Hybrid energy harvesting from ambient electromagnetic (EM), thermal, kinetic, photovoltaic (PV) and wind is a promising green energy solution for powering IoT devices to achieve battery-less operation. However, existing harvesters face limitations in efficiency, input power range and bandwidth. This paper proposes for the first time a cooperative voltage doubler rectifying topology using hybrid EM-PV energy harvesting to address these challenges. Theoretical analysis indicates that the proposed topology can realize high power conversion efficiencies (PCEs) and considerable efficiency gains (EGs) under hybrid input power excitation and with an extended power range. In addition, a post-matching technique for the proposed topology to extend operating bandwidth is presented for the first time. For validation, single-and wide-band cooperative rectifiers are fabricated and measured. In a room with a light intensity of 232 lux, the single-band cooperative rectifier maintains PCEs higher than 46% at 2.4 GHz with the input power range of 25 dB, while the wide-band one realizes PCEs of over 30% with an ultra-wide bandwidth of 0.8 to 2.4 GHz covering GSM, WCDMA, LTE and WLAN. Compared to existing state-of-the-art works, the implemented cooperative rectifiers achieve the largest input power range and widest bandwidth for hybrid ambient energy harvesting.

5G and future 6G wireless communication systems are pushing the practical limits on the modulation schemes that can be achieved in practice. These higher-level modulation schemes have further exacerbated the challenges in power amplifier (PA) design. One solution to linearly and efficiently amplify these modulation schemes that have high peak-to-average power ratios (PAPRs), is to use dual-input Outphasing PAs (OPAs). However, the output back-off (OBO) range of a conventional OPA is limited. To mitigate this issue, a new OPA based on an asymmetric architecture is proposed. This new asymmetric OPA is shown theoretically that the OBO range can be extended. To validate the proposal, an OPA is designed using two asymmetric sub-PAs and a modified Chireix combiner. The implemented OPA achieves drain efficiencies of 60.6% and 60% at saturation (44.1dBm) and at 8-dB OBO under CW excitation at 2.58 GHz, respectively. Measurement of the OPA using 20-MHz 5G NR signal with 8-dB PAPR yields an average drain efficiency of 58.3% with an adjacent channel power ratio (ACPR) below −45.1 dBc with digital pre-distortion (DPD).

A tradeoff between bandwidth and back-off range is commonly found in the Doherty power amplifier (DPA). This paper proposes an input phase control mechanism together with a cooperative asymmetric out-phased current load modulation technique. The cooperative asymmetric out-phased current load modulation can extend the output back-off range (OBO). The input phase control mechanism together with modified impedance transforming load modulation networks (LMNs) is used to widen the DPA bandwidth. An input coupled-line coupler is implemented to realize this phase requirement. For demonstration purposes, a DPA with an operating frequency from 1.7 GHz to 2.9 GHz and with a 9-dB OBO range is designed and fabricated using GaN HEMT devices. Continuous-wave measurements show that the implemented DPA exhibits a drain efficiency ranging from 54.5% to 75.6% at saturation and from 41% to 50.6% at 9-dB OBO across the operating bandwidth. When excited by a 20-MHz long-term evolution (LTE) signal with a 9-dB peak-to-average power ratio (PAPR), the implemented DPA achieves average drain efficiencies of 41%-53.8% with an adjacent channel leakage ratio (ACLR) better than -48.1 dBc after digital predistortion (DPD).

The parameter adaptation enhanced differential evolution (DE) algorithm has demonstrated promising performance for noiseless optimization. However, its efficiency degrades when confronted with noise in a noisy environment, which makes the fitness comparison for adaptation unreliable. To deal with the issue and improve the performance, this article proposes a fuzzy logic system (FLS)-assisted parameter adaptation for noisy optimization, inspired by the strength of FLS in handling uncertainties. The proposed FLS is fully informed by search feedback from both the objective and solution spaces, as well as their correlation, allowing for a more comprehensive estimation of parameters. Experimental studies confirm the superiority of the proposed method in noisy environments over adaptation methods that solely rely on fitness comparison. The constructed fully informed FLS-assisted noisy DE exhibits state-of-the-art performance compared to other evolutionary algorithms.

