Some possible directions for future works are listed below.
• In this thesis, narrowband systems were assumed. The proposed mmWave channel estimation methods in Chapter 3 and 4 may be extended to wideband scenarios [89], [212] in different manners. For example, they can be directly applied to the pilot subcarriers in an OFDM setting. They may also be combined with direction- finding methods such as the MUSIC [213] to estimate the angles of the propagation paths. The property that different subcarriers may share the same AoAs/AoDs [89] may then be exploited to offer a good initial guess for the proposed mmWave channel estimator, e.g., the GCG-Alt channel estimator, to reduce the solution space of CS-based estimators that aim to recover the paths’ information. In the case of uncalibrated arrays, direction finding methods that account for the unknown phase/gain errors, such as [214] and [215], may be exploited to improve robustness. • The mmWave channel estimators designed in this thesis only use the training pilots.
CHAPTER 6. CONCLUSIONS AND FUTURE WORKS
We may consider using the information data at the transmission phase to aid channel estimation and further improve the performance of our proposed channel estimators. Data-aided channel estimation has been considered in [83], [216] for fully digital systems. In [216], a few pilots are used for coarsely estimating the channel and then data symbols are used to improve the estimation accuracy. The data-aided scheme is realized by utilizing certain available prior data obtained from the output of the soft- input soft-output decoder. Recently, data-aided-based method has been proposed for mmWave MIMO systems [217], [218]. In [217], the channel estimation problem is formulated as a sparse vector recovery problem so that the channel estimation task becomes a sparse vector estimation task. The authors employ i.i.d Gaussian distribution and a correlated pattern to model the prior of the sparse vector and use the clustered sparse Bayesian learning (SBL) to estimate the sparse vector. The data-aided scheme is realized by using Gaussian approximation on the distribution of the data symbol. In [218], the channel is estimated through two steps: the AoDs are estimated in the first step through data symbols by using the SBL method and the channel path gains are estimated in the second step through pilot symbols by using the LS method. The AoD estimation in the first step is also a sparse vector recovery problem and the non-zero locations of the sparse vector are not affected by the transmitted data symbol. The authors of [218] model each element in the sparse vector as a zero-mean Gaussian random variable controlled by a variance, and the SBL method is used to learn the variances. The above three methods adopts different data-aided schemes, and in the future, we may explore the MC technique to incorporate different data-aided schemes to improve the channel
CHAPTER 6. CONCLUSIONS AND FUTURE WORKS
estimation performance.
• For the mmWave covariance estimator proposed in this thesis, the structure of the arrays was assumed to be known, indicating that the proposed method may suffer from performance degradation when arrays are not perfectly calibrated. To solve this issue, we may improve the robustness of our proposed estimator by firstly estimating the phase/gain errors from one instantaneous channel estimation. The methods that consider blind self-calibration problems, such as [219], can be used for estimating phase/gain errors. Then the arrays can be calibrated, and our proposed estimator can be implemented.
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