Massive Mimo

The millimeter wave (mmWave) frequency band is seen as a key enabler of multi-gigabit wireless access in future cellular networks. In order to overcome the propagation challenges, mmWave systems use a large number of antenna elements both at the base station and at the user equipment, which lead to high directivity gains, fully-directional communications, and possible noise-limited operations. The fundamental differences between mmWave networks and traditional ones challenge the classical design constraints, objectives, and available degrees of freedom. This paper addresses the implications that highly directional communication has on the design of an efficient medium access control (MAC) layer. The paper discusses key MAC layer issues, such as synchronization, random access, handover, channelization, interference management, scheduling, and association. The paper provides an integrated view on MAC layer issues for cellular networks, identifies new challenges and tradeoffs, and provides novel insights and solution approaches.

We study the problem of physical layer security in massive multiple-input multiple-output (MaMIMO) cognitive radio networks (CRNs). In particular, we investigate the design of a smart jamming attack on the uplink transmission of a CRN in the presence of a single antenna jammer. The jammer is aware of the transmission protocol as well as the pilot set used for channel training in the MaMIMO systems. It attacks both the training and data transmission phases but with different powers. To have the most destructive attack, the jammer optimally divides its power between two phases to minimize the maximum energy efficiency of the secondary system. The resulting power optimization is a non-convex problem and to solve it, we propose a method to transform it into a convex optimization problem. Numerical results illustrate the effectiveness of the proposed smart jamming attack in decreasing the energy efficiency of the secondary MaMIMO system.

Distributed antenna system (DAS) is a highly diversified concept that can help lower radiated power, maximize coverage, and improve spectral efficiency. Yet, the most exciting feature of DAS is the distribution of remote radio heads (RRH) or antennas over a wide geographical area, which allows the use of less complex signal processing techniques resulting in the reduction of systems’ size and cost. These features of DAS were utilized in network MIMO, distributed MIMO, multi-cell MIMO, and distributed-input distributed-output (DIDO) systems to move away from the centralized approach as in conventional multi-user and massive MIMO systems. In this tutorial paper, we first talk about MIMO systems with their relevant technologies by categorizing them into two main approaches according to their working principles and systematic architecture; the first approach is based on a centralized (or collocated) architecture, whereas the second is based on a decentralized (or distributed) architecture. We then deep dive into all the different technologies related to these architectures one by one and explain each in detail with much more focus on the similarities and differences between them. After that, we exposition the main target of this study, which is to answer the following question "Is cell-free massive MIMO (CF-MMIMO) equivalent to the already existing and commercialized pCell technology?".  To confidently answer this question, we comprehensively and deeply study the two technologies considering the differences in their system models and look into their conceptual formulations  as well as their respective channel models. More particularly, we detail CF-MMIMO and pCell architectures while giving special attention to pCell's SDR wireless platform and stating why it is crucial for the current wireless systems. Then, we present the understandings that are acquired after careful observations and provide recommendations based upon them. In short, this study aims to focus the reader attention on the fact that all the distributed and cell-free MIMO-related technologies work on the same principle, and the way these systems are built makes them fundamentally equivalent, yet, they are displayed quite differently using different terminologies and perspectives, which can cause confusion to readers and end up misleading future researchers.
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There have been four generations of wireless networks. Since its inception in the early 1980s, mobile communication has evolved with each succeeding generation emerging approximately every 10 years and introducing additional services to users. Demand for bandwidth capacity has grown with every generation. Available spectrum is being increasingly limited by competing demands, while the primary device for internet connection is steadily becoming wireless. The present mobile generation is gradually reaching its capacity limits, necessitating rigorous studies to find ways of providing greater capacity beyond the current 4G. This paper aims at proposing a 5G solution and analyzing its capacity and range.

