The powerful phase modulation capability of reconfigurable intelligent surfaces (RIS) endows them with the potential to replace future base station (BS) antenna arrays. However, current research on planar transmissive RIS-enabled BSs (PT-RIS-BSs) suffers from angle-sensitive limitations, failing to meet the demands of dynamic low-altitude communications. This paper proposes a spherical transmissive RIS-enabled BS (ST-RIS-BS) architecture, where traditional array antennas in BS are replaced by a combination of an omnidirectional antenna and a ST-RIS, achieving stable gain across all spatial directions. Specifically, considering angle-sensitive gain characteristics, three-dimensional (3D) signal incidence/departure angles, and the effective responsive elements of ST-RIS, we establish a ST-RIS-BS model tailored for dynamic low-altitude communications. Furthermore, through spatial integration methods, we derive the spatial average gain for both PT-RIS and ST-RIS, theoretically demonstrating a performance enhancement of up to 36.6% for the spherical configuration. Additionally, addressing a typical application scenario in the future low-altitude economy where uplink user communications coexist with unmanned aerial vehicle (UAV) data collection, we verify the performance of proposed ST-RIS-BS by the joint optimization of RIS phase shifts, transmit power, user scheduling, and UAV 3D trajectory, which is solved by using block coordinate descent and successive convex approximation techniques. This approach maximizes data collection while guaranteeing uplink user rates. Simulation results validate the effectiveness of the proposed scheme, showing that the ST-RIS solution achieves a 45% increase in data collection compared to the PT-RIS configuration.

Due to the down-tilt of BS antennas to serve ground users in existing networks, aerial user equipments (AUEs) are only supported through the side lobes, which leads to insufficient aerial coverage. Fortunately, intelligent reflecting surfaces (IRSs) possess the remarkable ability to offer extra signal power to enhance AUEs’ coverage. However, to fully realize the potential of the IRS, its deployment is contingent upon the radiation pattern of the BS which substantially modifies the network topology. This paper proposes a novel IRS-based network model to serve AUEs by constructing the virtual LoS air-to-ground links to provide targeted signal for AUEs, where IRSs are practically distributed in the BS main lobe to effectively utilize the BS radiation power. Specifically, the distribution of IRSs is modeled by a variant of the Matérn cluster process (MCP), based on which the correlated interference from BSs and IRSs introduced by their location correlation is theoretically characterized. Furthermore, based on a concise two-lobe model for BS antenna gain, the effective distribution area of IRSs is derived as a ring concentric with the cell, which exhibits tunability in accordance with the beamwidth and the tilt angle of the BS antenna. In addition, the framework is characterized for analyzing the correlated interference from BSs and IRSs through the accurate integration of the power correlation associated with both direct paths and IRS-reflected paths. Leveraging stochastic geometry, theoretical expressions of network metrics are derived on the IRS-assisted AUE networks. Numerical results demonstrate the effectiveness of the proposed scheme, as manifested by the significant elevation in the coverage probability of AUEs by 67% when the number of elements per IRS hitsN= 2000.

Rapid signal fluctuations due to blockage effects cause severe risks of handover failures (HOF) and ping pongs (PPs). By reconfiguring line-of-sight (LoS) Links through passive reflections, intelligent reflecting surface (IRS) has the potential to maintain link stability and resolve this issue. However, existing handover (HO) process analyses have not introduced both blockage effects and IRS reflections, thus fail to explore the potential of IRS in this aspect.This paper analyzes the IRS-aided HO process by tracking the Line-of-Sight (LoS) state of moving users, where effects of LoS state transitions are characterized by modifying the state transition probability of HO process. Specifically, LoS states involve LoS, non-LoS, and IRS-reconfigured LoS, and the state transition probabilities are obtained through exact blockage modeling and IRS reflection analysis, taking into account the correlation of adjacent moments. In addition, Markov Chain-based analytical models are designed for the process of HO, HOF, and PP considering HO parameters (Time-to-Trigger and HO margin), whose transition probabilities are integrated with all LoS states. The results indicate that under severe blocking effects, the trade-off of HO parameters becomes ineffective, with HOF and PP probabilities remaining high. However, after introducing IRS, a viable range of HO parameters emerges.

The capability of low-Earth Orbit (LEO) satellite networks is constrained by shadowing effects and highly dynamic channel conditions, and thus intelligent reflecting surface (IRS) is introduced to mitigate these challenges. However, existing single-IRS-assisted LEO satellite network models are unable to characterize performance fluctuations resulting from additional random scattering caused by multiple IRSs and interference from adjacent satellites. This paper proposes an analytical framework of multiple IRSs-aided LEO satellite networks, which captures the alterations in network topology and additional cascaded link gains via beamforming and random scattering introduced by IRSs, enabling the characterization of cumulative interference from nearby satellites and IRS-induced scattering. Specifically, the satellite positions are modeled using a Binomial Point Process (BPP) at a fixed altitudeHs, while the IRSs are randomly distributed on the ground following a Homogeneous Poisson Point Process (HPPP). To accurately capture the unique characteristics of space-ground propagation, the channel model is constructed based on the shadowed Rician (SR) distribution. Moreover, the characteristics of the cascaded link are analyzed by jointly considering the beamforming and the random scattering gains from multiple IRSs, facilitating the statistical modeling of signal power according to the Gamma distribution. In addition, the number of interfering satellites is determined by accounting for visibility constraints on the spherical surface, and the cumulative interference is quantified through the Laplace transform of power distribution, enabling the derivation of the formula of coverage probability (CP). The numerical results, which match well with Monte Carlo (MC) simulations show that the performance has been improved by approximately 61% through the aid of IRS under the optimal deployment parameters compared with networks without any aid of IRS.

