Adaptive pilot allocation for estimating sparse uplink MU-MIMO-OFDM channels


We consider uplink multiuser multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication. The transmit (Tx) side of the envisaged system consists of several single-antenna users or/and several multiple-antenna users. At the receive side, a multiple-antenna access point employs compressive sensing techniques to estimate the channel impulse response from the preamble portion of the observed packets. The traditional approach is that of orthogonal pilot allocation: during a short training period, each OFDM subcarrier is assigned exclusively to a single Tx antenna. In this case, the channel state information can conveniently be acquired on a per Tx antenna basis. To the best of our knowledge, all related research imposes that all Tx antennas are allocated the same amount of pilots (which must then be tailored for the most extreme channel conditions). However, in the considered system, Tx antennas may experience totally different channel conditions. Under these circumstances, the use of a fixed number of pilots per Tx antenna results in a lot of unnecessary overhead. To tackle this problem, our work addresses the design of efficient algorithms for adaptive orthogonal pilot allocation. The following design principles are applied: orthogonal pilot allocation, constant-modulus modulation, minimum measurement matrix mutual coherence optimization, and the condition that the number of pilot subcarriers allocated to each Tx antenna is adjusted to the channel conditions experienced by that Tx antenna. The paper tackles the problem of determining the optimal number of pilot subcarriers as well as the optimal positions of the pilots. To facilitate adaptive operation, we propose a reduced-complexity method to determine the optimal pilot positions. The performance of our algorithms is demonstrated by means of computer simulations, using both theoretical channel models and results from our own channel measurement campaign.