Physical layer protection, which protects data confidentiality using information-theoretic methods, has recently attracted a lot of research attention. Using the inherent randomness of the transmission channel to ensure protection in the physical layer is the core concept behind physical layer security. In 5G wireless communication, new challenges have arisen in terms of physical layer security. This paper introduces the most recent survey on various 5G technologies, including millimeter-Wave, massive multi-input multiple outputs, microcells, beamforming, full-duplex technology, etc. The mentioned technologies have been used to solve this technology, such as attenuation, millimeter-Wave penetration, antenna array architecture, security, coverage, scalability, etc. Besides, the author has used descriptions of the techniques/algorithms, goals, problems, and meaningful outcomes, and the results obtained related to this approach were demonstrated.

The downlink multiuser precoding of the multiple-input multiple-output (MIMO) method includes optimal channel state information at the base station and a variety of linear precoding (LP) schemes. Maximum ratio transmission (MRT) is among the common precoding schemes but does not provide good performance with massive MIMO, such as high bit error rate (BER) and low throughput. The orthogonal frequency division multiplexing (OFDM) and precoding schemes used in 5G have a flaw in high-speed environments. Given that the Doppler effect induces frequency changes, orthogonality between OFDM subcarriers is disrupted and their throughput output is decreased and BER is decreased. This study focuses on solving this problem by improving the performance of a 5G system with MRT, specifically by using a new design that includes weighted overlap and add (WOLA) with MRT. The current research also compares the standard system MRT with OFDM with the proposed design (WOLA-MRT) to find the best performance on throughput and BER. Improved system results show outstanding performance enhancement over a standard system, and numerous improvements with massive MIMO, such as best BER and throughput. Its approximately 60% more throughput than the traditional systems. Lastly, the proposed system improves BER by approximately 2% compared with the traditional system.

Getting higher data rates always remains a
challenging task and so is in the fifth generation which may
launch in 2021 to get seamless connectivity with ultra-high
speed and extremely low latency less than two milliseconds to
connect mobile devices along with other physical devices which
is about to cross the figure of nine billion over the globe. It is
highly essential to get extremely higher data rates to taste the
fruits of different kinds of services on the mobile handset using
technologies like LTE Advanced Pro, Carrier aggregation
the scheme, and 5G NR. Carrier aggregation will be one of the
better solutions along with massive mimo at the base station to
achieve peak data rates and faster throughput per user. With
5G carrier aggregation, we have 700 MHz bandwidth using
millimeter-wave frequency range which is much higher
compared to 100MHz of LTE advanced to get higher data rates
per user by using advanced higher-order modulation, spatial
multiplexing, and bigger transport block size. More than 1
Gbps data rate can be achieved on the downlink and 500 Mbps on
the uplink. The research paper gives an overall idea of carrier
aggregation in 5G and coexistence with legacy LTE and
Millimeter-wave system.

The role of interference in wireless networks has recently been profoundly rethought with the emergence of new techniques for combating it and exploit it to maximize the use efficiency of the physical resources. This paper presents a two-way relay channel using a lattice-based physical layer network coding scheme, a massive MIMO array, and in-band full-duplex, taking into account the residual self-interference that results after applying recently developed cancellation techniques for the loopback interference. The proposed scheme is able to ultimately exchange information across the TWRC in only one time slot, whereas four time slots would be needed in a conventional TWRC. The system's performance is shown to be mostly dependent on the number of antennas at the relay, and also dependent on the channel state information of all the channel matrices, including the one describing the loopback interference at the relay. For base-stations and relays with a few hundred antennas, the proposed scheme is feasible for wireless systems.
Index Terms—In-band full-duplex, massive multiple-input multiple-output (MIMO), Physical Layer Network Coding (PLNC), Two-way Relay Channel.

By exploiting large antenna arrays, massive MIMO (multiple input multiple output) systems can greatly increase spectral and energy efficiency over traditional MIMO systems. However, increasing the number of antennas at the base station (BS) makes the uplink joint channel estimation and data detection (JED) challenging in massive MIMO systems. In this paper, we consider the JED problem for massive SIMO (single input multiple output) wireless systems, which is a special case of wireless systems with large antenna arrays. We propose exact Generalized Likelihood Ratio Test (GLRT) optimal JED algorithms with low expected complexity, for both constant-modulus and nonconstant-modulus constellations. We show that, despite the large number of unknown channel coefficients, the expected computational complexity of these algorithms is polynomial in channel coherence time (T) and the number of receive antennas (N), even when the number of receive antennas grows polynomially in the channel coherence time (N = O(T 11) suffices to guarantee an expected computational complexity cubic in T and linear in N). Simulation results show that the GLRT-optimal JED algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity. Index Terms-Joint channel estimation and data detection, massive SIMO, generalized likelihood ratio test, tree search algorithm.

