The IEEE 802.11bn draft amendment to the 802.11 Wi-Fi standard introduces Non-Primary Channel Access (NPCA) to enhance spectrum efficiency and mitigate congestion in dense wireless environments. NPCA allows stations to transmit over non-primary channels when the primary channel is occupied, improving overall spectrum utilization and reducing transmission delays. This paper presents a detailed evaluation of NPCA, focusing on its effectiveness in scenarios involving hidden nodes—one of the key challenges limiting its performance. Our research models NPCA based on IEEE contributions and proposals, implementing realistic network topologies to analyze both its strengths and weaknesses. The findings provide critical insights into NPCA’s practical deployment, highlighting its potential benefits and identifying areas requiring further optimization to ensure fairness and adaptability in real-world networks.

This paper presents a new system model to evaluate the capacity and power consumption of multi-layer 6 G networks utilising the upper mid-band (FR3). The model captures heterogeneous$(4 \mathrm{G}, 5 \mathrm{G})$, and 6 G deployments, analyzing their performance under different deployment strategies. Our results show that strategic 6 G deployments, non-co-located with existing 5G sites, significantly enhance throughput, with median and peak user rates of 300 Mbps and exceeding 1 Gbps, respectively. We also emphasize the importance of priority-based cell reselection and beam configuration to fully leverage 6 G capabilities. While 6 G implementation increases power consumption by 33%, non-colocated deployments strike a balance between performance and power consumption.

Typically, energy-efficient operation of wireless networks is achieved by allocating resources, such as physical resource blocks (PRBs), transmit power, and number of base station (BS) antennas, in an energy-efficient manner. However, the allocation of these resources is tightly coupled with user (UE)-to-BS association, which impacts the performance and efficiency of the network. Therefore, in this work we focus on energy-efficient UE-to-BS association for multi-cell multi-user massive multiple input multiple output (mMIMO) heterogeneous networks (HetNets) and propose two bias-based UE-to-BS association algorithms. Firstly, a range expansion bias (REB) model is employed to increase the effective coverage area of each pico BS (PBS), improving overall network performance and energy efficiency (EE). Secondly, the sum of channel gains as a metric for UE-to-BS association is leveraged, seeking to maximize the total channel gain across UEs and BSs by prioritizing stronger connections. This approach aims to optimize the distribution of UEs among available BSs, thereby enhancing the quality of service provided to UEs. Our results, based on numerical system-level simulations describing a realistic metropolis (Berlin) scenario, demonstrate that the individual REBs (iREBs) per PBS lead to significant improvements in EE, while the channel-gainbased approach effectively boosts overall network throughput and EE, outperforming the greedy maximum channel gain, a baseline REB based, and the iREB based UE-to-BS association methods.

Mobility management in dense cellular networks is challenging due to varying user speeds and deployment conditions. Traditional 3GPP handover (HO) schemes, relying on fixed A3-offset and time-to-trigger (TTT) parameters, struggle to balance radio link failures (RLFs) and ping-pongs. We propose a data-driven HO optimization framework based on high-dimensional Bayesian optimization (HD-BO) and enhanced with transfer learning to reduce training time and improve generalization across different user speeds. Evaluations on a real-world deployment show that HD-BO outperforms 3GPP set-1 and set-5 benchmarks, while transfer learning enables rapid adaptation without loss in performance. This highlights the potential of data-driven, site-specific mobility management in large-scale networks.

We propose a data-driven approach for large-scale cellular network optimization, using a production cellular network in London as a case study and employing Sionna ray tracing for site-specific channel propagation modeling. We optimize base station antenna tilts and half-power beamwidths, resulting in more than double the 10%-worst user rates compared to a 3GPP baseline. In scenarios involving aerial users, we identify configurations that increase their median rates fivefold without compromising ground user performance. We further demonstrate the efficacy of model generalization through transfer learning, leveraging available data from a scenario source to predict the optimal solution for a scenario target within a similar number of iterations, without requiring a new initial dataset, and with a negligible performance loss.

Today, Wi-Fi is over 25 years old. Yet, despite sharing the same branding name, today’s Wi-Fi boasts entirely new capabilities that were not even on the roadmap 25 years ago. This article aims to provide a holistic and comprehensive technical and historical tutorial on Wi-Fi, beginning with Institute of Electrical and Electronics Engineers 802.11b (Wi-Fi 1) and looking forward to IEEE 802.11bn (Wi-Fi 8). This is the first tutorial article to span these eight generations. Rather than a generation-by-generation exposition, we describe the key mechanisms that have advanced Wi-Fi. We begin by discussing spectrum allocation and coexistence, and detailing the IEEE 802.11 standardization cycle. Second, we provide an overview of the physical layer (PHY) and describe key elements that have enabled data rates to increase by over 1000×. Third, we describe how Wi-Fi medium access control (MAC) has been enhanced from the original distributed coordination function (DCF) to now include capabilities spanning from frame aggregation to wideband spectrum access. Fourth, we describe how Wi-Fi 5 first broke the one-user-at-a-time paradigm and introduced multi-user (MU) access. Fifth, given the increasing use of mobile, battery-powered devices, we describe Wi-Fi’s energy-saving mechanisms over the generations. Sixth, we discuss how Wi-Fi was enhanced to seamlessly aggregate spectrum across 2.4-, 5-, and 6-GHz bands to improve throughput, reliability, and latency. Finally, we describe how Wi-Fi enables nearby access points (APs) to coordinate in order to improve performance and efficiency. In the Appendix, we further discuss Wi-Fi developments beyond 802.11bn, including integrated millimeter-wave (IMMW) operations, sensing, security and privacy extensions, and the adoption of artificial intelligence (AI)/machine learning (ML).

We address the challenge of designing cellular networks for uncrewed aerial vehicles (UAVs) corridors through a novel data-driven approach. We assess multiple state-of-the-art high-dimensional Bayesian optimization (HD-BO) techniques to jointly optimize the cell antenna tilts and half-power beamwidth (HPBW). We find that some of these approaches achieve over 20 dB gains in median SINR along UAV corridors, with negligible degradation to ground user performance. Furthermore, we explore the HD-BO’s capabilities in terms of model generalization via transfer learning, where data from a previously observed scenario source is leveraged to predict the optimal solution for a new scenario target. We provide examples of scenarios where such transfer learning is successful and others where it fails. Moreover, we demonstrate that HD-BO enables multi-objective optimization, identifying optimal design trade-offs between data rates on the ground versus UAV coverage reliability. We observe that aiming to provide UAV coverage across the entire sky can lower the rates for ground users compared to setups specifically optimized for UAV corridors. Finally, we validate our approach through a case study in a real-world cellular network, where HD-BO identifies optimal and non-obvious antenna configurations that result in more than double the rates along 3D UAV corridors with negligible ground performance loss.

In this article, we introduce a method to optimize 5G massive multiple-input multiple-output (mMIMO) connectivity for unmanned aerial vehicles (UAVs) on aerial highways through strategic cell association. UAVs operating in 3D space encounter distinct channel conditions compared to traditional ground user equipment (gUE); under the typical line of sight (LoS) condition, UAVs perceive strong reference signal received power (RSRP) from multiple cells within the network, resulting in a large set of suitable serving cell candidates and in low signal-to-interference-plus-noise ratio (SINR) due to high interference levels. Additionally, a downside of aerial highways is to pack possibly many UAVs along a small portion of space which, when taking into account typical LoS propagation conditions, results in high channel correlation and severely limits spatial multiplexing capabilities. In this paper, we propose a solution to both problems based on the suitable selection of serving cells based on a new metric which differs from the classical terrestrial approaches based on maximum RSRP. We then introduce an algorithm for optimal planning of synchronization signal block (SSB) beams for this set of cells, ensuring maximum coverage and effective management of UAVs cell associations. Simulation results demonstrate that our approach significantly improves the rates of UAVs on aerial highways, up to four times in achievable data rates, without impacting ground user performance.

Optimizing the design, performance, and resource efficiency of wireless networks (WNs) necessitates the ability to discern Line of Sight (LoS) and Non-Line of Sight (NLoS) scenarios across diverse applications and environments. Unmanned Aerial Vehicles (UAVs) exhibit significant potential in this regard due to their rapid mobility, aerial capabilities, and payload characteristics. Particularly, UAVs can serve as vital nonterrestrial base stations (NTBS) in the event of terrestrial base station (TBS) failures or downtime. In this paper, we propose CNN autoencoder resizer (CAR) as a framework that improves the accuracy of LoS/NLoS detection without demanding extra power consumption. Our proposed method increases the mean accuracy of detecting LoS/NLoS signals from 66% to 86%, while maintaining consistent power consumption levels. In addition, the resolution provided by CAR shows that it can be employed as a preprocessing tool in other methods to enhance the quality of signals.

Due to an ever-expansive network deployment, numerous questions are being raised regarding the energy consumption of the mobile network. Recently, Non-Terrestrial Networks (NTNs) have proven to be a useful, and complementary solution to Terrestrial Networks (TN) to provide ubiquitous coverage. In this paper, we consider an integrated TN-NTN, and study how to maximize its resource usage in a dynamic traffic scenario. We introduce BLASTER, a framework designed to control User Equipment (UE) association, Base Station (BS) transmit power and activation, and bandwidth allocation between the terrestrial and non-terrestrial tiers. Our proposal is able to adapt to fluctuating daily traffic, focusing on reducing power consumption throughout the network during low traffic and distributing the load otherwise. Simulation results show an average daily decrease of total power consumption by $45 \%$ compared to a network model following 3GPP recommendation, as well as an average throughput increase of roughly $250 \%$. Our paper underlines the central and dynamic role that the NTN plays in improving key areas of concern for network flexibility.

Drawing upon a data-driven methodology, deep learning has emerged as an innovative approach for dynamic resource allocation in large-scale cellular networks. This paper proposes an optimization strategy relying on domain adversarial networks to reduce the number of poorly performing base stations (BSs). The approach dynamically allocates radio resources to address real-time mobile traffic needs. We calculate the interference coefficients among BSs and design a performance classifier that evaluates BS performance with respect to provided traffic-resource pairs as either poor or good. Most importantly, we use well-performing BSs as source domain data to reallocate the resources of poorly performing ones through the domain adversarial neural network. Experimental results demonstrate that the proposed domain adversarial resource allocation strategy effectively decreases the number of poorly performing BSs in the cellular network, which in turn outperforms other benchmark algorithms in terms of both the ratio of poor BSs and radio resource consumption.