With the development of low-power devices, wireless power transfer and wireless energy harvesting techniques become increasingly important. As the critical component, the rectifier is required to maintain good performance under different scenarios. Most existing works focus on the improvement in bandwidth and input power range with a constant load value. However, the impedance of the driven devices cannot be predicted in practical applications, resulting in performance deterioration. Thus, an adaptive signal diversion approach is proposed to diver the injected signal to the parallel high and low load branches with distinct reactance compensation networks. This topology can be applied to both single- and dual-band rectifiers with simplicity and scalability in design. For validation, two rectifiers are designed, fabricated, and measured. The rectifier operating at 2.4 GHz achieves a load range of 0.16 k$\Omega $to 6 k$\Omega $(load variation ratio of 37.5). The dual-band rectifier working at 2.49 GHz and 5.14 GHz achieves load ranges from 0.21 to 5.4 k$\Omega $(load variation ratio of 25.7) and from 0.07 to 4.2 k$\Omega $(load variation ratio of 60), respectively. It can be found that the proposed rectifiers exhibit the largest load variation ratio with the minimum number of diodes and load compared with the state-of-the-art works.

Although a significant advancement of differential evolution (DE) for global optimization has been witnessed in the past two decades, the problems of premature convergence and stagnation are still open questions that hinder the performance. Both are likely to occur on complicated multimodal functions, but the phenomena differ. Premature convergence refers to a rapid loss of population diversity when attracted to a local minimum while stagnation happens even though the population is diverse. To deal with these problems, this article proposes a domain transform (DT) methodology. Different from existing fitness analysis which mainly utilizes the original fitness landscape information, DT yields a transformed fitness landscape with transform operation to the frequency domain and inverse transform operation back to the solution domain, between which the first few highest frequencies are removed. With the deletion operation, the transformed fitness landscape becomes smoother and facilitates the escape from local minima and stagnation on complicated multimodal functions. Simulation results show that DT significantly improves the population successful update rate and population convergence. The constructed DTDE algorithm consequently exhibits remarkable improvements on the baseline algorithm and outperforms several state-of-the-art DE variants. DT has also been extended for noisy optimization and it performs better than the baseline, the classic resampling method, state-of-the-art DE variants, as well as several popular noisy evolutionary optimization algorithms.

Confronting the challenge of replacing or recharging batteries for numerous low-power devices in the Internet of Things, the harvesting of ambient electromagnetic (EM) energy is considered a promising approach. Since EM energy is randomly distributed in the environment, omnidirectional antennas or multibeam antennas are better choices for their large coverages which enable them to collect as much energy as possible. However, omnidirectional antennas usually have low gains resulting in low receiving energy, and most multibeam antennas with high gains require complicated feeding networks. To address these issues, a six-beam antenna without a complex feeding network is proposed in this article. It consists of a triangular patch array layer and a surface waveguide layer. A peak gain of 8.3 dBi, as well as a 3-dB beamwidth of$62\mathbf {\mathrm {^\circ }}$in H-plane and$56\mathbf {\mathrm {^\circ }}$in E-plane have been achieved, affording full azimuthal coverage within a certain elevation angle range. For demonstration, an EM energy harvester is constructed based on the proposed antenna. It can be observed that the implemented device can harvest almost the same amount of EM energy in different horizontal orientations, and acquire multifold DC power in the case of multiple EM transmitters.

With the rapid development of wireless technologies, the electromagnetic energy scattered in the environment becomes one of the most attractive energy sources for low power devices which are highly demanded in various applications. Since the available electromagnetic signals may have different frequencies, the corresponding receiving antenna and rectifier are required to support multi-band operation to obtain as much energy as possible. However, existing multi-band rectifiers usually have a complex structure or unsatisfactory performance. For this issue, a self-matched rectifying structure is proposed. The theoretical analysis of the proposed configuration demonstrates the unique self-matched characteristics, which is important to implement multi-band operation with simplicity in design procedure and structure. For validation, two rectifiers are designed, fabricated and measured. The dual-band rectifier achieves measured conversion efficiencies of 69.37% and 55.28% at 2.38 GHz and 4.99 GHz with 5 dBm input power in actual measurement, respectively. Owing to the self-matched property, a tri-band design operating at 1.78 GHz, 2.35 GHz, and 4.97 GHz can be easily implemented and achieve efficiency of 60.95%, 71.37%, and 54.01% under test, respectively. Compared with the existing works, the proposed rectifier has high simplicity in both design and structure maintaining high conversion efficiencies at different frequencies.

