Pilot contamination mitigation by pilot assignment and adaptive linear precoding for massive MIMO multi-cell systems

We propose a novel location-aware pilot assignment scheme to mitigate pilot contamination in massive multiple-input multiple-output (MIMO) networks, where the channels are subjected to Rician fading. Our proposed scheme utilizes the location information of users as the input to conduct pilot assignment in the network. Based on the location information, we first determine the line of sight (LOS) interference between the intended signal and the interfering signal. Our analysis reveals that the LOS interference converges to zero as the number of antennas at the base station (BS) goes to infinity, whereas for finite number of antennas at the BS the LOS interference indeed depends on specific pilot allocation strategies. Following this revelation, we assign pilot sequences to all the users in the massive MIMO network such that the LOS interference is minimized for finite number of antennas at the BS. Our proposed scheme outperforms the random pilot assignment in terms of achieving a much higher uplink sum rate for reasonable values of the Rician K-factor. Moreover, we propose a new performance metric, which measures the strength of the LOS interference and demonstrate that it is a good metric to analyze the performance of pilot assignment schemes. Theoretical analysis is provided and verified through numerical simulations to support the proposed scheme.

Massive multiple-input multiple-output (MIMO) technology is expected to achieve significant gains in both signal-to-noise-plus-interference ratio (SINR) and throughput for 5G cellular wireless networks. Efficient and highly accurate channel state information (CSI) acquisition at the base stations (BS) is essential to achieve the potential benefits of massive MIMO systems. However, the inadequate number of orthogonal pilot sequences used for CSI estimation leads to erroneous channel estimation as it causes interference between pilot sequences. This phenomenon is coined pilot contamination and severely limits the system performance. Therefore, we address in this paper the pilot contamination problem in massive MIMO Cloud-Radio Access Networks systems. Leveraging on a real telecom operator data delivered by Call Detail Records (CDR), our objective is to maximize the average uplink achievable rate by reducing the effect of pilot contamination in massive MIMO Cloud-Radio Access Networks (C-RAN). As such a problem is a non-linear integer problem that has no solution in polynomial time, we develop a two-stage solution to solve it. First, a coalition game is proposed where Remote Radio Heads (RRHs) gather into clusters with random user-pilot allocation. Then, two greedy heuristics are applied to match each user in the cluster with a given pilot. The goal is to further improve the average uplink rate achieved in the first stage. Simulation results show that our heuristic solutions for pilot contamination mitigation outperform the traditional pilot allocation solution and a state-of-the-art pilot allocation scheme based on large-scale fading in terms of average uplink achievable rate and SINR.

This paper focuses on the issue of pilot assignment in cell-free massive multiple-input multiple-output systems to efficiently mitigate pilot contamination. A novel pilot assignment strategy based on quantum bacterial foraging optimization (QBFO) is proposed. The quantum bacterial population collapses to the ground state population by measuring to obtain the pilot assignment solutions. In this scheme, we first utilize adaptive quantum rotation gate to imitate chemotaxis. Then, the health of bacterium is introduced to record the cumulative fitness sum of each bacterium. Moreover, we consider elimination-dispersal operation in order to avoid being trapped in the local optimum. Numerical results show the superiority of the proposed scheme to other traditional schemes and validate that the proposed scheme can significantly improve system's average downlink throughput.

The performance of massive multiple input multiple output systems may be limited by inter-cell pilot contamination (PC) unless appropriate PC mitigation or avoidance schemes are employed. In this paper we develop techniques based on existing long term evolution (LTE) measurements - open loop power control (OLPC) and pilot sequence reuse schemes, that avoid PC within a group of cells. We compare the performance of simple least-squares channel estimator with the higher-complexity minimum mean square error estimator, and evaluate the performance of the recently proposed coordinated pilot allocation (CPA) technique (which is appropriate in cooperative systems). The performance measures of interest include the normalized mean square error of channel estimation, the downlink signal-to-interference-plus-noise and spectral efficiency when employing maximum ratio transmission or zero forcing precoding at the base station. We find that for terminals moving at vehicular speeds, PC can be effectively mitigated in an operation and maintenance node using both the OLPC and the pilot reuse schemes. Additionally, greedy CPA provides performance gains only for a fraction of terminals, at the cost of degradation for the rest of the terminals and higher complexity. These results indicate that in practice, PC may be effectively mitigated without the need for second-order channel statistics or inter-cell cooperation.

