Large AI models have been widely adopted in wireless communications for channel modeling, beamforming, and resource optimization. However, most existing efforts remain limited to single-modality inputs and channel-specific objec- tives, overlooking the broader potential of large foundation models for unified wireless sensing. To bridge this gap, we propose MMSense, a multi-modal, multi-task foundation model that jointly addresses channel-centric, environment-aware, and human-centered sensing. Our framework integrates image, radar, LiDAR, and textual data by transforming them into vision- compatible representations, enabling effective cross-modal align- ment within a unified feature space. A modality gating mecha- nism adaptively fuses these representations, while a vision-based large language model backbone enables unified feature align- ment and instruction-driven task adaptation. Furthermore, task- specific sequential attention and uncertainty-based loss weighting mechanisms enhance cross-task generalization. Experiments on real wireless scenario datasets show that our approach outper- forms both task-specific and large-model baselines, confirming its strong generalization across heterogeneous sensing tasks.

Since the start of 5G New Radio (NR) work in the 3rd Generation Partnership Project (3GPP) in early 2016, tremendous progress has been made in both standardization and commercial deployments. The first 5G NR release (Release 15) laid out a solid foundation in accommodating a diverse set of services, a wide range of spectra, and a variety of deployment scenarios, while being forward compatible. Expansion to vertical domain services [e.g., vehicle to everything (V2X), non-terrestrial networks (NTN)] was introduced in Release 16. Such an expansion was further accelerated in Release 17, with the standardization work being completed despite the extreme challenges due to COVID-19.

Since the start of 5G work in 3GPP in early 2016, tremendous progress has been made in both standardization and commercial deployments. 3GPP is now entering the second phase of 5G standardization, known as5G-Advanced, built on the 5G baseline in 3GPP Releases 15, 16, and 17. 3GPP Release 18, the start of 5G-Advanced, includes a diverse set of features that cover both device and network evolutions, providing balanced mobile broadband evolution and further vertical domain expansion and accommodating both immediate and long-term commercial needs. 5G-Advanced will significantly expand 5G capabilities, address many new use cases, transform connectivity experiences, and serve as an essential step in developing mobile communications towards 6G. This paper provides a comprehensive overview of the 3GPP 5G-Advanced development, introducing the prominent state-of-the-art technologies investigated in 3GPP and identifying key evolution directions for future research and standardization.

Reinforcement learning, particularly deep reinforcement learning, has made remarkable progress in recent years and is now used not only in simulators and games but is also making its way into embedded systems as another software-intensive domain. However, when implemented in a real-world context, reinforcement learning is typically shown to be fragile and incapable of adapting to dynamic environments. In this paper, we provide a novel dynamic reinforcement learning algorithm for adapting to complex industrial situations. We apply and validate our approach using a telecommunications use case. The proposed algorithm can dynamically adjust the position and antenna tilt of a drone-based base station to maintain reliable wireless connectivity for mission-critical users. When compared to traditional reinforcement learning approaches, the dynamic reinforcement learning algorithm improves the overall service performance of a drone-based base station by roughly 20%. Our results demonstrate that the algorithm can quickly evolve and continuously adapt to the complex dynamic industrial environment.

Evolving 5G New Radio (NR) to support non-terrestrial networks (NTNs), particularly satellite communication networks, is under exploration in 3GPP. The movement of the spaceborne platforms in NTNs may result in large timing varying Doppler shift that differs for devices in different locations. Using orthogonal frequency-division multiple access (OFDMA) in the uplink, each device will need to apply a different frequency adjustment value to compensate for the Doppler shift. To this end, the 3GPP Release-17 work on NTNs assumes that an NTN device is equipped with a global navigation satellite system (GNSS) chipset and thereby can determine its position and calculate the needed frequency adjustment value using its position information and satellite ephemeris data. This makes GNSS support essential for the NTN operation. However, GNSS signals are weak, not ubiquitous, and susceptible to interference and spoofing. We show that devices without access to GNSS signals can utilize reference signals in more than one frequency position in an OFDM carrier to estimate the Doppler shift and thereby determine the needed frequency adjustment value for pre-compensating the Doppler shift in the uplink. We analyze the performance, elaborate on how to utilize the NR reference signals, and present simulation results. The solution can reduce the dependency of NTN operation on GNSS with reasonable complexity and performance trade-off.

