Resource Optimization in Multi-Tier HetNets Exploiting Multi-Slope Path Loss Model

Existing cellular network analyses are based on the standard power law path loss model. If the base stations are modeled by a Poisson point process, this leads to a tractable analysis of coverage probability and other metrics for downlink cellular networks. Yet, it is also well-known that the standard path loss model is idealized and does not capture the distance-dependence of the path loss exponent. This paper considers a more precise and general model, the dual-slope path loss model, where the path loss exponents are different for short links and long links differentiated by a critical distance. We derive compact expressions on the coverage probability and its tight closed-form estimate under this model. The analytical results show that the SINR does not monotonically increase with network density (as under the standard path loss model). Rather, ultra-densification leads to worse or even zero coverage when the near-field path loss exponent is 2 or less.

Little research has been done to reliably model millimeter wave (mmWave) path loss in rural macrocell settings, yet, models have been hastily adopted without substantial empirical evidence. This paper studies past rural macrocell (RMa) path loss models and exposes concerns with the current 3rd Generation Partnership Project (3GPP) TR 38.900 (Release 14) RMa path loss models adopted from the International Telecommunications Union — Radiocommunications (ITU-R) Sector. This paper shows how the 3GPP RMa large-scale path loss models were derived for frequencies below 6 GHz, yet they are being asserted for use up to 30 GHz, even though there has not been sufficient work or published data to support their validity at frequencies above 6 GHz or in the mmWave bands. We present the background of the 3GPP RMa path loss models and their use of odd correction factors not suitable for rural scenarios, and show that the multi-frequency close-in free space reference distance (CI) path loss model is more accurate and reliable than current 3GPP and ITU-R RMa models. Using field data and simulations, we introduce a new close-in free space reference distance with height dependent path loss exponent model (CIH), that predicts rural macrocell path loss using an effective path loss exponent that is a function of base station antenna height. This work shows the CI and CIH models can be used from 500 MHz to 100 GHz for rural mmWave coverage and interference analysis, without any discontinuity at 6 GHz as exists in today's 3GPP and ITU-R RMa models.

It has been observed that the existing path loss models such as Hata, Okumura, and COST-231-Walfisch-Ikegami do not help in accurately predicting path loss for Indian terrain conditions. Since path loss values impacts the cost of operation along with Quality of Services (QoS) significantly, a new path loss model is desired. Modelling an accurate path loss model for Indian terrain requires terrain/city planning along with the building data, extensive drive tests for radio signal characterization, wireless telecom operator data related to Base Station (BTS) location, transmission power, height, tilt, etc. All these factors make the path loss modeling difficult and challenging. In this direction, we have proposed Tata Dense Urban Path loss (T-DUP) model which is a non-deterministic statistical model applicable for Indian cities. The proposed path loss model is validated through extensive drive tests at Bangalore with the help of a major Indian Telecom Operator operating at 1800 MHz. We have observed that the T-DUP model is statistically robust and is closely matching to the drive test results. We have also conducted statistical analysis and have illustrated the impact of various city planning parameters such as building heights, building separation, road widths, etc., on path loss model. We believe that T-DUP model can be extended to other frequency bands operating in India with appropriate tuning of parameters.

In this correspondence, we investigate unmanned aerial vehicle (UAV)-base stations (BSs) assisted and received signal strength (RSS) based mobile station (MS) localization. A practical air-to-ground path loss model is utilized, where the path loss exponent (PLE) varies with the elevation angle and altitude of UAV, and the accurate PLE estimate is often difficult to obtain. With unknown and unequal PLEs for different UAVs, the UAVs assisted localization problem becomes nonlinear and non-convex, which cannot be solved by the existing methods. We propose a piecewise convex approximation aided localization (PCAL) approach to convert the localization problem into linear and convex, without requiring the knowledge of PLE. The proposed PCAL approach with unknown and unequal PLEs achieves much higher accuracy than the existing methods which require perfectly known and equal PLE, due to its higher robustness against shadowing. In addition, a grid search aided ambiguity elimination (GSAE) method, which is more effective than the state-of-the-art methods, is proposed to determine the final MS localization estimate based on multiple tentative estimates derived from PCAL. The effectiveness of PCAL is also verified by the Cramer-Rao lower bound (CRLB) derived.

In this paper, first, path loss models are developed for infrared optical wireless transmission inside an aircraft cabin. Second, a cellular network in the aircraft is considered and signal-to-interference ratio (SIR) maps are determined via simulation. For this purpose, a Monte Carlo ray-tracing (MCRT) simulation is performed in a geometric computer-aided design (CAD) cabin model with defined position, azimuth (AZ), elevation (EL) and field of view (FOV) properties of transmitters and receivers. Mathematical models are developed for line-of-sight (LOS) and non-line-of-sight (NLOS) path losses along particular paths, including estimation of the path loss exponent and the shadowing component. The shadowing is modeled according to a log-normal distribution with zero mean and standard deviation sigma. The validity of this model is confirmed in the paper. It is shown that irradiance distribution under LOS conditions experiences an attenuation with a path loss exponent of 1.92 and a shadowing standard deviation of 0.81 dB. In NLOS conditions, however, the path loss exponent varies, depending on the nature of the NLOS cases considered. The presented NLOS scenarios yield path loss exponent values of 2.26 and 1.28, and shadowing standard deviation values of 1.27 dB and 0.7 dB, respectively. Finally, the cabin is divided into cells and SIR maps are presented for different frequency reuse factors. It is shown that at the edges of the circular cells with diameter of 2.8 m, a SIR of -5.5 dB is achieved in a horizontal cross section of the cabin for frequency reuse of 1, and -2 dB and 3 dB for frequency reuse factors of 2 and 3, respectively. This means that in an aircraft cabin, for reuse factors less than three, viable communication at the cell edges is not feasible without additional interference avoidance or interference mitigation techniques.

