Beyond-5G and 6G wireless networks exploit the millimeter-wave (mmWave) frequency bands to achieve significantly improved data rates, and existing mmWave systems rely on analog single-user beamforming (SUBF) or hybrid multi-user beamforming (MUBF). In this work, we focus on improving the performance of multi-user communication in mmWave networks by exploring analog MUBF using an array of subarrays (ASA) with reduced system overhead and hardware complexity as it eliminates digital beamforming and the need for estimating the channel state information (CSI). We present Mambas, a novel system that maneuvers analog MUBF using an ASA to support simultaneous communication with multiple users located in close proximity, e.g., within the half-power beamwidth of the ASA. In essence, Mambas effectively decouples the user selection, subarray allocation, and beamforming optimization based on a comprehensive understanding of the multi-user support determined by the ASA. We evaluate Mambas using a 28 GHz software-defined radio testbed and show that, compared to existing methods, Mambas can effectively support users that are 2× more closely spaced while achieving an improved sum rate of up to 2×, using only two subarrays. Large-scale ray tracing-based simulations also show that Mambas can achieve a sum rate gain of 1.92--3.86× and is able to maintain consistent performance with significantly increased user density.

5G new radio (NR) employs frequency range 2 (FR2) in the millimeter-wave (mmWave) bands, which employs a much shorter slot duration compared to FR1 (sub-7 GHz) systems and, therefore, poses significant challenges for softwarized baseband processing in virtualized radio access networks (vRANs). Existing systems supporting software baseband processing focus on enabling (massive) multiple-input and multiple-output (MIMO) using multi-core edge server(s). These solutions may fail to meet the more stringent processing deadline in FR2 or require more intensive computational resources. In this paper, we present Savannah, an efficient mmWave baseband processing framework using minimal and heterogeneous computing resources including CPU and eASIC. Savannah addresses the challenges associated with baseband processing in FR2 by applying techniques for vectorizing matrix operations and memory access patterns, supporting heterogeneous computation via offloading LDPC decoding to an eASIC, and enabling single-core operation. We show that Savannah, using a single CPU core and the ACC100 accelerator, can support a 2×2 MIMO link with 100 MHz bandwidth, yielding a data rate of up to 487 Mbps.

5G new radio (NR) frequency range 2 (FR2) in the millimeter-wave (mmWave) band has a much shorter baseband processing deadline compared to that in the sub-7 GHz FR1 band. This tight deadline requires an efficient real-time system for baseband processing using minimal computational resources. We demonstrate Savannah, a software framework for efficient mmWave baseband processing using minimal and heterogeneous computing resources, including CPU and eASIC. Savannah vectorizes matrix operations and memory access patterns in multi-input multi-output (MIMO) arithmetic, offloads low-density parity-check (LDPC) coding to an eASIC, and enables single-core operation. We demonstrate that Savannah, using a single CPU core and an eASIC, can support a 2×2 MIMO link with 100 MHz bandwidth under full uplink traffic load, yielding a data rate of up to 487 Mbps.

The past 40 years have seen an explosion of Radio Frequency (RF) transmitters, which motivates understanding their impacts on the natural world. The European honeybee, Apis Mellifera, has been shown to sense the Earth's magnetic field. Human Radio Frequency (RF) transmitters alter this field. For example, recent work demonstrated that human-created RF interferes with the common robin's ability to orient themselves. This work proposes an experimental design to determine if honeybees can sense RF transmissions in frequencies from 1 MHz (AM radio) to 6 GHz (WiFi). We deployed a custom-designed RF bee feeder near bee hives to test honeybees' RF sensing ability.

As rapid expansion of the low-cost next billion devices, wireless sensor networks (WSN) undertake much denser low-end internet of things (IoT) nodes nowadays. In the meantime, the future next 5 generation (5G) radio base stations (BS) are granted more capabilities. Distributed cloud radio access network (C-RAN) is becoming available for the future massive WSN. However, real-world distributed C-RAN is less explored for low-end IoT based WSN due to its difficulties in implementation. In this paper, we built a distributed C-RAN which has tens of distributed radio frontends using USRP N210s in a 20 × 20 × 3 m3 area. By exploiting the inherent hardware properties of low-end IoT devices and the spatial diversity of distributed C-RAN system, we show the distributed C-RAN can potentially decode collided signals from low-end IoT devices with all signal processing been done on the cloud.