Massive MIMO: An Introduction

Focused research and standardization work in wireless throughput, subscribers will increase day by day. One can prospect that, millions of users in a mega city will want to transmit and receive data, for instance, 100 megabits per second per user. Massive MIMO (Large Scale Antenna Systems) is a new technology which will be used for resolving the mentioned issue. Spectral efficiency improvements over fourth generation (4G) technology are frequently mentioned. Adding more antennas is always beneficial for increased throughput, reduced radiated power, increase the capacity everywhere in the cell and greater simplicity in signal processing. In these days the main problem is RF interference and noise which can be generated by almost any device that produces an electro-magnetic signal, such as cordless phones to Bluetooth headsets, microwave ovens, repeaters and even smart phones, which is caused call drop and bad quality in the network. In this article, the Signal-to-interference-plus-noise ratio (SINR) and the value of mean capacity in the Non-cooperative cellular wireless have been increased by using infinite number of base station antennas. While employing advanced features, this is illustrated by network densification, Multi-cell Multi-User MIMO and inter cell interference mitigation techniques. The propagation model is not clear for both terminals and base stations which is calculated taking in to consideration path loss, specular reflection, environment models, earth’s elevation, fast fading, log-normal shadowing fading and geometric attenuation. The conjugate transpose of the channel estimation is used for forward and reverse precoding. Numerical results show that, by using unlimited number of antennas in the base station, the inter-cell interference, the effect of uncorrelated noise and fast fading have been vanished, although the inter-cell interference that caused by reuse of the pilot sequence in other cells does not disappear. And also average capacity improves with increment of base station antennas. In this study, MATLAB based simulation tool has been developed to calculate the SIR and also the mean capacity.

Recent advances in massive multiple-input multiple-output (MIMO) communication show that equipping base stations (BSs) with large antenna arrays can significantly improve the performance of cellular networks. Massive MIMO has the potential to mitigate the interference in the network and enhance the average throughput per user. On the other hand, dynamic time-division duplexing (TDD), which allows neighboring cells to operate with different uplink (UL) and downlink (DL) subframe configurations, is a promising enhancement for the conventional static TDD. Compared with static TDD, dynamic TDD can offer more flexibility to accommodate various UL and DL traffic patterns across different cells, but may result in additional interference among cells transmitting in different directions. Based on the unique characteristics and properties of massive MIMO and dynamic TDD, we propose a marriage of these two techniques, i.e., to have massive MIMO address the limitation of dynamic TDD in macrocell (MC) networks. Specifically, we advocate that the benefits of dynamic TDD can be fully extracted in MC networks equipped with massive MIMO, i.e., the BS-to-BS interference can be effectively removed by increasing the number of BS antennas. We provide detailed analysis using random matrix theory to show that the effect of the BS-to-BS interference on UL transmissions vanishes as the number of BS antennas per user grows infinitely large. Last but not least, we validate our analysis by numerical simulations.

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.

