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—Massive MIMO systems can greatly increase spectral and energy efficiency over traditional MIMO systems by exploiting large antenna arrays. However, increasing the number of antennas at the base station (BS) makes the uplink noncoherent data detection very challenging in massive MIMO systems. In this paper we consider the joint maximum likelihood (ML)(More)
This paper considers the joint maximum likelihood (ML) channel estimation and data detection problem for massive SIMO (single input multiple output) wireless systems. We propose efficient algorithms achieving the exact ML non-coherent data detection, for both constant-modulus constellations and nonconstant-modulus constellations. Despite a large number of(More)
—Massive MIMO communication systems, by virtue of utilizing very large number of antennas, have a potential to yield higher spectral and energy efficiency in comparison with the conventional MIMO systems. In this paper, we consider uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. With increased number of antennas,(More)
We consider frequency selective channel estimation in the uplink of massive MIMO-OFDM systems, where our major concern is complexity. A low complexity distributed LMMSE algorithm is proposed that attains near optimal channel impulse response (CIR) estimates from noisy observations at receive antenna array. In proposed method, every antenna estimates the(More)
This paper investigates the joint maximum-likelihood (ML) data detection and channel estimation problem for space-time-block-coded (STBC) OFDM wireless systems with general constellation modulations. An efficient low-complexity algorithm is proposed based on recursive least squares (RLS) that renders exact ML estimates of both channel and the data. The(More)
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