Massive MIMO Systems With Non-Ideal Hardware: Energy Efficiency, Estimation, and Capacity Limits

Recently the combination of massive multiple-input multiple-output (MIMO) and spatial modulation (SM), has been considered as a promising concept for uplink transmission, in which each user equipment (UE) uses SM for uplink transmission and base station (BS) is equipped with massive antennas. In this paper, we evaluate a massive single-carrier (SC) SM-MIMO system with frequency domain equalization, where SC transmission is combined with SM (SC-SM) to combat the negative impact of broadband frequency-selective fading, and frequency domain equalization is utilized to mitigate the intersymbol-interference with a low complexity. With frequency domain processing, a framework is proposed to analyze the achievable uplink spectral efficiency (SE) of single-cell massive SC-SM MIMO systems. Based on this framework, the closed-form SE lower bound of frequency domain maximum ratio combining is derived, and both the derivation of framework and SE lower bound are much more complicated than those of systems with time domain combining. Monte Carlo simulations verify the tightness of proposed SE lower bound, and show that massive SC-SM MIMO systems can outperform the SE of conventional single transmit antenna (TA) massive MIMO systems. The systems can even have a better SE performance than massive MIMO systems with spatial multiplexing UEs in a low signal-to-noise ratio. Finally, the SE gain is found to be mainly dependent on the specific number of UE's TAs, which facilitates an SE maximization via optimizing the number of TAs.

Massive Multiple Input Multiple Output (MIMO) is one of the key technologies in 5G, and it is envisioned to have superior spectral and energy efficiencies. This paper is the first to evaluate Massive MIMO in realistic performance metrics in heterogeneous urban environments, i.e. 20 Macrocells and 20 Picocells, providing cellular services in the city of Bristol (UK). We base our study on a 3D ray-tracing propagation channel model that uses real city maps. We also convolve our channel model with individual 3D complex polarimetric antenna radiation patterns for both base station (BS) and User Equipment (UE). We consider a system configuration with 128 elements at the BS and up to 16 receive terminals (i.e. 16 single-antenna UEs or 8 dual-antenna UEs). Eigen-beamforming precoding and a Received Bit-level mutual Information Rate (RBIR) based abstraction simulator are used on a system level. Millions of cellular links were simulated to ensure statistically relevant results. We quantify the realistically achievable capacity in terms of cell size, number of user terminals, and rank of the users, as well as the gain over traditional 4G Long-Term Evolution (lTE) networks. Overall, 128Tx-16Rx Massive MIMO (with rank-2 UEs) was found to provide up to 434% and 478% more capacity over traditional LTE Single-User MIMO with 8Tx-8Rx configuration in Macrocells and Picocells respectively.

Large scale antenna arrays technology has the potential of bringing many advantages to future wireless systems. Energy and spectral efficiency are going to be the most important features. Hence, accurate estimate of channel state information (CSI) makes these advantages achievable. This paper investigates the effects of angular spread on the accuracy of channel estimation for massive multiple input multiple output (MIMO) wireless communication systems. The model we consider consists of user equipment (UE) and a base station with large antenna array. Linear minimum mean square error (LMMSE) is used to estimate the uplink channel of a massive MIMO system using a pilot signal. It is shown that higher spatial correlation (SC) positively affects the accuracy of channel estimation when the signal to noise ratio is kept constant.

The massive capacity and connectivity requirements of the fifth generation (5G) wireless communication systems, have motivated the application of hundreds of antenna elements at the base station (BS). This kind of system known as massive multiple-input multiple-output (MIMO), offers a huge spectral and energy efficiency against traditional wireless communication techniques. As the number of antennas increases significantly in massive MIMO systems, the previous MIMO channel models will not be valid longer. One of the main issues is that the planar wavefront assumption cannot be considered over the massive MIMO arrays while spherical wavefronts are experienced. In this paper, the channel matrix of a multi-user (MU) massive MIMO system is derived for both the plane wave and spherical wave assumptions in the line-of-sight (LOS) condition, where the base station (BS) is equipped with a large non-uniform linear array of antennas. This non-uniformity causes to increase the orthogonality between sub-channels and the rank of the channel matrix. The obtained channel capacities are investigated by some simulation results.