Millimeter-wave (mmWave) networks, integral to 5G communication, offer a vast spectrum that addresses the issue of spectrum scarcity and enhances peak rate and capacity. However, their dense deployment, necessary to counteract propagation losses, leads to high power consumption. An effective strategy to reduce this energy consumption in mobile networks is the sleep mode optimization (SMO) of base stations (BSs). In this paper, we propose a novel SMO approach for mmWave BSs in a 3D urban environment. This approach, which incorporates a neural network (NN) based contextual multi-armed bandit (C-MAB) with an epsilon decay algorithm, accommodates the dynamic and diverse traffic of user equipment (UE) by clustering the UEs in their respective tracking areas (TAs). Our strategy includes beamforming, which helps reduce energy consumption from the UE side, while SMO minimizes energy use from the BS perspective. We extended our investigation to include Random, Epsilon Greedy, Upper Confidence Bound (UCB), and Load Based sleep mode (SM) strategies. We compared the performance of our proposed C-MAB based SM algorithm with those of All On and other alternative approaches. Simulation results show that our proposed method outperforms all other SM strategies in terms of the 10thpercentile of user rate and average throughput while demonstrating comparable average throughput to the All On approach. Importantly, it outperforms all approaches in terms of energy efficiency (EE).

Intelligent omni-surfaces (IOSs) have been considered for assisting outdoor-to-indoor millimeter-wave (mmWave) communications. Nevertheless, the existing works have not adequately investigated how the number or the deployment locations of IOSs should be optimized for serving multiple indoor users. In this paper, we study IOS-assisted outdoor-to-indoor mmWave communications where IOSs are installed in an exterior wall of a building to refract mmWave signals from an outdoor base station (BS) to indoor users that locate among indoor blockages. Given a fixed total number of refracting elements, we formulate an optimization problem to maximize the downlink energy efficiency of the outdoor BS while satisfying the dowlink data rate requirements of the indoor users by jointly optimizing the number, locations and phase shifts of IOSs and the beamforming vectors of the BS. To address the varying dimensionality and the non-convexity of the optimization problem, we decompose it into two subproblems that optimize the IOSs’ phase shifts together with the BS beamforming vectors and the number and locations of IOSs, respectively, and devise successive convex approximation and Continuous Population-Based Incremental Learning-based algorithms to solve them alternately. Simulation results demonstrate that the proposed algorithms can obtain the optimal number and locations of IOSs, resulting in significantly enhanced energy efficiency of the outdoor BS compared to benchmark schemes.

For energy efficient operation of the massive multiple-input multiple-output (MIMO) networks, various aspects of energy efficiency maximization have been addressed, where a careful selection of number of active antennas has shown significant gains. Moreover, switching-off physical resource blocks (PRBs) and carrier shutdown saves energy in low load scenarios. However, the joint optimization of spectral PRB allocation and spatial layering in a heterogeneous network has not been completely solved yet. Therefore, we study a power consumption model for multi-cell multi-user massive MIMO 5G network, capturing the joint effects of both dimensions. We characterize the optimal resource allocation under practical constraints, i.e., limited number of available antennas, PRBs, base stations (BSs), and frequency bands. We observe a single spatial layer achieving lowest energy consumption in very low load scenarios, whereas, spatial layering is required in high load scenarios. Finally, we derive novel algorithms for energy efficient user (UE) to BS assignment and propose an adaptive algorithm for PRB assignment and power control. All results are illustrated by numerical system-level simulations, describing a realistic metropolis scenario. The results show that a higher frequency band should be used to support UEs with large rate requirements via spatial multiplexing and assigning each UE maximum available PRBs.

As traditional cellular base stations (BSs) are opti-mized for 2D ground service, providing 3D connectivity to un-crewed aerial vehicles (UAVs) requires re-engineering of the existing infrastructure. In this paper, we propose a new methodology for designing cellular networks that cater for both ground users and UAV corridors based on Bayesian optimization. We present a case study in which we maximize the signal-to-interference-plus-noise ratio (SINR) for both populations of users by optimizing the electrical antenna tilts and the transmit power employed at each BS. Our proposed optimized network significantly boosts the UAV performance, with a 23.4 dB gain in mean SINR compared to an all-downtilt, full-power baseline. At the same time, this optimal tradeoff nearly preserves the performance on the ground, even attaining a gain of 1.3 dB in mean SINR with respect to said baseline. Thanks to its ability to optimize black-box stochastic functions, the proposed framework is amenable to maximize any desired function of the SINR or even the capacity per area.

Mobile edge computing (MEC) and field-of-view (FoV) prediction are two key techniques to enable the wireless virtual reality (VR) service. On this basis, we investigate a practical issue, that is, how to efficiently achieve the user's pre-set quality-of-experience (QoE) requirement on both video quality and delay tolerance. A constrained reward-steering algorithm based on reinforcement learning is proposed in this work to solve this multi-objective optimization problem, which finds the optimal policy approaching the user's targeted QoE. Meanwhile, both an instantaneous service delay constraint and a long-term energy constraint are satisfied by the Lagrangian-based method. Simulation results demonstrate that the proposed algorithm outperforms conventional reinforcement learning relying on weights, i.e. achieving an average reward vector much closer to the user's targeted QoE, and meeting both constraints.

The fifth generation of the Radio Access Network (RAN) has brought new services, technologies, and paradigms with the corresponding societal benefits. However, the energy consumption of 5G networks is today a concern. In recent years, the design of new methods for decreasing the RAN power consumption has attracted interest from both the research community and standardization bodies, and many energy savings solutions have been proposed. However, there is still a need to understand the power consumption behavior of state-of-the-art base station architectures, such as multi-carrier active antenna units (AAUs), as well as the impact of different network parameters. In this paper, we present a power consumption model for 5G AAUs based on artificial neural networks. We demonstrate that this model achieves good estimation performance, and it is able to capture the benefits of energy saving when dealing with the complexity of multi-carrier base stations architectures. Importantly, multiple experiments are carried out to show the advantage of designing a general model able to capture the power consumption behaviors of different types of AAUs. Finally, we provide an analysis of the model scalability and the training data requirements.

The energy consumption of the fifth generation (5G) of cellular technology is concerning for the mobile industry and the entire society. To minimize the environmental footprint and economic costs of 5G, it is necessary to adapt the transmission capabilities of networks to end-users’ quality of service requirements. In this paper, we focus on the carrier shutdown approach that enables a base station (BS) to autonomously switch off during low traffic periods, by transferring its load to neighbouring active BSs. More specifically, we propose a data-driven framework, constructed through real network measurements, which statistically characterizes the user equipment (UE) transfer across neighbouring BSs, when carrier shutdown operates. The implementation of this framework allows the 5G system to determine a poor load distribution due to energy saving mechanisms, prevent drastic reductions in UE performance, and ultimately estimate energy savings when activating carrier shutdown.

With growing interest in integrating terrestrial networks (TNs) and non-terrestrial networks (NTNs) to connect the unconnected, a key question is whether this new paradigm could also be opportunistically exploited to augment service in urban areas. We assess this possibility in the context of an integrated TN-NTN, comprising a ground cellular deployment paired with a Low Earth Orbit (LEO) satellite constellation, providing sub-6 GHz connectivity to an urban area populated by ground users (GUEs) and uncrewed aerial vehicles (UAVs). Our study reveals that offloading UAV traffic to the NTN segment drastically reduces the downlink outage of UAVs from 70% to nearly zero, also boosting their uplink signal quality as long as the LEO satellite constellation is sufficiently dense to guarantee a minimum elevation angle. Offloading UAVs to the NTN also benefits coexisting GUEs, preventing uplink outages of around 12% that GUEs would otherwise incur. Despite the limited bandwidth available below 6 GHz, NTN-offloaded UAVs meet command and control rate requirements even across an area the size of Barcelona with as many as one active UAV per cell. Smaller UAV populations yield proportionally higher rates, potentially enabling aerial broadband applications.

Cellular connected unmanned aerial vehicles (CCUAVs) are expected to enable new disruptive verticals with a significant impact on different business sectors. In particular, to enable connected and safe operations, the concept of drone corridors has recently received attention. In general, CCUAVs suffer from poor received signal strength, and they perceive large interference due to the high line-of-sight probability with the interfering sectors. In this paper, we propose an ADAM-based algorithm to optimize the electronic tilt of base stations deployed in an LTE network to improve the quality of service in predefined aerial corridors. Importantly, the numerical analysis results indicate that it is feasible to re-tune antenna sector directions to distribute, in an optimized manner, more power in the targeted corridors while minimizing interference, with a minimum impact for the ground, allowing usage of an already deployed LTE network for beyond line of sight (BLoS) communication.

This work considers a multi-cell, multi-carrier massive MIMO network with carrier aggregation, and tackles the rate versus power consumption trade-off, by jointly optimizing the number of employed component carriers, active antennas, base station density, and transmit power. A provably convergent algorithm is developed together with closed-form results for the individual optimization of the considered resources. Numerical results show how carrier aggregation can effectively reduce the power consumption without sacrificing the rate performance.

To meet the targets of net-zero green house gas (GHG) emissions, future wireless networks must operate highly energy efficient. To this end, various aspects of energy efficiency (EE) maximization have been addressed. On the one hand, careful selection of active number of antennas in massive multiple-input multiple-output (MIMO) systems has shown significant gains. Whereas, switching off physical resource blocks (PRBs) and carrier shutdown saves energy in low load scenarios. However, the joint optimization of both dimensions, the spectral PRB allocation with carrier aggregation (CA) and spatial layering, has not been accounted for. In this paper, we propose a power consumption model that captures the joint effect of CA and spatial layering on the total power consumption of a 5G network. We characterize the optimal resource allocation in spatial and spectral dimensions under practical constraints. Our results show that only in very low load scenarios, a single spatial layer achieves the lowest energy consumption and in most cases with high rate requirements and more users, spatial layering is required with carefully optimized number of active antennas and active PRBs. The gains compared to activating all available antennas and using all available PRB resources are tremendous. Finally, we study the point where switching on another frequency band results in better EE depending on the attenuation model.

Wireless connectivity will soon enable people to consume augmented and virtual reality content anywhere and cloud-connected mobile robots to perform complex tasks collaboratively. This connectivity will come to enterprises, factory floors and digital homes first, powered by indoor networks that offer much higher data rates, greater reliability, and lower latency than today’s networks. In addition to providing traditional communication capabilities, the future indoor network will have a “sixth sense” that enables it to provide sensory information and insights to meet physiological needs such as lighting, heating, health and safety. It will serve as the core infrastructure for smart buildings and help enterprises operate more efficiently by further enabling capabilities such as immersive virtual workplaces, indoor navigation and asset tracking. This paper reviews current technologies, examines the enabling technologies of the future indoor network and presents our latest research results and our vision for implementing them in commercial and residential environments.

In this paper, we theoretically analyse the uplink (UL) and downlink (DL) performance of massive multiple-input and multiple-output (mMIMO) networks, in term of coverage probability, cell spectral efficiency and network area spectral efficiency, using stochastic geometry. A sophisticated but yet practical system model is considered, taking into account a path loss model differentiating line-of-sight and non-line-of-sight transmissions, an idle mode capability at the base stations and a finite user density. Our analysis pays particular attention to the existence of a finite number of UL pilots for channel estimation and the effect of pilot contamination. We study for the first time the joint impact of the number of UL pilot sequences, the user density, and the base-station density on the pilot contamination issue in a mMIMO network, which in turn characterizes the DL and UL network performance. Moreover, using the proposed framework, we investigate two different scheduling problems—UE and pilot scheduling—, to find the optimal simultaneously scheduled UE density per time-frequency resource as well as the optimal UL pilot number to maximise the spectral efficiency.