Massive multiple‐input multiple‐output is an attractive technology that aims to ameliorate both the energy and the spectrum efficiencies of fifth‐generation cellular networks. The main problem that limits the performance of a massive multiple‐input multiple‐output system is pilot contamination, which happens due to the limited number of orthogonal pilot sequences. In this paper, we assume that each cell is assigned a set of orthogonal pilot sequences and that these same pilot sequences are reused in other cells. Consequently, the contamination comes only from users in other cells, so that it is less powerful than an active user in the cell of interest. We propose a novel iterative algorithm to ensure the mitigation of the pilot contamination effect. This algorithm, dubbed as the “channel zooming algorithm”, is able to zoom through iterations on the signal of the strongest user by taking advantage of the difference in space signatures and transmitted data sequences. Moreover, we derive an analytical expression of the bit error probability, resulting after a large number of iterations of the proposed algorithm. The analytical results developed in our work are confirmed by simulation results.

Massive multiple-input multiple-output (MIMO) or large-scale MIMO is the magical technology that can provide the required large system capacity in the future fifth generation (5G) cellular networks. Massive MIMO is characterized by a large number of base station (BS) antennas, which makes it less susceptible to noise and fast fading. Unfortunately, this large antennas number does not enable the massive MIMO to get rid of the inter-cell interference originated from the problem of pilot contamination. In this article, we propose a spatiotemporal scenario that can mitigate the pilot contamination effects by avoiding synchronous pilot transmission in the time domain and following a pilot allocation algorithm in the spatial domain. We show that our proposed scenario provides a reduced level of inter-cell interference, which eventually gives it the ability to enhance the minimum and the mean achievable capacities per terminal.

In recent years cell-free massive multiple-input multiple-output system has attracted considerable attention for promising large spectral and power efficiency. However, pilot contamination, a well-known challenge in cell-free massive MIMO, limits these aspired gains. Alongside, and since 2000, Location Based Services (LBS) have rapidly emerged and enabled users to know their current location information for various purposes. In order to reduce pilot contamination and improve system performance in cell-free massive MIMO, this paper proposes an efficient location-based greedy pilot assignment (LBGPA) algorithm. Specifically, by following the principle that two users in close vicinity of each other are not assigned the same pilot sequence and utilizing the advantage of greedy pilot assignment (GPA), the pilot contamination can be reduced significantly. Numerical experiments show that the proposed algorithm outperforms other conventional pilot assignment schemes in typical cell-free massive MIMO systems.

Massive multiple-input-multiple-output (MIMO) system enhances area throughput by achieving high spectral efficiency. Pilot contamination limits the system performance. In this paper, we propose a pilot reuse factor adoption and applying multi-cell minimum mean square error (M-MMSE) precoding/combining scheme under the assumption of Laplacian correlation model to mitigate pilot contamination. Simulation results show that the proposed scheme outperforms the other existing precoding/combining schemes in decontaminating pilots and achieving larger spectral efficiency. The price to pay for interference rejection and spectral efficiency improvement is a small reduction in the desired signal power and an increase in the system computational complexity.

In this paper, an adaptive pilot assignment scheme is proposed to solve pilot contamination (PC) caused by the reuse of limited pilot sequences, while allowing massive access in the scalable Cell-free massive multiple-input multiple-output systems. The proposed allocation scheme consists of algorithms for determining initial serving relationships between access points (APs) and user equipments (UEs), and an adaptive group forming strategy where groups are formed with UEs that contain the least common associated APs. The proposed scheme guarantees the association of all UEs and better spectral efficiency (SE) by reducing PC efficiently with limited pilot sequences. Numerical results prove that the proposed allocation scheme outperforms the random assignment scheme and a recent pilot assignment proposal that allows massive access in respect to 95%-likely SE and average SE performance.

Thanks to its capability to provide a uniform service rate for the User Equipments (UEs), Cell-free (CF) massive Multiple-Input, Multiple-Output (mMIMO), has recently attracted considerable attention, both in academia and in industry, and so is considered as one of the potential technologies for beyond –5G and 6G. However, the reuse of the same pilot signals by multiple users can create the so-called pilot contamination problem, which can hinder the CF mMIMO from unlocking its full performance. In this paper, we address the challenge by formulating the pilot assignment as a maximally diverse clustering problem and propose an efficient yet straightforward repulsive clustering-based pilot assignment scheme to mitigate the effects of pilot contamination on CF mMIMO. The numerical results show the superiority of the proposed technique compared to some other methods with respect to the achieved uplink per-user rate.