The information growth we have experienced in the immediate past and which continues to increase has consequently brought about the big data era and when pooled with the vast increase in subscriber numbers has led to an ever-escalating demand for more efficient and high-capacity communication systems. The affinity for higher capacity and efficient networks has necessitated the initiation of wireless fifth generation (5G) networks. Among the key technologies underlying the wireless 5G network are massive Multiple-Input Multiple-Output (MIMO) and Cloud Radio Access Network (C-RAN) which enhances spectral efficiency, energy efficiency, security and robustness but suffers from pilot contamination and fronthaul finite capacity. There have been several attempts to minimize pilot contamination in massive MIMO system through linear precoding. But for those precoding schemes with good performance, they suffer from intricate problem of matrix inversion owing to large antenna numbers inherent in massive MIMO system, yet they do not render themselves readily to hardware parallelization. Also, channel state information estimation remains a challenge within massive MIMO networks. While the finite fronthaul capacity remains a bottleneck in C-RAN network systems. This study presents the formulation of iterative linear precoder that is efficiently parallelizable with efficient channel estimators for massive MIMO and massive MIMO partially centralised CRAN networks. The channel precoder was formulated and adapted using the iterative linear Rapid Numerical Algorithm (RNA). This model was then extended to include coordination among multicell massive MIMO system with receive combining computational complexity and efficiency evaluation. RNA model is again used to formulate improved linear and semi-blind channel estimators for massive MIMO systems in combination with the Fast Data Projection Method (FDPM). The semi-blind channel estimator is combined with compressed data channel estimator then extended based on Givens transformations and Data Projection Method (DPM) for massive MIMO partially centralised C-RAN networks. And finally, the estimation of the signal-to-interference-to-noise ratio, bit error rates, spectral efficiency, energy efficiency and normalised mean square error for the respective modelled components was realized. The models above were simulated using MATLAB for the analysis and validation. The TDD downlink massive MIMO system was considered with varying immediate channel state information qualities for the single cell and multicell systems. For single cell system, there was optimal performance with regard to the signal-to-interference-to-noise ratio and the bit error rate when rapid numerical algorithm was used to implement the matrix inversion process in comparison to existing methods. It also rendered the precoding process highly parallelizable further reducing the complexity. For instance, for base transceiver station with 128 antennas serving 32 user terminals at signal-to-interference-to-noise ratio = 20 the average per user terminal rate was: RNA = 5 bit/sec/Hz, Regularized Zero Forcing (RZF) = 5 bit/sec/Hz and Truncated polynomial Expansion (TPE at J = 2) = 2.9 bit/sec/Hz. For the case of the Bit Error Rate (BER), for base transceiver station with 128 antennas serving 32 user terminals at signalto-interference-to-noise ratio = 10 the BER was: RNA = 1, Regularized Zero Forcing (RZF) = 1 and TPE (J = 2) = 5. For the multicell massive MIMO, it was found that the performance of rapid numerical algorithm implementation gave a good spectral efficiency and energy efficiency performance in comparison to existing methods while lowering the complexity further through parallelization. The compressed data channel estimator gave comparable performance for the spectral efficiency and normalized mean square error when compared to the improved linear channel estimators. The semi-blind channel estimators for both massive MIMO and massive MIMO partially centralised C-RAN outperformed the linear channel estimators as well as the compressed data channel estimator. These results demonstrate that rapid numerical algorithm can effectively eliminate the intricate matrix inversion associated with linear precoding while rendering itself to efficient parallelization. It also shows that the compressed data channel estimator optimally estimates the channel covariance matrix while reducing the amount of channel state information transmitted in estimation process. The semi-blind channel estimators have the optimal performance with regard to the normalised mean square error. It was also illustrated that the Givens transformation based semi-blind estimator outperforms the FDPM based semi-blind channel estimator.

The fifth generation (5G) wireless communication system is considered a promising and recent research. Massive Multiple-Input Multiple-Output (MIMO) system has an influential role in improving game-changing enhancements in area throughput and energy efficiency (EE). EE refers to one of the easiest and most cost-effective ways to combat climate change, reduce energy costs for consumers, and improve the competitiveness of businesses. Deep Learning (DL) can significantly improve area throughput and EE. It plays a crucial role in the 5G wireless communication systems. Optical systems are not far from this system, which include the optical components which serve more accurately.To assess the overall power usage in up-link and down-link communications, a power dissipated model is introduced. The proposed model incorporates the overall power used by the base station (BS) power amplifier and circuit components as well as single antenna user equipment (UE). In this paper, EE and power consumption of massive MIMO systems are calculated based on Convolutional Neural Network hybrid with Long Short-Term Memory cell (CNNLSTM). This model is proposed to overcome the high complexity and over fitting by replacing the inner dense connections with convolution layers resulting in improved model performance. There are different linear processing schemes applied for detection and precoding, as Minimum Mean Squared Error (MMSE), Zero-Forcing (ZF), and Maximum Ratio Transmission/Maximum Ratio Combining (MRT/MRC). These schemes are applied to train our proposed CNNLSTM.It is observed the results are improved by 12.8% when using ZF (perfect CSI) and the system outperforms other schemes by 10%, 10.44% and 12.05% when using MRT, ZF (imperfect CSI), and MMSE, respectively, for the EE performance. The obtained results also reveal that an improvement of 7.5% is achieved when using MRT. It outperforms other schemes by 6.5%, 5% and 5%, respectively, when using ZF (perfect CSI), ZF (imperfect CSI), and MMSE for average power consumption per antenna using the CNNLSTM model. When using MRT, an improvement of 7.5% is achieved in the area throughput performance, and it outperformed the other schemes, ZF (perfect CSI), ZF (imperfect CSI) and MMSE, by 5.2%, 5% and 5.2%, respectively.

