Low Complexity MIMO Receivers by Lin Bai, Jinho Choi, Quan Yu (auth.)

By Lin Bai, Jinho Choi, Quan Yu (auth.)

Multiple-input multiple-output (MIMO) platforms can bring up the spectral potency in instant communications. notwithstanding, the interference turns into the most important difficulty that results in excessive computational complexity at either transmitter and receiver. particularly, the complexity of MIMO receivers could be prohibitively excessive. As an effective mathematical instrument to plot low complexity methods that mitigate the interference in MIMO structures, lattice aid (LR) has been extensively studied and hired over the past decade. The co-authors of this e-book are world's top specialists on MIMO receivers, and right here they proportion the major findings in their study over years. They element a number key ideas for receiver layout as a number of transmitted and obtained signs can be found. The authors first introduce the primary of sign detection and the LR in mathematical features. They then movement directly to talk about using LR in low complexity MIMO receiver layout with appreciate to varied facets, together with uncoded MIMO detection, MIMO iterative receivers, receivers in multiuser situations, and multicell MIMO systems.

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1. Consider a 2 × 2 MIMO system using 4-QAM modulation method for signaling. 1. Let the channel matrix and the noise 2+i 4 1/2 vector be given by H = and n = , respectively. 5 + i = . 1. 1 is chosen as the ML solution. −1 + i Therefore, the symbol vector has been detected correctly with the ML detection. 2 Linear Detection For complexity reduction, linear detectors can be considered. With linear detectors, the received signal y is filtered by a linear filter and each data symbol is detected individually.

4) that with {sm,l }, it is able to reproduce sm (t). 4) is called the Karhunen-Loéve (K-L) expansion. By using the K-L expansion, we can show that ⎡ T yl = 0 Y (t)φl→ (t)dt = sm,l + nl , where l = 1, 2, . . 7) N (t)φl→ (t)dt. 8) l=1 where 0 ≥ t < T and N˜ (t) denotes the noise component that cannot be represented L nl φl (t). 10) ⎥T respectively. It it noteworthy that 0 |φl (t)|2 dt = 1, while we assume the basis functions are orthonormal. By using the K-L expansion, the received signal can be represented by a vector form and given by y = [y1 y2 .

1 Signals in Vector Space Consider a vector as sm = [s1,m s2,m · · · s L ,m ]T . 1) Let sm (t) be a representation of the mth signal in a functional (or waveform) space. Then, it can be found that sm and sm (t) are equivalent, where sm becomes a representation in a vector space. 2) and ⎢⎡ T dm,k = |sm (t) − sk (t)| dt 2 ⎣ 21 0 = ||sm − sk ||, L. 3) 29 30 3 MIMO Detection: Vector Space Signal Detection respectively, which are both invariant. 1. Define a set of L orthonormal basis functions, {φl (t)}, l = 1, 2, .

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