSI) acquisition as well as backhaul overhead for CSI sharing. When regularized zero-forcing beamforming is considered to coordinate interference with the skewed codebook, average sum rate depends on not only regularization parameters, but also quantization error impacts of serving and interfering channels according to their channel covariance matrices. To improve the average sum rate by effectively controlling the desired signal strength and the interference cancellation, we propose joint optimization of regularization parameters and feedback bit allocation by leveraging adaptive feedback according to the channel covariance matrices.

In this paper, we consider a K-user multiple-input and single-output interference channel and propose a machine learning-based beamforming design. To circumvent the difficulties of beamforming design in interference channels, we consider a beamforming scheme that combines two well-known beamforming schemes, which are maximum ratio transmission and zero-forcing, with one of finite combining factors. However, this problem is still NP-hard, which requires brute-force search for optimal beamforming, so we adopt the machine learning to find the optimal beamforming vectors. Our machine learning design exploits a deep neural network structure, whose input nodes take channel vectors with transmit power, while the output nodes return the combining factors for the transmitters' beamforming. Our numerical results show that our proposed machine learning-based beamforming achieves a sum rate more than 99% of that with the best combining factors numerically found with brute-force search.

Learning continually without forgetting might be one of the ultimate goals for building artificial intelligence (AI). However, unless there are enough resources equipped, forgetting knowledge acquired in the past is inevitable. Then, we can naturally pose a fundamental question about how to control what knowledge and how much of it to forget to improve the overall accuracy. To give a clear answer to it, we propose a novel trainable network termed homeostatic meta-model. The proposed neuromorphic framework is a natural extension of the conventional concept Synaptic Plasticity (SP) for further optimizing the accuracy of continual learning. In the preceding works on SP and its variations, though they seek important network parameters for structural regularization, they care less about the intensity of regularization (IoR). Per contra, this work reveals that a careful selection of IoR during continual training can remarkably improve the accuracy of tasks. The proposed method balances IoR between newly learned knowledge and the previously-acquired ones rather than biasing it to a specific task or evenly balancing. To obtain effective and optimal IoRs for the real-time continual learning circumstances, we propose a homeostasis-inspired meta learning architecture. The proposed meta-model automatically controls the IoRs by capturing important parameters from the previous tasks and the current learning direction. We provide experimental results considering various types of continual learning tasks showing that the proposed method notably outperforms the conventional methods in terms of learning accuracy and knowledge forgetting. We also show that the proposed method is relatively stable and robust compared to the existing SP-based methods. Furthermore, the IoR generated by our model interestingly appears to be proactively controlled within a specific range, which resembles a negative feedback mechanism of homeostasis in synapses.

In conventional maximum distance separable (MDS) coded caching systems, storage-efficient caching strategy which minimizes unnecessarily redundant data is believed to be optimal. However, the conventional storage efficient caching strategy may not be optimal because redundantly stored data can be utilized as diversity sources to combat wireless channel impairments due to channel fading. This paper explores how MDS coded caching facilitates coordinated maximum ratio transmission (MRT) in cache enabled wireless networks. Optimizing the length of MDS coded sequence and the amount of redundant cached data, we demonstrate the gain of MDS coded caching leveraged by MRT outperforms conventional uncoded caching schemes. Keywords: Wireless caching, Coded caching, Maximum distance separable code, Maximum ratio transmission

A two-stage precoder is widely considered in frequency division duplex massive multiple-input and multiple-output (MIMO) systems to resolve the channel feedback overhead problem. In massive MIMO systems, users on a network can be divided into several user groups of similar spatial antenna correlations. Using the two-stage precoder, the outer precoder reduces the channel dimensions mitigating inter-group interferences at the first stage, while the inner precoder eliminates the smaller dimensions of intra-group interferences at the second stage. In this case, the dimension of effective channel reduced by outer precoder is important as it leverages the inter-group interference, the intra-group interference, and the performance loss from the quantized channel feedback. In this paper, we propose the machine learning framework to find the optimal dimensions reduced by the outer precoder that maximizes the average sum rate, where the original problem is an NP-hard problem. Our machine learning framework considers the deep neural network, where the inputs are channel statistics, and the outputs are the effective channel dimensions after outer precoding. The numerical result shows that our proposed machine learning-based dimension optimization achieves the average sum rate comparable to the optimal performance using brute-forcing searching, which is not feasible in practice.