Massive Multi-input And Multi-output Research Articles

Performance on massive MIMO enabled mobile edge computing networks: Parallel computing modeling

With the development of communication and computation technologies, mobile edge computing (MEC) has become an emerging technology being deployed in 5G/B5G mobile networks to improve the mobile user experience. In this paper, we study the asymptotic performance of parallel computing in MEC networks with massive multi-input and multi-output (MIMO) in the millimeter wave band. First, a massive MIMO enabled MEC network with a parallel-limited computing model is constructed, and the task completed probability is defined as a new network performance metric. In the proposed model, we derive the task offloading and computing probabilities via the probability generation functional of the Poisson point process, from which, the task completed probability is obtained. Then we propose a computing resource allocation optimization algorithm to maximize the task completed probability within a given delay constraint. The numerical simulation results prove the accuracy of the obtained theoretical results and verify the performance of the proposed algorithm.

New ultra-small design and high performance of an 8×8 massive MIMO antenna for future 6G wireless devices

The demand for the array antenna that consists of multiple ports has increased in recent years, because of its main importance in reducing noise and interference between users. In this paper, we propose a new method for designing an 8×8 (16-ports) multi-input and multi-output (MIMO) antenna. This method relied on the Micro Strip mechanism so that we presented a small antenna that operates at wide and multi-bands of millimeter waves (Mm-Waves). According to the information curves generated by the CST experimental software, it was observed that the proposed antenna operates well from 36 to 60 GHz. Therefore, the antenna achieved the best results in terms of many most important parameters, the reflection coefficient is <-10 dB, return loss is <-25 dB, and voltage standing wave ratio (VSWR) is < 2. In addition, the efficiency of the antenna for all frequencies from 70% to 97%, the envelope correlation coefficient (ECC) is <0.001, and the diversity gain (DG) is 10 dB for all frequencies, while the maximum gain achieved by the antenna is 9 dB at 46 GHz. All these good results achieved by the antenna make it the prominent and potential element in most of the future 6G wireless communication systems.

Detection in 5G Mobile Communication System Using Hybrid Technique

Massive multi-input and multi-output (MIMO) is considered as the most important technology to attain the high data rate for next-generation mobile communication. It comprises more than one antenna at the sender and recipient sides. The detection of the signal in these systems is complex. In this work, we suggest a novel QRM-MLD algorithm with maximum likelihood detection to reduce the latency and complexity of the massive MIMO system. The simulation results indicate that the proposed system accomplished a reduced latency and complexity with a trivial penalty of bit error rate performance as compared to the existed techniques.

Achievable Capacity with Spatial Channel Correlation in Massive MIMO Multi-Cell Systems

Massive Multi-Input and Multi-Output (MIMO) antenna system provides unlimited capacity by the spatial multiplexing and array gain. Since the data rate has been limited by the coherence interference due to pilot contamination (PC). In this paper, we propose transmit combine and precoding schemes to achieve asymptotic capacity in multi-cell scenario, when the number of base station antennas tends to infinity. The impact of spatial channel correlation on channel capacity is explored by considering the co-variance matrices of the user –terminals (UT)s .To do this, we presented linear processing schemes such as MMSE,MRC, and ZF.Where MMSE achieves high capacity in the presence of large-scale fading and PC. Since the diagonals of the channel covariance matrices were designed with non- zero Eigen values and linearly independent. The results outperform and obtain asymptotic limit, when the co-variance of UTs are linearly independent. The results were simulated by using MATLAB 2018b.

Massive MIMO: Achievable Energy Efficiency for 5G systems with Multi-user Environment

Massive Multi-Input and Multi-Output (MIMO) antenna system potentially provides a promising solution to improve energy efficiency (EE) for 5G wireless systems. The aim of this paper is to enhance EE and its limiting factors are explored. The maximum EE of 48 Mbit/Joule was achieved with 15 user terminal (UT)s. This problem is related to the uplink spectral efficiency with upper bound for future wireless networks. The maximal EE is obtained by optimizing a number of base station (BS) antennas, pilot reuse factor, and BSs density. We presented a power consumption model by deriving Shannon capacity calculations with closed-form expressions. The simulation result highlights the EE maximization with optimizing variables of circuit power consumption, hardware impairments, and path-loss exponent. Small cells achieve high EE and saturate to a constant value with BSs density. The MRC scheme achieves maximum EE of 36 Mbit/Joule with 12 UTs. The simulation results show that peak EE is obtained by deploying massive BS antennas, where the interference and pilot contamination are mitigated by coherent processing. The simulation results were implemented by using MATLAB 2018b.