An Internet of Things (IoT) non-terrestrial network (NTN) refers to an IoT network that utilizes airborne or spaceborne payload for communication. Using NTNs to serve IoT devices has the potential of further expanding the use cases as well as coverage for IoT. This has motivated the work in 3GPP on evolving Narrowband Internet of Things (NB-IoT) and Long Term Evolution for machine type communications (LTE-M) wireless access technologies to support NTN. While the topic of IoT over NTN has attracted much attention, the performance of IoT NTN has not been well studied. In this paper, we investigate the performance of IoT low Earth orbit (LEO) NTN utilizing LTE-M by evaluating the connection density. The evaluation results show that for a LEO satellite at 600 and 1200 km altitude, LTE-M can support 364 and 78 users per km2for a single narrowband in its coverage area, respectively. This implies that an LTE-M based LEO NTN can potentially serve IoT in remote areas, thereby complementing the coverage of terrestrial networks.

Direct air-to-ground (A2G) communications leveraging the fifth-generation (5G) new radio (NR) can provide high-speed broadband in-flight connectivity to aircraft in the sky. A2G network deployment entails optimizing various design parameters such as inter-site distances, number of sectors per site, and the up-tilt angles of sector antennas. The system-level design guidelines in the existing work on A2G network are rather limited. In this paper, a novel deep learning-based framework is proposed for efficient design and optimization of a 5G A2G network. The devised architecture comprises two deep neural networks (DNNs): the first DNN is used for approximating the 5G A2G network behavior in terms of user throughput, and the second DNN is developed as a function optimizer to find the throughput-optimal deployment parameters including antenna up-tilt angles and inter-site distances. Simulation results are provided to validate the proposed model and reveal system-level design insights.

A non-terrestrial network (NTN), a term coined by the 3rd Generation Partnership Project (3GPP), refers to a network utilizing airborne or spaceborne payload for communication. The use of NTN has the potential of facilitating providing connectivity to underserved areas. This has motivated the work in 3GPP on evolving the fifth generation (5G) wireless access technology, known as new radio (NR), to support NTN. The broadband opportunities promised by NTN with low Earth orbit (LEO) satellites have attracted much attention, but the performance of LEO NTN using 5G NR has not been well studied. In this paper, we address this gap by analyzing and evaluating the throughput and capacity performance of LEO NTN. The evaluation results show that the downlink capacity of a LEO satellite in S band with 30 MHz bandwidth serving handheld terminal is about 600 Mbps and the downlink capacity of a LEO satellite in Ka band with 400 MHz bandwidth serving very small aperture terminal (VSAT) is about 7 Gbps. For a LEO NTN similar to the Kuiper project proposed by Amazon, we find that, due to the large cell sizes in the LEO NTN, the area capacity density is moderate: 1-10 kbps/km2in the S band downlink and 14-120 kbps/km2in the Ka band downlink depending on latitude.

Flying drones can be used in a wide range of applications and services from surveillance to package delivery. To ensure robust control and safety of drone operations, cellular networks need to provide reliable wireless connectivity to drone user equipments (UEs). To date, existing mobile networks have been primarily designed and optimized for serving ground UEs, thus making the mobility support in the sky challenging. In this paper, a novel handover (HO) mechanism is developed for a cellular-connected drone system to ensure robust wireless connectivity and mobility support for drone-UEs. By leveraging tools from reinforcement learning, HO decisions are dynamically optimized using a Q-learning algorithm to provide an efficient mobility support in the sky. The results show that the proposed approach can significantly reduce (e.g., by 80%) the number of HOs, while maintaining connectivity, compared to the baseline HO scheme in which the drone always connects to the strongest cell.