Multi-user MIMO offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multi-user MIMO, as originally envisioned, with roughly equal numbers of service antennas and terminals and frequency-division duplex operation, is not a scalable technology. Massive MIMO (also known as large-scale antenna systems, very large MIMO, hyper MIMO, full-dimension MIMO, and ARGOS) makes a clean break with current practice through the use of a large excess of service antennas over active terminals and time-division duplex operation.

Incorporating full duplex operation in Multiple Input Multiple Output (MIMO) systems provides the potential of boosting throughput performance. However, the hardware complexity of the analog self-interference canceller in emerging full duplex MIMO designs mostly scales with the number of transmit and receive antennas, thus exploiting the benefits of analog cancellation becomes impractical for full duplex MIMO transceivers, even for moderate number of antennas. In this paper, we present two novel architectures for the analog canceller comprising of reduced number of cancellation elements, compared to the state of the art, and simple multiplexers for efficient signal routing among the transmit and receive radio frequency chains. One architecture is based on analog taps (tap refers to a line of fixed delay, variable phase shifter, and attenuator) and the other on AUXiliary (AUX) Transmitters (TXs) that locally generate the cancellation signal. In contrast to the available analog cancellation architectures, the values for each tap or each AUX TX and the configuration of the multiplexers are jointly designed with the digital transmit and receive beamforming filters according to certain performance objectives. Focusing on a narrowband flat fading channel model as an example, we present a general optimization framework for the joint design of analog self-interference cancellation and digital beamforming. We also detail the sum rate optimization objective together with its derived solution for the latter architectural components. Representative computer simulation results demonstrate the superiority both in terms of hardware complexity and achievable performance of the proposed low complexity full duplex MIMO schemes over the lately available ones.

In this paper, we study the problem of joint inband backhauling and interference mitigation in 5G heterogeneous networks (HetNets) in which a massive multiple-input multipleoutput (MIMO) macro cell base station equipped with a large number of antennas, overlaid with self-backhauled small cells is assumed. This problem is cast as a network utility maximization subject to wireless backhaul constraints. Due to the non-tractability of the problem, we first resort to random matrix theory to get a closed-form expression of the achievable rate and transmit power in the asymptotic regime, i.e., as the number of antennas and users grows large. Subsequently, leveraging the framework of stochastic optimization, the problem is decoupled into dynamic scheduling of macro cell users and backhaul provisioning of small cells as a function of interference and backhaul links. Via simulations, we evaluate the performance gains of our proposed framework under different network architectures and low/high...

The world is going digital by the day and the need for digital data communication such as high definition video streaming, web browsing and machine to machine communication due to internet of things (IoT) are on the increase continually, such applications require high data rate to deliver them and without increase in bandwidth which is an expensive resource constant. Also in order for 3G cellular system operators to compete favorably in the mobile data market with new competing technologies such as WIMAX, LTE etc especially in the future explosive IoT market and to preserve their investment a cost effective way is to utilize the Multiple Input and Multiple Output antenna system otherwise called MIMO to obtain the needed high data rates. In this paper we looked at the various data rate optimization techniques of MIMO and their applications in 3G networks. Using a drive test we conducted investigation and compare the impact of MIMO on the throughput of legacy Rel. 99, HSDPA and HSPA+ Rel. 9. The result of our investigation shows that while Rel. 99 offered a 0.403Mbps in the uplink, HSUPA offered 1.335Mbps with HSPA+ showing a throughput of 10.950Mbps. Likewise throughput in the downlink is 0.244Mbps, 1.041Mbps and 35.370Mbps for Rel. 99, HSDPA and HSPA+ respectively showing a progressive increase as the number of transmit antenna increases at the base station