In comparison to the unmanned aerial vehicle-mounted base station (BS), the unmanned aerial vehicle-mounted reconfigurable intelligent surface (UAV-RIS) is a potential technology for future high-quality communications due to its high gain and flexibility. At the same time, the inaccuracy of channel state information (CSI) estimation can cause fluctuations in rates. This poses a challenge for the deployment optimization of UAV-RIS. In this paper, we propose an outage-aware transmission scheme in multi-UAV-RIS assisted networks, where the outage probability of all users is constrained and the three-dimensional (3D) position optimization of UAV-RIS is taken into consideration. Specifically, by utilizing the Bernstein-Type inequality, the challenging outage probability constraint is approximated into a manageable explicit expression. We then decouple the resulting total power minimization problem and solve three sub-problems separately: beamforming at the BS, phase shifts at RISs, and 3D positions of UAV-RISs. Furthermore, we first consider the imperfect cascaded channels and assume perfect direct channels. By solving three sub-problems through techniques such as equivalent matrix transformation, semi-definite relaxation, and first-order Taylor expansion, an alternating optimization algorithm is formed. Subsequently, we extend the proposed algorithm to the case where direct channels are also inaccurate, and the complexity and convergence of the proposed algorithm are rigorously proven. Simulation results show that a reasonable configuration of UAV-RIS can effectively improve system performance. Compared to the baseline scheme, 3D position optimization of UAV-RIS and outage awareness can reduce the transmit power by 9.6 dB.

Intelligent reflecting surfaces (IRSs) have been proven to be high gain from the validation perspective of commercial mobile networks. However, the rotation of the IRS is ignored by most works, and real-time adjustment of IRS parameters for dynamic discrete user locations is difficult to achieve in practice due to the difficulty of the instantaneous cascaded channel estimation. In this paper, a new deployment design and experimental measurement for IRS-aided indoor communications is carried out, where the rotation of the IRS is considered to maximize the worst signal to noise ratio (SNR) over all locations in a target area, and the sub-optimal closed solution is obtained and verified by experiment. Specifically, the size of the target coverage area is modeled into the objective problem and an accurate IRS-aided wireless communication network model with IRS rotation is established. Furthermore, by dividing the IRS/area into an equal number of sub-IRS/sub-areas, theoretical closed-form formulas for sub-optimal IRS phase shift and rotation angle for area coverage are given by using the beam-broadening technique and the approximation of regional center alignment. Besides, a physical demonstration system is constructed and a large number of experimental measurements are carried out, which shows that the optimal rotation angle is very close to the theoretical formula given by us. Additionally, numerical comparison of simulation results show that our proposed scheme can obtain 14.1 dB gain for the worst SNR compared with the well-known baseline.

Owning to the reflection gain and double path loss featured by intelligent reflecting surface (IRS) channels, handover (HO) locations become irregular and the signal strength fluctuates sharply with variations in IRS connections during HO, the risk of HO failures (HOFs) is exacerbated and thus HO parameters require reconfiguration. However, existing HO models only assume monotonic negative exponential path loss and cannot obtain sound HO parameters. This paper proposes a discrete-time model to explicitly track the HO process with variations in IRS connections, where IRS connections and HO process are discretized as finite states by measurement intervals, and transitions between states are modeled as stochastic processes. Specifically, to capture signal fluctuations during HO, IRS connection state-dependent distributions of the user-IRS distance are modified by the correlation between measurement intervals. In addition, states of the HO process are formed with Time-to-Trigger and HO margin whose transition probabilities are integrated concerning all IRS connection states. Trigger location distributions and probabilities of HO, HOF, and ping-pong (PP) are obtained by tracing user HO states. Results show IRSs mitigate PPs by 46% but exacerbate HOFs by 91% under regular parameters. Optimal parameters are mined ensuring probabilities of HOF and PP are both less than 0.1%.

The integration of intelligent reflecting surfaces (IRS) onto structures takes advantage of its electrically thin and flexible properties, effectively transforming original obstructions into potential serving nodes. However, existing works model IRS as a planar array, which is not well coupled with the infrastructural shape, posing challenges to future large-scale deployment. Therefore, this paper introduces conformal IRS and proposes a reflection constraint model owing to the curvature imparted by the conformal deployment of IRS, based on which the conformal IRS-assisted network is modeled and system-level performance analysis is derived via stochastic geometry. Specifically, considering infrastructure facades non-planar feature, to balance the accuracy and analytical tractability, randomly located infrastructures are modeled as cylinders based on the Boolean scheme, and conformal IRSs are deployed around a fraction ρ of them. In addition, the surface curvature of conformal IRS modulates the distribution of Line-of-Sight paths within the system and its effective illumination. We obtain the successfully reflecting probability and the effective reflecting elements respectively under two different states of IRS perfect reflecting and random scattering, and further derive the coverage probability. Numerical results show that conformal IRS significantly improves network connectivity, though it slightly reduces coverage compared to 2D planar IRS.

Reconfigurable intelligent surfaces (RIS) are emerging technologies capable of manipulating impinging radio waves. However, most existing RIS studies use an idealized reflection model with a constant amplitude for all incident and reflection angles, overlooking the phase responses to be sensitive to the incident angle due to the unavoidable Floquet high-order diffraction inherent in the practical RIS board. Therefore, this paper proposes an angle-sensitive RIS channel model for non-ideal reflection and constructs a corresponding RIS-assisted communication prototype, based on which the coverage probability is derived to explore the impact of introducing angle factors on the overall system. Specifically, based on the surface energy flow, the reflection efficiency factor at different angles is calculated, and the optimal RIS deployment position is further deduced. To validate the angle-sensitive model, experimental measurements are conducted in both anechoic chamber and indoor environments. Additionally, an analytical framework is developed, where the reflection energy loss is considered in signal power distribution and the non-uniform RIS-reflected interference is characterized. Experimental results essentially match with the theoretical predictions, confirming the accuracy of the proposed model. Numerical simulations further reveal the adverse influence of the angle-sensitive effect on network coverage, shedding light on future RIS implementation.