Satellite communication has the potential to provide coverage over large geographical areas. This ubiquity enables potential for providing connectivity for currently unserved remote areas as well as for moving platforms such as airplanes and ships. However, many satellite systems were developed on private networks, each having their individual solution for the satellite systems and for the end user equipment (UE). In order to extent the satellite-industry-wide standards to global telecommunication standards, there are currently activities in the 3rd generation partnership project (3GPP) to study the feasibility and needed standard adaptations for the 5th generation (5G) new radio (NR) 3GPP standard. In this paper, we provide an overview of the ongoing activity in 3GPP RAN working groups with focus on RAN2 and RAN3 aspects for enabling NR communication over geosynchronous orbit (GEO) and non-GEO satellite systems.

Beyond visual line-of-sight connectivity is key for use cases of unmanned aerial vehicles (UAVs) such as package delivery, infrastructure inspection, and rescue missions. Cellular networks stand ready to support flying UAVs by providing wide-area, quality, and secure connectivity for UAV operations. Ensuring reliable connections in the presence of UAV movements is important for safety control and operations of UAVs. With increasing height above the ground, the radio environment changes. Using terrestrial cellular networks to provide connectivity to the UAVs moving in the sky may face new challenges. In this article, we share some of our findings in mobility support for cellular connected UAVs. We first identify how the radio environment changes with altitude and analyze the corresponding implications on mobility performance. We then present evaluation results to shed light on the mobility performance of cellular connected UAVs. We also discuss potential enhancements for improving mobility performance in the sky.

The emerging, practical and observed issue of how to detect rogue drones carrying terrestrial user equipment (UE) on mobile networks is addressed in this paper. This issue has drawn much attention since the rogue drones may generate excessive interference to mobile networks and may not be allowed by regulations in some regions. In this paper, we propose a novel machine learning approach to identify the rogue drones in mobile networks based on radio measurements. We apply two classification machine learning models, Logistic Regression, and Decision Tree, using features from radio measurements to identify the rogue drones. Simulation results show that the proposed machine learning solutions can achieve high rogue drone detection rate for high altitudes while not mis-classifying regular ground based UEs as rogue drone UEs.

Positioning of people and things is now being considered as one vital feature for many applications. The positioning support in cellular networks started with the regulatory requirements for emergency call, and has been enhanced over time to provide support with higher accuracy and better coverage including even deep indoor areas. Devices and use cases are not limited to regular users with mobile handsets, but include also very low cost, power and complexity devices on one hand, and advanced devices like robots, autonomous vehicles and factory equipments on the other. This paper aims to provide an overview of the positioning methods that have been introduced in the Third Generation Partnership Project (3GPP) up to the currently developed release (Rel.15), as well as different considered scenarios and deployment conditions. Also, the ambition is to describe use cases for and requirements on positioning in emerging and future cellular networks such as 5G.

Listen-before-talk (LBT) is enforced in the regions such as European Union and Japan to harmonize coexistence of cellular and incumbent systems in unlicensed spectrum. In this paper, we study how to exploit LBT strategies for frame based equipment (FBE) in unlicensed spectrum. We consider two optimization problems: Throughput optimal stopping and nominal throughput optimal stopping. We discover that the throughput optimal transmission strategy for FBE in unlicensed spectrum is to transmit whenever the channel is clear. In contrast, we find that the nominal throughput optimal transmission strategy is less aggressive: The FBE does not transmit until it finds that the channel is clear and the channel quality exceeds an optimized threshold.