In wireless communications, massive multiple-input multiple-output (MIMO) systems in which the base station (BS) is equipped with a large number of antennas can provide significant spectral and energy efficiency by means of simple signal processing. Therefore, massive MIMO is an attractive technology for next generation wireless communication systems and for green communications. There are two operation modes: time-division duplex (TDD) and frequency-division duplex (FDD) for massive MIMO systems, and the debate on the two modes is still discussed in the literature. The originator of massive MIMO systems, Marzetta, has presented the basic frame structures for both the TDD and the FDD modes. However, the basic frame structures do not carefully take the hardware impairments such as phase noise (PN) into account. In this paper, by taking the PN as the key hardware impairment into account, we recommend new and efficient frame structures for both the TDD and the FDD modes. The corresponding performance comparison between the two modes is also provided by using the data transmission efficiency (DTE) as the performance measurement. The comparison may help determine which mode should be used for massive MIMO systems.

Massive multiple-input multiple-output (MIMO) systems have been gaining interest recently due to their potential to achieve high spectral efficiency [1]. Despite their potential, they come with certain issues such as pilot contamination. Pilot contamination occurs when cells simultaneously transmit the same pilot sequences, creating interference. Unsynchronizing the pilots can reduce pilot contamination, but it can produce data to pilot interference. This thesis investigates the impact of pilot contamination and other interference, namely data to pilot interference, on the performance of finite massive MIMO systems with synchronized and unsynchronized pilots. Two unsynchronized pilot schemes are considered. The first is based on an existing time-shifted pilot scheme, where pilots overlap with downlink data from nearby cells. The second timeshifted method overlaps pilots with uplink data from nearby cells. Results show that if there are small numbers of users, the first time-shifted method provides the best sum rate performance. However, for higher numbers of users, the second time-shifted method provides better performance than the other methods. We also show that time-synchronized pilots are not necessarily the worst case scenario in terms of sum rate performance when shadowing effects are considered. The wireless channel can be time and frequency varying due to the Doppler effect from mobile user equipment (UE) and a multipath channel. These variations can be simulated by using a selective channel model, where the channel can vary within the coherence block in both time and frequency domains. The block fading channel model approximates these variations by assuming the channel stays constant within a coherence block, but changes independently between blocks [2]. Due to its simplicity, the block fading model is widely used in massive MIMO studies [3–8]. Our research compares the impact of block fading and time-selective fading channel models in massive MIMO systems. To achieve this, we derive a novel closed form sum rate expression for time-selective channels. Results show that there are significant differences in sum rate performance between these models. In addition to time variation from Doppler effect, the channel can also experience frequency variation due to delay spread from multipath signal propagation. The combination of time and frequency selective channels can be described as a doubly-selective channel. Hence, the sum rate expression for time-selective channels can also be extended

We consider a low complexity polynomial expansion (PE) detector in a massive multiple-input multiple-output (MIMO) uplink channel. In contrast to most massive MIMO systems in the literature, where single antenna user equipments (UEs) are assumed, multiple antenna UEs are employed in this paper. Moreover, the channel between a base station (BS) and a UE is a jointly correlated Rician fading channel. The PE detector reduces the computational complexity of the minimum mean square error (MMSE) detector by replacing the matrix inversion with an approximate matrix polynomial. The coefficients of the approximate matrix polynomial are computed from the deterministic equivalents of the moments of the channel Gram matrix. We use operator-valued free probability, which is a more general version of free probability, to derive the deterministic equivalents. In particular, we use the operator-valued moment-cumulant formula. The proposed low complexity PE detector is easy to compute. Simulation results show that the proposed detector can achieve performance close to the MMSE detector.

