Enormous data transfer over the Internet demand needs ultra-high-speed data rates and low latency networks, opening the door for 5G and beyond. Immigration towards high frequency bands seems to be an efficient solution due to their large swath of available spectrum. The use of the millimeter wave (mmWave) band and the visible light band, i.e., light-fidelity (Li-Fi), in wireless access are considered as the most promising solutions. Hence, there is an urgent need for efficient interworking among these candidate technologies to increase their usability by overcoming their challenges. In this paper, an efficient Li-Fi/mmWave integration paradigm orchestrated by Wi-Fi signaling is introduced, then a mmWave beamforming training (BT) based on Li-Fi localization is proposed. The wide spread of light emitting diode (LED) bulbs used as Li-Fi atto-cells accompanied with their high accurate positioning enable the use of too sharp (pencil) mmWave beams using a few number of beam switchings. This contributes in highly reducing the mmWave BT complexity while extremely increasing its coverage area. The effectiveness of the proposed mmWave BT, in terms of BT complexity, outage probability and misalignment probability, is proved mathematically and via numerical simulations over the exhaustive search BT and that based on the conventional Wi-Fi localization, especially in sharp beam scenarios (e.g., beamwidth is around 2.8°).

Li-Fi Positioning for Efficient Millimeter Wave Beamforming Training in Indoor Environment

Access point selection in beyond 5G hybrid MmWave/Wi-Fi/Li-Fi network

Millimeter wave (mmWave) communication has attracted considerable attention as a key technology for the next-generation wireless communications thanks to its exceptional advantages. MmWave leads the way to achieve a high transmission quality with directed narrow beams from source to multiple destinations by adopting different antenna beamforming (BF) techniques, which have a pivotal role in establishing and maintaining robust links. However, realizing such BF gains in practice requires overcoming several challenges, such as severe signal deterioration, hardware constraints, and design complexity. The elevated complexity of configuring mmWave BF vectors encourages researchers to leverage relevant machine learning (ML) techniques for better BF configurations deployment in 5G and beyond. In this article, we summarize mmWave BF strategies employed for future wireless networks. Then, we provide a comprehensive overview of ML techniques plus its applications and promising contributions toward efficient mmWave BF deployment. Furthermore, we discuss mmWave BF’s future research directions and challenges. Finally, we discuss a single and concurrent mmWave BF case study by applying multiarmed bandit to confirm the superiority of ML-based methods over conventional ones. 

Leveraging Machine Learning for Millimeter Wave Beamforming in Beyond 5G Networks

In this study, joint users selection and beamforming based on users positioning are proposed for enabling non-orthogonal multiple access (NOMA) in the millimetre-wave (mmWave) transmissions. In the proposed scheme, the mmWave users are arranged in descending order based on their estimated distances from the mmWave base station (BS), which guarantees the users ordering based on their line-of-sight availability, as well. Out of the ordered users, the two users having the minimum pairing angle and expected to lie within the specified mmWave beamwidth are selected as the two NOMA users. In this study, the users' pairing angle is defined as the maximum expected difference angle between two users considering their location-uncertainty areas. Consequently, the BS antenna beam is formed towards the two selected NOMA users by adjusting its boresight angle to lie in between their estimated location angles. Then, the fixed power allocation scheme is used to adjust the power allocation coefficients of the selected NOMA users. Mathematical analysis is performed to bound the performance of the proposed mmWave NOMA scheme. Also, numerical simulations are conducted to prove the mathematical findings and to show the superior performance of the proposed scheme over the well-known technique of using random beamforming.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-com.2019.1019

Joint users selection and beamforming in downlink millimetre-wave NOMA based on users positioning

To handle the exponentially growing service expectations from mobile users and circumvent the band switching slow rate, device-to-device (D2D) communication is receiving much research attention in the Internet of Things (IoT). While the emerging D2D nodes can support heterogeneous frequency bands [radio frequency (RF) including 2.4 GHz/5 GHz wireless local area network (WLAN), 38-GHz millimeter wave (mmWave), and visible light communication (VLC)], the physical constraints (e.g., blocking) require the user devices to dynamically switch between the bands in order to avoid the loss of connectivity and throughput degradation. In this article, we investigate an effective online link selection in hybrid RF-VLC scenarios for direct user data handling. First, we model the multiband selection issue as a multiarmed bandit (MAB) problem. The source/relay node acts as a player who gambles to maximize its long-term feedback/reward via selecting suitable arms, i.e., available bands (WLAN, mmWave, or VLC). Then, we propose an online, energy-aware band selection (EABS) methodology by leveraging three theoretically guaranteed MAB techniques [upper confidence bound (UCB), Thompson sampling (TS), and minimax optimal stochastic strategy (MOSS)] to derive optimal band selection policies. Based on these adopted policies, we propose three algorithms, namely, EABS-UCB, EABS-TS, and EABS-MOSS, to implement the EABS strategy, respectively. Extensive simulations demonstrate our proposed algorithms’ superior performance compared to the traditional link selection schemes regarding energy efficiency, average throughput, and convergence rate. In particular, EABS-MOSS emerges as the best algorithm as it exhibits near-optimal performance due to its flexibility to both stochastic and adversarial environments. 

