Embedded Files
Robust energy harvesting
References
A. Azarbahram, O. L´opez, P. Popovski, M. Latva-Aho, "On the Deployment of Multiple Radio Stripes for Large-Scale Near-Field RF Wireless Power Transfer", available at https://arxiv.org/pdf/2508.21640.
A. Khattak, A. Azarbahram, D. Kumar, M. Latva-aho, O. L´opez, "End-to-End Joint Waveform and Beamforming Optimization for RF Wireless Power Transfer with Hybrid Transmit Architecture and Non-Linear Energy Harvesters", available at https://d197for5662m48.cloudfront.net/documents/publicationstatus/274526/preprint_pdf/c31860f35e4d3c9d53b08bc416e53589.pdf.
On the deployment of multiple radio stripes for large-scale near field RF wireless power transfer
We formulate a joint clustering and radio stripe deployment problem that aims to maximize the minimum received power across all hotspots. To address the complexity, we decouple the problem into two stages: i) clustering for assigning radio stripes to hotspots based on their spatial positions and near-field propagation characteristics, and ii) antenna element placement optimization. In particular, we propose four radio stripe deployment algorithms. Two are based on general successive convex approximation (SCA) and signomial programming (SGP) methods. The other two are shape-constrained solutions where antenna elements are arranged along either straight lines or regular polygons, enabling simpler deployment.
The figures on the left illustrate the minimum received power versus f and radio stripe length. The proposed deployments consistently outperform the benchmarks under both MRT- and SDP-based precoders, with the polygon-shaped deployment yielding the best performance by a small margin. Among the proposed methods, the line-shaped deployment performs worst, as its longer effective distances reduce the 3D beam focusing capability. While the SCA- and SGP-based approaches are not limited to predefined shapes, their reliance on approximations (and the alternating structure of SCA) restricts optimization flexibility, leading to worse performance compared to the polygon-shaped deployment, which benefits from a more direct yet geometrically constrained optimization.
The figures on the left show the impact of b on system performance. Increasing b generally reduces the minimum received power, as the gain becomes more focused along the boresight and coverage for off-axis users declines. Interestingly, the line shaped deployment benefits from a larger b due to its wider spatial span and angular diversity, while other deployments maintain similar orientations and cannot exploit this effect. This trend, however, is scenario-dependent: in dense singlecluster deployments, higher b could enhance energy focusing. Thus, the optimal choice of b depends on the deployment and user configuration and requires further investigation.
End-to-end joint waveform and beamforming optimization for RF wireless power transfer and hybrid transmit architecture and non-energy harvesters
We consider a fully connected hybrid multi-antenna transmit architecture that aims to charge non-linear energy harvesters. First, we present a mathematical framework to determine the harvested power from multi-tone signal transmissions and the system’s power consumption. Then, we formulate a joint waveform and analog beamforming design problem to minimize system’s power consumption and fulfill user’s charging needs. With this in place, and due to the problem high-complexity, we propose a particle swarm optimization (PSO)-based solution. Moreover, we also model the problem as a Markov decision process and propose a solution based on deep deterministic policy gradient (DDPG). Numerical results demonstrate that the proposed algorithms converge to suboptimal solutions.
The figure on the top shows Pc versus B for different N, comparing the PSO-JWB and DDPG-JWB algorithms. It can be noticed that higher B for PSs and higher N enhances performance by offering more control over the transmitted signal’s direction and shape, resulting in more harvested power with reduced power consumption. Additionally, it can be observed that the PSO algorithm performs better in terms of power consumption reduction compared to the DDPG algorithm. This indicates that PSO is more effective in optimizing power efficiency while managing the system’s scalability.
The above figure illustrates Pc increasing with number of RF chains for different number of users, comparing the PSOJWB and DDPG-JWB algorithms. It can be also observed that increasing number of RF chains leads to increasing Pc. Moreover, the rate of increase becomes more pronounced as the number of users grows, indicating a compounded effect of multiple users and RF chains on power consumption. This trend highlights the importance of balancing system scalability and power efficiency in practical deployments. When comparing both algorithms, it is observed that while the trend of increasing Pc with the number of RF chains and users is consistent, the PSO algorithm performs better in terms of Pc reduction. This suggests that PSO is more effective in managing power efficiency as the system scales.
Page updated
Google Sites
Report abuse