With the development of intelligent connected vehicles (ICVs), there emerge many new services and applications which involve intensive computation. To support the intensive computation in vehicle-to-everything (V2X) communication system, the framework of edge computing networks has been proposed, which exploits the computation ability of edge nodes at the cost of wireless transmission. Hence, it is of vital importance to predict the wireless channel parameters, which can help schedule the system resource management and optimize the system performance in advance. To fulfil this challenge, this paper proposes a novel prediction model based on long short-term memory (LSTM) network, which is powerful in capturing valuable information in the sequence and hence is good at analyzing the spatio-temporal correlation in the channel parameters. To validate the proposed model, we conduct extensive simulations to show that the proposed model is quite effective in the channel prediction. In particular, the proposed model can outperform the conventional ones substantially.INDEX TERMS Vehicular network, edge computing, channel prediction, LSTM network.
I. INTRODUCTION
The fifth generation (5G) wireless communication systems promise to provide massive connectivity over the limited available spectrum. Various new transmission paradigms such as non-orthogonal multiple access (NOMA) and cognitive radio (CR) have emerged as potential 5G enabling technologies. These techniques offer high spectral efficiency by allowing multiple users to communicate on the same frequency channel, simultaneously. A combination of both techniques may further enhance the performance of the system. This work aims to maximize the achievable rate of a multi-user multi-channel NOMA based CR system. We propose an efficient user pairing, channel assignment and power optimization technique for the secondary users while the performance of primary users is guaranteed through interference temperature limits. The results show that, at small values of the power budget or high interference threshold, optimizing channel allocation and user pairing proves to be more beneficial than optimal power allocation to the user pairs. The proposed joint optimization technique provides promising results for all values of the power budget, interference threshold and rate requirement of the communicating users.
With the deployment and commercialization of the fifth-generation (5G) mobile communication network, the access nodes and data volume of wireless network show a massive and blowout growth trend. Taking beyond 5G (B5G) edge intelligent network as the research object, based on the deep integration of storage / computing and communication, this paper focuses on the theory and key technology of system intelligent transmission, so as to effectively support the related applications of B5G edge intelligent network in the future. This paper analyzes the research status of data storage, studies the real field distributed storage computing system, and designs the corresponding flashback shift code and error correction scheme with low storage space overhead.
In this paper, we present a novel approach for studying Boolean function in a graph-theoretic perspective. In particular, we first transform a Boolean function f of n variables into an induced subgraph H of the n -dimensional hypercube, and then, we show the properties of linearly separable Boolean functions on the basis of the analysis of the structure of H . We define a new class of graphs, called hyperstar, and prove that the induced subgraph H of any linearly separable Boolean function f is a hyperstar. The proposal of hyperstar helps us uncover a number of fundamental properties of linearly separable Boolean functions in this paper.
The effects of hardware impairments with imperfect channel estimation for cache-enabled UAV networks are studied in this paper. The effects of the setup parameters, such as the number of cached or relaying nodes, the hardware impairment factor, the channel estimation error, and the transmission SNR, on the outage probability are present by deriving the exact closed-form expressions on outage probability. Also, the asymptotic results are present when the transmission SNR is large enough. From the asymptotic analysis, we can see that there is an error floor introduced by the hardware impairments and imperfect channel estimation. Specifically, the error floor is dependent on the channel estimation error factor, the hardware impairment distortion factor, and the QoS requirements. And the diversity order of the error floor for the cached/relaying links is equal to the number of cached/relaying UAV nodes.
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