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This paper introduces the Quasi-Moment-Method (QMM) as a novel radiowave propagation pathloss model calibration tool, and evaluates its performance, using field measurement data from different cellular mobile communication network sites in Benin City, Nigeria. The QMM recognizes the suitability of component parameters of existing basic models for the definition of ‘expansion’ and ‘testing functions’ in a Galerkin approach, and simulations were carried out with the use of a FORTRAN program developed by the authors, supported by matrix inversion in the MATLAB environment. Computational results reveal that in terms of both Root Mean Square (RMS) and Mean Prediction (MP) errors, QMM-calibrated models performed much better than an ‘optimum’ model reported for the NIFOR (Benin City), by a recent publication. As a matter of fact, the QMM-calibrated COST231 (rural area) model recorded reductions in RMS error of between 31.5% and 71% compared with corresponding metrics due to the aforementioned ‘optimum’ model. The simulation results also revealed that of the five basic models (COST231-rural area and suburban city, ECC33 (medium and large sized cities), and Ericsson models) utilized as candidates, the two ECC33 models, whose performances were consistently comparable, represented the best models for QMM-model calibration in the Benin City environments investigated.

Radio frequency interference (RFI) constitutes a significant problem in achieving a good quality of service in radio links. Several techniques have been proposed to identify and mitigate RFI in wireless networks. However, most of these techniques are not generalized for all propagation environments due to varying geographical features from one environment to another. The need for extensive frequency scan measurements on the links to identify the available channels, evaluate the performances of the links, and detect RFI in the channels becomes imperative. This study presents a performance evaluation of frequency scan measurements from active microwave links comprising eighteen base stations. The measurements equipment included a spectrum analyzer and a 0.6 m antenna dish. The frequency scans were taken at 6 GHz, 7 GHz, and 8 GHz with full azimuth coverage of the horizontal and vertical polarization. Measured data were processed to determine the available frequencies and RFI in the channels. The histogram and probability density function of the frequency scans were computed. The cumulative distribution functions were determined, and the statistical error characteristics of the frequency scans for the estimated normal distribution and the estimated fitness curve were derived. The short-time Fourier transform of the noisy signal was obtained, and the signal without noise was recovered using the inverse short-time Fourier transform. Analysis of the scanned signals before and after the noise removal is demonstrated. The denoised signals compare favorably with related results in the preliminary literature. Overall, these frequency scans would be beneficial in evaluating RFI measurements and spectrum planning and hold great promise for designing robust RFI detection algorithms for future wireless systems.

One of the biggest challenges for wireless communication network operators is how to minimize or mitigate radio frequency interference (RFI) for efficient network services at the desired quality of service (QoS). Microwave radio links are highly susceptible to interference from narrow and wideband sources. Interference ultimately affects network quality and contributes to the colossal loss of usable mobile data, leading to substantial operational costs for network operators. Additionally, the implementation of high capacity microwave links could potentially force the channels to point towards the same direction, posing a significant interference source. Radio frequency interference issues on the microwave links should be prioritized for prompt resolution or mitigation to achieve the minimum QoS requirement for the growing network subscribers. Toward this end, frequency scans are required to accurately picture the available frequency plan and channels based on the allocated spectrum. This article presents experimental data on radio frequency interference of active microwave links at 6 GHz, 7 GHz, and 8 GHz. The extensive frequency scans were obtained from eighteen active base stations located in Kogi, Lagos, and Rivers States in Nigeria. The frequency scans were carried out using the Anritsu MS2724C spectrum analyzer and a 0.6-meter antenna dish with full azimuth coverage. The analyzer captures the horizontal and vertical polarization. The frequency scan measurements reported in this article would be significantly useful to radio frequency interference detection and mitigation, preliminary network equipment positioning, frequency selection and assignment, and microwave network planning.

Investigations in this paper focus on establishing the uniqueness properties of the Quasi-Moment-Method (QMM) solution to the problem of calibrating nominal radiowave propagation pathloss prediction models. Nominal (basic) prediction models utilized for the investigations, were first subjected to QMM calibrations with measurements from three different propagation scenarios. Then, the nominal models were recast in forms suitable for Singular Value Decomposition (SVD) calibration before being calibrated with both the SVD and QMM algorithms. The prediction performances of the calibrated models as evaluated in terms of Root Mean Square Prediction Error (RMSE), Mean Prediction Error (MPE), and Grey Relational Grade-Mean Absolute Percentage Error (GRG-MAPE) very clearly indicate that the uniqueness of QMMcalibrations of basic pathloss models is more readily observable, when the basic models are recast in forms specific to SVD calibration. In the representative case of calibration with indoorto-outdoor measurements, RMSE values were recorded for QMM-calibrated nominal models as 5.2639dB for the ECC33 model, and 5.3218dB for the other nominal models.Corresponding metrics for the alternative (rearranged) nominal models emerged as 5.2663dB for the ECC33 model and 5.2591dB for the other models. A similar general trend featured in the GRG-MAPE metrics, which for both SVD and QMM calibrations of all the alternative models, was recorded as 0.9131, but differed slightly (between 0.9138 and 0.9196) for the QMM calibration of the nominal models. The slight differences between these metrics (due to computational round-off approximations) confirm that when the components of basic models are linearly independent, the QMM solution is unique. Planning for wireless communications network deployment may consequently select any basic model of choice for QMM-calibration, and hence, identify relative contributions to pathloss by the model's component parts.