The complex indoor environment makes the deployment of indoor communication networks difficult and expensive. Thanks to the emergence of intelligent surfaces (ISs), including intelligent reflecting surfaces (IRSs), intelligent transmission surfaces (ITSs), and intelligent omni-surfaces (IOSs), a new technology has been brought to indoor communications. However, due to the irregularly scattered distribution of indoor walls, it is difficult to achieve splendid indoor communications using only one kind of ISs. Moreover, most existing works do not consider the location optimization of ISs, resulting in poor performance. This paper proposes a new hybrid IRS/ITS/IOS assisted indoor communication scheme, where the cooperative multi-hop is considered and IRSs/ITSs/IOSs are optimally deployed in specific areas for enhancing the minimum signal-to-interference-plus-noise ratio. Specifically, we first consider the case of hybrid deployment of IRSs/ITSs, the blockage factors between the base station (BS)/users and IRSs/ITSs are modeled, and the multi-hop cooperative channel model is expressed as an explicit function of the phase shifts of IRSs/ITSs by matrix transformation. Furthermore, to deal with the non-convexity and the strong coupling between BS beamforming, phase shifts and positions of IRSs/ITSs, an efficient iterative algorithm is developed based on the successive convex approximation and semi-definite relaxation. Additionally, we analyze and illustrate that our proposed scheme can be extended to the case of IRSs/ITSs/IOSs hybrid deployment. Simulation results prove the advantages of the proposed scheme and show a 45.9% performance enhancement compared with the well-known graph-based deployment.

Signal fluctuations caused by blockage effects induce numerous handover failures (HOFs) and ping-pongs (PPs). Intelligent reflecting surfaces (IRSs) have the potential to address this issue by constructing indirect Line-of-Sight (LoS) links. However, this potential remains underexploited since existing works lack dedicated handover (HO) parameter optimization approaches. This paper proposes an HO boundary generation model for optimizing the HO parameters of IRS-aided networks, where the locations of HO events (HO triggers, HOFs, and PPs) are explicitly modeled as geometric boundaries reshaped by signal blocking and IRS reflection to evaluate and minimize HOFs and PPs. Specifically, initial boundaries are generated by projecting received signal strength trigger conditions for HO events onto the geometric elements across all considered propagation conditions. Exact boundaries are then determined by preserving parts of the geometric elements where the propagation condition holds. By evaluating the distance between the boundaries of HO events, the upper bounds on HOF and PP probabilities are derived and minimized under the worst-case criterion to determine the optimal HO parameters (HO offset and time-to-trigger). Simulations and IRS prototype-based experimental verifications are conducted to validate the proposed algorithm. Field measurements in a typical corridor corner scenario confirm that the IRS inherently reduces HOFs and PPs, and the proposed algorithm further reduces incorrect HOs by at least 42.2% compared to existing algorithms.

In intelligent reflecting surface (IRS) assisted networks, most works select collaborative IRS only based on the nearest distance without considering the joint influence of double path loss. This paper proposes a tractable model of product-distance association in IRS-assisted networks, where the Cassini oval is leveraged to characterize the IRS association mechanism theoretically, and the system coverage probability is analyzed by stochastic geometry. Specifically, modeling the product-distance of IRS by the mathematical model of the Cassini curve and considering the physical limitations of the half-space reflection in IRS, the probability density function of the minimum product-distance of IRS that can successfully reflect the signal is derived by approximating the area of Cassini oval. Furthermore, a semi-closed expression of the coverage probability is obtained under Rician fading through the Gamma approximation of expected signal power and the Laplace transform of interference power. In addition, the analysis results indicate a trade-off between the number of IRS elements deployed in the network and the density of base stations, and the tendency of this trade-off changes with IRS density. The numerical results reveal the optimal coverage performance based on product-distance association is 28.8% higher than that of nearest-distance-based association.

In aerial reconfigurable intelligent surface (RIS) aided networks, high line-of-sight (LoS) and agility characteristics provide favorable conditions for RIS reflection. However, this also introduces extra LoS RIS reflection interference, especially when aerial RIS is fixedly deployed. This paper proposes a dynamic unmanned aerial vehicle (UAV)-mounted RIS aided multi-cell multi-user communication scheme, where the minimum ergodic rate of randomly mobile users is maximized through joint optimization of BS beamforming, RIS phases, user scheduling and UAV trajectory under the assumption of only statistical channel state information. Specifically, a closed-form ergodic rate approximation is derived, based on which the block coordinate descent framework is leveraged to solve the above four variables due to the non-convexity of the problem. Furthermore, the optimal beamforming is acquired in closed form utilizing fractional programming and quadratic transformation, RIS phases are optimized by the complex circle manifold, user scheduling is obtained by the reconstruction-based relaxation, and UAV trajectory is solved through the first-order Taylor expansion and successive convex optimization techniques. Moreover, the complexity of the proposed algorithm is analyzed and its convergence is carefully proved. The numerical results demonstrate our proposed scheme achieves a better ergodic rate than traditional methods and other aerial schemes with heuristic trajectories.