The energy consumption of cellular networks, despite the high energy efficiency of the fifth generation (5G) of mobile technology, is still a challenge. The fundamental problem arises due to the complexity of optimising the operation of the available rich set of energy efficiency features in large-scale deployments. To assist such optimisation, a large body of research -with the resulting understanding and algorithms-exists, particularly on the energy efficiency of single-cell massive multiple-input multiple-output systems. However, other funda-mental cellular features, such as those relating to multi-carrier systems, remain largely unexplored. In this paper, we show how multi-carrier features, such as carrier aggregation, can play a significant role in energy savings, and question the need for hundreds of antennas and transceiver chains at the base stations as an urgent solution to increase the energy efficiency of next generation networks.

The energy consumption and carbon footprint of the fifth-generation (5G) of mobile technology is a current concern to mobile network operators (MNOs). These are currently attempting to lower both their carbon emissions and electricity bills by investigating new schemes that allow adapting the network transmission capabilities to the end-users' quality of service (QoS) requirements. Many of such schemes rely on accurate traffic forecasting, and as a consequence, there is a large effort on investigating novel machine learning (ML) algorithms, which fed by network measurement data and empowered by the computing capabilities of dedicated hardware, can help modelling and predicting users' behaviours. Most of the works in the literature, however, focus on predicting the traffic when energy saving features, e.g. carrier shutdown, are not implemented or activated. However, the prediction task becomes much more challenging when energy saving features are adopted due to their impact to the actual measured traffic. In this paper, we consider a scenario in which part of the base stations implement energy saving schemes, which allow them to dynamically switch off part of their hardware to reduce their power consumption. Then, we present a ML framework based on graph convolutional networks (GCNs) for traffic forecasting in such dynamic scenarios, and compare its performance with other statistical and ML prediction algorithms. The proposed GCN framework provides significant accuracy gains. Moreover, we provide an analysis of the impact of spatial correlation-captured by the GCN model-on the achieved performance.

We consider an antenna tuning problem that is quite important to ensure a satisfying user experience in wireless networks. We aim to maximize the coverage ratio of a large service region by properly choosing the antenna angles. In order to embrace the true complexity of the practical networks and the radio channels, a system level simulator is required. The optimization algorithm has to be performed based on the stochastic numerical output of the simulator. We proposed a zeroth order continuation method to solve the challenging stochastic black-box non-convex optimization problem. The basic idea is to use two observations of the simulator output to generate a gradient estimator, that can be applied to optimize the smoothed version of the original objective function. We optimize a series of smoothed functions to make the solution progressively closer to the global optimum. The performance guarantee of the proposed algorithm has been investigated under weaker assumptions compared to those of the state-of-art analysis. Although the proposed algorithm can be applied to general problems, we perform simulations considering an ideal network model and present numerical results to corroborate our claim.

Intelligent reflecting surfaces (IRSs) have been proposed in recent years as a promising technology to enhance the quality of transmissions in high-frequency spectrum. Currently, the research on the performance of large networks with IRSs is still in its infancy. Different from the commonly-used stochastic geometry model for the study of traditional networks, where only transmitters and receivers are modeled as point processes, in an IRS network, the blockages and reflectors also need to be accounted for. In this paper, we study a bipolar network with a line segment object model, and derive the probability that an IRS can successfully reflect a signal from a transmitter to a receiver, as well as the distribution of the distance traveled by the reflected signal. With these analytic results, the signal to interference ratio (SIR) and the achievable rate are obtained in closed-form expressions. From the analysis, we can observe that IRSs have a great potential to enhance the network performance, as they are able to boost the signal power, while preventing the inter-cell interference from rising rapidly. More importantly, we find that even with a limited number of IRSs, the network can still achieve a higher achievable rate than a conventional one without IRSs.

We discover a new capacity scaling law in ultra-dense networks under practical system assumptions, such as a general multi-piece path loss model, a non-zero base station to user equipment antenna height difference, and a finite user equipment density. The intuition and implication of this new capacity scaling law are completely different from those found in the year 2011. That law indicated that the increase of the interference power caused by a denser network would be exactly compensated by the increase of the signal power due to the reduced distance between transmitters and receivers, and thus, network capacity should grow linearly with network densification. However, we find that both the signal and interference powers become bounded in practical ultra-dense networks, which leads to a constant capacity scaling law. Moreover, our new discovery on the constant capacity scaling law indicates three network optimization problems respectively for base station deployment, user equipment scheduling and base station coordination. These three optimization problems are justified and solved in this paper, shedding new light on the deployment and optimization of ultra-dense networks.

Wi-Fi technology is continuously evolving to keep the pace with the fast-increasing demand for new digital services and applications. In particular, Wi-Fi 6 is becoming widely available in residential and enterprise scenarios supporting up to eight spatial streams. At the same time, large antenna arrays have already shown their ability and benefits when serving a large number of devices simultaneously. Since enterprise owners are already upgrading their indoor private networks with the next generation of Wi-Fi products, it becomes crucial to understand the capacity limits and the benefits associated to different deployment strategies and large antenna array configurations. The scope of this paper is to provide concrete insights to internet service providers and enterprise owners on how to upgrade, deploy, and efficiently operate their networks with next-generation Wi-Fi access points, showing their potential performance improvements and providing a set of clear guidelines.

In this paper, we investigate the use of highly directional, hybrid antenna arrays in residential Wi-Fi mesh deployments to further improve their performance in challenging conditions where high wall losses are encountered. We also formulate a metric to quantify the quality of the Wi-Fi coverage, and present a deployment procedure to automatically select extender AP locations to maximise such metric. Using the proposed deployment procedure, the performance of highly directional, hybrid antenna arrays in residential Wi-Fi mesh deployments is evaluated through simulations. Compared with the benchmark of omni-directional antennas, we show that using the proposed array increases the areas achieving peak throughput by more than 3 times.

Indoor areas, such as offices and shopping malls, are a natural environment for initial millimeter-wave (mmWave) deployments. Although we already have the technology that enables us to realize indoor mmWave deployments, there are many remaining challenges associated with system-level design and planning for such. The objective of this article is to bring together multiple strands of research to provide a comprehensive and integrated framework for the design and performance evaluation of indoor mmWave systems. This article introduces the framework with a status update on mmWave technology, including ongoing fifth generation (5G) wireless standardization efforts and then moves on to experimentally validated channel models that inform performance evaluation and deployment planning. Together these yield insights on indoor mmWave deployment strategies and system configurations, from feasible deployment densities to beam management strategies and necessary capacity extensions.

Unmanned aerial vehicles (UAVs) are envisaged to play a new and important role in future cellular networks. In this paper, we analyze the uplink performance of heterogeneous networks with UAVs in terms of coverage probability and area spectral efficiency (ASE). To be more specific, we first investigate the system performance under a general channel model, with practical considerations such as (1) line-of-sight and non-line-of-sight components, (2) antenna height difference L between the UAVs and the base stations (BSs), and (3) idle mode capabilities (IMCs) at the BSs to mitigate inter-cell interference. Thereafter, we study the system performance under the latest UAV path loss model defined by the 3rd Generation Partnership Project. Under this special case, we provide a more detailed analysis of the coverage probability as well as the ASE, and explore the impacts of difference system parameters on the system performance. Numerical results validate the analytical expressions and show that (1) the IMC can improve the coverage probability and the ASE, especially when the network is dense, (2) the overall system performance degrades when L increases, and (3) the fractional power control factor has a negligible impact on the UAVs performance when L is large enough.

Accurate channel modelling is crucial for network performance analysis, particularly when considering unmanned aerial vehicles (UAVs). Measurement campaigns involving UAVs have shown that the path loss between UAVs and terrestrial base stations (BSs) is highly dependent on the UAV height, rendering previous line-of-sight (LoS) probability models inaccurate. In this paper, the coverage probability, average ergodic rate and area spectral efficiency are theoretically investigated based on a more realistic channel model, with height dependent LoS probabilities. Our results show that the long tail of the LoS probability function in UAV networks has an important performance impact on sparse networks where the BS density is low. Our new findings also show that the coverage of UAV-enabled networks may continuously decrease with the UAV density, since the LoS probability slowly declines with the link range.

The dense deployment of small cells has been considered as a promising approach to providing high quality of service (QoS) to mobile users. However, the resulting dense short-range line-of-sight (LoS) links bring new challenges to interference management, because the interfering links from neighbouring cells are also likely to be in LoS, thus leading to a severe inter-cell interference. In this paper, we propose a user-centric base station (BS) clustering strategy for a non-coherent joint transmissions (JTs) in dense small-cell networks. The main idea is to cluster BSs in LoS with a user to serve it, by selecting the BSs with their LoS probabilities to such user above a predefined threshold. As a result, strong LoS links serve the user, while non-line-of-sight (NLoS) links interfere it. The proposed BS clustering strategy is then evaluated in four different communication environments, as specified by their corresponding path loss models. Our simulation results show that the coverage probability and spectrum efficiency achieved by the proposed user-centric BS clustering strategy increase with the BS density even at extremely high BS densities in all four considered environments.

With the ever increasing cell densities in wireless networks, it is desired to enable more self-X features, such as self-configuration. Setting the network parameters in an autonomous manner is not only more time efficient but also increases network performance and reduces the probability of a human error. Among numerous network settings, one of the key parameters that requires such autonomous configuration is the cell ID (CID). It is used during fundamental procedures, such as network access, decoding, or handover, and therefore, its configuration is crucial for network operation. A complete CID management framework together with a centralized method for CID assignment is presented in this paper. It is not only applicable to multiple mobile standards but is also compatible with multi-vendor equipment, since it is based on the TR-069 management protocol. The proposed approach mitigates CID conflicts when there is a high level of reuse. Moreover, in the 4G case it also prevents neighbors from using different CID but with the same reference signal pattern, which avoids significant interference. The proposed method is evaluated in an enterprise small cell scenario, and in comparison with the baseline third generation partnership project approach, significant reductions of assignment conflicts are demonstrated.

A key aspect of the fifth-generation wireless communication network will be the integration of different services and technologies to provide seamless connectivity. In this paper, we consider using massive multiple-input multiple-output (mMIMO) to provide backhaul links to a dense deployment of self-backhauling (s-BH) small cells (SCs) that provide cellular access within the same spectrum resources of the backhaul. Through a comprehensive system-level simulation study, we evaluate the interplay between access and backhaul and the resulting end-to-end user rates. Moreover, we analyze the impact of different SCs deployment strategies, while varying the time resource allocation between radio access and backhaul links. We finally compare the above mMIMO-based s-BH approach to a mMIMO direct access (DA) architecture accounting for the effects of pilot reuse schemes, together with their associated overhead and contamination mitigation effects. The results show that dense SCs deployments supported by mMIMO s-BH provide significant rate improvements for cell-edge users (UEs) in ultra-dense deployments with respect to mMIMO DA, while the latter outperforms mMIMO s-BH from the median UEs' standpoint.

