This paper introduces a Medium Access Control (MAC) protocol for Underwater Acoustic Network (UAN) where one of the transmitter nodes equipped with a Deep Reinforcement Learning (DRL) agent learns the communication environment and adapts its transmission policy to maximize the network throughput. In contrast to a radio frequency (RF) wireless network where the propagation delay is ignored, the UAN experiences significant propagation delays in both transmission from source to sink and feedback from sink to source. When co-existing with a Time Division Multiple Access (TDMA) node or a slotted Aloha node, the DRL node learns to take advantage of the difference in propagation delays and occupy the spare time slots of the network to achieve minimum collision in the UAN environment. Computer simulation results demonstrate the performance gain of using the DRL agent in both synchronous and asynchronous timing models. CCS CONCEPTS• Networks → Network protocol design; Link-layer protocols.

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This paper considers power control problem based on Nash equilibrium (NE) to eliminate interference in multi-cell device-to-device (D2D) network. The power control problem is modeled as a non-cooperative game model, and a user residual energy factor is introduced in the formulation. Based on the proof of the existence and uniqueness of Nash equilibrium, a distributed iterative game algorithm is proposed to realize power control. Simulation results show that the proposed algorithm can converge to Nash equilibrium quickly, and obtain a better equilibrium income by adjusting the residual energy factor.

AES has been one of the most popular encryption and decryption algorithms for data security applications. At the same time, data randomization (or "homogeneous") technology was applied to reduce the bit error rate (BER) of MLC and TLC flash memory. Here, AES algorithm was found efficient to replace the orthogonal polynomials which normally carry out homogeneous function by scrambling data. This paper put forward a novel hardware architecture providing both homogeneous and data encryption/decryption functions concurrently by an embedded AES hardware engine while getting rid of randomization engine with Linear Feedback Shift Register (LFSR). It made a flash controller simple and reduced the die size because the independent homogeneous hard engine is no longer necessary for a flash memory system, in which AES security algorithm embedded. Finally a SSD controller designed in this architecture was silicon proven.

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Two kinds of lattice-basis reduction precoding schemes based on successive interference cancellation are proposed. The successive interference cancellation (SIC) structure can be obtained by either orthogonal and a right triangular matrix (QR) decomposition, or the Vertical Bell Labs Layered Space Time (VBLAST) algorithm which provides optimal user ordering. Moreover, the extended channel approach is applied to the proposed SICbased schemes. Simulation results show that the proposed schemes can achieve comparable BER performance to vector precoding (VP).