Memory-assisted Statistically-ranked RF Beam Training Algorithm for Sparse MIMO

This paper presents a novel radio frequency (RF) beam training algorithm for sparse multiple input multiple output (MIMO) channels using unitary RF beamforming codebooks at transmitter (Tx) and receiver (Rx). The algorithm leverages statistical knowledge from past beam data for expedited beam search with statistically-minimal training overheads. Beams are tested in the order of their ranks based on their probabilities for providing a communication link. For low beam entropy scenarios, statistically-ranked beam search performs excellent in reducing the average number of beam tests per Tx-Rx beam pair identification for a communication link. For high beam entropy cases, a hybrid algorithm involving both memory-assisted statistically-ranked (MarS) beam search and multi-level (ML) beam search is also proposed. Savings in training overheads increase with decrease in beam entropy and increase in MIMO channel dimensions.Comment: Under peer-review for IEEE Globecom 201

RSSI-Based Hybrid Beamforming Design with Deep Learning

Hybrid beamforming is a promising technology for 5G millimetre-wave communications. However, its implementation is challenging in practical multiple-input multiple-output (MIMO) systems because non-convex optimization problems have to be solved, introducing additional latency and energy consumption. In addition, the channel-state information (CSI) must be either estimated from pilot signals or fed back through dedicated channels, introducing a large signaling overhead. In this paper, a hybrid precoder is designed based only on received signal strength indicator (RSSI) feedback from each user. A deep learning method is proposed to perform the associated optimization with reasonable complexity. Results demonstrate that the obtained sum-rates are very close to the ones obtained with full-CSI optimal but complex solutions. Finally, the proposed solution allows to greatly increase the spectral efficiency of the system when compared to existing techniques, as minimal CSI feedback is required.Comment: Published in IEEE-ICC202

A Comprehensive Investigation of Beam Management Through Conventional and Deep Learning Approach

5G spectrum uses cutting-edge technology which delivers high data rates, low latency, increased capacity, and high spectrum utilization. To cater to these requirements various technologies are available such as Multiple Access Technology (MAT), Multiple Input Multiple Output technology (MIMO), Millimetre (mm) wave technology, Non-Orthogonal Multiple Access Technology (NOMA), Simultaneous Wireless Information and Power Transfer (SWIPT). Of all available technologies, mmWave is prominent as it provides favorable opportunities for 5G. Millimeter-wave is capable of providing a high data rate i.e., 10 Gbit/sec. Also, a tremendous amount of raw bandwidth is available i.e., around 250 GHz, which is an attractive characteristic of the mmWave band to relieve mobile data traffic congestion in the low frequency band. It has a high frequency i.e., 30 – 300 GHz, giving very high speed. It has a very short wavelength i.e., 1-10mm, because of this it provides the compact size of the component. It will provide a throughput of up to 20 Gbps. It has narrow beams and will increase security and reduce interference. When the main beam of the transmitter and receiver are not aligned properly there is a problem in ideal communication. To solve this problem beam management is one of the solutions to form a strong communication link between transmitter and receiver. This paper aims to address challenges in beam management and proposes a framework for realization. Towards the same, the paper initially introduces various challenges in beam management. Towards building an effective beam management system when a user is moving, various steps are present like beam selection, beam tracking, beam alignment, and beam forming. Hence the subsequent sections of the paper illustrate various beam management procedures in mmWave using conventional methods as well as using deep learning techniques. The paper also presents a case study on the framework's implementation using the above-mentioned techniques in mmWave communication. Also glimpses on future research directions are detailed in the final sections. Such beam management techniques when used for mmWave technology will enable build fast, efficient, and capable 5G networks

A review on massive MIMO Antennas for 5G communication systems on challenges and limitations

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 consideration

A review on massive MIMO antennas for 5G communication systems on challenges and limitations

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 MultipleOutput (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 multigigabit 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

Channel estimation and beam training with machine learning applications for millimetre-wave communication systems

