Gil Kedar

A novel tunable E-band (78–82 GHz) Reconfigurable Electro-Mechanical Reflectarray (REMR) is demonstrated for beam focusing and steering. This design is based on our previous study, which achieved a 350° phase dynamic range in simulations, and is experimentally validated here. The results confirm precise beam focusing and ±3° beam steering, showing excellent agreement with simulations. To further enhance performance, an innovative dual-patch unit cell with a flat ground plane is introduced, enabling nearly 360° phase coverage and extended beam steering up to ±6°. The simplified architecture reduces fabrication complexity and cost while providing a compact, robust, and power-efficient solution for E-band and millimeter-wave communication systems.

This study introduces a steerable metasurface reflector designed for the Ka-band, enabling one-dimensional and two-dimensional beam steering. The paper elaborates on the design considerations, manufacturing process, and experimental findings. The unit cell design incorporates a Varactor diode as the tuning element, facilitating a dynamic phase range exceeding 300° with minimal metasurface beam steering losses. Notably, the experimental results are in good agreement with the simulation outcomes. The advantages of employing this metasurface reflector include rapid beam steering, cost-effective production implementation, support for both one-dimensional and two-dimensional beam steering, low reflection loss, high-resolution beam steering, and continuous beam steering capabilities.

The deployment of wireless communication networks in the E band (60–90 GHz) requires highly flexible, real-time, and precise tunability to optimize power transmission amidst diffraction, obstacles, and scattering challenges. This paper proposes an innovative reconfigurable metasurface reflect array design capable of achieving a dynamic phase range of 312 degrees with less than 1 dB of loss. The design integrates two types of unit cells and employs piezoelectric crystal as the tuning element. Simulation results illustrate the feasibility of beam focusing and accurate beam steering within a range of ±3 degrees. Furthermore, the proposed reconfigurable metasurface reflector demonstrates an antenna gain comparable to that of a dish antenna with the same aperture size.

We consider the problem of of multi-flow transmission in wireless networks, where data signals from different flows can interfere with each other due to mutual interference between links along their routes, resulting in reduced link capacities. The objective is to develop a multi-flow transmission strategy that routes flows across the wireless interference network to maximize the network utility. However, obtaining an optimal solution is computationally expensive due to the large state and action spaces involved. To tackle this challenge, we introduce a novel algorithm called Dual-stage Interference-Aware Multi-flow Optimization of Network Data-signals (DIAMOND). The design of DIAMOND allows for a hybrid centralized-distributed implementation, which is a characteristic of 5G and beyond technologies with centralized unit deployments. A centralized stage computes the multi-flow transmission strategy using a novel design of graph neural network (GNN) reinforcement learning (RL) routing agent. Then, a distributed stage improves the performance based on a novel design of distributed learning updates. We provide a theoretical analysis of DIAMOND and prove that it converges to the optimal multi-flow transmission strategy as time increases. We also present extensive simulation results over various network topologies (random deployment, NSFNET, GEANT2), demonstrating the superior performance of DIAMOND compared to existing methods.

We study the problem of real-time scheduling in a multi-hop millimeter-wave (mmWave) mesh. We develop a model-free deep reinforcement learning algorithm called Adaptive Activator RL (AARL), which determines the subset of mmWave links that should be activated during each time slot and the power level for each link. The most important property of AARL is its ability to make scheduling decisions within the strict time slot constraints of typical 5G mmWave networks. AARL can handle a variety of network topologies, network loads, and interference models, it can also adapt to different workloads. We demonstrate the operation of AARL on several topologies: a small topology with 10 links, a moderately-sized mesh with 48 links, and a large topology with 96 links. We show that for each topology, we compare the throughput obtained by AARL to that of a benchmark algorithm called RPMA (Residual Profit Maximizer Algorithm). The most important advantage of AARL compared to RPMA is that it is much faster and can make the necessary scheduling decisions very rapidly during every time slot, while RPMA cannot. In addition, the quality of the scheduling decisions made by AARL outperforms those made by RPMA.