Reconfigurable Intelligent Surfaces (RIS) are a promising technology for creating smart radio environments by controlling wireless propagation. However, several factors hinder the integration of RIS technology into existing cellular networks, including the incompatibility of RIS control interfaces with 5G PHY/MAC procedures for synchronizing radio scheduling decisions and RIS operation, and the cost and energy limitations of passive RIS technology. This paper presents RISENSE, a system for practical RIS integration in cellular networks. First, we propose a novel, low-cost, and low-power RIS design capable of decoding control messages without complex baseband operations or additional RF chains, utilizing a power sensor and a network of microstrip lines and couplers. Second, we design an effective in-band wireless RIS control interface, compatible with 5G PHY/MAC procedures, that embeds amplitude-modulated (AM) RIS control commands directly into standard OFDM-modulated 5G data channels. Finally, we propose a low-overhead protocol that supports swift on-demand RIS re-configurability, making it adaptable to varying channel conditions and user mobility, while minimizing the wastage of 5G OFDM symbols. Our experiments validate the design of RISENSE and our evaluation shows that our system can re-configure a RIS at the same pace as users move, boosting 5G coverage where static or slow RIS controllers cannot.

Spatial frequency estimation from a mixture of noisy sinusoids finds applications in various fields. The widely used subspace-based methods provide super-resolution parameter estimation at a low computational cost. However, they require an accurate array calibration, which is difficult for large antenna arrays. Sparsity-based methods have been shown to be more robust than subspace-based methods in difficult scenarios, e.g., in the case with a small number of snapshots and/or correlated sources. In this paper, we consider the direction-of-arrival (DOA) estimation in partly calibrated rectangular arrays comprising several calibrated and identical subarrays. We derive a gridless sparse formulation for DOA estimation based on the shift-invariance properties of the array and develop an efficient algorithm in the alternating direction method of multipliers (ADMM) framework. Numerical simulations show the superior error performance of our proposed method compared to subspace-based methods.

Reconfigurable intelligent surfaces (RISs) have great potential to improve the coverage of mmWave networks; however, acquiring perfect channel state information (CSI) of a RIS-enabled mmWave network is very costly and should thus be done infrequently. At the same time, finding an optimal RIS configuration when CSI is outdated is challenging. To this end, this work aims to provide practical insights into the tradeoff between the outdatedness of the CSI and the system performance by using the effective capacity as analytical tool. We consider a RIS-enabled mmWave downlink where the base station (BS) operates under statistical quality-of-service (QoS) constraints. We find a closed-form expression for the effective capacity that incorporates the degree of optimism of packet scheduling and correlation strength between instantaneous and outdated CSI. Moreover, our analysis allows us to find optimal values of the signal-to-interference-plus-noise-ratio (SINR) distribution parameter and their impact on the effective capacity in different network scenarios. Simulation results demonstrate that better effective capacity can be achieved with suboptimal RIS configuration when the channel estimates are known to be outdated. It allows us to design system parameters that guarantee better performance while keeping the complexity and cost associated with channel estimation to a minimum.

Millimeter-Wave (mmWave) WiFi can provide very low latency and multi-Gbps throughput, but real-world deployments usually do not achieve the theoretically feasible performance. One main source of inefficiency is the contention-based random channel access, as it requires omni-directional reception which limits performance. Additionally, carrier sensing at mmWave frequencies is highly unreliable, leading to reduced channel usage. In this paper, we present SIGNalling in the PHY Preamble (SIGNiPHY) for efficient directional communications, a solution that allows to embed user identity in the preamble of data packets. It allows for true early user identification and then immediately steering the beam towards the transmitter while receiving the physical layer preamble. SIGNiPHY enables directional reception in random access mmWave networks, and additionally helps to quickly filter unwanted packets. It does not affect any preamble functions and is backward-compatible with legacy stations. We implement SIGNiPHY on an FPGA-based mmWave testbed and show that it achieves 99.6% decoding accuracy even under very low SINR conditions. We also implement SIGNiPHY in ns-3 to evaluate large networks and show that it achieves throughput gains between 13% and 230% compared to different baseline schemes, due to the lower packet loss rate and improved spatial sharing.