Reliable command and control channels to unmanned aerial vehicles (UAVs) are needed to allow beyond visual line of sight (LoS) operations. Cellular networks, with their almost ubiquitous coverage, are an obvious candidate to provide such conditions. However, up to which extent the current networks designed for ground users can support UAV communications is an open question. In this paper, we provide a comprehensive theoretical analysis, using stochastic geometry, of the performance that operators could expect from traditional cellular networks with omnidirectional antennas when supporting UAV downlink command and control channels. Our study employs the latest UAV height-dependent path loss model defined by the 3GPP, with LoS and non-LoS transmissions and a probabilistic model to switch between them. We derive analytical expressions for the coverage probability and area spectral efficiency, while accounting for base stations with idle mode capabilities, a practical finite UAV density, and different UAV heights. Results show that networks based on base stations with omnidirectional coverage can support low-height UAVs but will struggle with high-height ones. Network densification helps to provide a better performance.

In this paper, we investigate the impact of the absolute height difference between base station (BS) and user equipment (UE) antennas on the uplink performance of dense small cell networks. We use a path loss model adopted by the 3rd Generation Partnership Project to increase the accuracy of our analysis, where transmissions via both line-of-sight and non-line-of- sight paths are considered. To achieve more practical results, we also consider that UEs are associated with the BS that has the smallest path loss. Based on the performance derived, we then prove that the coverage probability and the area spectral efficiency (ASE) will continuously decrease to zero as the density of BSs grows. To prevent this ASE crash to zero, we also show that reducing the antenna height difference between the BSs and UEs is an effective method. Moreover, our results reveal that the existence of the BS-to-UE antenna height difference changes the optimal operation point of the fractional path loss compensation factor $\epsilon$.

The aggressive spatial spectrum reuse (SSR) by network densification using smaller cells has successfully driven the wireless communication industry onward in the past decades. In our future journey toward ultra-dense networks (UDNs), a fundamental question needs to be answered. Is there a limit to SSR? In other words, when we deploy thousands or millions of small cell base stations (BSs) per square kilometer, is activating all BSs on the same time/frequency resource the best strategy? In this paper, we present theoretical analyses to answer such question. In particular, we find that both the signal and interference powers become bounded in practical UDNs with a non-zero BS-to-UE antenna height difference and a finite UE density, which leads to a constant capacity scaling law. As a result, there exists an optimal SSR density that can maximize the network capacity. Hence, the limit to SSR should be considered in the operation of future UDNs.

In this paper, by means of simulations, we evaluate the uplink (UL) performance of an Internet of Things (IoT) capable ultra-dense network (UDN) in terms of the coverage probability and the density of reliably working user equipments (UEs). From our study, we show the benefits and challenges that UL IoT UDNs will bring about in the future. In more detail, for a low-reliability criterion, such as achieving a UL signal-to-interference-plus-noise ratio (SINR) above 0dB, the density of reliably working UEs grows quickly with the network densification, showing the potential of UL IoT UDNs. In contrast, for a high-reliability criterion, such as achieving a UL SINR above 10 dB, the density of reliably working UEs remains to be low in UDNs due to excessive inter-cell interference, which should be considered when operating UL IoT UDNs. Moreover, considering the existence of a non-zero antenna height difference between base stations (BSs) and UEs, the density of reliably working UEs could even decrease as we deploy more BSs. This calls for the usage of sophisticated interference management schemes and/or beam steering/shaping technologies in UL IoT UDNs.

Providing consistently high wireless capacity is becoming increasingly important to support the applications required by future digital enterprises. In this paper, we propose Eigen-direction-aware ZF (EDA-ZF) with partial coordination among base stations (BSs) and distributed interference suppression as a practical approach to achieve this objective. We compare our solution with Zero Forcing (ZF), entailing neither BS coordination or inter-cell interference mitigation, and Network MIMO (NeMIMO), where full BS coordination enables centralized inter-cell interference management. We also evaluate the performance of said schemes for three sub-6 GHz deployments with varying BS densities — sparse, intermediate, and dense — all with fixed total number of antennas and radiated power. Extensive simulations show that: (i) indoor massive MIMO implementing the proposed EDA-ZF provides uniformly good rates for all users; (ii) indoor network densification is detrimental unless full coordination is implemented; (iii) deploying NeMIMO pays off under strong outdoor interference, especially for cell-edge users.

To guarantee the success of massive multiple-input multiple-output (MIMO), one of the main challenges to solve is the efficient management of pilot contamination. Allocation of fully orthogonal pilot sequences across the network would provide a solution to the problem, but the associated overhead would make this approach infeasible in practical systems. Ongoing fifth-generation (5G) standardisation activities are debating the amount of resources to be dedicated to the transmission of pilot sequences, focussing on uplink sounding reference signals (UL SRSs) design. In this paper, we evaluate the performance of various UL SRS allocation strategies in practical deployments, shedding light on their strengths and weaknesses. Furthermore, we introduce a novel UL SRS fractional reuse (FR) scheme, denoted neighbour-aware (FR-NA). The proposed FR-NA generalizes the fixed reuse paradigm, and entails a trade-off between i) aggressively sharing some UL SRS resources, and ii) protecting other UL SRS resources with the aim of relieving neighbouring BSs from pilot contamination. Said features result in a cell throughput improvement over both fixed reuse and state-of-the-art FR based on a cell-centric perspective.

This paper studies the impact of the base station (BS) idle mode capability (IMC) on the network performance of multi-tier and dense heterogeneous cellular networks (HCNs). Different from most existing works that investigated network scenarios with an infinite number of user equipments (UEs), we consider a more practical setup with a finite number of UEs in our analysis. More specifically, we derive the probability of which BS tier a typical UE should associate to and the expression of the activated BS density in each tier. Based on such results, analytical expressions for the coverage probability and the area spectral efficiency (ASE) in each tier are also obtained. The impact of the IMC on the performance of all BS tiers is shown to be significant. In particular, there will be a surplus of BSs when the BS density in each tier exceeds the UE density, and the overall coverage probability as well as the ASE continuously increase when the BS IMC is applied. Such finding is distinctively different from that in existing work. Thus, our result sheds new light on the design and deployment of the future 5G HCNs.

In this paper, we conduct performance analysis for uplink (UL) massive multiple input and multiple output (mMI-MO) networks using stochastic geometry. With the consideration of practical system assumptions, such as sophisticated path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions and a finite user equipment (UE) density, we derive the coverage probability and the area spectral efficiency (ASE) performance. In particular, we adopt a practical user association strategy (UAS) based on the smallest pathloss since we differentiate LoS and NLoS transmissions, and we consider the correlation among the positions of UEs and base stations (BSs) in realistic networks. From our simulation and analytical results, we find that the performance impacts of the probabilistic LoS/NLoS transmissions and a finite UE density on UL mMIMO networks are significant. More specifically, the coverage probability performance suffers from a moderate decrease or even a severe degradation when the UE density becomes large in sparse mMIMO networks. Moreover, our results indicate that there exists an optimal BS density to maximize the sum spectral efficiency per BS. However, the ASE performance keeps growing with network densification.

In this paper, we propose a tractable semi-analytical approach for the network performance analysis of uplink (UL) cellular networks, which is based on a deterministic network analysis using a Gaussian approximation (DNA-GA). The key contribution of this paper is to investigate the UL signal-to-interference ratio (SIR) performance using the DNA-GA analysis. In particular, the SIR is modeled as a ratio of two random variables (RVs), representing the signal power and the aggregate interference power, respectively. The signal power is further characterized by a product of two RVs, i.e., a lognormal RV and an RV with an arbitrary distribution. The former RV comes from a common assumption of lognormal shadow fading, and the latter one takes the rest of random factors into account, such as random user positions, arbitrary types of multi-path fading, and so on. The aggregate interference power is approximated by an RV with a power lognormal distribution. The proposed DNA-GA analysis has several desirable features: 1) it naturally considers lognormal shadow fading; 2) it can treat arbitrary shape and/or size of cell coverage areas; 3) it can handle non-uniform user distributions; 4) it can cope with any type of multi-path fading; and 5) it can be applied to multi-antenna base stations. These features make the DNA-GA analysis very useful for the network performance analysis of the 5th generation systems with general cell deployment and user distribution.

In this paper, we study the impact of the base station (BS) idle mode capacity (IMC) on the network performance of multi-tier and dense heterogeneous cellular networks (HCNs) with both line-of-sight (LoS) and non-line-of-sight transmissions. Different from most existing works that investigated network scenarios with an infinite number of user equipments (UEs), we consider a more practical set-up with a finite number of UEs in our analysis. Moreover, in our model, the small BSs (SBSs) apply a positive power bias in the cell association procedure, so that macrocell UEs are actively encouraged to use the more lightly loaded SBSs. In addition, to address the severe interference that these cell range expanded UEs may suffer, the macro BSs (MBSs) apply enhanced inter-cell interference coordination, in the form of almost blank subframe (ABS) mechanism. For this model, we derive the coverage probability and the rate of a typical UE in the whole network or a certain tier. The impact of the IMC on the performance of the network is shown to be significant. In particular, it is important to note that there will be a surplus of BSs when the BS density exceeds the UE density, and thus a large number of BSs switch off. As a result, the overall coverage probability, as well as the area spectral efficiency, will continuously increase with the BS density, addressing the network outage that occurs when all BSs are active and the interference becomes LoS dominated. Finally, the optimal ABS factors are investigated in different BS density regions. One of major findings is that MBSs should give up all resources in favor of the SBSs when the small cell networks go ultra-dense. This reinforces the need for orthogonal deployments, shedding new light on the design and deployment of the future 5G dense HCNs.

In this paper, we theoretically study the proportional fair (PF) scheduler in the context of ultra-dense networks (UDNs). Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE) performance of dense small cell networks (SCNs) with the PF scheduler employed at base stations (BSs). The key point of our analysis is that the typical user is no longer a random user as assumed in most studies in the literature. Instead, a user with the maximum PF metric is chosen by its serving BS as the typical user. By comparing the previous results of the round-robin (RR) scheduler with our new results of the PF scheduler, we quantify the loss of the multi-user diversity of the PF scheduler with the network densification, which casts a new look at the role of the PF scheduler in UDNs. Our conclusion is that the RR scheduler should be used in UDNs to simplify the radio resource management (RRM).

Small cell networks (SCNs) are envisioned to embrace dynamic time division duplexing (TDD) in order to tailor downlink (DL)/uplink (UL) subframe resources to quick variations and burstiness of DL/UL traffic. The study of dynamic TDD is particularly important because it provides valuable insights on the full duplex transmission technology, which has been identified as one of the candidate technologies for the 5th-generation (5G) networks. Up to now, the existing works on dynamic TDD have shown that the UL of dynamic TDD suffers from severe performance degradation due to the strong DL-to-UL interference in the physical (PHY) layer. This conclusion raises a fundamental question: Despite such obvious technology disadvantage, what is the true value of dynamic TDD? In this paper, we answer this question from a media access control (MAC) layer viewpoint and present analytical results on the DL/UL time resource utilization (TRU) of synchronous dynamic TDD, which has been widely adopted in the existing 4th-generation (4G) systems. Our analytical results shed new light on the dynamic TDD in future synchronous 5G networks.

