Downlink cellular network analysis with a dual-slope path loss model

Current resource allocation techniques in cellular networks are largely based on single-slope path loss model, which falls short in accurately capturing the effect of physical environment. The phenomenon of densification makes cell patterns more irregular; therefore, the multi-slope path loss model is more realistic to approximate the increased variations in the links and interferences. In this paper, we investigate the impacts of multi-slope path loss models, where different link distances are characterized by different path loss exponents. We propose a framework for joint user association, power and subcarrier allocation on the downlink of a heterogeneous network (HetNet). The proposed scheme is formulated as a weighted sum rate maximization problem, ensuring the users' quality-of-service requirements, namely users' minimum rate, and the base stations' (BSs) maximum transmission power. We then compare the performance of the proposed approach under different path loss models with demonstrate the effectiveness of dual-slope path loss model in comparison to the single-slope path loss model. Simulation results show that the dual-slope model leads to significant improvement in network's performance in comparison to the standard single-slope model by accurately approximating the path loss exponent dependence on the link distance. Moreover, it improves the user offloading from macrocell BS to small cells by connecting the users to nearby BSs with minimal attenuation. It has been shown that the path loss exponents significantly influence the user association lying across the critical radius in the case of the dual-slope path loss model.

We consider a dense urban cellular network where the base stations (BSs) are stacked vertically as well as extending infinitely in the horizontal plane, resulting in a greater than two dimensional (2D) deployment. We use a dual-slope path loss model that is well supported empirically, wherein a “close-in” pathloss exponent $\alpha_0$ is used for distances less than a corner distance $R_c$ , and then changes to $\alpha_1 > \alpha_0$ outside $R_c$ . We extend recent 2D coverage probability and potential throughput results to $d$ dimensions, and prove that if the close-in path loss exponent $\alpha_0 , then the SINR eventually decays to zero. For example, $\alpha_0 \le 3$ results in an eventual SINR of 0 for all users in a 3D network, which is a troubling fact. We also show that the potential (i.e. best case) aggregate throughput decays to zero for $\alpha_0 . Both of these scaling results also hold for the more realistic case that we term 3D+, where there are no BSs below the user, as in a dense urban network with the user on or near the ground.

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.

Fifth generation wireless communication networks will demand massive bandwidth for high data rates. The millimeter wave frequency bands will play an important role in the 5G wireless networks due to the available huge chunk of bandwidth. Deployment of wireless networks will require an appropriate path loss model for site-specific environments such as indoor corridors. Structural design and building materials call for development of such models. This work presents a multi-frequency dual-slope large-scale path loss model based on measurements performed in two distinct indoor environments made of glass and concrete. Measurements were conducted at 14 to 22 GHz in typical indoor corridors using a costume-designed channel sounder based on Rohde and Schwarz SMF 100 A signal generator and FSIQ 40 signal analyzer with 120 MHz bandwidth for distances ranging 2 m to 24 m in line-of-sight and non-line-of-sight scenarios. The acquired measurement data of 36,000 directional power delay profiles for 18 transmitter-receiver combinations were analyzed to model a directional dual-slope path loss model for vertically polarized antennas. The directional dual-slope path loss was synthesized with consideration of propagation mechanisms such as reflection and diffraction effectively causing modal attenuation as the signal propagates along the corridor due to wave guiding effect. A breakpoint was established that demonstrates the effects of modal attenuation in a waveguide like corridor. The model was evaluated by performing a comparative analysis on the directional dual-slope path loss model on various indoor corridors with different structural design and materials with well-known alfa-beta-gamma model. The results were observed to give a better prediction of path loss at 14 to 22 GHz over ABG model as fitted to the measurement data. Moreover, the results show that the wave guiding effect is pronounced after the breakpoint due to both free space and multi-modal propagation.

Performance analysis of coverage-centric heterogeneous cellular networks using dual-slope path loss model

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.

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.

Recent cellular systems are moving towards heterogeneous cellular networks (HCNs) that consist of a mixture of miniature cells and legacy macro-cells to meet the requirements of wireless data traffic, owing to the immense amount of multi-purpose mobile applications. The inclusion of small cells is a cost-effective solution for enhancing the size and coverage of the existing macro-cellular network. This article assumes a heterogeneous cellular network consisting of two tiers of base stations (BSs): large-scale (macro) and small-scale (pico) BSs. The users are evenly distributed, and each tier of BSs and users creates a uniform Poisson point process (PPP). Practical third-generation partnership project (3GPP) models for path loss are considered, and three camping/association criteria are utilized to relate user equipment (UEs) to large or small-scale BSs, including coupled and decoupled camping criteria to study coverage. The impact of several system design parameters on coverage is investigated using the aforementioned heterogeneous cellular network, association criteria, and 3GPP path loss models. Our simulation results provide insights into the effect of infrastructure sharing between macro and pico-cells and user density on coverage. We also explore the impact of fractional power control (FPC) and signaling limits on coverage under all considered association strategies. Finally, we investigate the effect of open-loop UE transmission power, pico-density, and biasing on coverage. Specifically, we thoroughly explore the effect of empty BSs on coverage under all system design parameters.

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°.

Coverage probability of uplink millimeter wave cellular network with non-homogeneous interferers’ point process

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.

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.