The effort to extend cellular technologies to unlicensed spectrum has been gaining high momentum. Listen-before-talk (LBT) is enforced in the regions such as European Union and Japan to harmonize coexistence of cellular and incumbent systems in unlicensed spectrum. In this paper, we study throughput optimal LBT transmission strategy for load based equipment (LBE). We find that the optimal rule is a pure threshold policy: The LBE should stop listening and transmit once the channel quality exceeds an optimized threshold. We also reveal the optimal set of LBT parameters that are compliant with regulatory requirements. Our results shed light on how the regulatory LBT requirements can affect the transmission strategies of radio equipment in unlicensed spectrum.

Narrowband Internet of Things (NB-IoT) is a new radio access technology, recently standardized in 3GPP to enable support for IoT devices. NB-IoT offers a range of flexible deployment options and provides improved coverage and support for a massive number of devices within a cell. In this paper, we provide a detailed evaluation of the coverage performance of NBIoT and show that it achieves a coverage enhancement of up to 20 dB when compared with existing LTE technology.

This paper studies the interplay between massive MIMO and device-to-device (D2D) networking in a single cell setting, where cellular uplink resources are shared by D2D. The spatial positions of underlaid D2D transmitters are modeled by a Poisson point process. All the transmissions (both cellular and D2D) are SIMO (i.e., single-input multiple-output) with the base station (BS) having a very large antenna array. Assuming perfect channel state information at the receivers, we study cellular and D2D spectral efficiency. In the asymptotic regime where the number of BS antennas goes to infinity, we find that the received signal-to-interference-plus-noise ratio (SINR) of any cellular user increases unboundedly and the effects of noise, fast fading, and the interfering signals from the other co-channel cellular users and the infinite D2D transmitters vanish completely. In the non-asymptotic regime, we derive simple analytical lower bounds for both cellular and D2D spectral efficiency, which allow for efficient numerical evaluation.

Device-to-device (D2D) communication is a new feature being introduced to LTE in 3GPP Release 12. This feature will provide proximity services including support for public safety applications. For D2D communication within the network coverage area, network assistance is very beneficial for interference management purpose to ensure reliable communication. In this case, a simple algorithm can be used wherein the network manages the number and selection of simultaneous D2D transmissions within its cell. This will help manage interference from D2D transmission in case of time-frequency reuse to ensure that minimum performance requirement can be satisfied. In this paper, we outline a method for determining the maximum number of simultaneous D2D transmitters in a given cell. In addition, a method for selecting D2D pairs for simultaneous transmission is also provided. It is shown that, with network assistance, the percentage of reliable D2D communication links can be improved significantly.

This paper proposes a random network model for a device-to-device (D2D) underlaid cellular system using stochastic geometry and develops centralized and distributed power control algorithms. The goal of centralized power control is twofold: ensure that the cellular users have sufficient coverage probability by limiting the interference created by underlaid D2D users, while scheduling as many D2D links as possible. For the distributed power control method, the optimal on-off power control strategy is proposed, which maximizes the sum rate of the D2D links. Expressions are derived for the coverage probabilities of cellular, D2D links, and the sum rate of the D2D links in terms of the density of D2D links and the path-loss exponent. The analysis reveals the impact of key system parameters on the network performance. For example, the bottleneck of D2D underlaid cellular networks is the cross-tier interference between D2D links and the cellular user, not the D2D intratier interference when the density of D2D links is sparse. Simulation results verify the exactness of the derived coverage probabilities and the sum rate of D2D links.

This paper develops a novel analytical framework for asynchronous wireless networks deploying multicarrier transmission over flat-fading channels. Nodes in the network have different notions of timing; therefore, from the viewpoint of a typical receiver, the received signals from different transmitters are asynchronous, leading to a loss of orthogonality between subcarriers. We first develop a detailed link-level analysis based on OFDM, based on which we propose a tractable system-level signal-to-interference-plus-noise ratio (SINR) model for asynchronous OFDM networks. The proposed model is used to analytically characterize several important statistics in asynchronous networks with spatially distributed transmitters, including: (i) the number of decodable transmitters; (ii) the decoding probability of the nearest transmitter; and (iii) the system throughput. The system-level loss from lack of synchronization is quantified, and to mitigate the loss, we compare and discuss four possible solutions including extended cyclic prefix, advanced receiver timing, dynamic receiver timing positioning, and semi-static receiver timing positioning with multiple timing hypotheses. The model and results are general, and apply to ad hoc networks, cellular systems, and neighbor discovery in device-to-device (D2D) networks.

