Time Division Duplex Massive Massive Multiple-input Multiple-output Research Articles

Performance comparisons of FDD MIMO and 2.6 GHz TDD massive MIMO: An experimental analysis

Massive multiple-input multiple-output (MIMO) is considered as a breakthrough technology in 5G and beyond 5G systems. Some of its main advantages are providing high spectral efficiency to many users simultaneously in the same time–frequency blocks, strong directive signals towards short-range areas and little interference leaks. However, while massive MIMO exhibits interesting benefits, it is important to investigate its main gains through real deployment scenarios in an operator’s infrastructure. In this paper, we focus on performance comparisons of traditional frequency-division duplex (FDD)-based MIMO and 2.6 GHz Time-division duplex (TDD)-based massive MIMO deployments through experimental analysis under different spectrum and bandwidth in a total of three separate sites and one co-site in an operational infrastructure of an operator in Turkey. We also provide design guidelines and requirements for massive MIMO network deployment and proper acceptance of Key Performance Indicators (KPIs) collection and comparisons criteria. Our experimental results reveal up to 66%, 56% and 23% performance benefits in terms of downlink (DL) cell throughput of 2.6 Ghz TDD-based massive MIMO compared to FDD-based MIMO sites in 800 Mhz (site with approximately same number of User Equipment (UEs) compared with TDD massive MIMO), 1800 Mhz (site with higher number of UEs compared with TDD massive MIMO) and 2600 Mhz (site with lower number of UEs compared with TDD massive MIMO) respectively each having 10 Mhz bandwidth. On the other hand, LTE 1800 Mhz FDD MIMO at 20 Mhz can yield higher user throughput values in comparison to 2.6 GHz TDD-based massive MIMO at 10 Mhz. We also observed that the maximum paired layer reached 14 layers in DL of TDD-based massive MIMO. At the end of the paper, we address the main observations and takeaways of TDD-based massive MIMO deployments.

A Comparison of TDD and FDD Massive MIMO Systems Against Smart Jamming

Frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems introduce a large overhead in downlink channel estimation in contrast to the time division duplex (TDD) mode. This overhead results in a considerable spectral efficiency (SE) gap between the FDD and TDD modes. In this paper, we consider the performance of the TDD and FDD massive MIMO systems with a spatially correlated channel in the presence of jamming in the network. We show how a smart jammer can effectively design its attack signal to degrade the network performance in terms of the downlink SE. Since the jammer can obtain different information about the channels in the TDD and FDD modes, two distinct jamming strategies are proposed for each mode. In the numerical results, the performance of both the TDD and FDD modes under the optimized jamming designs are evaluated and compared. Our results show that despite more attention to the TDD mode in current massive MIMO systems, it is more vulnerable to smart jamming attacks compared to the FDD mode which results in a smaller SE-gap between the modes. Furthermore, a countermeasure technique is proposed to combat this jamming attack in both the TDD and FDD modes by estimating the jamming power, grouping users, and allocating power among them. The numerical results demonstrate the effectiveness of the proposed countermeasure techniques in both the TDD and FDD modes.

Pilot Decontamination in Multi-Cell Massive MIMO Systems via Combining Semi-Blind Channel Estimation With Pilot Assignment

In the multi-cell massive multiple-input multiple-output (MIMO) systems, the mitigation of pilot contamination is an important issue in the semi-blind channel estimations. For the pilot decontamination schemes, the performance of the semi-blind based estimation approaches is constrained by the ratio of the interference signal power to the target signal power. In order to eliminate pilot contamination effectively, the eigenvalues of the target signals should be separated from those of the interference signals. In this paper, we propose a novel uplink channel estimation for mitigating pilot contamination in time division duplex massive MIMO systems, which combines pilot assignments with semi-blind channel estimation methods. In order to reduce the search complexity for the optimal pilot assignment under the condition of a large number of users per cell, we proposed a sector-based pilot assignment method, including inter-sector pilot assignment and intra-sector pilot optimization. Simulation results verify that the joint pilot allocation and semi-blind channel estimation method is capable of improving the system achievable rates and the normalized mean square error performance.

