MIMO systems employing sphere decoding (SD) algorithm are known to achieve near maximum likelihood (ML) performance at a reduced complexity by restricting the candidate search space to a sphere of a certain radius. The performance of SD depends on the precise estimation of its soft output. In this paper, a low complexity modified Likelihood Ascent Search (LAS) algorithm is proposed to be used within a SD receiver in order to precisely estimate the counter‐hypothesis for its winner candidates. The LAS algorithm is modified to search for the best counter‐hypothesis in only one‐half of the signal lattice thereby improving the performance of MIMO receiver. Our results challenge the popular perception that for a SD receiver a large number of candidates within the search sphere is essential for good performance. Instead, it is shown that accurate estimation of the counter‐hypothesis is equally important and in fact, the performance of the proposed augmented SD receiver with only single candidate approaches that of a classical SD with multiple candidates. Bit error rate performance of the proposed method when compared with the existing research works on soft output generation for the same number of candidates shows that our proposed method outperforms them by upto 3 dB.

By exploiting large antenna arrays, massive MIMO (multiple input multiple output) systems can greatly increase spectral and energy efficiency over traditional MIMO systems. However, increasing the number of antennas at the base station (BS) makes the uplink joint channel estimation and data detection (JED) challenging in massive MIMO systems. In this paper, we consider the JED problem for massive SIMO (single input multiple output) wireless systems, which is a special case of wireless systems with large antenna arrays. We propose exact Generalized Likelihood Ratio Test (GLRT) optimal JED algorithms with low expected complexity, for both constant-modulus and nonconstant-modulus constellations. We show that, despite the large number of unknown channel coefficients, the expected computational complexity of these algorithms is polynomial in channel coherence time (T) and the number of receive antennas (N), even when the number of receive antennas grows polynomially in the channel coherence time (N=O(T11) suffices to guarantee an expected computational complexity cubic in T and linear in N). Simulation results show that the GLRT-optimal JED algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity.

In[1], the following line was missing: “Weiyu Xu and Haider Ali Jasim Alshamary contributed equally to this article.”

By virtue of large antenna arrays, massive MIMO systems have a potential to yield higher spectral and energy efficiency in comparison with the conventional MIMO systems. This paper addresses uplink channel estimation in massive MIMO-OFDM systems with frequency selective channels. We propose an efficient distributed minimum mean square error (MMSE) algorithm that can achieve near optimal channel estimates at low complexity by exploiting the strong spatial correlation among antenna array elements. The proposed method involves solving a reduced dimensional MMSE problem at each antenna followed by a repetitive sharing of information through collaboration among neighboring array elements. To further enhance the channel estimates and/or reduce the number of reserved pilot tones, we propose a data-aided estimation technique that relies on finding a set of most reliable data carriers. Furthermore, we use stochastic geometry to quantify the pilot contamination, and in turn use this information to analyze the effect of pilot contamination on channel MSE. The simulation results validate our analysis and show near optimal performance of the proposed estimation algorithms.