In a device-to-device (D2D) underlaid cellular network, the uplink spectrum is reused by the D2D transmissions, causing mutual interference with the ongoing cellular transmissions. Massive MIMO is appealing in such a context as the base station's (BS's) large antenna array can nearly null the D2D-to-BS interference. The multi-user transmission in massive MIMO, however, may lead to increased cellular-to-D2D interference. This paper studies the interplay between massive MIMO and underlaid D2D networking in a multi-cell setting. We investigate cellular and D2D spectral efficiencies under both perfect and imperfect channel state information (CSI) at the receivers that employ partial zero-forcing. Compared to the case without D2D, there is a loss in cellular spectral efficiency due to D2D underlay. With perfect CSI, the loss can be completely overcome if the number of canceled D2D interfering signals is scaled with the number of BS antennas at an arbitrarily slow rate. With imperfect CSI, in addition to pilot contamination, a new asymptotic effect termed underlay contamination arises. In the non-asymptotic regime, simple analytical lower bounds are derived for both the cellular and D2D spectral efficiencies.

This paper addresses two fundamental and interrelated issues in device-to-device (D2D) enhanced cellular networks. The first issue is how D2D users should access spectrum, and we consider two choices: overlay (orthogonal spectrum between D2D and cellular UEs) and underlay (non-orthogonal). The second issue is how D2D users should choose between communicating directly or via the base station, a choice that depends on distance between the potential D2D transmitter and receiver. We propose a tractable hybrid network model where the positions of mobiles are modeled by random spatial Poisson point process, with which we present a general analytical approach that allows a unified performance evaluation for these questions. Then, we derive analytical rate expressions and apply them to optimize the two D2D spectrum sharing scenarios under a weighted proportional fair utility function. We find that as the proportion of potential D2D mobiles increases, the optimal spectrum partition in the overlay is almost invariant (when D2D mode selection threshold is large) while the optimal spectrum access factor in the underlay decreases. Further, from a coverage perspective, we reveal a tradeoff between the spectrum access factor and the D2D mode selection threshold in the underlay: as more D2D links are allowed (due to a more relaxed mode selection threshold), the network should actually make less spectrum available to them to limit their interference.

Multicast device-to-device (D2D) transmission is important for applications like local file transfer in commercial networks and is also a required feature in public safety networks. In this paper we propose a tractable baseline multicast D2D model, and use it to analyze important multicast metrics like the coverage probability, mean number of covered receivers and throughput. In addition, we examine how the multicast performance would be affected by certain factors like dynamics (due to e.g., mobility) and network assistance. Take the mean number of covered receivers as an example. We find that simple repetitive transmissions help but the gain quickly diminishes as the number of repetitions increases. Meanwhile, dynamics and network assistance (i.e., allowing the network to relay the multicast signals) can help cover more receivers. We also explore how to optimize multicasting, e.g. by choosing the optimal multicast rate and the optimal number of retransmission times.

In this paper we jointly address two fundamental issues in device-to-device (D2D) overlaid cellular networks: 1) how to partition the spectrum assuming orthogonal cellular and D2D transmissions, and 2) what is the optimal threshold for distance-based D2D mode selection? To this end, we first derive rate expressions for both cellular and D2D users. Three design criteria - (weighted) max-sum, max-min and proportional fairness - are considered and optimal spectrum partition rules are derived. Finally we validate our model and give rules of thumb for optimizing the spectrum partition and mode selection.