As the number of antennas increases, the massive multiple-input multiple-output (MIMO) system can precisely point to user equipments (UEs) with narrow beams. Accurate and timely channel state information (CSI) feedback is crucial to keep UEs in service. Mobile UEs, however, may suffer from the narrow beam nature of the massive MIMO system since UEs can move out of the beam coverage. When intelligent reflecting surface (IRS) is applied to the massive MIMO system, the adjustment of the IRS cannot be frequent as the IRS is controlled remotely by the base station (BS). Limiting the number of CSI feedback and the number of both BS and IRS adjustments significantly reduces the overhead of transmission and computation to the system. In this paper, we consider the UEs’ mobility adaptation problem in an IRS assisted multiuser massive MIMO downlink system with infrequent CSI feedback. We propose a beam control method that adapts to UEs’ mobility. The problem is constructed as a sum rate problem where both the BS and IRS are taken into account to jointly optimize the beamforming matrices. Simulation results show that our proposed algorithm can converge quickly and provide satisfactory performance for mobile UEs. At the same time, our proposed algorithm reduces the frequency of updating the beamforming matrices effectively both at the BS and at the IRS.

In this paper, an optimal coordinated multipoint beamforming (CMBF) scheme is proposed for heterogeneous networks (HetNets) with massive multiple input multiple output (MIMO) systems for energy efficiency (EE), taking into account the maximum transmission power of the base station (BS) with the limitation on the power consumption of the static circuit. This paper analyzes the influence of the different parameters on the EE and derives a closed-form expression. We investigate the different cooperation schemes, considering the influence of the number of antennas of small base station (SBS) and macro base station (MBS), the quality of service (QoS), and the number of different service users. Therefore, the best parameters can be selected for optimizing EE with the different scenarios in HetNets with massive MIMO systems. Theoretical derivation and simulations show that the amount of BS antennas, achievable rate constraints, quantity of service users, and cooperative schemes all have the vital influences on the EE for the CMBF design. Therefore, the proposed optimization framework enables us to identify key system parameters, and obtain the better system design for the massive MIMO HetNets.

Massive multiple-input multiple-output (MIMO) has become a promising solution to provide unprecedented spectral efficiency (SE) to future cellular networks, based on the idea of equipping the base station (BS) with hundreds or thousands of antenna elements operating in a coherent fashion. At the doors of the future fifth generation mobile communication networks (5G), massive MIMO has been widely recognized as one of the cornerstones, given the ambitious key performance indicators of the future standard. The channel state information (CSI) acquisition is one of the core activities in massive MIMO, on which its entire performance depends to a great extent. Pilot contamination has been recognized as the main limiting factor to acquire an accurate CSI, becoming the focus of a large body of research. In this paper, we focus on the pilot contamination problem in massive MIMO systems. An approach is proposed to mitigate this problem based on the use of superimposed (SP) pilots in combination with time-multiplexed (TM) pilot sequences. Specifically, we use the contaminated channel estimates to reduce the amount of interference produced when SP pilots are used. Results show that the amount of interference caused by transmitting pilots alongside the data is substantially reduced when this method is put into place. In turn, the proposed method leads to mitigating the pilot contamination.

Systematic operations of Massive MIMO for Internet of Things networks

Massive-MIMO is an exciting and attractive technique enabler for upcoming 5G transmission systems due to the reason that it provides numerous orders of throughput and energy efficiency (EE) gains over recent LTE and LTE-Advanced systems. Massive-MIMO is a multiuser MIMO technique in which ‘K’ single antenna user equipments (UE’s) are treated simultaneously over the identical time-frequency resource by a base station (BS) deployed with a huge number ‘M’ antennas, i.e., M ≫ K. This paper evaluates the energy efficiency (EE) enhancements in the case when macro-cell topography is replaced with massive MIMO at the Base Station (BS) and cover with small-cell access points (SCAs).

One of focuses in green communication studies is the energy efficiency (EE) of massive multiple-input multiple-output (MIMO) systems. Although the massive MIMO technology can improve the spectral efficiency (SE) of cellular networks by configuring a large number of antennas at base stations (BSs), the energy consumption of radio frequency (RF) chains increases dramatically. The increment of energy consumption is caused by the increase of RF chain number to match the antenna number in massive MIMO communication systems. To overcome this problem, a generalized spatial modulation (GSM) solution is presented to simultaneously reduce the number of RF chains and maintain the SE of massive MIMO communication systems. A EE model is proposed to estimate the transmission and computation power of massive MIMO communication systems with GSM. Simulation results demonstrate that the EE of massive MIMO communication systems with GSM outperforms the massive MIMO communication systems without GSM. Besides, the computation power consumed by massive MIMO communication systems with GSM is effectively reduced.