The design, fabrication, and characterization of the microstrip circular antenna arrays were presented. The proposed antennas were designed for single band at 2.45 GHz and dual bands at 3.3 - 3.6 and 5.0 - 6.0 GHz to support WLAN/WiMAX applications. The proposed single and dual band antennas showed omnidirectional radiation pattern with the gain values of 3.5 dBi at 2.45 GHz, 4.0 dBi at 3.45 GHz, and 3.3 dBi at 5.5 GHz. The dual band antenna array was placed on both top and bottom layers to obtain the desired antenna characteristics. The proposed double-sided dual band antenna provides omnidirectional radiation pattern with high gain.
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.
In multiband transmitters where two or more radios share the same power amplifier (PA), interferences can usually be kept below an acceptable level with the help of multi-band digital predistortion (DPD) techniques. This article reports on novel frequency scenarios in dual-band transmitters where additional cross-modulation products not reported before, are found to overlap and interfer with both the lower and upper-band signals. These cross-modulation products arise in-band when the frequency separation between the bands is close to a sub-harmonic of the band frequencies. The leading nonlinear cross-modulation products are identified for various frequency plans and found to be of increasing order as the frequency interval between the two carriers shrinks. A PA model featuring the four leading cross-modulation products is proposed and found to exhibit an accuracy close to the measurement noise floor when applied to three different PAs. The additional in-band distortions generated by the PA under such operation cannot be filtered or compensated using conventional dual-band DPD. When using the proposed model architecture with the indirect-learning predistortion methodology for linearizing dual-band PAs, DPD is found experimentally to improve the ACPR and EVM by 25 dBc and 11 percentage points, respectively. These and the other experimental results reported indicate that the proposed linearization algorithm provides a suitable method with high efficacy for these special dual-band scenarios.
A novel elliptical patch resonator for a compact bandpass filter with tunable bandwidth is presented. This bandpass filter has the advantage of great flexibility in which the center frequency can be changed easily. The bandwidth of this filter can be modified by simply changing one variable, and this makes the proposed design unique. The order of the elliptical patch resonator can be increased, and three types of different orders of the same design are compared. The proposed filter can be used for future compact advanced wireless communication systems.
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).
Photovoltaic (PV) systems are one of the promising renewable energy sources that have many industrial applications; one of them is water pumping systems. This paper proposes a new application of a PV system for water pumping using a three-phase induction motor while maximizing the daily quantity of water pumped while considering maximizing both the efficiency of the three-phase induction motor and the harvested power from the PV system. This harvesting is performed through maximum power point tracking (MPPT) of the PV system. The proposed technique is applied to a PV-powered 3 phase induction motor water pumping system (PV-IMWPS) at any operating point. Firstly, an analytical approach is offered to find the optimal firing pattern of the inverter (V-F) for the motor through optimal flux control. This flux control is presented for maximizing the pump flow rate while achieving MPPT for the PV system and maximum efficiency of the motor at any irradiance and temperature. The provided analytical optimal flux control is compared to a fixed flux one to ascertain its effectiveness. The obtained feature of the suggested optimal flux control validates a significant improvement in the system performances, including the daily pumped quantity, motor power factor, and system efficiency. Then converting the data from the first analytical step into an intelligent approach using an adaptive neuro-fuzzy inference system (ANFIS). This ANFIS is trained offline with the input (irradiance and temperature) while the output is the inverter pattern to enhance the performance of the proposed pumping system, PV-IMWPS.
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