We consider cellular base stations (BSs) equipped with a large number of antennas and operating in the unlicensed band. We denote such system as massive MIMO unlicensed (mMIMO-U). We design the key procedures required to guarantee coexistence between a cellular BS and nearby Wi-Fi devices. These include: neighboring Wi-Fi channel covariance estimation, allocation of spatial degrees of freedom for interference suppression, and enhanced channel sensing and data transmission phases. We evaluate the performance of the so-designed mMIMO-U, showing that it allows simultaneous cellular and Wi-Fi transmissions by keeping their mutual interference below the regulatory threshold. The same is not true for conventional listen-before-talk (LBT) operations. As a result, mMIMO-U boosts the aggregate cellular-plus-Wi-Fi data rate in the unlicensed band with respect to conventional LBT, exhibiting increasing gains as the number of BS antennas grows.

Infrared thermography is emerging as a very interesting complementary technology for the condition monitoring of industrial electric motors. The expertise acquired from the infrared inspections that have been carried out over years in electric installations and static machines has been recently applied to electric motors, proving the potential of this technique to detect several types of common anomalies in these machines. Recent works have shown that infrared thermography provides very useful information for the diagnosis of cooling system problems, deficient bearing lubrication, transmission system problems or stator winding asymmetries, among others. The progressive incorporation of the infrared technology in electric motors predictive maintenance programs comes together with the necessity of new methods that can make it possible to detect the anomalies in the infrared images or, at least, that make the interpretation of these images easier. In this context, this work presents a representation method based on the infrared images which relies on the study of the isotherms. The method makes it easier to display the temperature gradient, as well as to locate the source of the anomaly. The methodology referred to in this paper is used to detect several types of faults in field motors operating in a petrochemical plant and the results confirm the potential of the proposed method.

3GPP LWIP Release 13 technology and its pre- standard version Wi-Fi Boost have recently emerged as an efficient LTE and Wi-Fi integration at the IP layer, allowing the combined use of LTE and Wi-Fi radio resources by the user. This solves the contention problems of Wi-Fi cell by routing uplink traffic over LTE, thus enabling an optimum usage of the unlicensed band for downlink. In this paper, we present a new feature of Wi-Fi Boost, its radio link management, which allows to smartly steer the downlink traffic between both LTE and Wi-Fi upon congestion detection. This customised congestion detection algorithm is based on IP probing, and can work with any Wi-Fi access point. Simulation results in a typical enterprise scenario show that LWIP R13 and Wi-Fi Boost can enhance network performance up to 5x and 6x over LTE-only, and 4x and 5x over Wi-Fi only networks, respectively, and that the proposed traffic flow control can further improve Wi-Fi Boost performance over LWIP R13 up to 19%. Based on the promising results, this paper suggests to enhance LWIP R13 user feedback mechanisms in future LTE releases.

In this paper, we conduct performance evaluation for Ultra-Dense Networks (UDNs), and identify which modelling factors play major roles and minor roles. From our study, we draw the following conclusions. First, there are 3 factors/models that have a major impact on the performance of UDNs, and they should be considered when performing theoretical analyses: i) a multi-piece path loss model with line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions; ii) a non-zero antenna height difference between base stations (BSs) and user equipments (UEs); iii) a finite BS/UE density. Second, there are 4 factors/models that have a minor impact on the performance of UDNs, i.e., changing the results quantitatively but not qualitatively, and thus their incorporation into theoretical analyses is less urgent: i) a general multi-path fading model based on Rician fading; ii) a correlated shadow fading model; iii) a BS density dependent transmission power; iv) a deterministic BS/user density. Finally, there are 5 factors/models for future study: i) a BS vertical antenna pattern; ii) multi-antenna and/or multi-BS joint transmissions; iii) a proportional fair BS scheduler; iv) a non-uniform distribution of BSs; v) a dynamic time division duplex (TDD) or full duplex (FD) network. Our conclusions can guide researchers to down-select the assumptions in their theoretical analyses, so as to avoid unnecessarily complicated results, while still capturing the fundamentals of UDNs in a meaningful way.

We propose to operate massive multiple-input multiple-output (MIMO) cellular base stations (BSs) in unlicensed bands. We denote such systems as massive MIMO unlicensed (mMIMO-U) ones. We design the key procedures required at a cellular BS to guarantee coexistence with nearby Wi-Fi devices operating in the same band. In particular, spatial reuse is enhanced by actively suppressing interference toward neighboring Wi-Fi devices. Wi-Fi interference rejection is also performed during an enhanced listen-before-talk phase. These operations enable Wi-Fi devices to access the channel as though no cellular BSs were transmitting, and vice versa. Under concurrent Wi-Fi and BS transmissions, the downlink rates attainable by cellular user equipment (UEs) are degraded by the Wi-Fi-generated interference. To mitigate this effect, we select a suitable set of UEs to be served in the unlicensed band accounting for a measure of the Wi-Fi/UE proximity. Our results show that the so-designed mMIMO-U allows simultaneous cellular and Wi-Fi transmissions by keeping their mutual interference below the regulatory threshold. Compared with a system without interference suppression, Wi-Fi devices enjoy a median interference power reduction of between 3 dB with 16 antennas and 18 dB with 128 antennas. With mMIMO-U, cellular BSs can also achieve large data rates without significantly degrading the performance of Wi-Fi networks deployed within their coverage area.

In this paper, we analyze the coverage probability and the area spectral efficiency (ASE) for the uplink (UL) of dense small cell networks (SCNs) considering a practical path loss model incorporating both line-of-sight (LoS) and non-line-ofsight (NLoS) transmissions. Compared with the existing work, we adopt the following novel approaches in this paper: 1) we assume a practical user association strategy (UAS) based on the smallest path loss, or equivalently the strongest received signal strength; 2) we model the positions of both base stations (BSs) and the user equipments (UEs) as two independent homogeneous Poisson point processes; and 3) the correlation of BSs' and UEs' positions is considered, thus making our analytical results more accurate. The performance impact of LoS and NLoS transmissions on the ASE for the UL of dense SCNs is shown to be significant, both quantitatively and qualitatively, compared with existing work that does not differentiate LoS and NLoS transmissions. In particular, existing work predicted that a larger UL power compensation factor would always result in a better ASE in the practical range of BS density, i.e., 101~ 103BSs/km2. However, our results show that a smaller UL power compensation factor can greatly boost the ASE in the UL of dense SCNs, i.e., 102~ 103BSs/km2, while a larger UL power compensation factor is more suitable for sparse SCNs, i.e., 101 ~ 102 BSs/km2.

In this paper, we present a new and significant theoretical discovery. If the absolute height difference between base station (BS) antenna and user equipment (UE) antenna is larger than zero, then the network performance in terms of both the coverage probability and the area spectral efficiency will continuously decrease toward zero as the BS density increases in ultra-dense networks (UDNs). Such findings are completely different from the conclusions in the existing works, both quantitatively and qualitatively. In particular, this performance behavior has a tremendous impact on the deployment of the 5th-generation UDNs. Network operators may invest large amounts of money in deploying more network infrastructure only to obtain even less network capacity. This paper's results reveal that one way to address this issue is to lower the BS antenna height to the UE antenna height. However, this requires a revolutionized approach of the BS architecture and deployment, which is also explored in this paper.

In this paper, we present a new and significant theoretical discovery. If the absolute height difference between base station (BS) antenna and user equipment (UE) antenna is larger than zero, then the network capacity performance in terms of the area spectral efficiency (ASE) will continuously decrease as the BS density increases for ultra-dense (UD) small cell networks (SCNs). This performance behavior has a tremendous impact on the deployment of UD SCNs in the 5th- generation (5G) era. Network operators may invest large amounts of money in deploying more network infrastructure to only obtain an even worse network performance. Our study results reveal that it is a must to lower the SCN BS antenna height to the UE antenna height to fully achieve the capacity gains of UD SCNs in 5G. However, this requires a revolutionized approach of BS architecture and deployment, which is explored in this paper too.

In this paper, we study the impact of the base station (BS) idle mode capability (IMC) on the network performance in dense small cell networks (SCNs). Different from existing works, we consider a sophisticated path loss model incorporating both line-of-sight (LoS) and non- line-of-sight (NLoS) transmissions. Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE) performance for SCNs with IMCs at the BSs. The upper bound, the lower bound and the approximate expression of the activated BS density are also derived. The performance impact of the IMC is shown to be significant. As the BS density surpasses the UE density, thus creating a surplus of BSs, the coverage probability will continuously increase toward one. For the practical regime of the BS density, the results derived from our analysis are distinctively different from existing results, and thus shed new light on the deployment and the operation of future dense SCNs.

Optimization of resource allocation is fundamental to implement orthogonal frequency division multiple access (OFDMA), which energy efficiency (EE) is very much desired. Wireless network virtualization recently emerges to flexibly enable high data rate services, but optimal cross-layer design with OFDMA remains open. While energy efficiency (EE) is a critical issue for OFDMA systems, the appropriate energy efficient design to enable emerging virtualization in wireless OFDMA networks remains open. In this paper, we focus on energy efficient subcarrier and power allocation with wireless network virtualization. To minimize power consumption and to maximize data rate, such virtual resource allocation can be modeled as a non-convex optimization of EE. Exploiting the non-convex fractional programming, we reformulate into a subtractive form as the equivalent optimization.We therefore obtain the iterative algorithm to achieve the optimality. In particular, subcarrier allocation, power allocation, and operator selection in each iteration can be derived by the Lagrange dual decomposition method. Simulations verify that the algorithms can significantly enhance EE in wireless network virtualization architecture.

© 2002-2012 IEEE. In this paper, we analyze the coverage probability and the area spectral efficiency (ASE) for the uplink (UL) of dense small cell networks (SCNs) considering a practical path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. Compared with the existing work, we adopt the following novel approaches in this paper: 1) we assume a practical user association strategy (UAS) based on the smallest path loss, or equivalently the strongest received signal strength; 2) we model the positions of both base stations (BSs) and the user equipments (UEs) as two independent homogeneous Poisson point processes; and 3) the correlation of BSs' and UEs' positions is considered, thus making our analytical results more accurate. The performance impact of LoS and NLoS transmissions on the ASE for the UL of dense SCNs is shown to be significant, both quantitatively and qualitatively, compared with existing work that does not differentiate LoS and NLoS transmissions. In particular, existing work predicted that a larger UL power compensation factor would always result in a better ASE in the practical range of BS density, i.e., 10-1∼ 10-3 BSs/km2. However, our results show that a smaller UL power compensation factor can greatly boost the ASE in the UL of dense SCNs, i.e., 10-2∼ 10-3 BSs/km2 , while a larger UL power compensation factor is more suitable for sparse SCNs, i.e., 10-1∼ 10-2,BSs/km-2.

