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Radio Stripes
References:
- A. Azarbahram, O. López, P. Popovski, and M. Latva-aho, "On the Deployment of Multiple Radio Stripes for Large-Scale Near-Field RF Wireless Power Transfer" in arXiv preprint arXiv:2508.21640 (submitted to IEEE Transactions on Wireless Communications), 2025.
- A. Azarbahram, O. López, P. Popovski, and M. Latva-aho, "On the Deployment of Multiple Radio Stripes for Large-Scale Near-Field RF Wireless Power Transfer" in arXiv preprint arXiv:2508.21640 (submitted to IEEE Transactions on Wireless Communications), 2025.
We addressed how to deploy radio stripes indoors to effectively power spatial energy hotspots in near-field LOS environments. A typical scenario is a restaurant wherein one may expect charging hotspots around the user tables. We proposed hotspot-aware clustering with strong near-field energy concentration in areas with dense or persistent power demand.
The deployment strategies allowed for far or less flexibility in shaping the radio stripes: from free-form shapes to straight-line and polygonal layouts. All of them converged, though with different characteristics. The free-form deployment solutions required more iterations, while the structured deployment designs based on straight-line and polygonal layouts converged more quickly and with significantly lower computational burden, making them attractive from a practical deployment standpoint.
Across a broad range of carrier frequencies and stripe lengths, optimized radio-stripe placement noticeably improved the minimum received power level compared to benchmark configurations. The geometry of the stripe layout played a key role in shaping the power delivery performance. Polygon-shaped configurations provided the strongest results overall by better surrounding hotspot regions and exploiting near-field focusing effects. In contrast, straight-line deployments displayed advantageous behavior under high boresight-gain conditions, where the increased aperture and broader angular footprint enabled more uniform coverage and improved hotspot illumination. These findings highlight that optimal deployment geometry depends on the antenna radiation characteristics and beam directivity, and that simple structured layouts can approach the performance of more complex optimization-based solutions while retaining ease of implementation.
Movable Antennas
References:
- O. Rosabal, O. López, M. Renzo, R. Souza, and H. Alves, "Movable Antennas-aided Wireless Energy Transfer for the Internet of Things," in arXiv preprint arXiv:2506.21966 (submitted to IEEE Communications Letters), 2025.
We minimize the transmit power of an analog beamforming power beacon equipped with movable antennas (MAs) for charging multiple single-antenna devices. To this end, we enforce a minimum separation among antennas and a minimum received power at the devices. The resulting optimization problem is nonlinear and nonconvex due to interdependencies among the variables. To tackle this, we propose a semidefinite program guided particle swarm optimization (SgPSO) algorithm where each particle represents an antenna configuration, and the fitness function optimizes the corresponding power allocation. SgPSO is utilized for configuring the MAs, which largely outperformed fixed array implementations, particularly with more antennas or devices. We consider independently-controlled MAs (IMAs) but also uniformly-spaced MAs (UMAs).
The transmit power pT increases with the number of devices. IMAs outperform fixed antennas and UMAs, with an increasing performance gap as the number of devices increases. Observe that the achieved solution with the URA slightly outperforms ULA, as the URA's three-dimensional beam-steering capability allows it to better align its analog beam to cover many spatially distributed devices, thereby reducing the required transmit power. UMAs outperform the arrays with fixed antennas due to their reconfiguration to meet devices' power needs. Notably, their limited degrees-of-freedom with respect to IMAs penalize their performance as the number of devices grows.
Variations in the antenna aperture and the distribution of devices across networks with different sizes influence channel conditions. The left-hand figure shows the likelihood of having the devices in the near-field region, i.e., the average portion of devices operating in such region, for different network sizes and for antenna arrays with 4 and 9 elements. Notice that increasing the number of antennas promotes the operation in the near-field. For the ULA case, the probability is either 0 or 1 as the aperture remains constant for a given number of antennas. When devices are closely clustered in small areas, the optimal placement of the MAs tends to be concentrated in a limited region. Conversely, as we expand the service area, the probability of having near-field conditions decreases despite the antennas being more widely spaced on average. This occurs because the devices can be positioned further away from the convex hull of the array, thereby increasing the operation distance compared to the array's aperture. This effect becomes more prominent for the IMAs architecture than for the UMAs, as in the former the antennas move independently.
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