<p>Over the past few years there has been an extensive growth in data traffic consumption devices. Billions of mobile data devices are connected to the global wireless network. Customers demand revived services and up-to-date developed applications, like real-time video and games. These applications require reliable and high data rate wireless communication with high throughput network. One way to meet these requirements is by increasing the number of transmit and/or receive antennas of the wireless communication systems. Massive multiple-input multiple-output (MIMO) has emerged as a promising candidate technology for the next generation (5G) wireless communication. Massive MIMO increases the spatial multiplexing gain and the data rate by adding an excessive number of antennas to the base station (BS) terminals of wireless communication systems. However, building efficient algorithms able to decode a coherently or non-coherently large flow of transmitted signal with low complexity is a big challenge in massive MIMO. In this dissertation, we propose novel approaches to achieve optimal performance for joint channel estimation and signal detection for massive MIMO systems. The dissertation consists of three parts depending on the number of users at the receiver side.</p><p>In the first part, we introduce a probabilistic approach to solve the problem of coherent signal detection using the optimized Markov Chain Monte Carlo (MCMC) technique. Two factors contribute to the speed of finding the optimal solution by the MCMC detector: The probability of encountering the optimal solution when the Markov chain converges to the stationary distribution, and the mixing time of the MCMC detector. First, we compute the optimal value of the “temperature'' parameter such that the MC encounters the optimal solution in a polynomially small probability. Second, we study the mixing time of the underlying Markov chain of the proposed MCMC detector.</p><p>We assume the channel state information is known in the first part of the dissertation; in the second part we consider non-coherent signal detection. We develop and design an optimal joint channel estimation and signal detection algorithms for massive (single-input multiple-output) SIMO wireless systems. We propose exact non-coherent data detection algorithms in the sense of generalized likelihood ratio test (GLRT). In addition to their optimality, these proposed tree based algorithms perform low expected complexity and for general modulus constellations. More specifically, despite the large number of the unknown channel coefficients for massive SIMO systems, we show that the expected computational complexity of these algorithms is linear in the number of receive antennas (N) and polynomial in channel coherence time (T). We prove that as $N \rightarrow \infty$, the number of tested hypotheses for each coherent block equals $T$ times the cardinality of the modulus constellation. Simulation results show that the optimal non-coherent data detection algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity.</p><p>In the part three, we consider massive MIMO uplink wireless systems with time-division duplex (TDD) operation. We propose an optimal algorithm in terms of GLRT to solve the problem of joint channel estimation and data detection for massive MIMO systems. We show that the expected complexity of our algorithm grows polynomially in the channel coherence time (T). The proposed algorithm is novel in two terms: First, the transmitted signal can be chosen from any modulus constellation, constant and non-constant. Second, the algorithm decodes the received noisy signal, which is transmitted a from multiple-antenna array, offering exact solution with polynomial complexity in the coherent block interval. Simulation results demonstrate significant performance gains of our approach compared with suboptimal non-coherent detection schemes. To the best of our knowledge, this is the first algorithm which efficiently achieves GLRT-optimal non-coherent detections for massive MIMO systems with general constellations.</p>

A large scale field trial is conducted to investigate the integrated performance of massive multiple-input multiple-output (MIMO) and polar code, which are two key technologies for 5th generation (5G) mobile system. A more simple and effective algorithm named polarization weight (PW) is used in polar construction. Based on uplink and downlink channel reciprocity in the time-division duplex (TDD) mode, the practical performance of single user (SU) massive MIMO prototype with 64 independent RF channels and 200 MHz bandwidth is investigated under different user equipment (UE) deployments, different layer numbers and moving speeds. Compared to massive MIMO with turbo code, it is shown that significant performance gains can be obtained in TDD massive MIMO system with polar code. For 3 layers scheduled in massive MIMO system with polar code, the maximum user throughput of 1.64 Gbps and spectrum efficiency of 11.64 bit/s/Hz can be achieved. Based on these observations, the feasibility and performance of TDD massive MIMO system with polar code are verified.

We propose a channel state information acquisition mechanism to reduce the training overhead in frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) downlink (DL). While the massive MIMO has demonstrated the great potentials in many aspects, the carrier multiplexing designs in massive MIMO mostly adopt time-division duplexing (TDD) system, which stems from a stereotype that the uplink/DL channel reciprocity only holds for TDD. On the other hand, FDD massive MIMO is generally considered infeasible due to the unaffordable temporal overhead for sending training symbols at base station. Our proposed FDD with reverse training (FDD-RT) modifies the DL band training procedure in typical FDD system to reduce pilot overhead, where the DL band channel reciprocity is exploited in FDD-RT. FDD-RT contributes to providing a feasible and relatively low complexity method to implement massive MIMO in FDD system. Without FDD-RT, communication operators who currently use FDD system may be enforced to change to TDD system in the future to accommodate massive MIMO, where the cost is prohibitive. The most different property of FDD-RT from other solutions is that its training overhead does not scale with the number of antennas at base station. The detail analysis/comparison of FDD-RT will be given in this paper.

Time-Division duplexing (TDD) allows to estimate the downlink channels for an arbitrarily large number of base station antennas by using a finite number of orthogonal pilot signals in the uplink, by exploiting channel reciprocity. Based on this observation, a recently proposed “Massive MIMO” scheme was shown to achieve unprecedented system spectral efficiency using a very simple beamforming scheme and a very large number of base station antennas per active user per cell. In this work, we consider a more sophisticated system design that partition the users' population in geographically determined “bins” and for each bin selects the best scheme in a family of network- MIMO schemes defined by the type of linear beamforming, cooperation among base stations, frequency reuse, pilot reuse and user loading factors. The spectral efficiency obtained by the proposed architecture is similar to that achieved by “Massive MIMO”, with a 10-fold reduction in the number of antennas at the base stations (roughly, from 500 to 50 antennas).

