An Improved Backoff Scheme and Its Performance Analysis for Full Duplex MAC Protocols in VLC Networks

IEEE 802.15.7 Visible Light Communication (VLC) networks suffer from performance degradation caused by the hidden device collisions due to the directional transmission with narrow beamwidth. One of the solutions for mitigating the hidden device collisions is to employ a full-duplex transmission technique. As a side effect of the full-duplex transmission in the VLC networks, however, the data-packet discard due to the retransmission limitation occurs frequently in the networks. This paper proposes an improved backoff scheme and its performance analysis to suppress the packet discard. The proposed backoff scheme increases the Backoff Exponent (BE) and the Number of Backoff stage (NB) in IEEE 802.15.7 only when the data packet transmission fails. To evaluate the system performance theoretically, this paper also provides the Markov-chain model for channel access with the proposed scheme. The performance evaluations through simulation and theoretical analysis show the effectiveness of the proposed scheme.

Capacity of Vehicular Ad Hoc Networks Based on Multibeam Directional Transmission

The development of multibeam directional transmission technology used in vehicular ad hoc networks is drawing much more attention in recent years due to its wider coverage ability than omnidirectional transmission. In this paper, we analyse the transport capacity of the vehicular network using different antenna modes in the transmitter and receiver end, respectively. We first construct the cross-layer model comprising the characteristic of the directional antenna model, arbitrary network model, and interference model. Then, based on scaling laws, we calculate the upper and lower bound of the network capacity with and without the directional multibeam transmission technology. In order to reduce the capacity lower bound computation complexity, several topology frameworks are constructed while taking various interferences into account included in the actual project. Finally, we analyse the capacity under changes of different parameters and also evaluate the law of capacity changes to discover how much improvement multibeam transmission technology can bring to the network performance. Analysis shows that compared with DTOR and OTDR mode, DTDR mode can continue to increase network capacity by 2 to 3 times on the basis of the above two modes.

Enhancing Energy Efficiency for Cellular-Assisted Vehicular Networks by Online Learning-Based mmWave Beam Selection

Millimeter-Wave (mmWave) technology is deemed as a feasible approach for future vehicular communications. However, mmWave signals are characterized by high path loss and penetration loss, which can be alleviated by directional communication. Directional transmission performance depends on beam alignment between transmitter and receiver, which is not easy to achieve in highly dynamic vehicular communications. The existing works addressed beam alignment problem by designing online learning-based mmWave beam selection schemes, which can be well adapted to high dynamic vehicular scenarios. However, this type of works does not take energy efficiency into account. Therefore, we propose an Energy efficiency-based FML (EFML) scheme to compensate for this shortfall, where the power consumption can be reduced as far as possible under the premise of meeting the basic data rate requirements of vehicle users and the users requesting the same content in close proximity can be organized into the same receiving group to share the same mmWave beam. The simulation results show that the EFML scheme improves both the network energy efficiency and the amount data of cellular-assisted vehicular networks at the cost of more beam performance update overhead. However, there is no difference in the cost of updating beam performance after adequate online learning.

BRNN-LSTM for Initial Access in Millimeter Wave Communications

The use of beamforming technology in standalone (SA) millimeter wave communications results in directional transmission and reception modes at the mobile station (MS) and base station (BS). This results in initial beam access challenges, since the MS and BS are now compelled to perform spatial search to determine the best beam directions that return highest signal levels. The high number of signal measurements here prolongs access times and latencies, as well as increasing power and energy consumption. Hence this paper proposes a first study on leveraging deep learning schemes to simplify the beam access procedure in standalone mmWave networks. The proposed scheme combines bidirectional recurrent neural network (BRNN) and long short-term memory (LSTM) to achieve fast initial access times. Namely, the scheme predicts the best beam index for use in the next time step once a MS accesses the network, e.g., transition from sleep to active (or idle) modes. The scheme eliminates the need for beam scanning, thereby achieving ultra-low access times and energy efficiencies as compared to existing methods.

Instantaneous Beam Prediction Scheme Against Link Blockage in mmWave Communications

Millimeter wave (mmWave) bands formulate the standalone (SA) operation mode in the new radio (NR) access technology of 5G systems. These bands rely on beamforming architectures to aggregate antenna array gains that compensate for dynamic channel fluctuations and propagation impairments. However, beamforming results in directional transmission and reception, thus resulting in beam management challenges, foremost initial access, handover, and beam blockage recovery. Here, beam establishment and maintenance must feature ultra-low latencies in the control and data planes to meet network specifications and standardization. Presently, existing schemes rely on arrays redundancy, multi-connectivity, such as dual-beam and carrier aggregation, and out-of-band information. These schemes still suffer from prolonged recovery times and aggregated power consumption levels. Along these lines, this work proposes a fast beam restoration scheme based on deep learning in SA mmWave networks. Once the primary beam is blocked, it predicts alternative beam directions in the next time frame without any reliance on out-of-band information. The scheme adopts long short-term memory (LSTM) due to the robust memory structure, which uses past best beam observations. The scheme achieves near-instantaneous recovery times, i.e., maintaining communications sessions without resetting beam scanning procedures.

A 3-dimensional fast machine learning algorithm for mobile unmanned aerial vehicle base stations

<p>The 5G technology is predicted to achieve the unoptimized millimeter Wave (mmWave) of 30-300 GHz bands. This unoptimized band because of the loss of mm-Wave bands, like path attenuation and propagation losses. Nonetheless, because of: (i) directional transmission paving way for beamforming to recompense for the path attenuation, and (ii) sophisticated placement concreteness of the base stations (BS) is the best alternative for array wireless communications in mmWave bands (that is to say 100-150 m). The advance in technology and innovation of unmanned aerial vehicles (UAVs) necessitates many opportunities and uncertainties. UAVs are agile and can fly all complexities if the terrains making ground robots unsuitable. The UAV may be managed either independently through aboard computers or distant controlled of a flight attendant on pulverized wireless communication links in our case 5G. Although a fast algorithm solved the problematic aspect of beam selection for 2-dimensional scenarios. This paper presents 3-dimensional scenarios for UAV. We modeled beam selection with environmental responsiveness in millimeter Wave UAV to accomplish close optimum assessments on the regular period through learning from the available situation.</p>