Compressive Beam Alignment for Indoor Millimeter-Wave Systems

TL;DR

This paper introduces a CS-based beam alignment technique that is agnostic to the user's antenna architecture, beamforming codebook, and channel and eliminates angle quantization errors by mapping the recovered angular directions directly onto the user's specific beamforming codebook.

Abstract

The dynamic nature of indoor environments poses unique challenges for next-generation millimeter-wave (mmwave) connectivity. These challenges arise from blockages due to mobile obstacles, mm-wave signal scattering caused by indoor surfaces, and user phased antenna array imperfections. Traditional compressed sensing (CS) based beam alignment techniques enable swift mm-wave connectivity with a limited number of measurements. These techniques, however, rely on prior knowledge of the communication channel model and the user's array manifold to design the sensing matrix and minimize angle quantization errors. This limits their effectiveness in dynamic environments. This paper proposes a novel CS-based beam alignment technique for mm-wave systems operating in indoor environments. Unlike prior work that rely on knowledge of the user's antenna architecture, communication codebook, and channel, the proposed technique is agnostic to these factors. The proposed formulation eliminates angle quantization errors by mapping the recovered angular directions onto the user's specific codebook. This is achieved by exploiting the energy compaction property of the Discrete Cosine Transform (DCT) to compress and identify the strongest cluster locations in the transform domain for robust beamforming. Experimental results at 60 GHz demonstrate successful recovery of the mm-wave power distribution in the angular domain, facilitating accurate beam alignment with limited measurements compared to exhaustive search solutions.

Figures (8)

• Figure 1: Received signal strength (RSS) measurements as a function of beam orientation for a 16-element phased antenna array operating at 60 GHz in a 58 square meter indoor laboratory environment.
• Figure 2: Normalized DCT coefficients of the RSS measurements shown in Fig. \ref{['fig:fig1']}. The DCT efficiently concentrates signal energy in low-frequency coefficients, resulting in a sparse representation which we exploit in this paper.
• Figure 3: View of the mm-wave propagation environment. The transmit and receive antennas were situated 4.3 meters (Location A) and 4.5 meters (Location B) apart. Antennas heights are set to 1.6 meters.
• Figure 4: Map and dimensions of the indoor environment.
• Figure 5: RSS versus the TX/RX beam orientation at location A when using (i) exhaustive search over all TX/RX angles (top left figure), (ii) CS recovery with ($m/n = 0.25$), (iii) CS recovery with ($m/n = 0.37$), and (iv) CS recovery with ($m/n = 0.47$).