This paper provides a method to build a deep learning image coding system based on inverse problem, choosing a suitable measurement operator to reduce the amount of information transmitted at the sender, and reconstructing the original image by tackling the inverse problem at the receiver. Unlike most compressed sensing (CS) methods, the proposed coding scheme does not rely on sparsity but uses the structural priors of the generative adversarial networks (GAN) to solve the inverse problem. The proposed model trains the GAN to learn a mapping from the latent space to the sample space formed by correlated images on the cloud. Then the measurements are used to localize the optimal latent variable in the representation space which corresponding to the original image in the sample space. The proposed method encodes and transmits the measurements instead of the original image, which greatly reduces the cost of transmission while ensuring the quality of the reconstructed the image at high compression ratios. To the best of our knowledge, this is the first time to introduce the GAN-based inverse problem in the field of the deep image coding area. The experimental results show that the visual quality of the images generated by the proposed scheme is better than the traditional encoding scheme JPEG2000. Especially in the case of extremely high compression ratios, the proposed scheme can still maintain good performance.

Caching high-frequency reuse contents at the edge servers in the mobile edge computing (MEC) network omits the part of backhaul transmission and further releases the pressure of data traffic. However, how to efficiently decide the caching contents for edge servers is still an open problem, which refers to the cache capacity of edge servers, the popularity of each content, and the wireless channel quality during transmission. In this paper, we discuss the influence of unknown user density and popularity of content on the cache placement solution at the edge server. Specifically, towards the implementation of the cache placement solution in the practical network, there are two problems needing to be solved. First, the estimation of unknown users’ preference needs a huge amount of records of users’ previous requests. Second, the overlapping serving regions among edge servers cause the wrong estimation of users’ preference, which hinders the individual decision of caching placement. To address the first issue, we propose a learning-based solution to adaptively optimize the cache placement policy without any previous knowledge of the user density and the popularity of the contents. We develop the extended multi-armed bandit (Extended MAB), which combines the generalized global bandit (GGB) and Standard Multi-armed bandit (MAB), to iteratively estimate both a global parameter, i.e., the user density, and individual parameters, i.e., the popularity of each content. For the second problem, a multi-agent Extended MAB based solution is presented to avoid the mis-estimation of parameters and achieve the decentralized cache placement policy. The proposed solution determines the primary time slot and secondary time slot for each edge server. The edge servers estimate expected satisfied user number of caching a content with the overlap information and determine the cache placement solution. The proposed strategies are proven to achieve the bounded regret according to the mathematical analysis. Extensive simulations verify the optimality of the proposed strategies when comparing with baselines.

Cloud VR (Virtual Reality) is a VR scheme based on cloud computing, which can reduce the computing burden on terminal equipment. It uses edge computing for adaptive streaming to minimize the response latency and bandwidth consumption. However, traditional adaptive streaming approaches based on preprocessing have some limitations. In this paper, we proposed a novel adaptive Cloud VR system with real-time processing. We design and implement a GPU acceleration scheme to perform efficient projection and coding, which makes the computing latency acceptable. The scheme is further extended to pipeline execution with simple orientation prediction to support higher frame rate. The real-time processing not only reduces the storage size, but also eliminates the drawbacks of pre-generating limited video versions. According to the experimental results, our scheme can provide a VR stream matching user viewport more precisely. Through optimized GPU algorithm, we shorten the processing time to 1/10 of the original. Compared to classic pyramid projection scheme, our system effectively reduces the average orientation deviation by 90.23%, thus provides more robust service.

Joint pushing and caching are commonly considered an effective way to adapt to tidal effects in networks. However, the problem of how to precisely predict users' future requests and push or cache the proper content remains to be solved. In this paper, we investigate a joint pushing and caching policy in a general mobile edge computing (MEC) network with multiuser and multicast data. We formulate the joint pushing and caching problem as an infinite-horizon average-cost Markov decision process (MDP). Our aim is not only to maximize bandwidth utilization but also to decrease the total quantity of data transmitted. Then, a joint pushing and caching policy based on hierarchical reinforcement learning (HRL) is proposed, which considers both long-term file popularity and short-term temporal correlations of user requests to fully utilize bandwidth. To address the curse of dimensionality, we apply a divide-and-conquer strategy to decompose the joint base station and user cache optimization problem into two subproblems: the user cache optimization subproblem and the base station cache optimization subproblem. We apply value function approximation Q-learning and a deep Q-network (DQN) to solve these two subproblems. Furthermore, we provide some insights into the design of deep reinforcement learning in network caching. The simulation results show that the proposed policy can learn content popularity very well and predict users' future demands precisely. Our approach outperforms existing schemes on various parameters including the base station cache size, the number of users and the total number of files in multiple scenarios.

This paper proposes a novel symbol-level combining hybrid automatic repeat request (HARQ) scheme based on analog chaotic code and named HARQ-Chaotic. The transmitter of HARQ-Chaotic adopts analog chaotic code to encode Quadrature Amplitude Modulation (QAM) symbols of retransmission packets to combat fading and noise. As the analog chaotic code can only handle sources with amplitudes in the range of [-0.5, 0.5], QAM symbols must be scaled into this range. We derived the optimal scaling factor through theoretical analysis. A joint algorithm combining with novel soft chaotic decoder and novel soft QAM demapper is proposed for the receiver to enhance the performance of the whole communication system. We implemented HARQ-Chaotic with LDPC codes and 16-QAM/64-QAM to carry out simulations in both AWGN channels and multipath fading channels. Massive simulation results demonstrate that the proposed HARQ-Chaotic has 1dB-4dB gain over traditional HARQ-Chase combining (HARQ-CC) scheme in block error rate (BLER) performance.