The explosive growth in data traffic is resulting in a spectrum crunch forcing many wireless network operators to look towards refarming their 2G spectrum and deploy more spectrally efficient Long Term Evolution (LTE) technology. However, mobile network operators face a challenge when it comes to spectrum refarming because 2G technologies such as Global System for Mobile (GSM) is still widely used for low bandwidth machine-to-machine (M2M) devices. In this paper we propose a novel solution to provide GSM connectivity within an LTE carrier through an efficient overlay by reserving a few physical resource blocks for GSM. With this approach, operators can refarm their 2G spectrum to LTE efficiently while still providing some GSM connectivity to their low data rate M2M customers. Furthermore, spectrum can be dynamically shared between LTE and GSM. An approach similar to that proposed in this paper can also be applied for other narrow band technology overlays over LTE.

Heterogeneous networks (HetNets) and carrier aggregation (CA) are two distinct features of next-generation cellular networks. Small cells in HetNets are vital for data off-loading and can significantly improve area and cell edge spectral efficiency compared to using just macrocells. CA further increases the transmission bandwidth and thus network capacity by aggregating multiple component carriers on the physical layer. In this paper, we propose a tractable multi-band multi-tier model to study multi-flow CA in HetNets, where base station positions in each tier follow an independent Poisson point process. Our model incorporates an appropriate notion of load which allows the study of the impact of biasing on HetNet performance. For a typical HetNet consisting of two tiers, macro cells and small cells, and two bands, 800MHz band and 2.4GHz band, we observe that the gain (in terms of UE ergodic rate) is about 35% to 40% if the small cells adopt optimal biasing factor compared to the case without biasing. Our model also incorporates the impact of band deployment. For the typical HetNet described, our study suggests that deploying the 800MHz band and 2.4GHz band in both the macro and small cells helps the HetNet best exploit the CA feature.

In this paper, we study the competition among the primary users (PUs) in a Dynamic Spectrum Leasing (DSL) system where multiple PUs lease spectrum to the secondary users (SUs) for monetary rewards. Considering the uncertainties of the PUs' channel gains and of the SUs' demands for spectrum, the competition among the PUs is formulated as a stochastic Nash game. Due to the uncertainties, the PUs aim to maximize their long term utilities which are related to the income from leasing spectrum and to their quality of service (QoS) conditions. Resorting to the stochastic variational inequality (SVI) theory, we investigate the existence and uniqueness of the stochastic Nash equilibrium (SNE). Based on the stochastic approximation theory, we propose a distributed learning algorithm for computing the SNE of the game. Rigorous convergence proof of the algorithm is provided. Besides, the features of the algorithm are demonstrated via numerical results.

Carrier aggregation (CA) and small cells are two distinct features of next-generation cellular networks. Cellular networks with different types of small cells are often referred to as HetNets. In this paper, we introduce a load-aware model for CA-enabled multi-band HetNets. Under this model, the impact of biasing can be more appropriately characterized; for example, it is observed that with large enough biasing, the spectral efficiency of small cells may increase while its counterpart in a fully-loaded model always decreases. Further, our analysis reveals that the peak data rate does not depend on the base station density and transmit powers; this strongly motivates other approaches e.g. CA to increase the peak data rate. Last but not least, different band deployment configurations are studied and compared. We find that with large enough small cell density, spatial reuse with small cells outperforms adding more spectrum for increasing user rate. More generally, universal cochannel deployment typically yields the largest rate; and thus a capacity loss exists in orthogonal deployment. This performance gap can be reduced by appropriately tuning the HetNet coverage distribution (e.g. by optimizing biasing factors).

