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To support the application of IoT and smart city, high data-rate wireless transmission is required. To meet the demand of high data-rate, the techniques of multiple antennas and mobile edge computing (MEC) networks have been proposed in order to enhance the data transmission rate significantly. However, there still exist lots of challenges array signal processing assisted MEC networks. In this paper, we propose an intelligent framework of offloading strategy for MEC networks assisted by array signal processing, where one user with multiple antennas has some computational tasks. These tasks can be computed by the user itself which however has limited computational capability, or computed by the nearby computational access points (CAPs) which has a powerful computational capability at the cost of wireless transmission. We consider the system cost by jointly taking into account the computational price, the energy consumption and the latency. By minimizing the system cost, we propose an intelligent offloading strategy based on ant colony optimization (ACO) algorithm, where the ants randomly visit the CAPs in order to obtain the final results. To further enhance the MEC network performance, the array signal processing is utilized at the user, where either the maximum ratio transmission (MRT) or selection combining (SC) is used to assist the data transmission from the user to CAPs. Simulation results with MRT and SC are finally demonstrated to verify the effectiveness of the proposed ACO-based offloading strategy and array signal processing schemes. INDEX TERMS Array signal processing, mobile edge computing, IoT, smart city.

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In this paper, we study an intelligent secure communication scheme for cognitive networks with multiple primary transmit power, where a secondary Alice transmits its secrecy data to a secondary Bob threatened by a secondary attacker. The secondary nodes limit their transmit power among multiple levels, in order to maintain the quality of service of the primary networks. The attacker can work in an eavesdropping, spoofing, jamming or silent mode, which can be viewed as the action in the traditional Qlearning algorithm. On the other hand, the system can adaptively choose the transmit power level among multiple ones to suppress the intelligent attacker, which can be viewed as the status of Q-learning algorithm. Accordingly, we firstly formulate this secure communication problem as a static secure communication game with Nash equilibrium (NE) between the main links and attacker, and then employ the Q-learning algorithm to select the transmit power level. Simulation results are finally demonstrated to verify that the intelligent attacker can be effectively suppressed by the proposed studies in this paper. INDEX TERMS Intelligent secure communication, Q-learning algorithm, Nash equilibrium.

This paper investigates a cache-aided mobile edge computing (MEC) network, where the source offloads the computation task to multiple destinations with computation capacity, with the help of a cache-aided relay. For the proposed cache-aided MEC networks, two destination selection criteria have been proposed to maximize the computation capacity of the selected destination, the channel gain of relay link and the channel gain of direct link, respectively. Similarly, three destination selection criteria have been proposed for the cache-free MEC networks based on the computation capacities of destinations and the channel gains of transmission links, respectively. To evaluate the system performance regarding the latency constraint, we provide the outage probability for the proposed network which is defined based on the transmission-plus-computation time. Our analysis suggests that caching can significantly alleviate the impact of increasing the size of computation task, since only half of the transmission time of cache-free network is required. However, the cache-aided network can not fully exploit the signal from both direct and relay links, thus the improvement by caching is less significant in the high signal-to-noise ratio (SNR) region, compared with the cache-free network employing the destination with maximal channel gain of direct link. Numerical results are given to validate our analysis.