Abstract:Abstract-We consider the problem of multiple-input multipleoutput (MIMO) sampling of multiband signals. In this problem, a set of input signals is passed through a MIMO channel modeled as a known linear time-invariant system. The inputs are modeled as multiband signals whose spectral supports are sets of finite measure and the channel outputs are sampled on nonuniform sampling sets. The aim is to reconstruct the inputs from the output samples. This sampling scheme is quite general and it encompasses various ot… Show more
“…For fixed and , define mod and mod for an arbitrary . Then, using the conditions (15), (16), and 25, we see that implying that for all and . Therefore, we obtain for all , where mod .…”
Section: Letmentioning
confidence: 98%
“…Since , (14) requires that have full column rank a.e. As in the continuous-time case [13], it can be easily verified that is continuous if and only if is continuous on , and the following periodicity conditions hold: (15) for all , where mod . As we will see later, in order to achieve continuity of , it is convenient to impose continuity on , and this produces a similar condition on .…”
Section: A General Casementioning
confidence: 98%
“…Then, is a Hilbert space equipped with the inner product for all Naturally, is the the norm of . We first review an important notion called stability of MIMO sampling [13], [15].…”
Section: A General Casementioning
confidence: 99%
“…Then, perfect reconstruction using an FIR reconstruction filter matrix is possible if and only if , and the minors of have no zero common to all except or . Proof: The coprimeness condition on the minors guarantees the existence of an FIR such that and the periodicity conditions (15) and (16) hold. This is a standard result that can be proved using Bezout's identity [40]- [42].…”
Section: Letmentioning
confidence: 99%
“…The continuous-time inputs can finally be recovered from the discrete-time sequences using a bank of conventional D/A converters. In this paper, we present sufficient conditions for perfect reconstruction in the discrete-time model with uniform subsampling (see [15] and [16] for necessary density conditions on arbitrary nonuniform sampling) and conditions and a solution to the related filter design problem. We will consider only uniform subsampling of the channel outputs.…”
We address the problem of finite impulse response (FIR) filter design for uniform multiple-input multiple-output (MIMO) sampling. This scheme encompasses Papoulis' generalized sampling and several nonuniform sampling schemes as special cases. The input signals are modeled as either continuous-time or discrete-time multiband input signals, with different band structures. We present conditions on the channel and the sampling rate that allow perfect inversion of the channel. Additionally, we provide a stronger set of conditions under which the reconstruction filters can be chosen to have frequency responses that are continuous. We also provide conditions for the existence of FIR perfect reconstruction filters, and when such do not exist, we address the optimal approximation of the ideal filters using FIR filters and a min-max 2 end-to-end distortion criterion. The design problem is then reduced to a standard semi-infinite linear program. An example design of FIR reconstruction filters is given.
“…For fixed and , define mod and mod for an arbitrary . Then, using the conditions (15), (16), and 25, we see that implying that for all and . Therefore, we obtain for all , where mod .…”
Section: Letmentioning
confidence: 98%
“…Since , (14) requires that have full column rank a.e. As in the continuous-time case [13], it can be easily verified that is continuous if and only if is continuous on , and the following periodicity conditions hold: (15) for all , where mod . As we will see later, in order to achieve continuity of , it is convenient to impose continuity on , and this produces a similar condition on .…”
Section: A General Casementioning
confidence: 98%
“…Then, is a Hilbert space equipped with the inner product for all Naturally, is the the norm of . We first review an important notion called stability of MIMO sampling [13], [15].…”
Section: A General Casementioning
confidence: 99%
“…Then, perfect reconstruction using an FIR reconstruction filter matrix is possible if and only if , and the minors of have no zero common to all except or . Proof: The coprimeness condition on the minors guarantees the existence of an FIR such that and the periodicity conditions (15) and (16) hold. This is a standard result that can be proved using Bezout's identity [40]- [42].…”
Section: Letmentioning
confidence: 99%
“…The continuous-time inputs can finally be recovered from the discrete-time sequences using a bank of conventional D/A converters. In this paper, we present sufficient conditions for perfect reconstruction in the discrete-time model with uniform subsampling (see [15] and [16] for necessary density conditions on arbitrary nonuniform sampling) and conditions and a solution to the related filter design problem. We will consider only uniform subsampling of the channel outputs.…”
We address the problem of finite impulse response (FIR) filter design for uniform multiple-input multiple-output (MIMO) sampling. This scheme encompasses Papoulis' generalized sampling and several nonuniform sampling schemes as special cases. The input signals are modeled as either continuous-time or discrete-time multiband input signals, with different band structures. We present conditions on the channel and the sampling rate that allow perfect inversion of the channel. Additionally, we provide a stronger set of conditions under which the reconstruction filters can be chosen to have frequency responses that are continuous. We also provide conditions for the existence of FIR perfect reconstruction filters, and when such do not exist, we address the optimal approximation of the ideal filters using FIR filters and a min-max 2 end-to-end distortion criterion. The design problem is then reduced to a standard semi-infinite linear program. An example design of FIR reconstruction filters is given.
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