PhaseMO: Future-Proof, Energy-efficient, Adaptive Massive MIMO

The rapid proliferation of devices and increasing data traffic in cellular networks necessitate advanced solutions to meet these escalating demands. Massive MIMO (Multiple Input Multiple Output) technology offers a promising approach, significantly enhancing throughput, coverage, and spatial multi-plexing. Despite its advantages, massive MIMO systems often lack flexible software controls over hardware, limiting their ability to optimize operational expenditure (OpEx) by reducing power consumption while maintaining performance. Current software-controlled methods, such as antenna muting combined with digital beamforming and hybrid beamforming, have notable limitations. Antenna muting struggles to maintain throughput and coverage, while hybrid beamforming faces hardware constraints that restrict scalability and future-proofing. This work presents PhaseMO, a versatile approach that adapts to varying network loads. PhaseMO effectively reduces power consumption in low-load scenarios without sacrificing coverage and overcomes the hardware limitations of hybrid beamforming, offering a scalable and future-proof solution. We will show that PhaseMO can achieve up to 30% improvement in energy efficiency while avoiding about 10% coverage reduction and 5dB increase in UE transmit power.

CommRad: Context-Aware Sensing-Driven Millimeter-Wave Networks

Millimeter-wave (mmWave) technology is pivotal for next-generation wireless networks, enabling high-data-rate and low-latency applications such as autonomous vehicles and XR streaming. However, maintaining directional mmWave links in dynamic mobile environments is challenging due to mobility-induced disruptions and blockage. While effective, the current 5G NR beam training methods incur significant overhead and scalability issues in multi-user scenarios. To address this, we introduce CommRad, a sensing-driven solution incorporating a radar sensor at the base station to track mobile users and maintain directional beams even under blockages. While radar provides high-resolution object tracking, it suffers from a fundamental challenge of lack of context, i.e., it cannot discern which objects in the environment represent active users, reflectors, or blockers. To obtain this contextual awareness, CommRad unites wireless sensing capabilities of bi-static radio communication with the mono-static radar sensor, allowing radios to provide initial context to radar sensors. Subsequently, the radar aids in user tracking and sustains mobile links even in obstructed scenarios, resulting in robust and high-throughput directional connections for all mobile users at all times. We evaluate this collaborative radar-radio framework using a 28 GHz mmWave testbed integrated with a radar sensor in various indoor and outdoor scenarios, demonstrating a 2.5x improvement in median throughput compared to a non-collaborative baseline.

Two beams are better than one: Enabling reliable and high throughput mmWave links

Millimeter-wave communication with high throughput and high reliability is poised to be a gamechanger for V2X and VR applications. However, mmWave links are notorious for low reliability since they suffer from frequent outages due to blockage and user mobility. Traditional mmWave systems are hardly reliable for two reasons. First, they create a highly directional link that acts as a single point of failure and cannot be sustained for high user mobility. Second, they follow a `reactive' approach, which reacts after the link has already suffered an outage. We build mmReliable, a reliable mmWave system that implements smart analog beamforming and user tracking to handle environmental vulnerabilities. It creates custom beam patterns with multiple lobes and optimizes their angle, phase, and amplitude to maximize the signal strength at the receiver. Such phase-coherent multi-beam patterns allow the signal to travel along multiple paths and add up constructively at the receiver to improve throughput. Of course, multi-beam links are resilient to occasional blockages of few beams in multi-beam compared to a single-beam system. With user mobility, mmReliable proactively tracks the motion in the background by leveraging continuous channel estimates without affecting the data rates. We implement mmReliable on a 28 GHz testbed with 400 MHz bandwidth and a 64 element phased-array supporting 5G NR waveforms. Rigorous indoor and outdoor experiments demonstrate that mmReliable achieves close to 100% reliability providing 1.5 times better throughput than traditional single-beam systems.