A Geometry-aided Message Passing Method for AoA-Based Short Range MIMO Channel Estimation

Abstract

Short range channels commonly arise in millimeter wave (mmWave) wearable settings, where the length of the antenna arrays can be comparable to the distance between the radios. Conventional mmWave MIMO channel estimation techniques based on the far field assumption may perform poorly in short range settings due to the large angular spread and, hence, high available rank. We propose a geometry-aided message passing algorithm that exploits structure in short range line-ofsight (LoS) channels for spatial sub-Nyquist channel estimation. Our approach parametrizes the channel using angle-of-arrivals (AoAs) that are locally defined for subarrays of an antenna array. Furthermore, it leverages the dependencies between the local AoAs using factors based on the array geometry. We show that the LoS MIMO channel can be reconstructed using the derived local AoA estimates and the known transceiver geometry. The proposed approach achieves a reasonable rate with greatly reduced pilot transmissions when compared to exhaustive beam search-based local AoA estimation.

Message Passing-Based Link Configuration in Short Range Millimeter Wave Systems

“…Similar to standard message passing, DCS-AMP performs iterative message flows between the variable nodes and the factors to estimate the marginal posteriors of {s k [n]} k,n for the channel measurements {y k [m]} k,m . We explain the sequence of message flows using f 1 [1]. .…”

“…. These messages are appropriately combined and sent to the next layer, i.e., factor graph for subchannel 2, through b 2 [1] and q 2 [1]. Then, message passing is performed in layer 2.…”

“…The prior parameters in DCS-AMP are learned independently for the masked beamspace locations in S 1 and S 2 , using the EM algorithm [10]. 1 We now discuss about the complexity of DCS-AMP with ZC-based subchannel acquisition. In a single pass of DCS-AMP, the complexity of message passing iterations corresponding to each subchannel is determined by matrix-vector multiplications involving A k and A * k [10].…”

“…Similar to standard message passing, DCS-AMP performs iterative message flows between the variable nodes and the factors to estimate the marginal posteriors of {s k [n]} k,n for the channel measurements {y k [m]} k,m . We explain the sequence of message flows using f 1 [1]. The factors b 1 [1] and q 1 [1] send their messages, i.e., probability distributions, to f 1 [1] through β 1 [1] and In practice, the prior parameters , κ, ξ, ζ and ρ are unkown apriori.…”

“…We explain the sequence of message flows using f 1 [1]. The factors b 1 [1] and q 1 [1] send their messages, i.e., probability distributions, to f 1 [1] through β 1 [1] and In practice, the prior parameters , κ, ξ, ζ and ρ are unkown apriori. To overcome this issue, DCS-AMP embeds the message passing algorithm within an Expectation-Maximization (EM) block that learns the parameters online [10].…”