Internet of Things (IoT) task offloading involves conflicting objectives of energy consumption and delay. We formulate a bi-objective optimization model for a multi-UAV-assisted mobile edge computing (MEC) system, jointly optimizing resource allocation, task offloading decisions, and UAV deployment to minimize both energy consumption and delay. As the number of offloaded tasks increases, finding feasible solutions becomes more challenging. To address this, we develop an Information Feedback Evolutionary Algorithm (IFEA) that leverages feedback driven guidance to enhance the diversity and convergence of the Pareto front (PF). Simulation results show that IFEA achieves better trade offs than the other four multi-objective algorithms.

Non-orthogonal multiple access (NOMA) is one of the key technology of 5G system to enhance the capacity and spectral efficiency. However, the fast changing wireless channel makes the ideal power allocation be a challenging task in practice. A chaotic shape-forming filter (CSF) based NOMA system is proposed and a deep learning (DL) based method is used to estimate the user channel gains for power allocation in this paper. The contributions of the work lie in: 1) The CSF and the corresponding matched filter (MF) enhance the noise resistance performance of the NOMA system. 2) The autocorrelation function (ACF) of the superposition signal composed by the chaotic signals generated by the CSF of different users is proved to be the same as the ACF of the base function of the CSF, which is an interesting and important base point to use the previous theoretical result for blind channel identification. 3) For the flat fading channel assumed in the NOMA system, an analytical formula of the channel gain, sole parameter in this type of the channel, with respect to the ACFs of the received signal and the base function of the CSF is derived for the first time, which provides the underlying mechanism to use deep neural network (DNN) for channel gain prediction. Simulation results show that 1) the chaos-based NOMA using the CSF and corresponding MF achieves better performance as compared to the conventional binary phase shift keying NOMA (BPSK-NOMA) with root-raised cosine (RRC) filter; 2) the DNN with simplified structure and the less input neuron as compared to that for the frequency selective fading channel is capable of achieving superior performance for both blind channel identification and NOMA system in the sense of low bit error rate (BER).

Abstract It was reported recently that chaos properties could be used to relieve inter‐symbol interference caused by multipath propagation in chaos‐based wireless communication system. Although there exists the optimal decoding threshold to theoretically eliminate the inter‐symbol interference, its practical implementation is still a challenge due to the strong requirement to know the future symbols to be transmitted. To tackle this almost ‘impossible’ task, convolutional neural network with deep learning structure is proposed to predict future symbols based on the received signal, to further reduce inter‐symbol interference and to obtain a better bit error rate performance. Due to the short time predictability of chaotic signal, the proposed method is able to predict short‐term future symbols and get a better threshold suitable for the time‐variant channel. The analytical bit error rate of the proposed method is derived. The contributions of the paper are as follows: firstly, a convolutional neural network with deep learning structure is proposed for the first time to predict the future symbols in the chaos baseband wireless communication system, which does not require much training in this important application; secondly, the future bits predicted by the trained convolutional neural network are used together with the past decoded bits to calculate more accurate decoding threshold compared with the existing methods, yielding a better bit error rate performance. Numerical simulations and experimental results validate the effectiveness of our theory and the superiority of the proposed method.

The inter-symbol interference (ISI) caused by multipath propagation in wireless channel is one of the main reasons for high bit error rate (BER) in wireless communication system. Chaos was proved to be an ideal communication baseband signal because of its special properties, including the corresponding simple matched filter and multipath resistance ability. Although the ISI caused by the multipath is promised to be completely eliminated by using the optimal symbol decoding threshold, the future symbols are needed to be known for calculating the optimal threshold, which is hard to be practically implemented. To deal with such problem, an echo state network (ESN), because of its short-term memory ability, is proposed to predict the future chaotic baseband signal based on the signal received after the matched filter. Thus, the first future symbol is estimated, which is used to improve the accuracy of the decoding threshold in the chaotic baseband wireless communication system (CBWCS) in order to further relieve the effect of ISI. Compared to the sub-optimal decoding threshold considering only past symbols, the improved threshold proposed here considers not only the ISI from past symbols, but also the ISI from one future symbol. The simulation in both the static and time-varying wireless channels are performed, the results show that the BER performance of CBWCS is improved more significantly in multipath channels as compared to single path channel. The experiments based on wireless open-access research platform verify the ISI resistance performance under the practical scenarios. Both simulation and experimental results show the effectiveness and the superiority of the proposed method.