In existing research of cache-enabled UAV networks, cell association is content-centric regardless of UAV line-of-sight (LOS)/non-line-of-sight (NLOS) channel dynamics, and the fixed content distribution hinders system performance. This paper proposes user-centric cooperation for cache-enabled UAV networks to exploit both contents and transmission diversity, and optimizes the probabilistic caching distribution to minimize system outage probability considering UAV interference topology dynamics. Specifically, user-centric UAV group (UAVG) is formed for each user to increase both aggregated cache size and transmission resources, considering UAVs can’t realize inter-nodes cache file transform due to limited wireless backhaul. Users will search the required content within the UAVG and associate to the cache hit UAV that provides the strongest signal-to-interference ratio (SIR), thus the intrinsic gain of transmission diversity can be achieved. If the required content can’t be successfully transmitted by UAVG, the macro base stations (MBSs) will help transmit the target content. In addition, based on analytical results, the optimal content distribution (OCD) probability is optimized by Lagrange multiplier, thus the tradeoff between content diversity gain and limited cache size is exploited. Analytical results show that, compared with caching the most popular contents policy, our proposed OCD model can decrease system average outage probability by 44.8%.

Due to the time-varying channels with line-of-sight (LoS) and non-LoS (NLOS) components, the handover (HO) trigger locations of unmanned aerial vehicle (UAV)-assisted networks are irregular and fluctuating, which causes frequent HOs and HO failures (HOFs). However, existing works only consider path loss and cannot obtain sound HO parameters. In this paper, the HO events trigger locations are explicitly modeled as time-varying boundaries, where the UAV altitude and LoS/NLoS components are projected to geometric boundaries satisfying the conditions of HO events, and the fluctuation of boundaries is introduced by modeling their radii as random variables with fast fading coefficients. Based on specific received signal ratios of HO events, the distributions of HO event boundaries are derived via the projection criterion. Specifically, the HO trigger location is analyzed through the HO boundary considering the measurement period, then HO status during Time-to-Trigger is analyzed via HO event boundaries and user-UAV geometric relationship. Expressions of HO events in UAV-assisted networks, including HO, HOF, and ping-pong (PP), are obtained with and without fast fading. The results show more HOs, HOFs, and PPs occur with fast fading, which increase by 1.7×, 1.4×, and 3.1× when densities of the MBS and UAV are 10/km2and 100/km2.

Wireless sensor nodes (SNs) are usually energy-limited, a low energy efficiency of SN may be caused by the occlusion of transmission link in the unmanned aerial vehicle (UAV)-enabled data collection. Reconfigurable intelligent surfaces (RISs) provide potential communication opportunities by reflecting and enhancing the signal, thus increasing SNs’ energy efficiency. This paper investigates energy-efficient data collection aided by$N$RIS elements, where a height-fixed and flight time-constrained UAV is considered as the collector, a tractable air-to-ground channel model is established considering the cascaded RIS channel and time-varying trajectory of UAV, and maximum energy consumption of SNs is minimized. Specifically, coupling two intractable fourth-order moment variables, the air-to-ground channel model is processed into an easily computable expression through the Gamma function. Furthermore, based on the proposed two RIS deployment schemes, maximum energy consumption of SNs minimization problem is formulated as a non-convex mixed-integer nonlinear programming (NMNP) problem with optimization of UAV trajectory and SNs wake-up schedules. Additionally, through the slack technique and Taylor expansion, the NMNP problem is decoupled as linear programming and quadratically constrained quadratic programming, and an effective algorithm is designed to obtain the sub-optimal solution. Simulations prove the validity of our algorithm and RIS deployment insights are given for SNs energy saving.

Featured by flexible deployment, unmanned aerial vehicles (UAVs) can form multi-tier networks in 3D space, enhancing the capacity, but this heterogeneity causes more unnecessary handovers (HOs), i.e., ping-pongs (PPs) and handover failures (HOFs). However, existing studies have not modeled the HO process in multi-tier UAV networks. This paper proposes an equivalent model for the HO process in$K$-tier UAV networks, where nonlinear mapping rules are established from the altitude and transmit power to equivalent horizontal distances specific to HO events, then HO status during time-to-trigger (TTT) is analyzed via the mapping rules. Specifically, each trigger condition of the HO events is transformed into a circular boundary centered on the projection of the target UAV with the corresponding trigger radii. Considering the practical HO process, the HO trigger location is tractably modeled as a random variable of the path length from the start, and the signal-to-interference ratio during TTT and the residence time are analyzed through equivalent geometric relations. The results show that there exists an altitude ratio that causes the most frequent HO, which increases by 25% to 150% under different configurations. Trade-offs between HOF and PP exist in the altitude ratio and transmit power ratio, besides the HO parameters.

In the existing works on performance analysis of cache-enabled unmanned aerial vehicle (UAV) networks, caching distribution is fixed, and the joint cache-level and transmission-level cooperation has not been analyzed in literature. Considering UAVs agility and line-of-sight (LOS)/non-line-of-sight (NLOS) channel characteristics, this article proposes a tunable caching distribution model, where content placement and transmission can be flexibly adjusted to match interference topology dynamics and derives semi-closed expression of caching coverage probability via stochastic geometry. Specifically, UAVs will take the partition coefficient$\rho $to determine caching distribution, where the most popular content files are reserved in every UAV within the cooperation group and jointly transmit to the user, while other files are probabilistic stored in the remaining cache space to achieve content diversity. In addition, when requiring the less popular files, the user will always connect to the UAV with the best signal-to-interference (SIR) ratio among cache hit UAVs within the cooperation group. The theoretical analysis investigates the effects of various system parameters such as UAVs height, cooperative radius, and content popularity on the caching coverage probability and optimal$\rho $for can be found. It can be seen that optimal$\rho $will be decreased first and then increased along with the increase of UAVs height, which reveals the tradeoff between cache hit probability and transmission reliability. Analytical results show that our proposed cache and transmission policy can achieve 34% gain of coverage probability compared to the policy of probabilistic cached.

