Time-variant channel estimation using discrete prolate spheroidal sequences

Pilot-Assisted Time-Varying Channel Estimation for OFDM Systems

“…As a compromise, one can derive a BEM that is based on a general approximation for all kinds of channel statistics. For instance, the discrete prolate spheroidal BEM (DPS-BEM) corresponds to the DKL-BEM with a rectangular spectrum [6]. It is featured by a set of orthogonal spheroidal functions that are perfectly band-limited but have maximal time concentration within the considered interval.…”

“…References [8], [12], and [21] belong to the few works that focus on blind BEM channel estimation. References [6], [10], [15], and [22] propose pilot-assisted channel estimators based on different BEM assumptions, where commonly, pilots are clustered in the time domain such that the channel estimation can be realized without interference from neighboring data symbols. For frequency-domain communication systems such as OFDM, it is not clear what is the "optimum" strategy to place the pilots.…”

Space-time block coding for frequency-selective and time-varying channels

“…The nice algebraic structure of this block fading CCE-BEM channel model allows us to extend the Alamouti code to doubly-selective channels enabling the full space-delay-Doppler diversity, as mentioned in Section III-B. Although this channel model was useful to design and analyze our STBC, it does not perfectly model reallife doubly-selective channels under all circumstances [10]. Hence, the receiver processing discussed in Section III-B can only be applied if we approximate the true channel by its best possible fit to a block fading CCE-BEM channel.…”

Structured Compressed Sensing for Channel Estimation under High-Speed Mobile Environment

“…In the BEM, the time-variation of each channel tap is expressed as a superposition of a few fixed basis functions, so that only L Q BEM coefficients need to be estimated, where Q is the number of the basis functions. Several BEM variates are proposed in the literature, e.g., the complex-exponential BEM (CE-BEM) [4], the generalized CE-BEM (GCE-BEM) [5], the polynomial BEM (P-BEM) [6], the Karhunen-Loeve BEM (KL-BEM) [7] and the discrete prolate spheroidal BEM (DPS-BEM) [8]. Although the last two BEMs are closest to the true scenario, they require statistical channel knowledged, which has led to the model is usually unavailable in practice.…”