A systematic and thorough report on the growth of the different wireless communication technologies for mobile technology is the main objective of this paper. The paper deals with mobile network development that helped to develop mobile and connectivity 5G technology. This paper also addressed the countries that use and test 5G technology, as well as the companies that undertook 5G growth. Fifth generation (5G) technology having all advanced wireless communication features that make 5G technologies more prominent for the future. This paper summarizes main technologies that can be utilized in futuristic 5G wireless communication equipment. Few examples are massive MIMO system as well as millimeter wave and communication from device to device. New directions for study will contribute to fundamental changes in the architecture of future cellular networks of fifth generation (5G). This article describes five developments that can results into changes into disruptive design of software as well as hardware: architecture based on device centric, communication between machine to machine, massive MIMO and millimeter wave, massive MIMO and smarter devices. In addition to 5G network prospective impact on 5G and the remaining research challenges, the key ideas for each technology are described.

In last decade, there have been a developed interest in the demand for the higher data rates which is caused by an increased mobile data traffic due to the rise of high number of mobile data users, this has prompted to the consideration of fifth generation (5G) technology as the future mobile communication standard or protocol because of its capability to handle of its capability to handle vigorous high transmission data rates and utilizing low power. In this paper, analysis of the Bit Error rate (BER) using Maximum Ratio Transmit (MRT) linear precoding scheme under imperfect Channel State Information (CSI) is carried out for massive Multiple-Input Multiple-Output (MIMO) under Time Division Duplex (TDD) mode of operation in the downlink transmission. Analysis using MATLAB have been carried out after deriving the formula relating BER and other parameters of interest for simulations. Most of the results showed that, the large the value of accuracy of channel estimation causes the degradation of the performance of massive MIMO in terms of quality of signal, which in this paper was measured through BER. In the future analysis of the BER must be extended to other linear precoding schemes like extending the analysis using Zero-Forcing (ZF) or Minimum Mean Square Error (MMSE) or even going to non-linear precoding schemes like dirty paper Coding (DPC).

In this chapter, we presented a summary and future research direction of massive multiple input multiple output (MIMO) for fifth generation (5G). Massive MIMO technology, where a base station (BS) equipped with very large number of antennas for serving many users in the same time-frequency resource, can meet the demand of high throughput, spectral efficiency, energy consumption of wireless communications, and hence it is a promising technology for next generation of wireless communications such as 5G cellular systems. Furthermore, we also presented, where massive MIMO could lead to significant improvement of 5G wireless Communication systems.

Cell-free (CF) Massive multiple-input multiple-output (MIMO) system is one of the most promising technologies for the fifth generation (5G) wireless communication and beyond, in which the concept of cell boundaries is not respected. This new paradigm aims to cover the users simultaneously through many distributed access points (APs) over the same time/frequency resources based on time-division duplex (TDD) system. In this paper, the uplink spectral efficiency (SE) of a CF Massive MIMO system is investigated based on the stochastic geometry (SG) tool and over Rician fading channels. The distribution of APs is assumed to be random based on the Poisson point process (PPP) to emulate the real AP behavior over the mobile network which has not been considered previously. However, deploying the network's APs irregularly may worsen the phase noise effect and thus the SE of the system. The uplink SE of cell free massive MIMO is derived considering the maximum ratio combining (MRC) at the AP receivers and minimum mean-square error (MMSE) to estimate the channels stats. The simulation results have confirmed that CF Massive MIMO system provides higher SE gain when the APs density is unevenly and largely distributed. However, a considerable cost of the uplink SE is observed when the length of the uplink training period increases for the perfect Channel State Information (CSI) case. Moreover, the performance gap between the perfect and imperfect (CSI) converges when the uplink training increases.

We propose a new physical-layer public key encryption scheme and establish a trapdoor one-way function through Massive MIMO techniques and precoding designs. Under standard arguments, we show that the eavesdropper's decoding complexity grows exponentially with the number of antennas, while the legitimate receiver's decoding complexity grows only quadratically. The proposed scheme builds a bridge between information-theoretic security and cryptographic security. Compared to the traditional physical-layer security, the proposed scheme is secure when the number of the eavesdropper's antennas is infinite or much larger than the number of transmitter/receiver antennas, provided that the eavesdropper's distance from the legitimate receiver is less than one-half of the wavelength, or that the channel estimation process between the sender-receiver pair is broken by the eavesdropper. Because the scheme is based on lattice, not on channel reciprocity, it can be applied to both time-division duplex and frequency-division duplex channels, and utilizes the simple physical layer characteristics of Massive MIMO to resist the currently known quantum attacks. The proposed scheme is adapted to the future requirements of 6G for the security of communication, and provides a new idea for the post-quantum cryptosystem. The simulation results show that the proposed scheme has a decoding bit error rate (BER) close to 0.5 at the eavesdropper and almost 0 at the legitimate receiver.