Energy efficiency is a key concern of proposing next-generation wireless communications since it has a significant impact on human existence on the earth's surface due to two causes, namely global warming caused by CO2 emissions and rising sea levels. Additionally, this challenge has become more concerning as the high demand for data rates, spectral efficiency, and QoS integrated with massive IoT communications. As a result, it is necessary to implement green mobile communications technologies that may decrease energy consumption while increasing battery life, as well as improved capacity to allow the fast development of small cell integrated base stations. So previously mentioned, featureenabled wireless communication networks is known as 5G green cellular communication networks. To achieve the green cellular communication, several techniques have been proposed and discussed in this article like as small cell, wireless power and data transmission, mm Wave, massive MIMO, and beamforming.

We study channel-aware decision fusion over a
multiple-input multiple-output (MIMO) channel in the large-
array regime at the decision-fusion center (DFC). Inhomogeneous
large-scale fading between the sensors and the DFC is consider
in addition to the small-scale fading, and pilot-based channel
estimation is performed at the DFC. Linear processing techniques
are analyzed in order to design low-complexity alternatives
to the optimum log-likelihood ratio test (LLRT). Performance
evaluation based on Monte Carlo simulations are presented.

Multiuser Multiple-Input Multiple-Output (MIMO) offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multiuser MIMO, as originally envisioned with roughly equal numbers of service-antennas and terminals and frequency division duplex operation, is not a scalable technology. Massive MIMO (also known as "Large-Scale Antenna Systems", "Very Large MIMO", "Hyper MIMO", "Full-Dimension MIMO" and "ARGOS") makes a clean break with current practice through the use of a large excess of service-antennas over active terminals and time division duplex operation. Extra antennas help by focusing energy into ever-smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include the extensive use of inexpensive low-power components, reduced latency, simplification of the media access control (MAC) layer, and robustness to intentional jamming. The anticipated throughput depend on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly-joined terminals, the exploitation of extra degrees of freedom provided by the excess of service-antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This paper presents an overview of the massive MIMO concept and of contemporary research on the topic. 1 Background: MultiUser MIMO Maturing MIMO, Multiple-Input Multiple Output, technology relies on multiple antennas to simultaneously transmit multiple streams of data in wireless communication systems. When MIMO is used to communicate with several terminals at the same time, we speak of multiuser MIMO. Here, we just say MU-MIMO for short.

Massive MIMO systems are now well known for their energy efficiency. The ability to focus the energy to a targeted user not only increases the gain of useful signal but also reduces inter-user interference to the point where Spatial Division Multiple Access becomes possible.
Using such systems at high frequencies, thus greatly reducing the array form factor, seems a smart mix and we give an insight into how well an angle based precoder fares compared to well known massive MIMO linear precoders in a highly Line-of-Sight (LOS) environment.

In this paper we prove the optimality of this angle based precoder, called Digital Beamsteering (DBS), in an ideal pure LOS configuration and then we show how it performs in a more realistic environment. Furthermore, its robustness against angle estimation error is illustrated for a specific scenario and serves to back up the importance such a solution represents for future dense 5G networks, angular information being easier to get and especially to keep track of.

In MIMO millimeter-wave (mmWave) systems, while the hybrid digital/analog precoding structure provides the ability to increase the reach rate, it also faces the challenge of reducing the channel time limit due to the large number of horns on both sides of the Tx / Rx.. In this paper, channel measurement is done by searching with multiple beams, and a new hierarchical multi-beam search system is proposed, using a pre-designed analog codebook. Performance tests show that, compared to a highperformance system, the proposed system not only achieves a high level of success in getting multiple beams under normal system settings but also significantly reduces channel estimation time

Abstract: This article pretends to provide an easy-to-understand overview of the requirements and the technologies envisioned to support 5G cellular systems. Some meaningful statistics regarding the way traffic behaved during the year 2015 and a forecast up to year 2020 are presented. This allows a better understanding of the requirements for 5G that are currently being outlined and the applications that will be enabled. Given the orientation of this paper, two general aspects have been considered: a) How to increase and reach the required capacity of 5G systems by combining more spectrum, more cells and enhancing spectral efficiency by dealing with interference; and, b) The main aspects of the overall architecture of 5G systems, including discussions of the access network, backhaul and core. For each case several technologies that are under enhancements and optimization and others that are just being researched and still are in their initial steps are introduced and discussed.