Energy-Aware Hybrid RF-VLC Multiband Selection in D2D Communication: A Stochastic Multiarmed Bandit Approach

5G and beyond will occupy a big position in wireless communication future, because of the high end-user data rate over the Internet demand. Millimeter wave (mmWave) plays the major role as a 5G networks enabler. To overcome mmWave band propagation losses and increase its coverage area, beamforming training (BT) is typically used in conjunction with mmWave transmissions. IEEE 802.11ad standard defines an exhaustive search BT to find out the best transmit (TX)/receive (RX) beam identifications (IDs). Narrow, i.e., pencil, beams guarantee a high gain, i.e., a wide coverage area, but at the expense of very high setup time due to the exhaustive searching process. Localization provides a promising solution for finding out the best mmWave beams within a small setup time. However, localization techniques having high localization errors compared with the coverage area of the sharp beams (e.g., 5 degrees) are not suitable for these scenarios. Motivated by the high publicity of light emitting diode (LED) lambs and their high accurate positioning (e.g., around 10 cm), a mmWave BT technique based on light fidelity (Li-Fi) multilateral positioning is proposed in this paper to work efficiently in mmWave sharp beam scenarios. Via mathematical and simulation analysis, the proposed mmWave BT achieves a few number of beam switchings with a comparable performance with the exhaustive search BT. Besides, it achieves a lower outage probability than that comes from using Wi-Fi Localization.

Millimeter Wave Beamforming Training Based on Li-Fi Localization in Indoor Environment

Millimeter wave (MmWave) is a promising technology nowadays because it provides users with ultra-high data rate links with low latency time. Hence, internet of things, tactile internet and cloud computing applications which becomes an essential matter in 5G networks can be supported using it. MmWave standard, IEEE 802.11 ad, proposed using scheduled medium access control (MAC) based on time division multiple access (TDMA) to share radio resources among active mmWave users. MmWave has a highly dynamic time-variant channel with high blocking probability which highly degrades the entire system performance. Hence, applying simple channel independent scheduling schemes, e.g., round robin (RR), will be inefficient. Thus, an efficient radio resource scheduling with considering the changes in mmWave channel is needed for maximum benefit from available resources. This paper presents the performance evaluation of one of the channel sensitive scheduling schemes, proportional fairness scheduling (PFS), in downlink mmWave network by executing numerical simulation in different channel conditions and various mmWave beamwidths regarding the total system data rate and fairness between users.

Performance Evaluation of Proportional Fairness Scheduling in MmWave Network

Implementing intelligent reflecting surfaces (IRSs), in high frequency based beyond 5G networks, has become a necessity to overcome the harsh blockage issues that exist in these bands. IRSs can supply user equipment (UEs) with multi alternative virtual line of sight (LOS) links, hence enhancing the spectral efficiency (SE) of the system. As a result of deploying multi IRSs as communication assistants, the step of IRSs-UEs association is required to optimally assign each UE to its best IRS; consideration of the interference between different links is needed, to maximize the system performance. However, this process will be a time and power consuming problem, if conventional schemes, which exhaustively search all possible association patterns to find the optimum one for communication, is adapted. Although iterative search based schemes can reduce this complexity, they still need feedback signaling in real time. Hence, they will be inefficient in terms of power consumption and delay. Moreover, optimal placement of the multi-IRSs in the network, to enlarge the system performance, is still an open issue and needs to be studied. Consequently, in this paper, to handle the IRSs-UEs association problem, we propose a neural network (NN) based scheme using a multi-IRSs aided multi input multi output (MIMO) system. In this system, the estimated angles of arrival (AoAs) of UEs are used as input features for the NN, which is trained to associate each UE to its best IRS based on this information; then, within each IRS, passive beamforming is performed. Adapting this NN in online mode guarantees obtaining better performance while relaxing the complexity of association and increasing response time, giving a performance comparable to the exhaustive and iterative search based schemes. The proposed NN based scheme determines the association pattern without searching or feedback signals. Moreover, the proposed approach maintains the system SE nearly similar to the optimum performance obtained by the conventional scheme. Secondly, a criterion is suggested for optimal deployment of multi IRSs in the network, depending on maximizing the average summation UEs signal-to-interference-plus-noise ratio (SINR). Numerical results prove that this strategy outperforms a reference one, which aims to guarantee certain performance by maximizing minimum UE SINR. In contrast the proposed strategy achieves better system and per UE spectral efficiency.

/pdf/neural-network-based-irss-ues-association-and-irss-optimal-2ahkl2op.pdf

Ahmed M. Nor

Papers

Li-Fi Positioning for Efficient Millimeter Wave Beamforming Training in Indoor Environment

Millimeter Wave Beamforming Training Based on Li-Fi Localization in Indoor Environment

Access point selection in beyond 5G hybrid MmWave/Wi-Fi/Li-Fi network

Performance Evaluation of Proportional Fairness Scheduling in MmWave Network

Neural Network Based IRSs-UEs Association and IRSs Optimal Placement in Multi IRSs Aided Wireless System