In this paper, we propose a new approach of network performance analysis, which is based on our previous works on the deterministic network analysis using the Gaussian approximation (DNA-GA). First, we extend our previous works to a signal-to-interference ratio (SIR) analysis, which makes our DNA-GA analysis a formal microscopic analysis tool. Second, we show two approaches for upgrading the DNA-GA analysis to a macroscopic analysis tool. Finally, we perform a comparison between the proposed DNA-GA analysis and the existing macroscopic analysis based on stochastic geometry. Our results show that the DNA-GA analysis possesses a few special features: (i) shadow fading is naturally considered in the DNA-GA analysis; (ii) the DNA-GA analysis can handle non-uniform user distributions and any type of multi-path fading; (iii) the shape and/or the size of cell coverage areas in the DNA-GA analysis can be made arbitrary for the treatment of hotspot network scenarios. Thus, DNA-GA analysis is very useful for the network performance analysis of the 5th generation (5G) systems with general cell deployment and user distribution, both on a microscopic level and on a macroscopic level.

Self-configuration of network parameters is a desired network management feature, especially in dense small cell deployments. One of the key parameters that requires such autonomous set up is the cell ID. Its configuration to a large extent influences the network performance affecting such mechanisms as network access, decoding, handover, and is therefore of a very high importance. In this paper a new centralised method for Physical Layer Cell Identity (PCI) assignment is presented. The proposal is based on the network information available via the TR-069 management protocol, which makes it applicable also to multi-vendor deployments. In contrast with the existing schemes, the proposed algorithm mitigates not only the colliding and confusing assignments but also prevents neighbours from using different PCIs but introducing overlap in their reference signal pattern, which has not been addressed before. The results show a significant reduction of assignment conflicts when compared with the baseline 3rd Generation Partnership Project (3GPP) approach.

Mechanical faults amount to a significant occurrence rate in industrial induction motors. Typically, vibration data analysis has been employed for the detection of this type of failures. However, this technique has some drawbacks (need of sensor installation, difficult discrimination between certain faults, etc...) that may make the application of follow-up diagnostic techniques advisable. In this context, the analyses of currents or temperature data have revealed to be very interesting informational sources for obtaining a more reliable diagnosis of this type of faults. This work presents several case studies that illustrate the application of infrared thermography to detect mechanical faults in induction motors; all these cases are referred to field motors operating in a petrochemical plant and prove the potential of the technique to detect several types of mechanical failures (bearing faults, misalignments, belt transmission problems, etc...) even in some cases in which vibration data analysis was not conclusive enough to indicate the presence of the problem.

Wireless fidelity (Wi-Fi) benefits from large downlink bandwidth but suffers from inefficient uplink contention, while long term evolution (LTE) benefits from efficient scheduling in the uplink but suffers from bandwidth scarcity in the downlink. In this paper, we quantify the performance gain of Boost, a new technology that blends the advantages of Wi-Fi and LTE in an efficient manner to overcome their individual disadvantages. By redirecting uplink traffic from Wi-Fi to LTE, Boost avoids resource waste due to Wi-Fi contention in the uplink. Since the radio channel is now fully under the control of an scheduler, downlink controlled by Wi-Fi AP scheduler and uplink controlled by LTE scheduler, delays and rates can be guaranteed. Simulation results show that in typical enterprise scenarios, Boost significantly decreases packet delay up to 85 % and increases user throughput up to 3.5x, which is beneficial for delay-sensitive and best effort applications, respectively. This positions Boost as a strong candidate for realising the all-wireless enterprise.

Cooperative inter-working of the long-term evolution (LTE) and the wireless fidelity (Wi-Fi) networks have drawn much attention recently, and several strategies have been proposed to enhance their network capacity. In this paper, we propose a new framework to analyze the network performance of several inter-working strategies for the LTE and the Wi-Fi. The proposed framework considers both the LTE and the Wi-Fi systems, both the downlink (DL) and the uplink (UL) transmissions, and the generated interference in both the time and the spatial domains. Based on such a framework, we theoretically analyze for the first time the performance of a Wi-Fi network, taking into account the intra-cell time efficiency and the signal and inter-cell interference with spatial randomness. Moreover, we study the performance of: 1) a coexisting architecture where Wi-Fi coexists with an ideal carrier sense multiple access (CSMA) duplex system, which represents an upper bound performance for the LTE Release 14 licensed assisted access network and 2) a brand-new architecture that allows UL on LTE and DL on Wi-Fi, referred to as the Boost architecture. We derive analytical results for both the DL and the UL network performances in terms of the signal quality distribution and the total area system throughput (AST) in these two architectures, and quantify their performance gain compared with the traditional disjoint LTE Wi-Fi architecture. Simulation results validate our analysis results, and show that, in a typical outdoor scenario, the coexisting architecture and the Boost architecture can, respectively, increase the total AST up to 11% and 25%, compared with the traditional disjoint LTE Wi-Fi.

In heterogeneous cellular networks for mobile communications, small cells are deployed within the coverage range of primary macro cells to provide for a localized capacity boost in traffic hotspots. Sharing of communication channels among the small-cell and macro-cell tiers is spectrally efficient, but causes failures of control signaling and data channels due to unmitigated co-channel interference. Consequently, the small-cell coverage range and capacity deteriorate. In Long Term Evolution (LTE) networks, the performance of control channels such as the physical downlink control channels (PDCCH) is of particular concern because they are protected only by simple interference averaging based on pseudo-random subcarrier allocation. Observing that the randomization algorithms are primarily seeded by the physical cell identifiers (PCIs) and cell radio network temporary identifier (C-RNTIs), we show that efficient interference-aware scheduling of control transmissions can be enabled by optimized allocation of PCIs, C-RNTIs and PDCCH resources. Simulations of a 3 GPP-compliant heterogeneous network show that the small-cell size can be doubled for a better macro-cell traffic offload by trading the number of active PDCCHs for a higher small-cell expansion bias. Alternatively, the small-cell PDCCH capacity can be at least tripled for high-load applications such as Voice over LTE by using selective macro-cell PDCCH muting.

In this paper, we analytically derive an upper bound on the error in approximating the uplink (UL) single-cell interference by a lognormal distribution in frequency division multiple access (FDMA) small cell networks (SCNs). Such an upper bound is measured by the Kolmogorov-Smirnov (KS) distance between the actual cumulative density function (CDF) and the approximate CDF. The lognormal approximation is important because it allows tractable network performance analysis. Our results are more general than the existing works in the sense that we do not pose any requirement on 1) the shape and/or size of cell coverage areas; 2) the uniformity of user equipment (UE) distribution; and 3) the type of multipath fading. Based on our results, we propose a new framework to directly and analytically investigate a complex network with practical deployment of multiple BSs placed at irregular locations, using a power lognormal approximation of the aggregate UL interference. The proposed interference analysis is particularly useful for the 5th generation (5G) systems with more general cell deployment and UE distribution beyond the widely used Poisson distribution.

In this paper, we introduce a sophisticated path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions to study their impact on the performance of dense small cell networks (SCNs). Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE), assuming both a general path loss model and a special case with a linear LoS probability function. The performance impact of LoS and NLoS transmissions in dense SCNs in terms of the coverage probability and the ASE is significant, both quantitatively and qualitatively, compared with the previous work that does not differentiate LoS and NLoS transmissions. Our analysis demonstrates that the network coverage probability first increases with the increase of the base station (BS) density, and then decreases as the SCN becomes denser. This decrease further makes the ASE suffer from a slow growth or even a decrease with network densification. The ASE will grow almost linearly as the BS density goes ultra dense. For practical regime of the BS density, the performance results derived from our analysis are distinctively different from previous results, and thus shed new insights on the design and deployment of future dense SCNs.

Higher order sectorization (HOS), which splits macrocells into a larger number of smaller sectors, are receiving significant interest as a cost-effective means of improving network capacity. Potentially, the capacity gain with HOS is proportionally linear to the number of sectors per cell due to spatial reuse, but factors such as non-ideal antenna radiation patterns together with inter-cell interference can significantly reduce this capacity gain. We develop a statistical model to theoretically characterize the performance of HOS deployments in wireless networks using orthogonal frequency division multiple access. Moreover, a fractional frequency reuse scheme is considered, which aids to mitigate inter-cell interference. The model provides a fast and effective tool for studying network performance in terms of user signal quality, site throughput, and outage probability, and it can be used to speed up network planning and optimization. In addition, we consider the impact of user classification methods in the analysis, and propose a new spectrum efficiency-based user classification method that improves resource utilization and allocation fairness. Performance results indicate that the proposed model is accurate, and shows a diminishing performance gain of HOS deployments with the number of sectors. The proposed user classification method improves network performances with respect to the state-of-the-art approaches.

In this paper, for the first time, we analytically prove that the uplink (UL) inter-cell interference in frequency division multiple access (FDMA) small cell networks (SCNs) can be well approximated by a lognormal distribution under a certain condition. The lognormal approximation is vital because it allows tractable network performance analysis with closed-form expressions. The derived condition, under which the lognormal approximation applies, does not pose particular requirements on the shapes/sizes of user equipment (UE) distribution areas as in previous works. Instead, our results show that if a path loss related random variable (RV) associated with the UE distribution area, has a low ratio of the 3rd absolute moment to the variance, the lognormal approximation will hold. Analytical and simulation results show that the derived condition can be readily satisfied in future dense/ultra-dense SCNs, indicating that our conclusions are very useful for network performance analysis of the 5th generation (5G) systems with more general cell deployment beyond the widely used Poisson deployment.

In this paper, we introduce a sophisticated path loss model into the stochastic geometry analysis incorporating both line-of-sight (LoS) and non- line-of-sight (NLoS) transmissions to study their performance impact in small cell networks (SCNs). Analytical results are obtained on the coverage probability and the area spectral efficiency (ASE) assuming both a general path loss model and a special case of path loss model recommended by the 3rd Generation Partnership Project (3GPP) standards. The performance impact of LoS and NLoS transmissions in SCNs in terms of the coverage probability and the ASE is shown to be significant both quantitatively and qualitatively, compared with previous work that does not differentiate LoS and NLoS transmissions. From the investigated set of parameters, our analysis demonstrates that when the density of small cells is larger than a threshold, the network coverage probability will decrease as small cells become denser, which in turn makes the ASE suffer from a slow growth or even a notable decrease. For practical regime of small cell density, the performance results derived from our analysis are distinctively different from previous results, and shed new insights on the design and deployment of future dense/ultra-dense SCNs. It is of significant interest to further study the generality of our conclusion in other network models and with other parameter sets.

In system-level simulations, it is vital to appropriately model spatially correlated shadow fading, especially the auto-correlation, so as to emulate realistic scenarios. However, as the size of the network increases, the complexity of the straightforward approach involving the Cholesky decomposition grows exponentially. Alternatively, low-complexity oriented schemes to generate correlated shadow fading value (SFV) maps for large networks are more preferable. However, the existing schemes do not consider wrap-around, which should be taken into account in the generation of SFV maps to facilitate user equipment (UE) mobility simulation and avoid the reduced interference in the outer-rim region. In order to prevent discontinuous shadow fading across the border of the simulated area and the wrap-around area, new methods to efficiently generate SFV maps are required. In this paper, a novel scheme to generate SFV maps with wrap-around is proposed, and its significant accuracy is analysed using computer simulations.