Resumen: Este artículo pretende dar una visión fácil de entender sobre los requisitos y las tecnologías que se prevé brindarán soporte a los sistemas celulares de 5G. Se presentan algunas estadísticas de importancia relacionadas a la forma en la que el tráfico se comportó durante el año 2015 y el pronóstico hasta el año 2020. Esto permite una mejor comprensión de los requisitos para sistemas 5G que se están delineando actualmente y las aplicaciones que se podrán especificar. Dada la orientación de este trabajo, se han considerado dos aspectos generales: a) Cómo aumentar y alcanzar la capacidad de los sistemas 5G combinando más espectro, más celdas y mejorando la eficiencia espectral disminuyendo la interferencia; y, b) Los aspectos principales de la arquitectura de los sistemas 5G, incluyendo discusiones sobre la red de acceso, backhaul y el core de la red. Para cada caso, se presentan y discuten algunas tecnologías que están siendo mejoradas y optimizadas y otras que están siendo investigadas y que aún están en sus etapas iniciales.

We study the spectral efficiency (SE) of a mixed-ADC massive MIMO system in which K single-antenna users communicate with a base station (BS) equipped with M antennas connected to N high-resolution ADCs and M − N one-bit ADCs. This architecture has been proposed as an approach for realizing massive MIMO systems with reasonable power consumption. First, we investigate the effectiveness of mixed-ADC architectures in overcoming the channel estimation error caused by coarse quantization. For the channel estimation phase, we study to what extent one can combat the SE loss by exploiting just N ≪ M pairs of high-resolution ADCs. We extend the round-robin training scheme for mixed-ADC systems to include both high-resolution and one-bit quantized observations. Then, we analyze the impact of the resulting channel estimation error in the data detection phase. We consider random high-resolution ADC assignment and also analyze a simple antenna selection scheme to increase the SE. Analytical expressions are derived for the SE for maximum ratio combining (MRC) and numerical results are presented for zero-forcing (ZF) detection. Performance comparisons are made against systems with uniform ADC resolution and against mixed-ADC systems without round-robin training to illustrate under what conditions each approach provides the greatest benefit.

Multiuser Multiple-Input Multiple-Output (MIMO) offers big advantages over conventional point-to-point MIMO: it works with cheap single-antenna terminals, a rich scattering environment is not required, and resource allocation is simplified because every active terminal utilizes all of the time-frequency bins. However, multiuser MIMO, as originally envisioned with roughly equal numbers of service-antennas and terminals and frequency division duplex operation, is not a scalable technology. Massive MIMO (also known as "Large-Scale Antenna Systems", "Very Large MIMO", "Hyper MIMO", "Full-Dimension MIMO" and "ARGOS") makes a clean break with current practice through the use of a large excess of service-antennas over active terminals and time division duplex operation. Extra antennas help by focusing energy into ever-smaller regions of space to bring huge improvements in throughput and radiated energy efficiency. Other benefits of massive MIMO include the extensive use of inexpensive low-power components, reduced latency, simplification of the media access control (MAC) layer, and robustness to intentional jamming. The anticipated throughput depend on the propagation environment providing asymptotically orthogonal channels to the terminals, but so far experiments have not disclosed any limitations in this regard. While massive MIMO renders many traditional research problems irrelevant, it uncovers entirely new problems that urgently need attention: the challenge of making many low-cost low-precision components that work effectively together, acquisition and synchronization for newly-joined terminals, the exploitation of extra degrees of freedom provided by the excess of service-antennas, reducing internal power consumption to achieve total energy efficiency reductions, and finding new deployment scenarios. This paper presents an overview of the massive MIMO concept and of contemporary research on the topic. 1 Background: MultiUser MIMO Maturing MIMO, Multiple-Input Multiple Output, technology relies on multiple antennas to simultaneously transmit multiple streams of data in wireless communication systems. When MIMO is used to communicate with several terminals at the same time, we speak of multiuser MIMO. Here, we just say MU-MIMO for short.

