Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 hen 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.

Abstract: Vision-aided wireless communications systems are provided. Embodiments disclosed herein leverage visual data sensors (such as red-blue-green (RGB)/depth cameras) to adapt communications (e.g., predict beamforming directions) in large-scale antenna arrays, such as used in millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) systems. These systems face two important challenges: (i) a large training overhead associated with selecting an optimal beam and (ii) a reliability challenge due to high sensitivity of mmWave and similar signals to link blockages. Interestingly, most devices that employ mmWave antenna arrays, such as 5G phones, self-driving vehicles, and virtual/augmented reality headsets, will likely also use cameras. Therefore, an efficient olution is presented which uses cameras at base stations and/or handsets to help overcome the beam selection and blockage prediction challenges.

Abstract: Wireless transmitter identification in visual scenes is provided. This technology enables important wireless communications and sensing applications such as (i) fast beam/blockage prediction in fifth generation (5G)/sixth generation (6G) systems using camera data, (ii) identifying cars and people in a surveillance camera feed using joint visual and wireless data processing, and (iii) enabling efficient autonomous vehicle communication relying on both the camera and wireless data. This is done by developing multimodal machine learning based frameworks that use the sensory data obtained by visual and wireless sensors. More specifically, given some visual data, an algorithm needs to perform the following: (i) predict whether an object responsible for a received radio signal is present or not, (ii) if it is present, detect which object it is out of the candidate transmitters, and (iii) predict what type of signal the detected object is transmitting.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Apparatuses, methods, and systems for precoding multi-carrier signals are disclosed. One method includes obtaining a transmission channel matrix between a terminal and a plurality of separate users, wherein the transmission channel matrix includes channel estimates for a plurality of subcarriers of the multi-carrier signal, wherein the terminal is one of a plurality of terminals interfaced with a central processing unit, and wherein the terminal communicates with the plurality of spatially separate user with the multi-carrier signals. The method further comprises determining a precoding for the terminal based on a distribution of user signal power across the transmission channel between the terminal and the plurality of spatially separate user, and determining a precoding for the central processing unit based on the precoding for the terminal and based on the transmission channel matrix, wherein a precoding matrix for the central processing unit is multi-carrier signal dependent.

Abstract: Apparatuses, methods, and systems zone precoding are disclosed. One method includes determining a transmission zone for each of the plurality of users, wherein the transmission zone includes an angle of direction of a directional beam to each user, and a deviation of the angle of direction. Determining a precoding of transmission signals to each of the plurality of users from the base station based on the transmission zone associated with the user, and constructing the precoding for each user by adjusting the initial precoding for each user based on the transmission zone determined for each of the other users.

Abstract: Apparatuses, methods, and systems zone precoding are disclosed. One method includes determining a transmission zone for each of the plurality of users, wherein the transmission zone includes an angle of direction of a directional beam to each user, and a deviation of the angle of direction. Determining a precoding of transmission signals to each of the plurality of users from the base station based on the transmission zone associated with the user, and constructing the precoding for each user by adjusting the initial precoding for each user based on the transmission zone determined for each of the other users.

Abstract: Apparatuses, methods, and systems zone precoding are disclosed. One method includes determining a transmission zone for each of the plurality of users, wherein the transmission zone includes an angle of direction of a directional beam to each user, and a deviation of the angle of direction. Determining a precoding of transmission signals to each of the plurality of users from the base station, including determining an initial precoding for each of the users based on the transmission zone associated with the user, and constructing the precoding for each user by adjusting the initial precoding for each user based on the transmission zone determined for each of the other users.

Abstract: Apparatuses, methods, and systems zone precoding are disclosed. One method includes determining a transmission zone for each of the plurality of users, wherein the transmission zone includes an angle of direction of a directional beam to each user, and a deviation of the angle of direction. Determining a precoding of transmission signals to each of the plurality of users from the base station, including determining an initial precoding for each of the users based on the transmission zone associated with the user, and constructing the precoding for each user by adjusting the initial precoding for each user based on the transmission zone determined for each of the other users.

Abstract: Apparatuses, methods, and systems zone precoding are disclosed. One method includes training a transmission channel between a base station and each of a plurality of users and determining a precoding of transmission signals to each of the plurality of users from the base station. The training includes determining a transmission zone for each of the plurality of users, wherein the transmission zone includes an angle of direction of a directional beam to each user, and a deviation of the angle of direction. Determining a precoding of transmission signals to each of the plurality of users from the base station, includes determining an initial precoding for each of the users based on the transmission zone associated with the user, and constructing the precoding for each user by adjusting the initial precoding for each user based on the transmission zone determined for each of the other users.

Abstract: Apparatuses, methods, and systems for precoding multi-carrier signals are disclosed. One method includes obtaining a transmission channel matrix between a terminal and a plurality of separate users, wherein the transmission channel matrix includes channel estimates for a plurality of subcarriers of the multi-carrier signal, wherein the terminal is one of a plurality of terminals interfaced with a central processing unit, and wherein the terminal communicates with the plurality of spatially separate user with the multi-carrier signals. The method further comprises determining a precoding for the terminal based on a distribution of user signal power across the transmission channel between the terminal and the plurality of spatially separate user, and determining a precoding for the central processing unit based on the precoding for the terminal and based on the transmission channel matrix, wherein a precoding matrix for the central processing unit is multi-carrier signal dependent.

Abstract: Apparatuses, methods, and systems for precoding multi-carrier signals are disclosed. One method includes obtaining a transmission channel matrix between a terminal and a plurality of separate users, wherein the transmission channel matrix includes channel estimates for a plurality of subcarriers of the multi-carrier signal. A channel dimension reduction matrix is determined based upon a composite of the channel estimates for the plurality of subcarriers, wherein dimensions of the channel dimension reduction matrix are less than dimensions of the transmission channel matrix. The method further includes determining a precoding matrix for the terminal based on the channel dimension reduction matrix, wherein the precoding matrix is multi-carrier signal independent, determining an effective channel based on a channel estimate of the transmission channel and based on the precoding matrix of the terminal, and determining a precoding matrix for the central processing unit based on the effective channel.

Abstract: A mobile device includes a receiver configured to perform hybrid precoding on signals received through a large bandwidth communication. The receiver includes a plurality of antennas configured to receive wireless communications signals through the large bandwidth communication. The receiver also includes a number of radio frequency (RF) chains, each including a low-bit analog to digital converter (ADC) configured to preform precoding to receive the data and control signals. The receiver further includes a baseband processor configured to perform baseband detection.