Single-cell Massive MIMO System Research Articles

Statistical Learning Based Joint Antenna Selection and User Scheduling for Single-Cell Massive MIMO Systems

Large number of antennas and radio frequency (RF) chains at the base stations (BSs) lead to high energy consumption in massive MIMO systems. Thus, how to improve the energy efficiency (EE) with a computationally efficient approach is a significant challenge in the design of massive MIMO systems. With this motivation, a learning-based stochastic gradient descent algorithm is proposed in this article to obtain the optimal joint uplink and downlink EE with joint antenna selection and user scheduling in single-cell massive MIMO systems. Using Jensen's inequality and the characteristics of wireless channels, a lower bound on the system throughput is obtained. Subsequently, incorporating the power consumption model, the corresponding lower bound on the EE of the system is identified. Finally, learning-based stochastic gradient descent method is used to solve the joint antenna selection and user scheduling problem, which is a combinatorial optimization problem. Rare event simulation is embedded in the learning-based stochastic gradient descent method to generate samples with very small probabilities. In the analysis, both perfect and imperfect channel side information (CSI) at the BS are considered. Minimum mean-square error (MMSE) channel estimation is employed in the study of the imperfect CSI case. In addition, the effect of a constraint on the number of available RF chains in massive MIMO system is investigated considering both perfect and imperfect CSI at the BS.

Study of single-cell massive MIMO systems with channel aging and prediction

In this paper we consider a single-cell massive multiple-input-multiple-output scenario, where the number of base station (BS) antennas is larger than the number of single antenna user terminals (UTs). We study and investigate common impairment named as channel aging caused by the relative movement between BS antenna and UTs. To acquire channel state information we apply minimum-mean-square-error detector. After that we study some commonly used channel predictors. Then we apply Wiener predictor and ARMA predictor to mitigate the effects of channel aging. We provide signal-to-interference-plus-noise ratios for each channel predictor and demonstrate their overall performance. We apply matched filtering for precoding. We perform thorough analysis with both predictors and observe that Wiener predictor performs better than ARMA predictor in all aspects such as achievable rates, cumulative distribution function and with higher Doppler spreads. The calculated results are deterministic and simulations show that they are even more accurate for system dimensions practically.

Vulnerabilities of Massive MIMO Systems to Pilot Contamination Attacks

We consider a single-cell massive MIMO system in which a base station (BS) with a large number of antennas transmits simultaneously to several single-antenna users in the presence of an attacker.The BS acquires the channel state information (CSI) based on uplink pilot transmissions. In this work, we demonstrate the vulnerability of CSI estimation phase to malicious attacks. For that purpose, we study two attack models. In the first model, the attacker aims at minimizing the sum-rate of downlink transmissions by contaminating the uplink pilots. In the second model, the attacker exploits its in-band full-duplex capabilities to generate jamming signals in both the CSI estimation and data transmission phases. We study these attacks under two downlink power allocation strategies when the attacker knows and does not know the locations of the BS and users. The formulated problems are solved using stochastic optimization, Lagrangian minimization, and game-theoretic methods. A closed-form solution for a special case of the problem is obtained. Furthermore, we analyze the achievable individual secrecy rates under a pilot contamination attack, and provide an upper bound on these rates. Our results indicate that the proposed attacks degrade the throughput of a massive MIMO system by more than half.

Theoretical error for asynchronous multi‐user large‐scale MIMO channel estimation

In this study, the effects of pilot asynchronism on channel estimation of multi-user multiple-input multiple-output systems are analysed. Classical linear sequences such as Gold, Walsh–Hadamard and quaternary were deployed as pilots in large-scale MIMO context aiming to trace their behaviour in asynchronous scenarios. Through this study, it was derived a closed form expression for the channel estimate error, suggesting that asynchronism on channel estimation may introduce deviations from the actual channel and spatial correlation, which decreases the overall performance. Numerical results also demonstrated performance degradation of the single-cell massive MIMO system as asynchronism increases, besides of highlighting the effect of the bit-error-rate floor as the number of base stations antennas increases in such scenarios.