Despite the central role of mobility in wireless networks, analytical study on its impact on network performance is notoriously difficult. This paper aims to address this gap by proposing a random waypoint (RWP) mobility model defined on the entire plane and applying it to analyze two key cellular network parameters: handover rate and sojourn time. We first analyze the stochastic properties of the proposed model and compare it to two other models: the classical RWP mobility model and a synthetic truncated Levy walk model which is constructed from real mobility trajectories. The comparison shows that the proposed RWP mobility model is more appropriate for the mobility simulation in emerging cellular networks, which have ever-smaller cells. Then we apply the proposed model to cellular networks under both deterministic (hexagonal) and random (Poisson) base station (BS) models. We present analytic expressions for both handover rate and sojourn time, which have the expected property that the handover rate is proportional to the square root of BS density. Compared to an actual BS distribution, we find that the Poisson-Voronoi model is about as accurate in terms of mobility evaluation as hexagonal model, though being more pessimistic in that it predicts a higher handover rate and lower sojourn time.

The explosive growth in data traffic is resulting in a spectrum crunch forcing many wireless network operators to look towards refarming their 2G spectrum and deploy more spectrally efficient Long Term Evolution (LTE) technology. However, mobile network operators face a challenge when it comes to spectrum refarming because 2G technologies such as Global System for Mobile (GSM) is still widely used for low bandwidth machine-to-machine (M2M) devices. M2M devices typically have long life cycles, e.g. smart meters, and it is expensive to migrate these devices to newer technology since a truck roll will typically be required to the site where a device is deployed. Furthermore, with cost of 2G modules several times less than that of LTE, even newly deployed M2M devices tend to adopt 2G technology. Nevertheless, operators are keen to either force their 2G M2M customers to migrate so that they can refarm the spectrum or set aside a portion of the 2G spectrum for continuing operating 2G and only refarm the rest for LTE. In this paper we propose a novel solution to provide GSM connectivity within an LTE carrier through an efficient overlay by reserving a few physical resource blocks for GSM. With this approach, operators can refarm their 2G spectrum to LTE efficiently while still providing some GSM connectivity to their low data rate M2M customers. Furthermore, spectrum can be dynamically shared between LTE and GSM. An approach similar to that proposed in this paper can also be applied for other narrow band technology overlays over LTE.

Mobility modeling and analysis is a key issue in wireless networks. In this paper we propose a new and quite general random waypoint (RWP) mobility model which is valid over the entire plane. We derive key properties of the proposed mobility model including transition length, transition time and spatial node distribution. Then the RWP mobility model is applied to study the handover rate in cellular networks under both deterministic (hexagonal) and random (Poisson) base station (BS) models. Closed form expressions for handover rate can be obtained. These results show the expected property that the handover rate is proportional to the square root of base station density. Also, we find that Poisson-Voronoi model for BS coverage areas is about as accurate in terms of mobility (particularly handover) evaluation as the ubiquitous hexagonal model.

In this paper, we extend the distributed power control problem for one-to-one transmissions in Gaussian interference channels to one-to-many transmission scenarios. We assume a user-centric wireless network where the end users play the roles of decision makers. We formulate the power control problem as a noncooperative game. New challenges arise due to the coupling issues among power strategy spaces of distributed end users, which make standard Nash equilibrium based noncooperative game approach inapplicable. Indeed, our problem turns out to be a generalized Nash equilibrium problem (GNEP). Resorting to variational inequality theory, we show several fundamental properties of the GNEP. Then we propose a penalty-based distributed algorithm IP^2JA, which possesses favorable properties for practical implementation. Numerical results are provided to verify our arguments and proposed algorithm.

Adaptive cooperation with conflict-free relay assignment in a network setting is a challenging problem. The problem becomes even more difficult when users have quality-of-service (QoS) requirements but the available spectrum resource is limited, where admission control may be required. In this paper, we jointly study relay assignment and admission control in cooperative radio networks (CRNs). A one-stage optimization problem is formulated to integrate our multiple objectives. Since the problem in question is prohibitively difficult, we resort to an appropriate decomposition approach after a careful analysis on the structure of the formulated problem. A simple distributed algorithm is also proposed to overcome the inherent drawbacks of the centralized scheme. Numerical results demonstrate the benefits attained by jointly considering relay assignment and admission control, as well as the effectiveness of our proposed algorithms.