Communication in millimeter wave spectrum is an integral component to meet the ever increasing user throughput demand in fifth generation and beyond wireless cellular networks. Current millimeter wave basestation deployments typically use active phased array antennas which employ a codebook of predetermined directional beams to service cellular users. Highly directional beams are needed to overcome the high pathloss of the wireless medium in millimeter wave spectrum. However, drawbacks of such antenna systems include the signaling and measurement overhead required to select appropriate beams from the codebook for each user, as well as grouping users that can be covered by the same beam across the operating band of the array. Digital and hybrid beamforming systems are being actively researched to overcome these issues, however there is a scarcity of multi-antenna channel measurements in the millimeter wave spectrum needed to effectively optimize digital beamforming design parameters. In this paper, we present a novel fully digital 32 channel uniform planar array testbed, that is capable of performing wideband outdoor channel measurement in millimeter wave spectrum. Our testbed can also be calibrated to maintain precise phase and amplitude synchronization across all elements of the antenna array. Preliminary results from an outdoor channel sounding campaign show that digital beamforming receive algorithms provide significantly higher beamforming gain over traditional directional beamforming.

Wireless communication in millimeter wave spectrum is poised to provide the latency and bandwidth needed for advanced use cases unfeasible at lower frequencies. Despite the market potential of vehicular communication networks, investigations into the millimeter wave vehicular channel are lacking. In this paper, we present a detailed overview of a novel 1 GHz wide, multi-antenna vehicle to vehicle directional channel sounding and measurement platform operating at 28 GHz. The channel sounder uses two 256-element phased arrays at the transmitter vehicle and four 64-element arrays at the receiver vehicle, with the receiver measuring 116 different directional beams in less than 1 millisecond. By measuring the full multi-beam channel impulse response at large bandwidths, our system provides unprecedented insight in instantaneous mobile vehicle to vehicle channels. The system also uses centimeter-level global position tracking and 360 degree video capture to provide additional contextual information for joint communication and sensing applications. An initial measurement campaign was conducted on highway and surface streets in Austin, Texas. We show example data that highlights the sensing capability of the system. Preliminary results from the measurement campaign show that bumper mounted mmWave arrays provide rich scattering in traffic as well a provide significant directional diversity aiding towards high reliability vehicular communication. Additionally, potential waveguide effects from high traffic in lanes can also extend the range of mmWave signals significantly.

Massive MIMO deployed with large channel bandwidths in mid-band spectrum is fundamental to high throughput and near ubiquitous coverage required by users in Fifth generation cellular networks. Network operators are also looking past existing monolithic radio access network architectures and looking towards functionally split architectures that allow for flexible, scalable, and cost-effective network deployments. Functional split of the baseband in massive MIMO deployments requires new and innovative approaches to distribute signal processing algorithms, and to optimize the information exchange across the fronthaul while maintaining acceptable levels of network performance. We consider a baseband split standardized by the Open RAN Foundation and investigate the issue of channel information being present at one side of the split, yet needed at the other side in order to perform uplink beamforming. We provide an analysis of air-interface performance degradation caused by the delay between determining beamforming weights from uplink sounding reference signals and applying these weights to the physical uplink shared channel. We also propose a novel beamforming weight design algorithm that can provide good tradeoff between air-interface performance and fronthaul throughput overhead. Our analysis and results are supported by both simulations and a massive MIMO prototyping testbed that emulates an outdoor environment with a high-speed user.

Current wireless networks employ sophisticated multi-user transmission techniques to fully utilize the physical layer resources for data transmission. At the MAC layer, these techniques rely on a semi-static map that translates the channel quality of users to the potential transmission rate (more precisely, a map from the Channel Quality Index to the Modulation and Coding Scheme) for user selection and scheduling decisions. However, such a static map does not adapt to the actual deployment scenario and can lead to large performance losses. Furthermore, adaptively learning this map can be inefficient, particularly when there are a large number of users. In this work, we make this learning efficient by clustering users. Specifically, we develop an online learning approach that jointly clusters users and channel-states, and learns the associated rate regions of each cluster. This approach generates a scenario-specific map that replaces the static map that is currently used in practice. Furthermore, we show that our learning algorithm achieves sub-linear regret when compared to an omniscient genie. Next, we develop a user selection algorithm for multi-user scheduling using the learned user-clusters and associated rate regions. Our algorithms are validated on the WiNGS simulator from AT&T Labs, that implements the PHY/MAC stack and simulates the channel. We show that our algorithm can efficiently learn user clusters and the rate regions associated with the user sets for any observed channel state. Moreover, our simulations show that a deployment-scenario-specific map significantly outperforms the current static map approach for resource allocation at the MAC layer.

We propose an online algorithm for clustering channel-states and learning the associated achievable multiuser rates. Our motivation stems from the complexity of multiuser scheduling. For instance, MU-MIMO scheduling involves the selection of a user subset and associated rate selection each time-slot for varying channel states (the vector of quantized channels matrices for each of the users) — a complex integer optimization problem that is different for each channel state. Instead, our algorithm clusters the collection of channel states to a much lower dimension, and for each cluster provides achievable multiuser capacity trade-offs, which can be used for user and rate selection. Our algorithm uses a bandit approach, where it learns both the unknown partitions of the channel-state space (channel-state clustering) as well as the rate region for each cluster along a pre-specified set of directions, by observing the success/failure of the scheduling decisions (e.g. through packet loss). We propose an epoch-greedy learning algorithm that achieves a sub-linear regret, given access to a class of classifying functions over the channel-state space. We empirically validate our approach on a high-fidelity 5G New Radio (NR) wireless simulator developed within AT&T Labs. We show that our epoch-greedy bandit algorithm learns the channel-state clusters and the associated rate regions. Further, adaptive scheduling using this learned rate-region model (map from channel-state to the set of feasible rates) outperforms the corresponding hand-tuned static maps in multiple settings. Thus, we believe that auto-tuning cellular systems through learning-assisted scheduling algorithms can significantly improve performance in real deployments.