Existing deep learning-based beamforming models’ input sizes are fixed, and they can only be used in multiple-input multiple-output (MIMO) networks containing a fixed number of users and base stations. Therefore, they cannot work and need retraining when the number of users is dynamic. In this letter, a fully convolutional beamforming model named FC-BFNet is proposed, where data of different dimensions can be addressed to adapt to the dynamic environment without retraining the model. Specifically, FC-BFNet has two computational parts which are mainly made up of convolution layers because the convolution operation is not sensitive to the size of the input data. Differently and importantly, the first part extra uses max-pooling to calculate the underlying features of the input and stores max-pooling indices, while the second part extra leverages up-pooling based on stored indices to obtain the expected results from the underlying features and ensure the size consistency of input and output. Extensive experiments demonstrate FC-BFNet has better dynamic environment adaptability and sum-rate performance than other baselines, while maintaining great computing efficiency.

Exploiting multiple-input multiple-output (MIMO) technology in UAV networks will mine the potential of spatial multiplexing. However, considering UAVs 3D distribution and line-of-sight (LOS)/non-line-of-sight (NLOS) channel dynamics, multi-antenna transmission will exacerbate network irregularity and render interference management a complicated problem to solve. This paper proposes 3D coordination model for interference management via multi-cell beamforming and signal-level cooperation in multi-antenna UAV networks, and analyzes system performance by deriving semi-closed expression of coverage probability using stochastic geometry. Specifically, UAVs are deployed according to 3D Poisson point process (PPP) with maximum height limit$L$, and user-centric cell group selection is considered according to specific signal threshold$\eta $for interference coordination with the consideration of LOS/NLOS channel conditions. Zero-forcing beamforming and joint transmission is performed within the UAV cluster for interference mitigation with limited channel state information feedback taken account. In addition, considering the irregularity of UAV group, system performance is construed as three scenarios and coverage probability is derived by Gamma approximation with proposed novel coordinate system transformation. Numerical results match well with simulations and provide optimal deployment parameters for UAV deployments, where coverage probability can reach 92% when SIR threshold$T=0$dB.

In multiple unmanned aerial vehicles (UAVs)-aided networks, the time-varying channel caused by the impact of the line of sight (LOS) and nonline of sight (NLOS) leads to signal fluctuations; moreover, the UAV's agility and the constraint of the spatial distribution of blind areas on the UAV's deployment result in interference topology fluctuations. Accordingly, the instantaneous signal-to-interference ratio is fluctuant and cannot accurately reflect network performance. Considering a multi-UAV-aided network with UAV spatial dependence, this article derives the semiclosed expression of long-term average throughput considering the fluctuations of the channel and topology, which are measured by calculating correlation coefficients of the signal and interference. Specifically, the time-varying channel is characterized by incorporating the link-state conversions and randomness of small-scale fading into channel gains, the time-varying network topology is quantified by considering the effect of distance variation caused by mobility on interference topology. In addition, considering multiple UAVs are clustered in corresponding blind areas, UAV networks are modeled as matern cluster processes (MCP), and time-averaged performances at intracluster and extracluster are analyzed, respectively. The numerical results show the effects of clustering parameters and deployment parameters on temporal correlation and long-term average network performance.

User‐centric network (UCN) is regarded as a promising candidate to approach the challenges of more radio link failures (RLFs) due to the ultradense deployment of small base stations (SBSs) and meet the requirements of ultrahigh throughput, ultrahigh reliability, and ultralow latency for the 6G system. In this paper, soft mobility is proposed for UCN with the split of control and user plane (C/U‐plane) and shared physical cell identifier (PCI) to achieve the goal of zero handover failure (HOF) probability, where transparent handover (HO) within a cell is realized with user configuration duplication and measurement enhancement. Specifically, the cell is composed of several SBSs around the user, where one anchor SBS is selected for controlling, and others act as slave SBSs for transmission with duplicated UE configuration from the anchor SBS. Based on the proposed architecture, the user measures downlink channel quality for cells and SBSs distinguishingly, via SS/PBCH Block (SSB) and channel‐state information‐reference signal (CSI‐RS), respectively, and then makes the HO decision. Results show that soft mobility can reduce the number of HOF by about 50% over the current system, and the HOF probability is lower than 1% for TTT = 40 ms and offset = −1 dB.

The limited wireless backhaul capacity has become the major bottleneck for UAV communications, while in existing researches of UAV networks, the relation between cell selection and backhaul capacity has not been modeled. This paper proposes cell group selection with backhaul‐aware biasing for UAV networks and analyzes system performance by deriving the rate outage probability via stochastic geometry, where the user’s maximum data rate is constrained by backhaul capacity. Specifically, cell group selection is no longer distance‐based and considered with backhaul capacity bias factor, where UAVs with higher backhaul capacity will have a larger bias factor to match the backhaul variance. In addition, the dynamic UAV group is organized with the N largest bias reference signal receiving power (BRSRP), where users can utilize the diversity gain by adjusting serving UAV dynamically as the channel conditions change. Analytical results show that the outage probability is decreased by 58% when cell group size N = 3 and UAV optimal density λu = 600/km2 when UAV height h = 150 m.

In 5G ultradense heterogeneous networks, wireless backhaul, as one of the important base station (BS) resources that affect user services, has attracted more and more attention. However, a user would access to the BS which is the nearest for the user based on the conventional user association scheme, which constrains the network performance improvement due to the limited backhaul capacity. In this paper, using backhaul‐aware user association scheme, semiclosed expressions of network performance metrics are derived in ultradense heterogeneous networks, including coverage probability, rate coverage, and network delay. Specifically, all possible access and backhaul links within the user connectable range of BSs and anchor base stations (A‐BSs) are considered to minimize the analytical results of outage probability. The outage for the user occurs only when the access link or backhaul link which forms the link combination with the optimal performance is failure. Furthermore, the theoretical analysis and numerical results evaluate the impact of the fraction of A‐BSs and the BS‐to‐user density ratio on network performance metric to seek for a more reasonable deployment of BSs in the practical scenario. The simulation results show that the coverage probability of backhaul‐aware user association scheme is improved significantly by about 2× compared to that of the conventional user association scheme when backhaul is constrained.

