With the rapid adoption of the Internet of Things, it is necessary to go beyond fifth-generation applications and apply stringent high reliability and low latency requirements, closely related to strict delay demands. These requirements support massive network connectivity for multiple Internet of Things devices. Hence, in this paper, we optimize energy efficiency and achieve quality-of-service requirements by mitigating co-channel interference, performing efficient power control of transmitters, and harvesting energy using timeslot exchanges. Due to a nonconvex optimization problem, we propose an iterative algorithm for power allocation and time slot interchange to reduce the computational complexity. To achieve a high degree of ultra-reliability and low latency with quality-of-service-aware instantaneous reward under massive connectivity, we efficiently employ multiagent reinforcement learning by addressing the intelligent resource management problem via a novel Double Deep Q Network. The network prioritizes experience replay to exploit the best policy and maximize accumulative rewards. It also learns the optimal policy and enhances learning efficiency by maximizing its reward function to make decisions with high intelligence and guarantee strict ultra-reliability and low latency. The simulation result shows that the Double Deep Q Network with prioritized experience replay can guarantee stringent ultra-reliability and low latency. As a result, the cochannel interference between transmission links and the high-power consumption density associated with the massive connectivity of the Internet of Things devices are mitigated.INDEX TERMS Internet of things, beyond fifth-generation, energy efficiency, massive connectivity.

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Distributed arithmetic (DA) is an efficient look-up table (LUT) based approach. The throughput of DA based implementation is limited by the LUT size. This paper presents two highthroughput architectures (Type I and II) of non-pipelined DA based least-mean-square (LMS) adaptive filters (ADFs) using two's complement (TC) and offset-binary coding (OBC) respectively. We formulate the LMS algorithm using the steepest descent approach with possible extension to its power-normalized LMS version and followed by its convergence properties. The coefficient update equation of LMS algorithm is then transformed via TC DA and OBC DA to design and develop non-pipelined architectures of ADFs. The proposed structures employ the LUT pre-decomposition technique to increase the throughput performance. It enables the same mapping scheme for concurrent update of the decomposed LUTs. An efficient fixedpoint quantization model for the evaluation of proposed structures from a realistic point-of-view is also presented. It is found that Type II structure provides higher throughput than Type I structure at the expense of slow convergence rate with almost the same steady-state mean square error. Unlike existing non-pipelined LMS ADFs, the proposed structures offer very high throughput performance, especially with large order DA base units. Furthermore, they are capable of performing less number of additions in every filter cycle. Based on the simulation results, it is found that 256 th order filter with 8 th order DA base unit using Type I structure provides 9.41× higher throughput while Type II structure provides 16.68× higher throughput as compared to the best existing design. Synthesis results show that 32 nd order filter with 8 th order DA base unit using Type I structure achieves 38.76% less minimum sampling period (MSP), occupies 28.62% more area, consumes 67.18% more power, utilizes 49.06% more slice LUTs and 3.31% more flip-flops (FFs), whereas Type II structure achieves 51.25% less MSP, occupies 21.42% more area, consumes 47.84% more power, utilizes 29.10% more slice LUTs and 1.47% fewer FFs as compared to the best existing design.INDEX TERMS Adaptive filter (ADF), distributed arithmetic (DA), finite-impulse response (FIR), least mean square (LMS), look-up table (LUT).

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A miniaturized frequency agile multi-band antenna based on BST varactors is presented. The radiation patterns and frequency responses of this antenna are characterized. The measured tunability was 2.1%, 9.1%, and 6.6% for the first, second, and third band respectively. The lowest resonant frequency corresponds to an antenna size of . The size reduction of the antenna was achieved by employing the novel antenna structure and the thin film BST varactors. The measured -3 dB bandwidth was between 3.1% and 7.4% for all bands.