This letter expands the previously proposed High Time Resolution Carrier Interferometry (HTRCI) to estimate a larger amount of channel status information (CSI). HTRCI is based on a comb-type pilot symbol on OFDM and CSI for null subcarriers are interpolated by time-domain signal processing. In order to utilize such null pilot subcarriers for increasing estimable CSI, they should generally be separated in frequency-domain prior to estimation and interpolation processes. The main proposal is its separation scheme in conjunction with the HTRCI treatment of the temporal domain. Its effectiveness is verified by a pilot de-contamination on downlink two-cell MIMO transmission scenario. Binary error rate (BER) performance can be improved in comparison to conventional HTRCI and zero padding (ZP) which replaces the impulse response alias with zeros.

This paper addresses pilot contamination in massive multiple-input multiple-output (MIMO) uplink. Pilot contamination is caused by reuse of identical pilot sequences in adjacent cells. To solve pilot contamination, the base station utilizes differences between the transmission frames of different users, which are detected via joint channel and data estimation. The joint estimation is regarded as a bilinear inference problem in compressed sensing. Expectation propagation (EP) is used to propose an iterative channel and data estimation algorithm. Initial channel estimates are attained via time-shifted pilots without exploiting information about large scale fading. The proposed EP modifies two points in conventional bilinear adaptive vector approximate message-passing (BAd-VAMP). One is that EP utilizes data estimates after soft decision in the channel estimation while BAd-VAMP uses them before soft decision. The other point is that EP can utilize the prior distribution of the channel matrix while BAd-VAMP cannot in principle. Numerical simulations show that EP converges much faster than BAd-VAMP in spatially correlated MIMO, in which approximate message-passing fails to converge toward the same fixed-point as EP and BAd-VAMP.

This paper expands our previously proposed semi-blind uplink interference suppression scheme for multicell multiuser massive MIMO systems to support multi modulus signals. The original proposal applies the channel state information (CSI) aided blind adaptive array (BAA) interference suppression after the beamspace preprocessing and the decision feedback channel estimation (DFCE). BAA is based on the constant modulus algorithm (CMA) which can fully exploit the degree of freedom (DoF) of massive antenna arrays to suppress both inter-user interference (IUI) and inter-cell interference (ICI). Its effectiveness has been verified under the extensive pilot contamination constraint. Unfortunately, CMA basically works well only for constant envelope signals such as QPSK and thus the proposed scheme should be expanded to cover QAM signals for more general use. This paper proposes to apply the multi modulus algorithm (MMA) and the minimum mean square error weight derivation based on data-aided sample matrix inversion (MMSE-SMI). It can successfully realize interference suppression even with the use of multi-level envelope signals such as 16QAM with satisfactorily outage probability performance below the fifth percentile.

Pilot contamination is addressed in massive multiple-input multiple-output (MIMO) uplink. The main ideas of pilot decontamination are twofold: One is to design transmission timing of pilot sequences such that the pilot transmission periods in different cells do not fully overlap with each other, as considered in previous works. The other is joint channel and data estimation via approximate message-passing (AMP) for bilinear inference. The convergence property of conventional AMP is bad in bilinear inference problems, so that adaptive damping was required to help conventional AMP converge. The main contribution of this paper is a modification of the update rules in conventional AMP to improve the convergence property of AMP. Numerical simulations show that the proposed AMP outperforms conventional AMP in terms of estimation performance when adaptive damping is not used. Furthermore, it achieves better performance than state-of-the-art methods based on subspace estimation when the power difference between cells is small.

This paper improves our previously proposed semi-blind uplink interference suppression scheme for multicell multiuser massive MIMO systems by incorporating the beamspace approach. The constant modulus algorithm (CMA), a known blind adaptive array scheme, can fully exploit the degree of freedom (DoF) offered by massive antenna arrays to suppress inter-user interference (IUI) and inter-cell interference (ICI). Unfortunately, CMA wastes a lot of the benefit of DoF for null-steering even when the number of incoming signal is fewer than that of receiving antenna elements. Our new proposal introduces the beamspace method which degenerates the number of array input for CMA from element-space to beamspace. It can control DoF expended for subsequent interference suppression by CMA. Optimizing the array beamforming gain and null-steering ability, can further improve the output signal-to-interference and noise power ratio (SINR). Computer simulation confirmed that our new proposal reduced the required number of data symbols by 34.6%. In addition, the 5th percentile SINR was also improved by 14.3dB.

Pilot contamination due to pilot reuse in adjacent cells is a very serious problem in massive multi-input multiple-output (MIMO) systems. Therefore, proper pilot allocation is essential for improving system performance. In this paper, we formulate the pilot allocation optimization problem so as to maximize uplink sum rate of the system. To reduce the required complexity inherent in finding the optimum pilot allocation, we propose a low-complexity pilot allocation algorithm, where the formulated problem is decoupled into multiple subproblems; in each subproblem, the pilot allocation at a given cell is optimized while the pilot allocation in other cells id held fixed. This process is continued until the achievable sum rate converges. Through multiple iterations, the optimum pilot allocation is found. In addition, to improve users' fairness, we formulate fairness-aware pilot allocation as maximization problem of sum of user's logarithmic rate and solve the formulated problem using a similar algorithm. Simulation results show that the proposed algorithms match the good performance of the exhaustive search algorithm, meanwhile the users' fairness is improved.

In this paper, we study the spectral efficiency of the uplink multi-user large-scale distributed antenna systems (DAS) with imperfect channel state information. We propose the system model of multi-user DAS and illustrate the necessity of pilot reuse. Then, we derive the sum-rate of the system under pilot contamination. Furthermore, we investigate the asymptotical performance when the number of antennas goes to infinity. To reduce the pilot contamination, we present two novel pilot assignment algorithms to improve the spectral efficiency. Finally, we evaluate our proposed strategies through extensive simulations which show that compared with random pilot reuse, the min-max algorithm shows impressive performance with low complexity.