This paper presents a novel three-dimensional (3D) time-variant hybrid channel model for massive multiple input multiple output (M-MIMO) wireless systems. The main aim of the proposed model is to find channel characteristics in a simple and precise manner. To achieve this purpose, this channel model consists of two deterministic and stochastic modes. By using the idea of equivalent planes and ray tracing method, the channel multipath components (MPCs) are calculated in the deterministic mode. In the stochastic mode, those parts of the propagation environment that are too complex to be modeled in the deterministic mode, are modeled based on the cluster concept. Then, the MPCs characteristics are calculated by utilizing appropriate random distributions. The doppler effect is also taken into account due to the probability of existence relative velocities among channel components. This model is validated by comparing simulation results with those of the previously developed channel models. Finally, the channel model is applied to a real scenario to extract some of the important characteristics of the propagation environment.

To meet future demands on user experience and traffic volumes, mobile networks need to evolve towards providing higher capacities and data rates. In this paper we investigate the feasibility of incorporating higher frequency bands (15 GHz) and beamforming to support this evolution. We see that using user-specific beamforming, the challenging propagation conditions at higher frequencies are mitigated and outdoor-in coverage is often possible. In places where 15 GHz coverage is not satisfactory, swift fallback to a lower frequency band is essential. This is seamlessly provided by carrier aggregation with a 2.6 GHz band. Together these components provide a ten-fold increase in capacity over a reference system operating only at 2.6 GHz.

In upcoming trends of wireless communications,
such as massive MIMO, the number of antennas at the transmitter (TX) and receiver (RX) are expected to increase dramatically, allowing a substantial improvement in system performance and spectral efficiency. However, an increase in the number of antennas, also results in an increase in hardware, computational complexity and energy dissipation of the MIMO system. Hence, an intelligent utilisation of the array elements is crucial. To this end, this paper presents an insight into the relation between the array geometry and various performance metrics, such as detection, resolution and data-rate maximization. Based on the requirements of each performance metric, a suitable selection of the array geometry that provides an optimum trade-off between the objective of the task and constraints imposed by the system
is proposed.

As the wireless communication network is driven
toward densification with small cell deployments, massive MIMO technology shows great promises to boost capacity and energy efficiency. This paper aims at showing how raising carrier
frequencies impacts the performance of the linear precoders used in Massive MIMO systems. By means of a geometrical deterministic channel model, we simulate a dense outdoor scenario and highlight the influence of the direct and multi-paths components.
More importantly we prove that, in a Line-of-Sight configuration, the discriminating skills of the well known Zero Forcing precoder is much more sensitive to the antenna array architecture and the user location than the Conjugate precoder.

There have been four generations of wireless networks. Since its inception in the early 1980s, mobile communication has evolved with each succeeding generation emerging approximately every 10 years and introducing additional services to users. Demand for bandwidth capacity has grown with every generation. Available spectrum is being increasingly limited by competing demands, while the primary device for internet connection is steadily becoming wireless. The present mobile generation is gradually reaching its capacity limits, necessitating rigorous studies to find ways of providing greater capacity beyond the current 4G. This paper aims at proposing a 5G solution and analyzing its capacity and range.