Pilot allocation scheme based on coalition game for TDD massive MIMO systems

Pilot contamination is a major factor that restricts the system performance of time division duplex (TDD) massive multiple-input multiple-output (MIMO) systems. Reasonable pilot allocation is an effective method to reduce pilot contamination. However, most of the existing pilot allocation methods focus on cells of the same size, a regular shape, an equal number of users per cell, and/or independent fading channels. When they are generalized or modified to actual scenes (in which cells have irregular shapes, users are randomly distributed, and channels are correlated fading channels), they are inflexible and cannot guarantee the target performance requirement. Considering both flexibility of pilot allocation and system performance, this paper introduces the idea of a coalition game into pilot allocation and proposes a pilot allocation scheme based on the coalition game. Different utility functions are analyzed under correlated fading channels; the adjustment principle of coalition structure and the coalition formation algorithm are given. Simulation results show that the proposed scheme can be used in any actual scene as well as guarantee the target performance requirement.

Joint Pilot Allocation and Power Control to Enhance Max-Min Spectral Efficiency in TDD Massive MIMO Systems

In time division duplex (TDD) massive multiple-input multiple-output (MIMO) systems, pilot contamination seriously limits the improvement of spectral efficiency (SE). In order to provide good communication quality for every user, this paper devotes to enhance minimum spectral efficiency of the system. Firstly, a multi-cell multi-user system model is established to derive the objective function of Max-Min spectral efficiency. Through analysis of the mathematical expressions, we found that it is not a convex optimization problem for joint pilot allocation and power control, but a non-deterministic polynomial (NP) problem that is difficult to deal with. In order to solve the problem, a new algorithm is proposed to decompose the problem into two easy-to-handle sub-problems. We employ weight graph coloring (WGC) algorithm to deal with the sub-problem of pilot allocation, where uplink transmission power is fixed. The geometric programming (GP) approach is utilized to solve the sub-problem of power control, in which the large-scale fading factor is fixed. Then the computational complexity of the proposed algorithm is analyzed. Compared with the ideal optimal solution algorithm that requires exhaustive search, our algorithm has much less computational complexity. Finally, the simulation results demonstrate that the proposed algorithm significantly improves the minimum spectral efficiency, which is close to the ideal optimal solution.

Energy- and spectral-efficiency of zero-forcing beamforming in massive MIMO systems with imperfect reciprocity calibration: bound and optimization

In a time-division duplex (TDD) system with massive multiple input multiple output (MIMO), channel reciprocity calibration (RC) is generally required in order to cope with the reciprocity mismatch between the uplink and downlink channel state information. Currently, evaluating the achievable spectral efficiency (SE) and energy efficiency (EE) of TDD massive MIMO systems with imperfect RC (IRC) mainly relies on exhausting Monte Carlo simulations and it is infeasible to precisely and concisely quantify the achievable SE and EE with IRC. In this study, a novel method is presented for tightly bounding the achievable SE of massive MIMO systems with zero-forcing beamforming under IRC. On the basis of the analytical results, we demonstrate key insights for practical system design with IRC in three aspects: the scaling rule for interference power, saturation region of the SE, and the bound on the SE loss. Finally, the trade-off between spectral and energy efficiencies in the presence of IRC is determined with algorithms developed to optimize SE (EE) under a constrained EE (SE) value. The loss of optimal total SE and EE due to IRC is also quantified, which shows that the loss of optimal EE is more sensitive to IRC in a typical range of transmit power values.

User Grouping and Pilot Allocation for Spatially Correlated Massive MIMO Systems

This paper proposes a novel approach to deal with pilot contamination for spatially correlated massive multiple-input multiple-output (MIMO) systems with insufficient pilots. Specifically, a typical multi-cell multi-terminal time division duplex massive MIMO system is modeled first. To maximize the average achievable system capacity, a user grouping method which progressively merges the individual terminals with the largest angle of arrival similarity is proposed to mitigate the intra-cell interference. Furthermore, in order to alleviate the inter-cell interference, a pilot allocation method is proposed, including two algorithms, i.e., group matching algorithm and graph coloring-based pilot allocation algorithm. Compared with the existing pilot allocation schemes, the simulation results validate that the proposed approach can effectively improve the complexity-performance trade-off with a finite number of the base station antennas.