This dissertation develops and analyzes several techniques for improving channel estimation and tracking performance in distributed multi-input multi-output (D-MIMO) wireless communication systems. D-MIMO communication systems have been studied for the last decade and are known to offer the benefits of antenna arrays, e.g., improved range and data rates, to systems of single-antenna devices. D-MIMO communication systems are considered a promising technology for future wireless standards including advanced cellular communication systems. This dissertation considers problems related to channel estimation and tracking in D-MIMO communication systems and is focused on three related topics: (i) characterizing oscillator stability for nodes in D-MIMO systems, (ii) the development of an optimal unified tracking framework and a performance comparison to previously considered sub-optimal tracking approaches, and (iii) incorporating independent kinematics into dynamic channel models and using accelerometers to improve channel tracking performance. A key challenge of D-MIMO systems is estimating and tracking the time-varying channels present between each pair of nodes in the system. Even if the propagation channel between a pair of nodes is time-invariant, the independent local oscillators in each node cause the carrier phases and frequencies and the effective channels between the nodes to have random time-varying phase offsets. The first part of this dissertation considers the problem of characterizing the stability parameters of the oscillators used as references for the transmitted waveforms. Having good estimates of these parameters is critical to facilitate optimal tracking of the phase and frequency offsets. We develop a new method for estimating these oscillator stability parameters based on Allan deviation measurements and compare this method to several previously developed parameter estimation techniques based on innovation covariance whitening. The Allan deviation method is validated with both simulations and experimental data from low-precision and high-precision oscillators. The second part of this dissertation considers a D-MIMO scenario with $N_t$ transmitters and $N_r$ receivers. While there are $N_t imes N_r$ node-to-node pairwise channels in such a system, there are only $N_t + N_r$ independent oscillators. We develop a new unified tracking model where one Kalman filter jointly tracks all of the pairwise channels and compare the performance of unified tracking to previously developed suboptimal local tracking approaches where the channels are not jointly tracked. Numerical results show that unified tracking tends to provide similar beamforming performance to local tracking but can provide significantly better nullforming performance in some scenarios. The third part of this dissertation considers a scenario where the transmit nodes in a D-MIMO system have independent kinematics. In general, this makes the channel tracking problem more difficult since the independent kinematics make the D-MIMO channels less predictable. We develop dynamics models which incorporate the effects of acceleration on oscillator frequency and displacement on propagation time. The tracking performance of a system with conventional feedback is compared to a system with conventional feedback and local accelerometer measurements. Numerical results show that the tracking performance is significantly improved with local accelerometer measurements.
Worcester Polytechnic Institute
Electrical & Computer Engineering
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David, R. A. (2015). Improving Channel Estimation and Tracking Performance in Distributed MIMO Communication Systems. Retrieved from https://digitalcommons.wpi.edu/etd-dissertations/229
Kalman, SDR, channel estimation, accelerometer compensation, tracking, beamforming, filter, nullforming, oscillators, stability, estimation, Allan deviation, acceleration, inertial tracking, software defined radio, short term stability, long term stability