In heterogeneous cellular networks for mobile communications, small cells are deployed within the coverage range of primary macro cells to provide for a localized capacity boost in traffic hotspots. The sharing of communication channels among the small-cell and macro-cell tiers is spectrally efficient, but causes failures of control signaling and data channels due to unmitigated co-channel interference. Consequently, the small-cell coverage range and capacity is reduced. In LTE networks, the performance of some control channels (namely, PDCCH) is of particular concern because the LTE standard does not allow interference coordination in those, but only allows for interference averaging based on pseudo-random subcarrier allocation. Observing that the randomization algorithms are primarily seeded by the UE cell identifiers (the so-called C-RNTI), we show that simple C-RNTI optimization and selective scheduling can be used to enable efficient frequency-selective interferenceaware scheduling also in these control channels. If combined with our previously formulated concept of orthogonally-filled subframes [1], the control channel reliability and capacity can be significantly improved, especially under high cell-selection biases.

The most promising approach to enhance network capacity for the next generation of wireless cellular networks (5G) is densification, which benefits from the extensive spatial reuse of the spectrum and the reduced distance between transmitters and receivers. In this paper, we examine the performance of different schedulers in ultra dense small cell deployments. Due to the stronger line of sight (LOS) at low inter-site distances (ISDs), we discuss that the Rician fading channel model is more suitable to study network performance than the Rayleigh one, and model the Rician K factor as a function of distance between the user equipment (UE) and its serving base station (BS). We also construct a cross-correlation shadowing model that takes into account the ISD, and finally investigate potential multi-user diversity gains in ultra dense small cell deployments by comparing the performances of proportional fair (PF) and round robin (RR) schedulers. Our study shows that as network becomes denser, the LOS component starts to dominate the path loss model which significantly increases the interference. Simulation results also show that multi-user diversity is considerably reduced at low ISDs, and thus the PF scheduling gain over the RR one is small, around 10\% in terms of cell throughput. As a result, the RR scheduling may be preferred for dense small cell deployments due to its simplicity. Despite both the interference aggravation as well as the multi-user diversity loss, network densification is still worth it from a capacity view point.

The articles in this special issue focus on recent advances in the field of heterogeneous cellular networking.

Small cells are envisioned to embrace dynamic time division duplexing (TDD) in order to tailor downlink (DL)/uplink (UL) subframe resources to the quick variations and burstiness of their DL/UL traffic. In this paper, and for the first time, we perform a theoretical analysis on the signal-to-interference-plus-noise ratio performance of dynamic TDD transmissions in homogeneous small cell networks. The significant accuracy of the proposed theoretical analysis is corroborated by simulations. Based on our work, a partial interference cancellation scheme is proposed to reduce the outage probabilities, and it is shown that cancelling one interfering small cell on average is good enough when the traffic load is low to medium to avoid radio link failures.

Future wireless communication systems feature heterogeneous networks (HetNets), with small cells underlying existing macrocells. In order to maximize the off-loading benefits of small cells and mitigate the interference from the macrocell tier to the small cell tier, cell range expansion (CRE) and almost blank subframes (ABSs) have been designed for small cells and macrocells, respectively. Besides, enhanced 4th generation (4G) networks are also envisaged to adopt dynamic time division duplexing (TDD) transmissions for small cells to adapt their communication service to the fast variation of downlink (DL) and uplink (UL) traffic demands. However, up to now, it is still unclear whether it is technically feasible to introduce dynamic TDD into HetNets. In this paper, we investigate this fundamental problem and propose a feasible scheme to enable small cell dynamic TDD transmissions in HetNets. Simulation results show that compared with the static TDD scheme with CRE and ABS operations, the proposed scheme can achieve superior performance gains in terms of DL and UL packet throughputs when the traffic load is low to medium, at the expense of introducing the DL-to-UL interference cancellation (IC) functionality in macrocell base stations (BSs) and/or small cell BSs.

In heterogeneous cellular networks (HetNets) for mobile communications, small cells (SC) are deployed within the coverage range of primary macro cells (MC) to provide for a localized capacity boost in traffic hotspots. Focusing on LTE systems and co-channel SC deployments reusing the communication channels of the hosting MCs, we show that the performance of the LTE control signaling - the main performance bottleneck under inter-tier interference due to an inflexible and interference-unaware resource allocation - can be significantly improved by optimizing the seeds of the standardized pseudo-random mapping algorithms - the physical cell identity (PCI) and user radio network temporary identifier (RNTI). As a generalized result, we propose the standard-compliant concept of orthogonally-filled subframes (OFS) that employs power-control/blanking of UE-specific (PDCCH) channels to ensure co-existence in LTE HetNets. Simulations of a 3GPP-compliant HetNet deployed in Dublin show that compared to the standardized subframe-blanking approaches, configuring OFSs allows (i) extending the SC coverage range up to 3-times, i.e. to offload better the MC traffic, and/or (ii) increasing the control-channel capacity up to 8-times, e.g. to enable high-load voice-over-LTE applications.

In order to realise the potential benefits of co-channel deployments in Heterogeneous Networks (HetNets) and achieve an efficient spectrum reuse, a joint optimisation of small cell and macrocell network operation is highly beneficial. In this paper, a novel cellular network and frequency reuse configuration for both small cells and macrocells is proposed to address inter-cell interference and handover issues in HetNets. The proposed configuration complements Release 10 enhanced inter-cell interference coordination, and further increases its interference mitigation capabilities. Simulations for LTE show that compared to a network that uses standardised Release 10 Almost Blank Subframes (ABS), the proposed configuration can improve expanded-region User Equipments (UEs) performance by 50 % for a given ABS duty cycle. Moreover, for a given targeted expanded-region UEs performance, the proposed configuration can decrease the ABS duty cycle by up to a factor of 2, thus improving macrocell UEs performance up to 31 %. The proposed configuration also mitigates handover failures by 46 %, since handovers start at a higher signal quality.

In heterogeneous cellular networks (HetNets) for mobile communications, small cells are deployed within the coverage range of primary macro cells to provide for a localized capacity boost in traffic hotspots. The sharing of communication channels among the macro-cell and the small-cell tiers is spectrally efficient but causes failures of control signaling in LTE networks due to unmitigated co-channel interference. Consequently, the small-cell coverage range and control channel capacity is reduced. The present study explores to what extent the control channel bottleneck in inter-tier co-existence can be eliminated by using orthogonalized scheduling of the user-specific physical downlink control channels (PDCCH). Implementation-wise, the optimization of PDCCH scheduling is closely related to the management of the cell radio network temporary identifiers (C-RNTIs) as these determine the PDCCH resource allocation. Simulations of a 3GPP-compliant HetNet in a Dublin scenario show that the small-cell size can be doubled for a better macro-cell traffic offload by trading the number of active PDCCHs for a higher small-cell expansion bias. Alternatively, the small-cell PDCCH capacity can be tripled for high-load applications such as Voice over LTE by using macro-cell PDCCH muting.

With the introduction of femtocells, cellular networks are moving from the conventional centralized network architecture to a distributed one, where each network cell should make its own radio resource allocation decisions, while providing inter-cell interference mitigation. However, realizing such distributed network architecture is not a trivial task. In this paper, we first introduce a simple self-organization rule, based on minimizing cell transmit power, following which a distributed cellular network is able to converge into an efficient resource reuse pattern. Based on such self-organization rule and taking realistic resource allocation constraints into account, we also propose two novel resource allocation algorithms, being autonomous and coordinated, respectively. Performance of the proposed self-organization rule and resource allocation algorithms are evaluated using system-level simulations, and show that power efficiency is not necessarily in conflict with capacity improvements at the network level. The proposed resource allocation algorithms provide significant performance improvements in terms of user outages and network capacity over cutting-edge resource allocation algorithms proposed in the literature.

Macrocellular networks with frequency reuse across sectors of the same eNodeB and neighbouring eNodeBs suffer from high interference at the cell boundaries, resulting in an uneven distribution of User Equipment (UE) throughputs. Recently, it has been shown that for two-carrier HSDPA networks a horizontal sector offset configuration for the second carrier can significantly enhance network performance at the cost of an increased number of handovers. In this paper, this sector offset configuration is extended to LTE through a novel approach, which allows deploying offset antennas in eNodeBs without significantly increasing the number of handovers. The proposed sector offset configuration is compatible with current channel dependent schedulers, and provides significant gains. Simulation results show that for LTE the proposed sector offset configuration can increase the average UE throughput and 5%-tile UE throughput by up to 22% and 32%, respectively, while slightly increasing the number of handovers compared to the traditional eNodeB configuration. Moreover, results show that the new configuration can significantly reduce the handover failure rate by up to 69%.

This paper introduces the problem associated to long neighbour cell lists as a result of introducing and reserving identity codes for small cells in heterogeneous networks. It is shown that the number of reserved codes needs to be sufficiently high or identity confusions and collisions are inevitable. This results in long neighbouring lists that effectively increases measurement periods and hand over failures subsequently. To address the issue, a standard compliant mechanism is proposed in that the neighbouring cell list contains only relevant macrocell neighbours and is updated dynamically according to the users' measurements. The simulation scenario used in this study refers to a part of the 3G network in south east of Berlin and belongs to a top tier mobile operator in Germany. The result confirms performance improvement both in terms of handover success rate as well as impact on battery life of users' equipment when the proposed scheme is deployed.

We propose a distributed and coordinated radio resource allocation algorithm for orthogonal frequency division multiple access (OFDMA)-based cellular networks to self-organize efficient and stable frequency reuse patterns. In the proposed radio resource allocation algorithm, each cell independently and dynamically allocates modulation and coding scheme (MCS), resource block (RB), and transmit power to its users in a way that its total downlink (DL) transmit power is minimized, while users' throughput demands are satisfied. Moreover, each cell informs neighboring cells of the RBs that have been scheduled for its cell-edge users' DL transmissions through message passing. Accordingly, the neighboring cells abstain from assigning high transmit powers to the specified RBs. Extensive simulation results attempt to demonstrate that DL power control on a per-RB basis may play a key role in future networks, and show that the distributed minimization of DL transmit power at each cell, supported by intercell interference coordination, is able to provide a 20% improvement of network throughput, considerably reduce the number of user outages, and significantly enhance spatial reuse, as compared to cutting-edge resource allocation schemes.

In this paper, we consider the joint subchannel and power allocation problem in both the uplink and the downlink for two-tier networks comprising spectrum-sharing macrocells and femtocells. A joint subchannel and power allocation scheme for co-channel femtocells is proposed, aiming to maximize the capacity for delay-tolerant users subject to delay-sensitive users' quality of service and interference constraints imposed by macrocells. The joint subchannel and power allocation problem is modeled as an mixed integer programming problem, then transformed into a convex optimization problem by relaxing subchannel sharing, and finally solved by a dual decomposition approach. The effectiveness of the proposed approach is verified by simulations and compared with existing scheme.