Different from the classic differential evolution (DE) and many of its variants, collective information powered DE (CIPDE) is characterized by the utilization of collective information of population in the mutation and crossover processes of DE. This paper proposes a restart mechanism for CIPDE, to improve its robustness for solving the 100-digit challenge on single objective numerical optimization. Restart based CIPDE (rCIPDE) restarts the population when the unsuccessful update of the population exceeds a threshold value. Simulations on the challenge show that the restart mechanism enhances the performance on five out of the total 10 benchmark functions. According to the competition rule, rCIPDE achieves a total score of 85 (10 marks on each of F1-F7 and F10, 2 marks on F8 and 3 marks on F9).

The wireless power transmission and harvesting techniques is becoming more and more important in industrial applications. A wide operating bandwidth is always expected for these two techniques. However, the existing rectifying structures usually can operate only over a narrow bandwidth. To achieve the wideband and efficient rectifying, the variation of optimum input impedance for the rectifier over the operating band should be as small as possible. For this issue, the voltage doubler configuration is analyzed and proved theoretically to exhibit slowly changing optimum input impedance. Based on this unique property, the simplified real frequency is utilized as the universal design approach together with the theoretical calculation. For demonstration, two rectifiers have been designed, fabricated, and measured for different input power levels. The proposed rectifiers showed the good characteristics in both efficiency and bandwidth. The rectifier applied in the high-power scenario kept conversion efficiency higher than 50% from 0.6 to 3 GHz, which corresponds to a relative bandwidth of 133%. The other rectifier designed for the low-power scenario maintained conversion efficiency higher than 40% within a wide bandwidth of 73.1%. The simulated and measured results agree well with each other, which validates the proposed structure and design methodology.

Differential evolution (DE) is recognized as a simple but powerful algorithm in the family of evolutionary algorithms. Over the past two decades, many advanced DE variants with significantly improved performance have been proposed. However, the variants may only achieve the best performance on a certain type of functions. Moreover, a specific optimizer may not always be suitable for the whole optimization process. To overcome these weaknesses, this paper proposes a multiple variants coordination (MVC) framework with two mechanisms, namely, the multiple variants adaptive selecting mechanism and the multiple variants adaptive solutions preserving mechanisms (MV-APM). In MVC, the evolution process is divided into nonoverlap segments with equal numbers of generations. Each segment includes the learning generations (LGs) and executing generations (EGs). In LG, all the candidate DE optimizers are utilized independently. The best performing optimizer is determined and then utilized in EG in the same segment. Furthermore, MV-APM maintains the population by adaptively preserving promising solutions generated by multiple optimizers. Numerical experiments on the CEC2014 benchmark suit show that the proposed MVC framework can significantly improve the performance of the baseline algorithms and the resulted algorithm significantly outperform the start-of-the-art and up-to-date DEs. Moreover, as a general framework, MVC can also be applied to coordinate multiple improved DE variants to further enhance their performance.

Differential evolution (DE) is a simple yet powerful evolutionary algorithm for both single objective and multiobjective optimizations (MOPs). In nature, good parents are more likely to produce good offspring, because genes from good individuals propagate throughout the population. Inspired by this phenomenon, a two-step subpopulation strategy is proposed, in which individuals in the current population are sorted based on evaluation metrics, and are divided into superior and inferior subpopulations. The inferior subpopulation evolves to generate offspring. If the generated offspring has better evaluation metric values than individuals in the superior subpopulation, they will replace the latter and be used as vectors for mutation strategies. The proposed strategy is incorporated into several advanced DE variants for both single-objective optimization (SOP) and MOPs to verify its effectiveness. Experiments are conducted on 25 single objective, 5 bi-objective, and 4 tri-objective Deb, Thiele, Laumanns and Zitzler (DTLZ) benchmark problems. Results indicate that the proposed subpopulation strategy is capable of improving the performance of both single objective and multiobjective algorithms. The application of the proposed approach is demonstrated by solving a microwave circuit design problem with stringent requirements. The better performance achieved by the proposed approach in quality of solutions, convergence rate, and diversity is verified by the performance comparison with competitive optimization algorithms in the literature.