Deep learning methods can decrease power allocation algorithm computational complexity, but when the number of users dynamically changed, deep learning model needs to be retrained, which makes them not a practically useful solution. In this paper, a power allocation scheme based on deep QNetwork (DQN) with padding is proposed, which can maximize system's sum-rate under dynamically changing number of users. Specifically, when a user is inactive, it's state information cannot get and the input matrix does not match model requirements, but with padding, the missing part in the input matrix will be filled with zeros, which makes this model can manage the dynamic changing number of users. In addition, base stations (BSs) can transmit information with multiple-path, which means users can be severed by several base stations at the same time. The results show that the total system capacity is increased by 18.4% when the scenario have 1 macrocell base station (MBS), 7 smallcell base stations (SBSs), and 20 users, compare with the WMMSE algorithm, 50.0% compare with the max-rss algorithm, and this model can manage uncertain number of users properly.

In the existing works, the finite-area distribution of Unmanned Aerial Vehicle (UAV) and the effects of LoS and NLoS in air-to-ground channels have not been modeled when analyzing the temporal correlation in UAV networks, which makes the current analyses unsuitable for the practical deployment in hotspots. This paper analyzes the temporal correlation by deriving the expression of the interference correlation and joint coverage probability in mobile UAV networks, where all UAVs move independently in a finite area and the channel fading is calculated based on air-to-ground channel model with LoS and NLoS. Specifically, the temporal correlation is measured by incorporating the fluctuations caused by probability variation of LoS and the reliable network topology caused by finite mobility into the wireless channels. Furthermore, the non-uniform distribution caused by random mobility of UAVs is considered over different time slots. The results show that the interference correlation decreases as the moving distance of UAVs increases, and the decreasing interference correlation offsets part of the decrease in joint coverage probability caused by increasing moving distance of UAVs.

In user-centric network, traditional typical user analysis method based on spatial average results is no longer applicable due to the flexible spatial difference, which is the large fluctuations in user performance with spatial location. Especially because of power control leading to keen spatial competition, the spatial difference becomes much significantly, so that fine-grained analysis method is needed to evaluate its performance. This paper analyzes the spatial difference in user-centric network with power control through meta distribution from many different fine-grained perspectives to reveal that power control improves the performance not only in the sense of the spatial average, but also in the complete spatial distribution. Specifically, the complementary cumulative distribution function (CCDF) of the conditional transmitting success probability, the mean local delay and the 5%-tile users performance are given to depict power control effect on the individual links. This analysis provides the optimal values of the area and intensity for power control deployment in user-centric network. Numerical results show that after applying power control the users of high coverage probability can be improved at most by 38%, the mean local delay decreases by 2x and 4x gains can be obtained as for the 5%-tile user's performance.

User-centric ultra-dense network (UUDN) is a promising research direction for 5G, which organizes a dynamic transmission point group (TPG) consisting of potential transmission points (TPs) for each user. This paper is the first to give a general result of UUDN performance to characterize the gain obtained from the increased TP diversity within TPG, without considering cell coordination. Specifically, the outage probability and average achievable rate under general assumptions are analyzed and simple closed-form expressions for Rayleigh fading are derived, which gives a theoretical performance bound as the TPG's member is adjusted adaptively with the change of the instantaneous channel conditions. The numerical results show that the outage performance can be improved by 75% with low signal-to-interference-plus-noise ratio (SINR) threshold and the average achievable rate can be improved by 16% in comparison with ultra-dense network (UDN). In addition, the optimal TPG radius can be decided according to the requirements of users, which can provide guidance for TPG forming/reforming in UUDN.

The requirements of higher data rate and quality-of-service (QoS) enhancement for edge users lead to the densification of the small base stations (SBSs), which shortens the service distance. Due to the low transmission power of the SBSs, the technique of cell range expansion (CRE) is used for offloading macro base station (MBS) users to SBSs, reducing the throughput of the biased user on the other hand. In this paper, a tractable model based on dual connectivity (DC) is proposed in two-tier heterogeneous networks to enhance the SINR of the biased users, and analytical expressions are derived in terms of coverage probability, average throughput and area spectral efficiency (ASE). Taking advantage of DC, the MBS and the SBS serve the biased user simultaneously, which increases the throughput by improving the utilization of radio resources across two BSs. By including the handover cost derived with handover rate in the analysis, the mobility-aware average per-user throughput and ASE for mobile users are obtained. The results show that in the proposed network architecture, the average throughput of the biased user can be improved by 2× and the per-user throughput shows a maximum increase of 15% over the network without DC.

User-centric dynamic base station (BS) clustering can efficiently avoid cell edge effect, which results in overlapped clusters and introduces the tremendous complexity in beamforming optimizations. However, existing works mainly focus on addressing the problem in a centralized manner with the assumption that channel state information (CSI) is perfectly known at the central controller, which is not practical in real-world systems. In this paper, a distributed user-centric clustering and beamforming design for sum-rate maximization is proposed, where the optimization problem is split by introducing interference pricing and solved subsequently in the anchor of each cluster. In particular, based on interference pricing, the problem is decomposed into parallel subproblems by decoupling interference constraints and power constraints, where beamforming matrices are optimized by standard iterative minimization of mean square error; moreover, in case that beamforming matrices calculated at the anchor could be outdated before delivered to the cooperating BSs, a novel solution of joint transmission (JT) is proposed to avoid the degradation of performance. Simulation results demonstrate the performance gain in terms of sum rate and 5%-tile user throughput compared to BS-centric and centralized schemes; besides, the impacts of rate constraints are also studied in this paper.