In this paper, in order to tackle major challenges of spectrum exploration & allocation in Cognitive Radio (CR) networks, we apply the general framework of Decision Fusion (DF) to wideband collaborative spectrum sensing based on Orthogonal Frequency Division Multiplexing (OFDM) reporting. At the transmitter side, we employ OFDM without Cyclic Prefix (CP) in order to improve overall bandwidth efficiency of the reporting phase in networks with high user density. On the other hand, at the receiver side (of the reporting channel) we device the Time-Reversal Widely Linear (TR-WL), Time-Reversal Maximal Ratio Combining (TR-MRC) and modified TR-MRC (TR-mMRC) rules for DF. The DF Center (DFC) is assumed to be equipped with a large antenna array, serving a number of unauthorized users competing for the spectrum, thereby resulting in a "virtual" massive Multiple-Input Multiple-Output (MIMO) channel. The effectiveness of the proposed TR-based rules in combating (a) inter-symbol and (b) inter-carrier interference over conventional (non-TR) counterparts is then examined, as a function of the Signal-to-Interference-plus-Noise Ratio (SINR). Closed-form performance, in terms of system false-alarm and detection probabilities, is derived for the formulated fusion rules. Finally, the impact of large-scale channel effects on the proposed fusion rules is also investigated, via Monte-Carlo simulations.

Massive multiple-input multiple-output (MIMO) avails of simple transceiver design which can tackle many drawbacks of relay systems in terms of complicated signal processing, latency, and noise amplification. However, the cost and circuit complexity of having one radio frequency (RF) chain dedicated to each antenna element are prohibitive in practice. In this paper, we address this critical issue in amplify-and-forward (AF) relay systems using a hybrid analog and digital (A/D) transceiver structure. More specifically, leveraging the channel long-term properties, we design the analog beamformer which aims to minimize the channel estimation error and remain invariant over a long timescale. Then, the beamforming is completed by simple digital signal processing, i.e., maximum ratio combining/maximum ratio transmission (MRC/MRT) or zero-forcing (ZF) in the baseband domain. We present analytical bounds on the achievable spectral efficiency taking into account the spatial correlation and imperfect channel state information at the relay station. Our analytical results reveal that the hybrid A/D structure with ZF digital processor exploits spatial correlation and offers a higher spectral efficiency compared to the hybrid A/D structure with MRC/MRT scheme. Our numerical results showcase that the hybrid A/D beamforming design captures nearly 95% of the spectral efficiency of a fully digital AF relaying topology even by removing half of the RF chains. It is also shown that the hybrid A/D structure is robust to coarse quantization, and even with 2-bit resolution, the system can achieve more than 93% of the spectral efficiency offered by the same hybrid A/D topology with infinite resolution phase shifters.

In upcoming trends of wireless communications, such as massive MIMO, the number of antennas at the transmitter (TX) and receiver (RX) are expected to increase dramatically, aiming to provide a substantial improvement in system performance and spectral efficiency. However, an increase in the number of antennas also results in an increase in hardware, computational complexity and energy dissipation of the MIMO system. Therefore, the antenna array geometry plays a crucial role in the overall system performance. This paper is concerned with planar antenna array geometries with emphasis given to the family of 2D "grid" arrays and presents an insight into the relation between the array geometry and various performance metrics, such as detection, resolution and data-rate maximization, that may be used in different applications.

Although the three-dimensional (3D) channel model considering the elevation factor has been used to analyze the performance of multiuser multiple-input multiple-output (MU-MIMO) systems, less attention is paid to the effect of the elevation variation. In this paper, we elaborate the sum rate of MU-MIMO systems with a 3D base station (BS) exploiting different elevations. To illustrate clearly, we consider a high-rise building scenario. Due to the floor height, each floor corresponds to an elevation. Therefore, we can analyze the sum rate performance for each floor and discuss its effect on the performance of the whole building. This work can be seen as the first attempt to analyze the sum rate performance for high-rise buildings in modern city and used as a reference for infrastructure.

