To support the development of low altitude economy, the air-ground integrated sensing and communication (ISAC) networks need to be constructed to provide reliable and robust communication and sensing services. In this paper, the sensing capabilities in the cooperative air-ground ISAC networks are evaluated in terms of area radar detection coverage probability under a constant false alarm rate, where the distribution of aggregated sensing interferences is analyzed as a key intermediate result. Compared with the analysis based on the strongest interferer approximation, taking the aggregated sensing interference into consideration is better suited for pico-cell scenarios with high base station density. Simulations are conducted to validate the analysis.

Diagnosing the root causes of Quality of Experience (QoE) degradations in operational mobile networks is challenging due to complex cross-layer interactions among kernel performance indicators (KPIs) and the scarcity of reliable expert annotations. Although rule-based heuristics can generate labels at scale, they are noisy and coarse-grained, limiting the accuracy of purely data-driven approaches. To address this, we propose DK-Root, a joint data-and-knowledge-driven framework that unifies scalable weak supervision with precise expert guidance for robust root-cause analysis. DK-Root first pretrains an encoder via contrastive representation learning using abundant rule-based labels while explicitly denoising their noise through a supervised contrastive objective. To supply task-faithful data augmentation, we introduce a class-conditional diffusion model that generates KPIs sequences preserving root-cause semantics, and by controlling reverse diffusion steps, it produces weak and strong augmentations that improve intra-class compactness and inter-class separability. Finally, the encoder and the lightweight classifier are jointly fine-tuned with scarce expert-verified labels to sharpen decision boundaries. Extensive experiments on a real-world, operator-grade dataset demonstrate state-of-the-art accuracy, with DK-Root surpassing traditional ML and recent semi-supervised time-series methods. Ablations confirm the necessity of the conditional diffusion augmentation and the pretrain-finetune design, validating both representation quality and classification gains.

The synergy between Federated Learning (FL) and Foundation Models (FMs) holds great promise in enhancing privacy protection and improving the generalization capabilities of AI systems. However, the high computational and communication overhead of FMs hinders effective deployment in real-world scenarios. Although some pioneering research has proposed using proxy sub-Foundation Models (sub-FMs) to reduce the computational and communication costs when fine-tuning FMs in FL environments, it overlooks the challenges posed by heterogeneous mobile devices with varying computational and communication capabilities, and by dynamic changes in their operational conditions, which cause very long FL training delay. Motivated by these challenges, we propose a novel federated fine-tuning of Foundation Models design via adaptive pruning (FedFTAP). FedFTAP introduces a pruning method specifically designed for FMs, combined with parameter-efficient fine-tuning modules to enhance communication and computational efficiency. FedFTAP further addresses system heterogeneity and system dynamic changes by adaptively tailoring heterogeneous sub-FMs suitable for local training on mobile devices. Moreover, FedFTAP introduces a method for aligning heterogeneous sub-FMs with the global FM. The experimental results show that FedFTAP effectively reduces computational and communication costs in federated fine-tuning scenarios.

To support the development of air-ground integrated sensing and communication (ISAC), network-level performance analysis is needed for providing an essential guide on the network design. Following the widely adopted orthogonal frequency-division multiplexing (OFDM) technology in existing wireless systems, a cooperative air-ground wireless network based on OFDM-ISAC is introduced in this paper, where the ISAC-enabled base stations (BSs) following the two-dimensional homogeneous Poisson point process (HPPP) distribution serve the terrestrial communication users while sensing the aerial targets. In particular, cooperative beamforming schemes are designed for mitigating the interference among ISAC BSs. First, we analyze the communication as well as sensing performances in terms of different metrics including area communication coverage probability, area communication spectral efficiency, area radar detection coverage probability, and average Cramér-Rao Bound. Simulation results are then presented to validate the theoretical analysis and illustrate the effects of key system parameters on the network performance. It is observed that both the communication and sensing (C&S) performances depend on the BS density and height, while the sensing performance also depends on the height of sensing target together with the numbers of OFDM subcarriers and symbols. Moreover, there exists a tradeoff between the C&S performances with respect to the BS density and height. The results of this paper provide useful guidance to the design and implementation of air-ground wireless network for harnessing the dual benefits of ISAC.

Addressing the communication and sensing demands of sixth-generation (6G) mobile communication system, integrated sensing and communication (ISAC) has garnered traction in academia and industry. With the sensing limitation of single base station (BS), multi-BS cooperative sensing is regarded as a promising solution. The coexistence and overlapped coverage of macro BS (MBS) and micro BS (MiBS) are common in the development of 6G, making the cooperative sensing between MBS and MiBS feasible. Since MBS and MiBS work in low and high frequency bands, respectively, the challenges of MBS and MiBS cooperative sensing lie in the fusion method of the sensing information in high and low-frequency bands. To this end, this paper introduces a symbol-level fusion method and a grid-based three-dimensional discrete Fourier transform (3D-GDFT) algorithm to achieve precise localization of multiple targets with limited resources. Simulation results demonstrate that the proposed MBS and MiBS cooperative sensing scheme outperforms traditional single BS (MBS/MiBS) sensing scheme, showcasing superior sensing performance.