The fifth generation (5G) wireless system will extend the capabilities of the fourth generation (4G) standards to serve more users and provide timely communication. To this end, the carriers of 5G systems will be able to operate at higher frequency bands, such as the millimetre-wave (mmWave) bands that span from 30 GHz to 300 GHz, to obtain greater bandwidths and higher data rates. As a result, the deployment of 5G networks is required to accommodate more antennas and offer pervasive coverage with controlled power consumption. The complexity of 5G systems introduces new challenges to traditional signal processing techniques. To address these challenges, a major step is to integrate machine learning (ML) algorithms into wireless communication systems. ML can learn patterns from datasets to achieve control and optimisation of complex radio frequency (RF) networks. This PhD thesis focuses on developing efficient channel estimation methods and beam training strategies with the application of ML algorithms for mmWave wireless systems. Firstly, the channel estimation and signal detection problem is investigated for orthogonal frequency-division multiplexing (OFDM) systems that operate at mmWave bands. A deep neural network (DNN)-based joint channel estimation and signal detection approach is proposed to achieve multi-user detection in a one-shot process for non-orthogonal multiple access (NOMA) systems. The DNN acts as the receiver, which can recover the transmitted data by learning the channel implicitly from suitable training. The proposed approach can be adapted to work for both single-input and single-output (SISO) systems and multiple-output and multipleoutput (MIMO) systems. This DNN-based approach is shown to provide good performance for OFDM systems that suffer from severe inter-symbol interference or where small numbers of pilot symbols are used. Secondly, the beam training and tracking problem is studied for mmWave channels with receiver mobility. To reduce the signalling overhead caused by frequent beam training, a lowcomplexity beam training strategy is proposed for mobile mmWave channels, which searches a set of selected beams obtained based on the recent beam search results. By searching only the adjacent beams to the one recently used, the proposed beam training strategy can reduce the beam training delay significantly while maintaining high transmission rates. The proposed strategy works effectively for channel datasets generated using either the stochastic or the raytracing channel model. This strategy is shown to approach the performance for an exhaustive beam search while saving up to 92% on the required beam training overhead. Thirdly, the proposed low-complexity beam training strategy is enhanced with the use of deep reinforcement learning (DRL) for mobile mmWave channels. A DRL-based beam training algorithm is proposed, which can intelligently switch between different beam training methods such that the average beam training overhead is minimised while achieving good spectral efficiency or energy efficiency performance. Given the desired performance requirement in the reward function for the DRL model, the spectral efficiency or energy efficiency can be maximised for the current channel condition by controlling the number of activated RF chains. The DRL-based approach can adjust the amount of beam training overhead required according to the dynamics of the environment. This approach can provide a good overhead-performance trade-off and achieve higher data rates in channels with significant levels of signal blockage

Sixth Generation (6G)Wireless Networks: Vision, Research Activities, Challenges and Potential Solutions

The standardization activities of the fifth generation communications are clearly over and deployment has commenced globally. To sustain the competitive edge of wireless networks, industrial and academia synergy have begun to conceptualize the next generation of wireless communication systems (namely, sixth generation, (6G)) aimed at laying the foundation for the stratification of the communication needs of the 2030s. In support of this vision, this study highlights the most promising lines of research from the recent literature in common directions for the 6G project. Its core contribution involves exploring the critical issues and key potential features of 6G communications, including: (i) vision and key features; (ii) challenges and potential solutions; and (iii) research activities. These controversial research topics were profoundly examined in relation to the motivation of their various sub-domains to achieve a precise, concrete, and concise conclusion. Thus, this article will contribute significantly to opening new horizons for future research direction

Integrated Data and Energy Communication Network: A Comprehensive Survey

OAPA In order to satisfy the power thirsty of communication devices in the imminent 5G era, wireless charging techniques have attracted much attention both from the academic and industrial communities. Although the inductive coupling and magnetic resonance based charging techniques are indeed capable of supplying energy in a wireless manner, they tend to restrict the freedom of movement. By contrast, RF signals are capable of supplying energy over distances, which are gradually inclining closer to our ultimate goal – charging anytime and anywhere. Furthermore, transmitters capable of emitting RF signals have been widely deployed, such as TV towers, cellular base stations and Wi-Fi access points. This communication infrastructure may indeed be employed also for wireless energy transfer (WET). Therefore, no extra investment in dedicated WET infrastructure is required. However, allowing RF signal based WET may impair the wireless information transfer (WIT) operating in the same spectrum. Hence, it is crucial to coordinate and balance WET and WIT for simultaneous wireless information and power transfer (SWIPT), which evolves to Integrated Data and Energy communication Networks (IDENs). To this end, a ubiquitous IDEN architecture is introduced by summarising its natural heterogeneity and by synthesising a diverse range of integrated WET and WIT scenarios. Then the inherent relationship between WET and WIT is revealed from an information theoretical perspective, which is followed by the critical appraisal of the hardware enabling techniques extracting energy from RF signals. Furthermore, the transceiver design, resource allocation and user scheduling as well as networking aspects are elaborated on. In a nutshell, this treatise can be used as a handbook for researchers and engineers, who are interested in enriching their knowledge base of IDENs and in putting this vision into practice