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This paper suggests a MATLAB-based television white space (TVWS) database with forecastingcapabilities. The availability of multiple TV frequencies for a secondary user, depending on the day and time ofthe inquiry, location, and device, was forecasted using reinforcement learning (RL) software. A MATLAB livescript RL application was created and tested. After passing numerous tests, the algorithm was integrated into aMATLAB App Designer application so that an SU could request projections for spectrum availability. Threecategories might be used to categorize the forecast—medium-term (MT), long-term (LT), and short-term (LT).The forecast for that day is referred to as ST, the following day as MT, and the days after that as LT. Based onthe SU's device, the program was designed to predict the spectrum availability in relation to the SU's query time,location, and transmission details. The SU device is a fixed white space device with an EIRP power cap of 4watts. The database and AI are both necessary for accurate predictions. The forecast's results indicated 100%accuracy, assuming the database is frequently updated. Both the forecasting and reinforcement learningprograms were created using MATLAB's App Designer.

Wireless multipath clustering is an important area in channel modeling, and an accurate channel model can lead to a reliable wireless environment. Finding the best technique in clustering wireless multipath is still challenging due to the radio channels’ time-variant characteristics. Several clustering techniques have been developed that offer an improved performance but only consider one or two parameters of the multipath components. This study improved the K-PowerMeans technique by incorporating weights or loads based on the principal component analysis and utilizing the Minkowski distance metric to replace the Euclidean distance. K-PowerMeans is one of the several methods in clustering wireless propagation multipaths and has been widely studied. This improved clustering technique was applied to the indoor datasets generated from the COST 2100 channel Model and considered the multipath components’ angular domains and their delay. The Jaccard index was used to determine the new method’s accuracy performance. The results showed a significant improvement in the clustering of the developed algorithm than the standard K-PowerMeans.

Television white spaces (TVWSs) refer to the unused part of the spectrum under the very high frequency (VHF) and ultra-high frequency (UHF) bands. TVWS are frequencies under licenced primary users (PUs) that are not being used and are available for secondary users (SUs). There are several ways of implementing TVWS in communications, one of which is the use of TVWS database (TVWSDB). The primary purpose of TVWSDB is to protect PUs from interference with SUs. There are several geolocation databases available for this purpose. However, it is unclear if those databases have the prediction feature that gives TVWSDB the capability of decreasing the number of inquiries from SUs. With this in mind, the authors present a reinforcement learning-based TVWSDB. Reinforcement learning (RL) is a machine learning technique that focuses on what has been done based on mapping situations to actions to obtain the highest reward. The learning process was conducted by trying out the actions to gain the reward instead of being told what to do. The actions may directly affect the rewards and future rewards. Based on the results, this algorithm effectively searched the most optimal channel for the SUs in query with the minimum search duration. This paper presents the advantage of using a machine learning approach in TVWSDB with an accurate and faster-searching capability for the available TVWS channels intended for SUs.

As the world advances into 5G networks, significant scientific research accomplishments are being conducted for a communication system that could further enhance the current limit of data transmission capacity. Currently, the communication systems with the highest data rate are optical fiber systems. Due to the recent advancement of coherent optical fiber communications by exploiting time, wavelength, phase, amplitude, polarization, and space, optical engineering can break the petabit barrier data rate. Thus, coherent optical fiber communications is a hot topic due to its very high data rate that could be applied or a requirement in 5G and big data analytics. This paper focuses on a comparative survey of the current applied fundamental techniques in fiber communication channels. These fundamental techniques that could be further studied and exploited to increase the bandwidth performance, decrease the error rate and energy consumption are coding, multiplexing, and equalization. At the end of this paper, a comparative result is discussed to explain the difference among the current techniques in the literature for the optical engineering community to improve collective coding, multiplexing, and equalization in coherent fiber systems.

Proposed datasets for the interim goal of validating and comparing multipath clustering approaches are put forward based on the COST 2100 channel model (C2CM), which is a geometry-based stochastic channel model (GSCM) that can replicate multiple-input multiple-output (MIMO) channels. This paper provides descriptions and characteristics of the multipaths drawn from the Matlab implementation of the C2CM in eight indoor and semi-urban channel scenarios at 5.3 GHz and 285 MHz, respectively. In addition to the multipaths generated, the meaning and extraction method of the multipath components (MPCs) and the multipath clusters are elucidated. The suggested datasets are at the IEEE DataPort repository for use widely for the propagation engineering community.

Models of the radio channel power delay profile are proposed for low-complexity 60 GHz communications systems that employ beam tracking capability in realistic indoor non-line-of-sight scenarios. A single-carrier transmission system that does not employ equalization, but only uses forward error correction is considered. The channel models were obtained from measurements done in a novel way in a conference room in which non-line-of-sight scenarios are created through a human-sized blocking object, and in which beamforming is applied at the receiver to track and obtain reflected paths. By deploying beamforming antennas and then switching to the best path when the LoS path is cut, it was found through measurements and models that very low delay spreads could be achieved. As a result, not only the new channel models are verified, but the proposed model could replicate the measured channel with narrow antenna beamwidth together with beam tracking and switching. The low delay spread performance could lead to low-power consuming devices and multi-gigabits-per-second transmission by a single carrier with simple FEC.

Power line communication is an emerging technology in the field of communications that aims to use the power line as a medium to send and receive data. Several studies have been conducted to characterize the power lines [1], [2], [3], [4],. These studies guide PLC modem designers to create a more robust design. However, it is not practicable to test modems in a live power line network. This research aims to create an emulator capable of replicating the behavior of a typical indoor power line channel and use it as a test bed for PLC modem The emulator is implemented on Virtex 6 FPGA and FMC 151 for the analog front end. The power line channel model uses Zimmerman's channel transfer function [5] as reference. Analysis are done by comparing theoretical results with the hardware results which are done in MATLAB and Xilinx respectively. Several features such as addition of noise are also within the emulator and can be selected by the user to improve the realism of the emulator. Lastly, channel parameters such as SNR (signal to noise ratio) and RMS (root mean square) delay spread are obtained to ensure the quality of the channel produced.

Assistive devices for the hearing impaired have been developed to assist them through various techniques such as peripheral awareness and alerting systems. The disadvantage of presently available alerting systems is its fixed wired or wireless connection to the alert transmitter. This study focused on providing an alternative to these alerting systems in overcoming the transmitter dependence limitation. The researchers developed and integrated a real-time landmark-based audio fingerprinting and recognition algorithm for the implementation in a portable transmitter-less device which would notify the hearing impaired through combination of visual and vibrational cues when recognizing audio alert or warning signals.

This paper presents clusterwise polarization characteristics based on an estimated MIMO channel of a small urban macrocell at 4.5 GHz. Polarization path gain, cross-polarization ratio, and co-polarization ratio characteristics as influenced by channel parameters such as delay, azimuth and elevation angle of arrival were investigated. Hypothesis testing was used as a verification of the results. This statistical verification clarifies the independence of polarization characteristics from an azimuth angle of arrival, whereas some correlation was found between the cross-polarization ratio and the elevation angle of arrival. The results also confirmed a log-normal cross-polarization ratio.