In this paper, we investigate the system performance of a multi-cell multi-user (MU) hybrid millimeter wave (mmWave) multiple-input multiple- output (MIMO) network adopting the channel estimation algorithm proposed in [1] for channel estimation. Due to the reuse of orthogonal pilot symbols among different cells, the channel estimation is expected to be affected by pilot contamination, which is considered as a fundamental performance bottleneck of conventional multicell MU massive MIMO networks. To analyze the impact of pilot contamination on the system performance, we derive the closed-form approximation expression of the normalized mean squared error (MSE) of the channel estimation performance. Our analytical and simulation results show that the channel estimation error incurred by the impact of pilot contamination and noise vanishes asymptotically with an increasing number of antennas equipped at each radio frequency (RF) chain deployed at the desired BS. Thus, pilot contamination is no longer the fundamental problem for multi-cell hybrid mmWave systems.

A scalable cell-free massive MIMO (SCF-mMIMO) system where all user equipments (UEs) and access points (APs) employ finite resolution digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) over correlated Rician fading is presented and analysed in this paper. A closed-form expression for the uplink (UL) spectral efficiency (SE) using maximal-ratio combining (MRC) detection for centralized scheme is first derived. Moreover, a novel low complexity partial MMSE (P-MMSE) detector is proposed, which achieves very similar SE performance and maintains much less computational complexity in comparison with the original partial MMSE (P-MMSE) detector. In addition, a joint algorithm consisting of AP cluster formation, pilot assignment, and power control policy is proposed, which yields much higher SE performance than random pilot assignment and user-group based pilot assignment policies do, and meanwhile improves the quality of service (QoS) fairness for all accessing UEs as compared to the equal power transmit policy.

We propose a novel algorithm to design user load-achieving pilot sequences that mitigate pilot contamination in multi-cell massive multiple-input multiple-output (MIMO) networks. To this end, we first derive expressions for the user load and the load region of the network considering both small-scale and large-scale propagation effects. We then develop the pilot sequence algorithm for multi-cell massive MIMO networks as per the rules of generalized Welch bound equality design. Notably, we find that our algorithm and the corresponding downlink power allocation ensure that the user load is achieved when the signal-to-interference-plus-noise ratio (SINR) requirements for the users lie within the load region. Furthermore, we demonstrate the performance advantage of our proposed design relative to the existing designs, in terms of a larger load region and a higher maximum permitted SINR. Finally, we show that our proposed design can satisfy the pre-defined SINR requirements for users with a finite number of antennas at the base station (BS), while the existing designs cannot satisfy the same requirements even with an infinite number of antennas at the BS.

Massive multiple-input multiple-output (MIMO) is a promising technology to improve spectral efficiency (SE). However, because of limited pilot resources for channel estimation, pilot reuse among a large number of users is inevitable. It causes pilot contamination and will limit the system's performance. This paper proposes a channel charting (CC)-based pilot assignment (PA) method that assigns orthogonal pilot sequences to adjacent users on CC greedily to alleviate pilot interference with spatially correlated channels, where adjacent users on CC have stronger angular correlation. Moreover, a feature is explored from the second-order channel statistics for CC to extract more effective angular correlation among users. Pilot contamination is further mitigated by choosing the first assigned user which is prone to interference. Simulation results show that the proposed method has superiority over other PA methods, especially under the scenario that a large number of users need to be served.

The performance of multi-cell massive multiple- input multiple-output (MIMO) downlink (DL) with nonorthogonal multiple access (NOMA) is investigated. A novel pilot assignment for NOMA users is proposed to mitigate intra-cluster pilot contamination and to alleviate the detrimental impact of inter-cluster in the useful operating regime. The user nodes grouped into the same cluster based on the spatial-direction information are assigned orthogonal pilots to mitigate intracluster pilot contamination. These pilots are shared among the clusters to reduce the training overhead. However, the intercluster pilot contamination is mitigated by exploiting the distinct spatial directions of clusters. This pilot assignment ensures that the number of NOMA users that can be served simultaneously in the same time-frequency-spatial resource block is not limited by the length of coherence interval. A max-min fairness optimal transmit power allocation algorithm is proposed to guarantee user-fairness. The achievable DL sum rates are derived, and thereby, the cumulative impact of channel estimation errors, imperfect successive interference cancellation, and statistical channel knowledge at users is quantified. Our analysis reveals that the proposed massive MIMO NOMA can support massive access without compromising the spectral efficiency gains.