Arizona State University

Ahmed Alkhateeb

Umut Demirhan

Georgios Trichopoulos

Xiaoyan Ying

Muhammad Alrabeiah

Abdelrahman Taha

Relay-aided intelligent reconfigurable surfaces (IRSs) are provided. A novel relay-aided intelligent surface architecture is described herein that has the potential of achieving the promising gains of IRSs with a much smaller number of elements, opening the door for realizing these surfaces in practice. A half-duplex or full-duplex relay is connected to one or more IRSs. This merges the gains of relays and reconfigurable surfaces and splits the required signal-to-noise ratio (SNR) gain between them. This architecture can then significantly reduce the required number of reconfigurable elements in the IRS(s) while achieving the same spectral efficiencies. Consequently, the proposed relay-aided intelligent surface architecture needs far less channel estimation/beam training overhead and provides enhanced robustness compared to traditional IRS solutions.

Relay-aided intelligent reconfigurable surfaces

A communications network system is disclosed. The system may include a central processing unit (CPU) in data communication with a first access point (AP) configured to enable a data communication between the CPU and a first user equipment (UE). The CPU may include a processor configured to select a first group of APs including the first AP, establish a first data communications link over a first frequency band between the CPU- and the first AP, cause the first AP to establish a second data communications link over a second frequency band between the first AP and the first UE, and transmit a portion of data to the first AP over the first data communications link. The first data communications link may be a wireless data communications link. The first frequency band may include higher frequency levels than those of the second frequency band.

Massive MIMO systems with wireless fronthaul

A communications network system is disclosed. The system may include a central processing unit (CPU) in data communication with a first access point (AP) configured to enable a data communication between the CPU and a first user equipment (UE). The CPU may include a processor configured to select a first group of APs including the first AP, establish a first data communications link over a first frequency band between the CPU and the first AP, cause the first AP to establish a second data communications link over a second frequency band between the first AP and the first UE, and transmit a portion of data to the first AP over the first data communications link. The first data communications link may be a wireless data communications link. The first frequency band may include higher frequency levels than those of the second frequency band.

Large intelligent surfaces (LISs) with sparse channel sensors are provided. Embodiments described herein provide efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. Consequently, an LIS architecture based on sparse channel sensors is provided where all LIS elements are passive reconfigurable elements except for a few elements that are active (e.g., connected to baseband). Two solutions are developed that design LIS reflection matrices with negligible training overhead. First, compressive sensing tools are leveraged to construct channels at all the LIS elements from the channels seen only at the active elements. These full channels can then be used to design the LIS reflection matrices with no training overhead. Second, a deep learning-based solution is deployed where the LIS learns how to optimally interact with the incident signal given the channels at the active elements, which represent the current state of the environment and transmitter/receiver locations.

Large intelligent surfaces with sparse channel sensors

Mapping and localization using image processing of wireless signals is provided. Embodiments of the present disclosure provide a novel approach for high accuracy mapping of an environment around an antenna (or antenna array) coupled to a radio frequency (RF) transceiver through image processing of RF signals. The image processing includes constructing a map of line-of-sight (LOS) and non-line-of-sight (NLOS) objects in the environment by distinguishing NLOS objects and correctly projecting their positions relative to the LOS objects. In some examples, a three-dimensional (3D) image of the environment around the antenna (or antenna array) is produced. Aspects disclosed herein can further provide simultaneous localization and mapping (SLAM) for a wireless device.

Growing community of inventors

Chandler, AZ, United States of America

Ahmed Alkhateeb