The unmanned aerial vehicle (UAV) communication has become a promising technology to enhance network coverage thanks to the high mobility and flexibility of UAV. However, the dense deployment of UAVs and the time-varying aerial environment will introduce frequent handovers. This paper proposes a tractable equivalent model for 3D UAV networks, and derives compact expressions of the user association probability and the handover probability through stochastic geometry analysis. Modeling the 3D wireless network of UAVs as a multi-tier network where the locations of UAVs in each tier follow a Poisson point process (PPP), the equivalent horizontal distance is introduced to eliminate the information loss brought by the projective process. Based on the proposed model, the per-tier user association probability is first derived with the maximum average received power criterion. Furthermore, taking advantage of the equivalent model, handover probability between UAVs with different altitudes is analyzed. The results show that there is an optimal density in the 3D UAV networks for minimal handover probability, and the handover probability decreases as the deployment altitude increases.

In unmanned aerial vehicle (UAV) networks, line-of-sight (LOS) links dominate the communication among UAV transmission points (TPs) and user equipments (UEs), which tremendously improves link quality but also leads to strong interference. This paper proposes a power control strategy to combat interference in user-centric UAV networks, where a dynamic UAV transmission point group (TPG) is organized serve each user seamlessly. Based on the strategy, semi-closed expression of network downlink coverage probability is derived leveraging stochastic geometry analysis with the consideration that different TPGs can overlap with each other. Specifically, each UE would choose the closest UAV as its serving UAV to transmit data while other UAVs within the TPG will mitigate interference by reducing transmitting power. Two coefficients μ and β are applied to determine the area and intensity for power control, which can realize the tradeoff between interference mitigation and resource utilization. The results show that with the power control strategy we proposed, coverage probability can be improved at most 66% when the SIR threshold is 10 dB.

In existing works, temporal interference correlation and joint coverage probability have been extensively studied in the infinite-area cellular network. However, in the actual networks, base stations (BSs) are deployed non-uniformly in a finite area, which leads to the network performance differences at different locations. Utilizing stochastic geometry, this paper analyzes the temporal correlation in the finite-area network with a non-uniform Poisson point process (PPP) model by deriving the exact expression of interference correlation coefficient and joint coverage probability between two different slots at arbitrary location in the finite region. Specifically, interference and coverage are temporally correlated in different slots due to the stability of network topology and the large-scale fading. In addition, the randomness introduced by independent small-scale fading weakens the temporal correlation to some extent, and non-uniform BS distribution in the finite network area leads to the location-dependence of temporal correlation of interference and coverage. Numerical results show the effects of BS densities, path-loss factors, and the location of a typical user on temporal correlation of interference and joint coverage probability.

As densification and heterogeneity are the promising trends of future mobile networks, deployment of base stations (BSs) becomes increasingly difficult due to the laborious procedures of alternating optimization between field measurements and coverage evaluation via propagation models. In this paper, we present a coverage evaluation tool based on machine learning (ML) which is free from propagation model. In particular, received signal strengths (RSSs) reported by users and factors that affect the transmitted signal, such as distance, geography and configuration parameters of BS, are collected and used to train a classifier which allows to predict the RSS at the user side. In this way, the complicated procedures to obtain propagation model can be skipped, which reduces the great cost in coverage evaluation. Moreover, in order to acquire better prediction performance, hyper-parameters optimization is performed via exhaustive grid search methods in each learning algorithm. Simulations are conducted by using vast real-world data for a better training result. We also compare the performance by means of different machine learning algorithms and the numerical results show that Support Vector Machine (SVM) outperforms other classifiers in terms of prediction accuracy, which is up to 86.7%.

User‐centric network (UCN) organises a dynamic femto access point group (FAPG) for each user to meet the exponential data demands in 5G. However, there exists spatial correlation in the scheduling of femto access points (FAPs) due to the overlap of different users' FAPGs, which has not yet been analysed in existing literature. In this study, considering this spatial correlation by using Matérn‐like model, simple yet accurate semi‐closed form expressions of rate coverage probability and area average goodput of power control strategy in UCN are derived, respectively, using stochastic geometry tools. In this user‐centric power control strategy, each user equipment (UE) determines its FAPG radius considering the distance to the nearest FAP, and FAPs within the FAPG employ power control to mitigate dominant interference for the UE. Further, Matérn‐like model guarantees that two FAPs in the same FAPG will not transmit at full power simultaneously, and thus whether a FAP performs power control is correlated with other FAPs that belong to the same FAPG. The analytical results demonstrate significant performance gains of power control strategy in UCN compared with the non‐coordination baseline, e.g. area average goodput is improved by about 44% when FAP‐to‐UE density ratio equals 1.

Dense deployment of small cells is considered as a key enabling technique for the emerging 5G cellular networks. However, the dense and random deployments of femtocells in ultra-dense network (UDN) induce severe intercell inference that in turn reduces spectral efficiency. Traditional frequency reuse scheme is not fit for the dynamic network topology. In this paper, we propose a flexible resource reuse scheme based on opportunistic algorithm to coordinate interference. The scheme dynamically adjusts the number of reused sub-channels and determines which sub-channel should be reused with the variation of interference. Due to strong interference, some subchannels that cannot be reused will be allocated orthogonally. For those reused channels, the scheme selects best users to improve the sum rate, which will match resources with appropriate users. Hence, it can be seen as an optimization of enhanced inter-cell interference coordination (eICIC). Additionally, with the density of small cells increasing, non-ideal backhaul will be deployed extensively. However, the limited backhaul capacity constrains the throughput of small cells. We investigate the impact of backhaul on the sum rate. Our simulation shows that with the variation of signal-to-noise-ratio (SNR), the optimal reuse ratio is different. The sum rate of optimal reuse ratio is higher than traditional orthogonal allocation. In practical, we can choose the reuse ratio in respond to the interference situation. Besides, even when the backhaul capacity is constrained, the performance of our scheme is still better than orthogonal allocation.