An exponential increase in the data rate demand, prompted several technical innovations. Multi User Multiple Input Multiple Output (MU-MIMO) is one of the most promising schemes. This has been evolved into Massive MIMO technology in 5G to further stretch the network throughput. Massive MIMO tackles the rising data rate with the increase in the number of antenna. This comes at the price of a higher energy consumption. Moreover, the high velocity users in MU-MIMO scheme, experiences a frequent unpredictable change in the channel condition that degrade it‘s downlink performance. Therefore, a proper number of antenna selection is of paramount importance. This issue has been addressed using machine learning techniques and Channel State Information (CSI) but only for static users. In this study, we propose to introduce antenna diversity in spatial multiplexing MU-MIMO transmission scheme by operating more number of reception antenna compare to the number of transmission antenna. The diversity improves the downlink performance of high velocity users. In general, our results can be interpreted for large scale antenna systems like Massive MIMO. The proposed method can be easily implemented in the existing network architectures with minimal complexity. Also, it has the potential for solving real-life problems like call drops and low data rate to be experienced by cellular users traveling through high-speed transportation systems like Dhaka MRT project.

High data rate transfers, high-definition streaming, high-speed internet, and the expanding of the infrastructure such as the ultra-broadband communication systems in wireless communication have become a demand to be considered in improving quality of service and increase the capacity supporting gigabytes bitrate. Massive Multiple-Input Multiple-Output (MIMO) systems technology is evolving from MIMO systems and becoming a high demand for fifth-generation (5G) communication systems and keep expanding further. In the near future, massive MIMO systems could be the main wireless systems of communications technology and can be considered as a key technology to the system in daily lives. The arrangement of the huge number of antenna elements at the base station (BS) for uplink and downlink to support the MIMO systems in increasing its capacity is called a Massive MIMO system, which refers to the vast provisioning of antenna elements at base stations over the number of the single antenna of user equipment. Massive MIMO depends on spatial multiplexing and diversity gain in serving users with simple processing signal of uplink and downlink at the BS. There are challenges in massive MIMO system even though it contains numerous number of antennas, such as channel estimation need to be accurate, precoding at the BS, and signal detection which is related to the first two items. On the other hand, in supporting wideband cellular communication systems and enabling low latency communications and multi-gigabit data rates, the Millimeter-wave (mmWave) technology has been utilized. Also, it is widely influenced the potential of the fifth-generation (5G) New Radio (NR) standard. This study was specifically review and compare on a few designs and methodologies on massive MIMO antenna communication systems. There are three limitations of those antennas were identified to be used for future improvement and to be proposed in designing the massive MIMO antenna systems. A few suggestions to improve the weaknesses and to overcome the challenges have been proposed for future considerations.

Time division duplex (TDD) massive multiple-input multiple-output (massive MIMO) has been regarded as a fantabulous technology for fifth generation wireless cellular networks (5G) to improve spectral efficiency (SE) and network performance using tens of hundreds of antennas and terminals attached to the base station (BS). On the other hand, the concept of pilot contamination (PC) is believed to be an acquainted gainsay in massive MIMO caused by channel estimation error, which disrupts and demarcates these required objectives. This paper projects an innovative strategy for pilot allocation i.e. cell splitting and sectorization based pilot assignment (CSS-PA) strategy to mitigate the PC. This strategy is based on exploiting signal to interference plus noise ratio (SINR), the users are first categorized into cell center and edge zones, followed by the subsequent sectorization of edge zones of each cell. Then, each cell center zone users are allotted with identical pilot sequences. On contrast, for edge zone sectors, the appointed sequences are mutually orthogonal in different cells. With the assistance of mitigated PC, we have ascertained the approximate system capacity, which shows precision for the unlimited number of antennas at the BS. The outcome of simulation reveals that our proposed CSS-PA strategy would effectively weaken PC. Moreover, the proposed idea has achieved higher system throughput, low Mean Square Error (MSE) and Normalized MSE (NMSE) at higher signal to noise ratio (SNR) and maximum number of BS antennas in comparison to the traditional pilot sequences allocation strategy with the marked sequences reuse rate of one or three.