We study wireless sensor network (WSN) node placement in an environment where RF signal losses vary with position. This reflects real–world outdoor environments where vegetation and topography cause nonuniform path loss. Many techniques that solve for a variety of objective functions subject to various constraints have previously been proposed for node placement. However, many of these methods make simplifying assumptions such as all nodes having the same transmission range. Our goal is to take the insights and approaches of this previous ork and extend it to real–world environments. The present work assumes we have a map that quantifies the path loss behaviour of the real environment. Based on this map, and a path loss model that accounts for spatial variations in the path loss exponent, we propose a node placement algorithm for two–tiered WSNs that maximises the area covered by a specified number of relay nodes and sensor nodes.

Large-scale fading models play an important role in estimating radio coverage, optimizing base station deployments and characterizing the radio environment to quantify the performance of wireless networks. In recent times, multi-frequency path loss models are attracting much interest due to their expected support for both sub-6 GHz and higher frequency bands in future wireless networks. Traditionally, linear multi-frequency path loss models like the ABG model have been considered, however such models lack accuracy. The path loss model based on a deep learning approach is an alternative method to traditional linear path loss models to overcome the time-consuming path loss parameters predictions based on the large dataset at new frequencies and new scenarios. In this paper, we proposed a feed-forward deep neural network (DNN) model to predict path loss of 13 different frequencies from 0.8 GHz to 70 GHz simultaneously in an urban and suburban environment in a non-line-of-sight (NLOS) scenario. We investigated a broad range of possible values for hyperparameters to search for the best set of ones to obtain the optimal architecture of the proposed DNN model. The results show that the proposed DNN-based path loss model improved mean square error (MSE) by about 6 dB and achieved higher prediction accuracy R2 compared to the multi-frequency ABG path loss model. The paper applies the XGBoost algorithm to evaluate the importance of the features for the proposed model and the related impact on the path loss prediction. In addition, the effect of hyperparameters, including activation function, number of hidden neurons in each layer, optimization algorithm, regularization factor, batch size, learning rate, and momentum, on the performance of the proposed model in terms of prediction error and prediction accuracy are also investigated.

The objective of this work is to simulate and mathematically model both path loss and transmitted energy in a robotic wireless sensor network (WSN). The simulation and analysis showed an increase in both path loss and transmitted energy as a function of distance. The correlation between transmitted energy and path loss proved to be exponential relationship with both logarithmic and power relationships between path loss and distance. Both expressions describing path loss, using close-in (CI) dual model and transmitted energy, using wireless body area network (WBAN) model, are modified and combined in one single expression to enable optimization of energy management. The newly developed expression is simulated and produced reliable results, relating effect of frequency and message size on transmitted energy as a function of distance. Combining these results with the results showing effect on path loss on transmitted energy, enables a better optimization of energy management of nodes on robotic body. The main objective of this work, which is the development of a single expression relating transmitted energy to critical parameters (frequency, path loss exponent, message size, distance) is achieved and is logically derived and based on analysis using two separate models for path loss and transmitted energy.

This paper presents outdoor radio channel measurements at 140 GHz using a spectrum analyzer-based channel sounder operational in the D-band, ranging from 110 to 170 GHz. We measure directional Line-of-Sight (LOS) path loss (PL) in a suburban environment for distances ranging from 2 m to 95 m, using an omnidirectional transmit antenna and directional receive antenna. Every 10 m, a full angular scan is performed by physically rotating the receive antenna in steps of 12°, which corresponds to the antenna's half-power beamwidth. A floating-intercept PL model is created, resulting in a fitted intercept <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$PL_{0}$</tex> at 1 m of 80.7 dB, and PL exponent <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> 1.9. The azimuthal angular spread varies between 7° and 45°, with a mean angular spread of 19.7°.