Machine Learning-Based Spectrum Efficiency Hybrid Precoding With Lens Array and Low-Resolution ADCs

Hybrid precoding is an important issue in millimeter wave (mmWave) massive multi-input and multi-output (MIMO) system. Specially, energy-saving hybrid precoding architectures and efficient hybrid precoding schemes provide ideas for solving this issue. In this paper, we propose a hybrid precoding/combining architecture that is low-cost and easy to implement. Specifically, a hybrid precoding architecture is realized by the lens sub-arrays at the base station (BS). Moreover, a hybrid combining architecture applies the low-resolution analog-to-digital converters (ADCs) at the front end of the radio frequency (RF) chains at the receiving terminal. Based on the hybrid precoding/combining architecture, the hybrid precoder and combiner are jointly optimized to maximize the spectrum efficiency (SE) in the downlink systems, which is a combinatorial optimization problem due to hardware constraints. The cross-entropy (CE) approach in machine learning (ML) is a simple way to solve the combinatorial optimization problem benefiting from its adaptive update procedure. Therefore, we propose an adaptive hybrid precoder/combiner design scheme (AHDS), in which a hybrid precoding algorithm based on the improved CE (ICE) inspired by ML is adopted to design the optimal hybrid precoder, and an approximate optimization method (AOM) is suggested when designing the hybrid combiner. In general, compared with the existing hybrid design schemes, the proposed AHDS is demonstrated to have significant advantage in SE with low computational complexity.

Research and Implementation of Rateless Spinal Codes Based Massive MIMO System

The potential performance gains promised by massive multi‐input and multioutput (MIMO) rely heavily on the access to accurate channel state information (CSI), which is difficult to obtain in practice when channel coherence time is short and the number of mobile users is huge. To make the system with imperfect CSI perform well, we propose a rateless codes‐aided massive MIMO scheme, with the aim of approaching the maximum achievable rate (MAR) as well as improving the achieved rate over that based on the fixed‐rate codes. More explicitly, a recently proposed family of rateless codes, called spinal codes, are applied to massive MIMO systems, where the spinal codes bring the benefit of approximately achieving the MAR with sufficiently large encoding block size. In addition, the multilevel puncturing and dynamic block‐size allocation (MPDBA) scheme is proposed, where the block sizes are determined by user MAR to curb the average retransmission delay for successfully decoding the messages, which further enhances the system retransmission efficiency. Multilevel puncturing, which is MAR dependent, narrows the gap between the system MAR and the related achieved rate. Theoretical analysis is provided to demonstrate that spinal codes with the MPDBA can guarantee the system retransmission efficiency as well as achieved rate, which are also verified by numerical simulations. Finally, a simplified but comparable MIMO testbed with 2 transmit antennas and 2 single‐antenna users, based on NI Universal Software Radio Peripheral (USRP) and LabVIEW communication toolkits, is built up to demonstrate the effectiveness of our proposal in realistic wireless channels, which is easy to be extended to massive MIMO scenarios in future.

Using Massive MIMO and Small Cells to Deliver a Better Energy-Efficient Cognitive Radio Network

Energy efficiency in cognitive radio networks is gradually receiving a lot of significant attention lately due to the gradual increase in the energy consumption of the network. In order to enable a more efficient energy network and to facilitate higher spectral utilization, adequate measures need to be employed to foster and improve energy efficiency. In this work, a massive multi-input and multi-output (MIMO) antenna base station co-existing with small cells base stations are introduced to efficiently optimize the energy in cognitive radio networks. An energy efficiency optimization problem was formulated and Dinkelbach method is used to solve the problem iteratively. Our simulation results show that the inclusion of more antennas in the macro base station can significantly reduce the total power consumed in the network and also that the energy efficiency of the network can be greatly improved when an optimal base station is selected for transmission.