This paper analytically reviews the performance of massive multiple input multiple output (MIMO) system for communication in highly mobility scenarios like high speed Railways. As popularity of high speed train increasing day by day, high data rate wireless communication system for high speed train is extremely required. 5G wireless communication systems must be designed to meet the requirement of high speed broadband services at speed of around 500 km/h, which is the expected speed achievable by HSR systems, at a data rate of 180 Mbps or higher. Significant challenges of high mobility communications are fast time-varying fading, channel estimation errors, doppler diversity, carrier frequency offset, inter carrier interference, high penetration loss and fast and frequent handovers. Therefore, crucial requirement to design high mobility communication channel models or systems prevails. Recently, massive MIMO techniques have been proposed to significantly improve the performance of wireless networks for upcoming 5G technology. Massive MIMO provide high throughput and high energy efficiency in wireless communication channel. In this paper, key findings, challenges and requirements to provide high speed wireless communication onboard the high speed train is pointed out after thorough literature review. In last, future research scope to bridge the research gap by designing efficient channel model by using massive MIMO and other optimization method is mentioned.

A rapid increase in the wireless internet-based applications led to an enormous increase in wireless data rates. Intensification of future wireless networks faces a great challenge to meet such growing demand for payload data. A suggested solution that can be used to resolve this issue is to overlay small cell networks with macro cell networks to provide higher network capacity and better coverage. Small cell networks experience large interference from macro cell base stations (BSs) making data rates received by the small cell users not reliably. In this paper, an antenna selection scheme based on small cell user's (SCU) channel gain is proposed. Whereas, the two tiers use the same network bandwidth resources; the macro BS selects a subset of antennas which has a minimum interfering effect to the SCU based on a pilot sent from SCU to macro cell. The proposed selection scheme has been compared with convex optimization antenna selection scheme. Simulation results show that the SCU data rates are significantly improved using proposed scheme. Execution time required for antenna selection is reduced significantly using the proposed scheme.

The physical layer security is a new security paradigm based on the principles of information theory. Several methods for achieving secrecy in physical layer is proposed. This paper proposes PLS based on the deep learning architecture. In which the deep learning model will transform the channel coefficients, the beamforming based on this transformed channel coefficients can be decoded using deep learning architecture in the receiver. The secrecy rate and secrecy outage probability of proposed system is compared with the zero forcing based beamforming and superior performance is verified by the simulation using popular deep learning library TensorFlow.

Massive multiple-input multiple-output (MIMO) systems are considered a promising solution to minimize multiuser interference (MUI) based on simple precoding techniques with a massive antenna array at a base station (BS). This paper presents a novel approach of beam division multiple access (BDMA) which BS transmit signals to multiusers at the same time via different beams based on hybrid beamforming and user-beam schedule. With the selection of users whose steering vectors are orthogonal to each other, interference between users is significantly improved. While, the efficiency spectrum of proposed scheme reaches to the performance of fully digital solutions, the multiuser interference is considerably reduced.

The integration of Wireless Body Area Networks with a cloud computing platform creates a new digital ecosystem with advanced features called Cloud-assisted Wireless Body Area Networks. This ecosystem enables users to globally access e-healthcare services at competitive costs. However, the secure data communications between the cloud and Wireless Body Area Networks are critical because the data is related to users' privacy information. In this paper, we propose the Multi-valued and Ambiguous Scheme to capture data confidentiality in the Cloud-assisted Wireless Body Area Networks since it is the most important issue. The approach combining the scheme with existing encryption schemes provides a general paradigm for deploying applications. The obtained results show that secure data communications between the cloud and Wireless Body Area Networks can be achieved.

Beamspace channel estimation mechanism for massive MIMO (multiple input multiple output) antenna system presents a major process to compensate the 5G spectrum challenges caused by the proliferation of information from mobile devices. However, this estimation is required to ensure the perfect channel state information (CSI) for lower amount of Radio Frequency (RF) chains for each beam. In addition, phase shifter (PS) components used in this estimation need high power to select the beam in the desired direction. To overcome these limitations, in this work, we propose Regular Scanning Support Detection (RSSD) based channel estimation mechanism. Moreover, we utilise a 3D lens antenna array having metallic plate and a switch in our model which compensates the limitation of phase shifters. Simulation results show that the proposed RSSD based channel estimation surpasses traditional technique and SD based channel estimation even in lower SNR area which is highly desirable in the millimeter wave (mmWave) massive MIMO systems.