Outage probabilities in wireless networks depend on various factors: the node distribution, the MAC scheme, and the models for path loss, fading, and transmission success. In prior work on outage characterization for networks with randomly placed nodes, most of the emphasis was put on networks whose nodes are Poisson-distributed and where ALOHA is used as the MAC protocol. In this paper, we provide a general framework for the analysis of outage probabilities in the high-reliability regime. The outage probability characterization is based on two parameters: the intrinsic spatial contention γ of the network, introduced by Haenggi in a previous work, and the coordination level achieved by the MAC as measured by the interference scaling exponent κ introduced in this paper. We study outage probabilities under the signal-to-interference ratio (SIR) model, Rayleigh fading, and power-law path loss and explain how the two parameters depend on the network model. The main result is that the outage probability approaches γη <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">κ</sup> as the density of interferers η goes to zero, and that κ assumes values in the range 1 ≤ κ ≤ α/2 for all practical MAC protocols, where α is the path-loss exponent. This asymptotic expression is valid for all motion-invariant point processes. We suggest a novel and complete taxonomy of MAC protocols based mainly on the value of κ. Finally, our findings suggest a conjecture that bounds the outage probability for all interferer densities.

This paper presents the path loss model for 300 - 320 GHz wireless channels.The single-slope large-scale path loss model that includes log-normal shadow fading is devised from the large set of measured data. A good match between the modelled and the measured path loss is observed. The results show that for short distances, the path loss exponent is around 2 and the shadow fading is the smallest at the center of the frequency band of interest.

An on- and off- combined body channel path loss (PL) model, along with its link realistic antennas effects, is developed in this paper. Based on a heterogeneous human body phantom and the verified antenna structures, the PL parameters of this model are extracted through the SEMCAD simulated results. This PL model is finally verified experimentally on a volunteer body.

Vehicle-to-infrastructure (V2I) communication may provide high data rates to vehicles via millimeter-wave (mmWave) microcellular networks. This paper uses stochastic geometry to analyze the coverage of urban mmWave microcellular networks. Prior work used a pathloss model with a line-of-sight probability function based on randomly oriented buildings, to determine whether a link was line-of-sight or non-line-of-sight. In this paper, we use a pathloss model inspired by measurements, which uses a Manhattan distance pathloss model and accounts for differences in pathloss exponents and losses when turning corners. In our model, streets are randomly located as a Manhattan poisson line process and the base stations (BSs) are distributed according to a Poisson point process. Our model is well suited for urban microcellular networks where the BSs are deployed at street level. Based on this new approach, we derive the coverage probability under certain BS association rules to obtain closed-form solutions without much complexity. In addition, we draw two main conclusions from our work. First, non-line-of-sight BSs are not a major benefit for association or source of interference most of the time. Second, there is an ultra-dense regime where deploying active BSs does not enhance coverage.

Little is known about millimeter wave (mmWave) path loss in rural areas with tall base station antennas; yet, as shown here, surprisingly long distances (greater than 10 km) can be achieved in clear weather with less than 1 W of power. This paper studies past rural macrocell (RMa) propagation models and the current third generation partnership project (3GPP) RMa path loss models for frequencies from 0.5 to 30 GHz adopted from the International Telecommunications Union-Radiocommunication Sector (ITU-R). We show that 3GPP and ITU-R RMa path loss models were derived for frequencies below 6 GHz, yet are being asserted for use up to 30 GHz. Until this paper, there has not been published data to support mmWave RMa path loss models. In this paper, 73-GHz measurements in rural Virginia are used to develop a new RMa path loss model that is more accurate and easier to apply for varying transmitter antenna heights than the existing 3GPP/ITU-R RMa path loss models, and may be used for frequencies from 0.5 to 100 GHz. The measurement system used here has a measurement range comparable to a wideband (800-MHz radio frequency bandwidth) channel sounder with 21.7-dBW effective isotropic radiated power. Measured data verify a new path loss model that uses a close-in free space reference distance with a novel height-dependent path loss exponent (CIH model). This work shows that the CIH model is accurate and stable, and is frequency-independent beyond the first meter of propagation, and effectively models the path loss dependence on base station height in rural channels.

Modern cellular communication networks are already being perturbed by large and steadily increasing mobile subscribers in high demand for better service quality. To constantly and reliably deploy and optimally manage such mobile cellular networks, the radio signal attenuation loss between the path lengths of a base transmitter and the mobile station receiver must be appropriately estimated. Although many log-distance-based linear models for path loss prediction in wireless cellular networks exist, radio frequency planning requires advanced non-linear models for more accurate predictive path loss estimation, particularly for complex microcellular environments. The precision of the conventional models on path loss prediction has been reported in several works, generally ranging from 8–12 dB in terms of Root Mean Square Error (RMSE), which is too high compared to the acceptable error limit between 0 and 6 dB. Toward this end, the need for near-precise machine learning-based path loss prediction models becomes imperative. This work develops a distinctive multi-layer perception (MLP) neural network-based path loss model with well-structured implementation network architecture, empowered with the grid search-based hyperparameter tuning method. The proposed model is designed for optimal path loss approximation between mobile station and base station. The hyperparameters examined include the neuron number, learning rate and hidden layers number. In detail, the developed MLP model prediction accuracy level using different learning and training algorithms with the tuned best values of the hyperparameters have been applied for extensive path loss experimental datasets. The experimental path loss data is acquired via a field drive test conducted over an operational 4G LTE network in an urban microcellular environment. The results were assessed using several first-order statistical performance indicators. The results show that prediction errors of the proposed MLP model compared favourably with measured data and were better than those obtained using conventional log-distance-based path loss models.