User-centric network (UCN) forms a dynamic femto access point group (FAPG) for each user equipment (UE) to provide consistent user experience in 5G. However, femto access point (FAP) conflict problem occurs in UCN when the same FAP is included in different UEs' FAPGs. This paper proposes a power control strategy in UCN using Matern-like model to avoid FAP conflict, and derives semiclosed form expression of coverage probability leveraging tools from stochastic geometry. In this user-centric power control strategy, each UE determines its FAPG radius considering the UE's distance to the nearest FAP, and FAPs within UE's FAPG decrease transmission power by a power control factor to mitigate strong interference for the UE. Furthermore, whether a FAP performs power reduction is determined using Matern-like model, where two FAPs in the same FAPG do not transmit at full power simultaneously in order to avoid severe interference, thus resolving FAP conflict. The analysis shows that, there exist certain values of FAP-to-UE density ratio and FAPG radius where further increasing them would only bring about very small gain in coverage probability. When FAP-to-UE density ratio equals 1 and FAPG radius coefficient equals 1.5, the coverage probability of user-centric power control strategy is improved by about 49% compared with the non-coordination baseline.

Ultra-dense network (UDN) in 5G provides more potential transmission points (TPs) for users. To take advantage of dense TPs, 5G will shift the network paradigm from traditional cell-centric to user-centric. Virtual cell redefines the cell from the view of users to build user-centric networks. Virtual cell is a logical cell consisting of multi physical TPs. By data synchronization as a way of cooperation between TPs at different protocol layers, virtual cell can enhance spectral efficiency. Data synchronization will utilize backhaul resources. However, non-ideal backhaul will be extensively deployed, which will take a significant share of the energy consumption in future systems. Therefore, TP selection in a virtual cell should take spectral efficiency (SE) and energy efficiency (EE) into account. To trade off between the SE and EE, we present an analysis of the backhaul power consumption and put forward an adaptive algorithm for TP selection in a virtual cell. We define the normalized outage capacity as goodput to represent the SE. Then, we consider the power of backhaul link as the energy consumption. Thus, the objective of TP selection is maximizing the defined energy efficiency. A dynamic ergodic method is used to solve the problem. Our simulation shows the dynamic selection scheme has better performance than static scheme and random scheme. Besides, with the size of virtual cell increasing, the growth rate of energy efficiency is decreasing. Thus, the size of virtual cell and TP numbers should be controlled.

Future mobility management is expected to provide high data rate, low delay, and dense deployment to offer seamless coverage. As a main issue for the future network, Ultra-Dense Networks (UDNs) leads to frequent measurement, intolerable handover failure (HOF) rate and ping-pong (PP) rate. Meanwhile, most existing works either focus on handover decision rules design or handover parameters adjustment without considering the redundant handover and signaling overhead causing by the dense deployment environment. To solve this problem, mobility enhancement based on Virtual Cell (VC) design with dense deployment is firstly proposed in our work to provide seamless coverage and improve handover performance, which is promising for future 5G networks. The relationship between key handover parameters and mobility performance in UDNs is illustrated to seek for the optimal configuration of Time-to-Trigger (TTT) and A3 Offset and achieve low HOF rate and PP rate. VC forming/reforming process is optimized in terms of user's moving direction and speed to satisfy its preference. And we evaluate the impact of VC size and angle threshold for prediction on handover performance to provide basis for specific VC forming/reforming by which most users will have an SINR value higher than a fixed threshold, thus maximizing the residence time in the serving cell. Simulation results show that the system HOF rate is minimizing by optimizing the configuration of handover parameters applying for UDNs. In addition, PP rate achieves a decrease of more than 50% for moderate-speed users based on VC with dense deployment compared with handover in LTE system.

Femtocells are envisioned as a key solution to embrace the ever-increasing high data rate and thus are extensively deployed. Given that numerous femtocell access points (FAPs) deployed in ultra- dense networks (UDNs) lead to significant energy consumption, boosting their energy efficiency (EE) becomes an important issue to be addressed. However, most existing works either focus on homogeneous networks or assume that FAPs are equally distributed, which is not realistic in a dense network as random deployments cause severe interference. This paper explores the realistic scenario of randomly distributed FAPs in heterogeneous networks and proposes a clustering approach combined with an active FAP selection algorithm to boost both spectral and energy efficiency without manual configuration. Taking into account traffic load and interference, the paper reduces the complexity from the Bell Number to polynomial time by exploiting a graph-based Min-Cut strategy to cluster FAPs and allocate orthogonal resources in one cluster to mitigate interference that in turn improves EE. Simulation results confirm the effectiveness of the framework.

In the existing papers on cooperative diversity, the fading channels are idealistically assumed to be Rayleigh fading channel. Taking into account the realistic propagation environments in the presence of spatial fading correlation, the performance of cooperative protocols using distributed space-time block code (DSTBC) in the two-ring scattering model is analyzed. The time division multiple access scheme is proposed with half duplex mode. Cooperative scheme is proposed, because cooperative diversity can combat the fading correlation by spatial decorrelation which is produced by independent multipaths and scattering paths between source nodes (SNs) and destination nodes (DNs). Cooperative relaying is shown to increase the link stability (or, alternatively, decrease the link outage probability) significantly. Therefore, the outage capacity and average bit error rate (BER) of cooperation are presented by applying various cooperative schemes in the correlated channel. Based on theoretical analyses and simulation results, the cooperative diversity yields significant performance gain in correlated channel, i.e., reducing the spatial correlation of each path and enhancing the overall performance of virtual MIMO system by high quality of cooperative interuser channel.