In this paper, we characterize the relation between handover failure and ping-pong rates in a 3GPP heterogeneous network scenario as a function of relevant system parameters such as time-to-trigger, user equipment velocity, range expansion bias, etc. Under the assumptions that the picocell coverage and radio link failure areas are circular regions, and that users follow linear trajectories, handover failure and ping-pong rates are derived in closed-form expressions. Simulation results verify the accuracy of our analytical derivations, which shed new light on key aspects of the handover process in a heterogeneous network.

In order to mitigate inter-cell interference and cope with network, traffic, and channel fluctuations in space and time, Orthogonal Frequency Division Multiple Access (OFDMA) based cellular networks should be equipped with dynamic frequency and power allocation algorithms. Although dynamic frequency and power allocation has been well studied separately, very few results have been reported on dynamic joint frequency and power allocation. In this paper, we propose a dynamic frequency and power allocation algorithm for the DownLink (DL) of OFDMA-based cellular networks, where frequency resources are allocated to cells by a central broker to minimize long-term cell-edge inter-cell interference. Moreover, each cell independently assigns its available frequency resources and power to users in a way that its total DL transmission power is minimized while meeting the throughput demands of its users. Simulation results show that the proposed algorithm provides significant improvements in network outage performance, capacity, and throughput as compared with existing schemes.

Because following current cell selection procedures, User Equipments (UEs) connect to the cell that provides the strongest DownLink (DL) Received Signal Strength (RSS), in a Heterogeneous Network (HetNet) comprised of macrocells and picocells, the large difference in DL transmit powers among macro Base Stations (BSs) and pico BSs may significantly reduce the DL service coverage area of picocells. In order to expand the DL coverage of picocells in the presence of macrocells, the concept of range expansion has been recently proposed, in which a positive offset is added to the DL RSSs of picocell pilot signals to increase their DL footprints. Although range expansion may increase the DL coverage of picocells, it may also create significant DL inter-cell interference in the Expanded Region (ER) of picocells, since pico ER UEs do not connect to the cells that provide the strongest DL RSSs. In this paper, we propose a novel cooperative macrocell-picocell scheduling approach to mitigate macrocell DL inter-cell interference towards pico ER UEs. Simulation results show that expanded region picocells supported by the proposed cooperative macrocell-picocell scheduling approach are able to mitigate UE outages and significantly improve spatial reuse of spectrum.

This article investigates the problem of the allocation of modulation and coding, subcarriers and power to users in LTE. The proposed model achieves inter-cell interference mitigation through the dynamic and distributed self-organization of cells. Therefore, there is no need for any a prior frequency planning. Moreover, a two-level decomposition method able to find near optimal solutions is proposed to solve the optimization problem. Finally, simulation results show that compared to classic reuse schemes the proposed approach is able to pack more users into the same bandwidth, decreasing the probability of user outage.

With the introduction of femtocells, cellular networks are moving from the conventional centralised architecture to a distributed one, where each network cell should make its own radio resource management decisions, while providing inter-cell interference mitigation. However, realising this distributed cellular network architecture is not a trivial task. In this paper, we first introduce a simple self-organisation rule under which a distributed cellular network is able to converge into an efficient resource allocation pattern, then propose a novel resource allocation model taking realistic resource allocation constraints into account, and finally evaluate the performance of the proposed self-organisation rule and resource allocation model using system-level simulations.

Due to considerations of spectrum availability and network infrastructure, existing macrocell networks may have to share the spectrum with overlaid femtocells. In spectrum-sharing macro and femto networks, different transmit powers used by macro base stations (MBS) and femto access points, together with potentially densely deployed femtocells, may create dead spots where reliable coverage cannot be guaranteed to either macro or femto users. In this paper, we devise a decentralized strategy to regulate femtocell management of transmit power and usage of radio resources depending on its distance from the closest MBS. Simulation results for the orthogonal frequency division multiple access (OFDMA) downlink (DL) of spectrum-sharing macro and femto networks show that the proposed decentralized femtocell regulating strategy is able to guarantee reliable DL coverage over targeted macro and femto service areas while providing superior spatial spectrum reuse, for even a large number of spectrum sharing femtocells deployed per cell site.

Femtocells have been considered by the wireless industry as a cost-effective solution not only to improve indoor service providing, but also to unload traffic from already overburdened macro networks. Due to spectrum availability and network infrastructure considerations, a macro network may have to share spectrum with overlaid femtocells. In spectrum-sharing macro and femto networks, inter-cell interference caused by different transmission powers of macrocell base stations (MBSs) and femtocell access points (FAPs), in conjunction with potentially densely deployed femtocells, may create dead spots where reliable services cannot be guaranteed to either macro or femto users. In this paper, based on a thorough analysis of downlink (DL) outage probabilities (OPs) of collocated spectrum-sharing orthogonal frequency division multiple access (OFDMA) based macro and femto networks, we devise a decentralized strategy for an FAP to self-regulate its transmission power level and usage of radio resources depending on its distance from the closest MBS. Simulation results show that the derived closed-form lower bounds of DL OPs are tight, and the proposed decentralized femtocell self-regulation strategy is able to guarantee reliable DL services in targeted macro and femto service areas while providing superior spatial reuse, for even a large number of spectrum-sharing femtocells deployed per cell site.

This work presents a novel approach for the avoidance of cross-tier interference in two-tier networks comprised of Orthogonal Frequency Division Multiple Access (OFDMA) macrocells and femtocells. This new technique is based on the use of Intracell HandOvers (IHOs), and it makes possible that either a macrocell or a femtocell can reassign its sub-channel or power allocation upon the detection of cross-tier interference. Simulation results show that this approach is able to cope with the cross-tier interference issues intrinsic to closed access femtocells, and the increased number of HandOvers (HOs) resulting from open access femtocells.

During the network planning process, it is very important to identify the key network design factors and define how to take them into account to achieve optimally performing networks. The introduction of Relay Stations (RSs) in OFDMA-based networks will bring numerous advantages such as coverage extension and capacity enhancement due to the deployment of new cells and the reduction of distance between transmitter and receiver. However, the use of RSs will also bring new challenges to the network designers, for example, interference management in multihop communications. This work establishes the fundamentals of a new approach that will adapt the tilt, azimuth, and frequency assignment in OFDMA networks - in the form of subchannel planning - according to the changing conditions of the traffic and the radio channel in a novel two-step process for network design. An example of deployment is presented in this paper.

This paper introduces a dynamic system-level simulator for WIMAX (Wireless Interoperability for Microwave Access) networks that can be used to analyze the behavior of several network procedures. Moreover, the performance of a new approach to the frequency assignment problem, called DFP (Dynamic Frequency Planning), is studied using this simulation platform. DFP is able to run from a few times a day down to a frame by frame basis, balancing the radio resources between neighbouring BSs (Base Stations) and providing interference mitigation. We demonstrate the efficacy of DFP compared to off-the-shelf FRSs (Frequency Reuse Schemes) in terms of average network capacity and delay.

Adaptive multi-user resource allocation is one of the key features towards high speed wireless network based on Orthogonal Frequency Division Multiplexing Access (OFDMA). According to IEEE 802.16e (WiMAX) standard radio, resource allocation problem has to be performed on a two-dimensional space (frequency and time) with the constraint that each transmitted burst should occupy a contiguous portion of the frame. The paper introduces two different strategies and it investigates the advantages, drawbacks and challenges of the radio resource allocation procedure in two dimensions.

This work presents 2 novel approaches for the self-organization of Orthogonal Frequency Division Multiple Access (OFDMA) femtocells, in which the femtocell is able to dynamically sense the air interface and tune its sub-channel allocation in order to reduce inter-cell interference and enhance system capacity. In the sensing phase, these techniques make use of either messages broadcast by the femtocells or measurements reported by the users, while in the tuning phase, they provide a good solution for the frequency assignment problem. Results shows that it is recommend to use information collected at the user position (measurement reports), when devising self-organization algorithms for tuning the parameters of femtocells.

Femtocells are a promising solution for the provision of high indoor coverage and capacity. Furthermore, OFDMA-based femtocells have proven to be highly versatile when dealing with cross-layer co-channel interference thanks to the allocation of frequency subchannels. However, concerns still exist related to the impact of the different access methods to femtocells in an overlayed network. Femtocells based on a Closed Subscribers Group, where only device owners are allowed connectivity introduce severe interference to macrocell users. On the other hand, Open Access femtocells where any user can connect, does not bring many advantages to the femtocell owner. In this paper, an intermediate access method based on a limited access is proposed. The performance of the model is evaluated throughout system-level simulations and it is shown that limited access contributes to seriously reduce cross-layer interference while guaranteeing a minimum performance to the femtocell subscribers.

In order to avoid inter-cell interference, OFDMA networks are flexible in terms of radio resource management techniques, supporting different frequency reuse schemes (FRSs), which in turn, may decrease inter-cell interference and increase network performance. However, because most of them are based on fix patterns, these FRSs cannot cope with the uneven distribution and dynamic behavior of the traffic throughout the day. This work introduces a novel approach to the frequency assignment problem called dynamic frequency planning (DFP) tailored to OFDMA networks. The proposed approach dynamically adapts the radio frequency parameters to the environment taking the user and channel conditions into account. Moreover, a variant of DFP, called vertical DFP, based on the fractional frequency reuse schemes (FFRSs) concept is proposed. In comparison to the traditional FRSs, these techniques notably mitigate inter-cell interference and enhance network performance.

Finite-difference electromagnetic methods have been used for the deterministic prediction of radio coverage in cellular networks. This paper introduces an approach that exploits the spatial power distribution obtained from these techniques to characterize the random variations of fading due to multipath propagation. Although the presented method is equally applicable to any finite-difference algorithm able of computing electromagnetic field patterns (e.g. PSTD, ParFlow, ...), it has been exemplified here by means of FDTD. Furthermore, in order to test the reliability of this approach, the predicted fading distributions are compared against real measurements in a residential indoor-to-outdoor scenario. Finally, the practical usability of this fading prediction approach is tested throughout implementation in a WiMAX femtocells system-level simulator.

The network planning aims to allow the maximum number of users and throughput in a network. This imposes a demanding performance requirement on the new relay-based architecture for mobile WiMAX, in which the capacity estimation is complex. In the network design process, where interference avoidance plays an important role, WiMAX supports reconfiguration of the subchannel usage with some frequency reuse schemes and subchannel allocation techniques already proposed in the standard and existing literature. However, these fixed strategies are not the most suitable for multihop scenarios, where some of the advantages that relay can provide are network load balancing or capacity improvement in variable conditions. The scope of this paper is first, to describe and evaluate the main two frame structures proposed in the IEEE 802.16j draft to allow multihop communications; second, to analyze the capacity improvement provided by a new optimization framework for frequency planning which considers the relay WiMAX specific characteristics that influence this process. In addition, we introduced efficient capacity calculations that will allow intensive system level simulations in a Mobile WiMAX multihop environment. Numerical results are given for various configurations allowing interesting observations about WiMAX radio efficiency. These results can be used as an order of magnitude of the real case of IEEE 802.16j network design.