Downlink Performance of Superimposed Pilots in Massive MIMO systems

In this paper, we investigate the downlink throughput performance of a massive multiple-input multiple-output (MIMO) system that employs superimposed pilots for channel estimation. The component of downlink (DL) interference that results from transmitting data alongside pilots in the uplink (UL) is shown to decrease at a rate proportional to the square root of the number of antennas at the BS. The normalized mean-squared error (NMSE) of the channel estimate is compared with the Bayesian Cram\'{e}r-Rao lower bound that is derived for the system, and the former is also shown to diminish with increasing number of antennas at the base station (BS). Furthermore, we show that staggered pilots are a particular case of superimposed pilots and offer the downlink throughput of superimposed pilots while retaining the UL spectral and energy efficiency of regular pilots. We also extend the framework for designing a hybrid system, consisting of users that transmit either regular or superimposed pilots, to minimize both the UL and DL interference. The improved NMSE and DL rates of the channel estimator based on superimposed pilots are demonstrated by means of simulations.Comment: 28 single-column pages, 6 figures, 1 table, Submitted to IEEE Trans. Wireless Commun. in Aug 2017. Revised Submission in Feb. 201

Performance Analysis and Optimal Power Allocation for Linear Receivers Based on Superimposed Training

In this paper, we derive a performance comparison between two training-based schemes for Multiple-Input Multiple-Output (MIMO) systems. The two schemes are thetime-division multiplexing scheme and the recently proposed data-dependent superimposed pilot scheme. For both schemes, a closed-form expressions for the Bit Error Rate (BER) is provided. We also determine, for both schemes, the optimal allocation of power between pilot and data that minimizes the BER

Spectral and Energy Efficiency of Superimposed Pilots in Uplink Massive MIMO

Next generation wireless networks aim at providing substantial improvements in spectral efficiency (SE) and energy efficiency (EE). Massive MIMO has been proved to be a viable technology to achieve these goals by spatially multiplexing several users using many base station (BS) antennas. A potential limitation of Massive MIMO in multicell systems is pilot contamination, which arises in the channel estimation process from the interference caused by reusing pilots in neighboring cells. A standard method to reduce pilot contamination, known as regular pilot (RP), is to adjust the length of pilot sequences while transmitting data and pilot symbols disjointly. An alternative method, called superimposed pilot (SP), sends a superposition of pilot and data symbols. This allows to use longer pilots which, in turn, reduces pilot contamination. We consider the uplink of a multicell Massive MIMO network using maximum ratio combining detection and compare RP and SP in terms of SE and EE. To this end, we derive rigorous closed-form achievable rates with SP under a practical random BS deployment. We prove that the reduction of pilot contamination with SP is outweighed by the additional coherent and non-coherent interference. Numerical results show that when both methods are optimized, RP achieves comparable SE and EE to SP in practical scenarios.Comment: 32 pages, 12 figures, 3 tables. Submitted in March 2017 to IEEE Transactions on Wireless Communication

Joint Decision-Directed Channel and Noise-Variance Estimation for MIMO OFDM/SDMA Systems Based on Expectation-Conditional Maximization

A joint channel impulse response (CIR) and noise-variance estimation scheme is proposed for multiuser multiple-input–multiple-output (MIMO) orthogonal frequency-division multiplexing/space-division multiple access (OFDM/SDMA) systems, which is based on the expectation-conditional maximization (ECM) algorithm. Multiple users communicating over fading channels exhibiting a range of different characteristics are considered in this paper. Channel estimation becomes quite challenging in this scenario since an increased number of independent transmitter–receiver links having different statistical characteristics have to be simultaneously estimated for each subcarrier. To cope with this scenario, we design an ECM-based joint CIR and noise-variance estimator for multiuser MIMO OFDM/SDMA systems, which is capable of simultaneously estimating diverse CIRs and noise variance. Furthermore, we propose a forward error code (FEC)-aided decision-directed channel estimation scheme based on the ECM algorithm, which further improves the ECM algorithm by exploiting the error correction capability of an FEC decoder for iteratively exchanging information between the decoder and the ECM algorithm