Driven by the rapid development of deep learning approaches, many novel WiFi sensing based applications have emerged, such as human activity recognition, pose estimation and indoor localization. However, due to the limited richness of collected WiFi data, the performance of WiFi sensing based models still lags behind conventional vision based models in terms of recognition accuracy and generalization. To break through the bottleneck of insufficient WiFi data, we propose a diffusion model based data augmentation scheme for human activity recognition task, in which the training dataset is composed of both real data and synthetic data. In particular, to reduce training overheads of the diffusion model, it is trained by taking activity classes as input conditions. Therefore, a single model is able to generate multiple types of WiFi data corresponding to activities, thereby avoiding the need to train separate models for each individual activity. Simulation results show that the generated WiFi data samples are visually indistinguishable from real ones, even when the model is trained on a small-scale dataset. Moreover, it also shows that adding an appropriate amount of synthetic data into training dataset can indeed improve the performance of WiFi sensing in most cases.

Prior works on near-field beam training mostly assume dedicated polar-domain codebooks and on-grid range estimation, however, this may incur large training overhead and deteriorated estimation accuracy. In this paper, we propose a new and efficient beam training scheme with off-grid range esti-mation based on conventional discrete Fourier transform (DFT) codebook, which greatly reduces the beam training overhead. In particular, we first analyze the received beam pattern at the user when far-field beamforming vectors are used for beam scanning, and reveal an interesting result that this beam pattern contains useful user angle and range information. Then, an efficient scheme was proposed to jointly estimate the user angle and range using DFT codebook. This scheme estimates the user angle based on a defined angular support and resolves the user range by leveraging an approximated angular support width. Finally, numerical simulations show that our proposed scheme significantly reduces the near-field beam training overhead and improves the range estimation accuracy compared with various benchmark schemes.

As the proliferation of sophisticated task models in 5G empowered digital twin, it yields significant demands on fast and accurate model training over resource-limited wireless networks. It is vital to investigate how to accelerate the training process based on the salient features of practical systems, including heterogeneous data distributions and system resources both across devices and over time. To study the non-trivial coupling between participating device selection and their appropriate training parameters, we first characterize the dependency of convergence performance bound on system parameters, i.e., statistical structure of local data, mini-batch size and gradient quantization level. Based on the theoretical analysis, a training efficiency optimization problem is formulated subject to heterogeneous communication and computation capabilities among devices. To realize online control of training parameters, we propose an adaptive batch-size assisted device scheduling strategy, which prioritizes the selection of devices that offer good data utility and dynamically adjust their mini-batch sizes and gradient quantization levels adapting to network conditions. Simulation results demonstrate our proposed strategy can effectively speed up the training process as compared with benchmark algorithms.

Semantic communication shows great promise in reducing network traffic and alleviating spectrum shortage. While many semantic theories have been put forward, how to measure the importance of semantic information theoretically remains an open issue. In this paper, we propose semantic value, a metric that measures the importance of semantic information, for text transmission. First, we model a semantic communication system for text transmission, in which semantic information is represented by semantic triplets. Then, we propose a hybrid communication mechanism to ensure the success of text transmission. Finally, we compare the performances of the conventional mode and the semantic mode in terms of latency and derive conditions leading to minimum latency.

The performance of multiple-input multiple-output (MIMO) integrated sensing and communication systems (ISAC) can be evaluated from the perspectives of information theory and estimation theory to provide more fundamental insights. In this paper, we study the relationship between mutual information (MI) and minimum mean square error (MMSE) by characterizing the Pareto boundary for a general ISAC scenario, a dual-functional BS simultaneously estimates the target response matrix while communicating with a user. First, optimization problems are formulated to achieve MI Pareto boundary and MMSE Pareto boundary, respectively. Then, we show that under the same maximum transmit power constraint and set of transmit filters, MI Pareto bounary can be transformed to MMSE Pareto boundary with optimized MSE-weights in ISAC with colored Gaussian noise. Subsequently, based on unified MI and MMSE performance, we propose Data-dependent alternate algorithm (DDA) to obtain the MI Pareto boundary with colored Gaussian noise. In order to reduce complexity, we propose Data-independent alternate algorithm (DIA) when noise degenerates into white Gaussian noise. Finally, simulation results show DDA almost achieves the MI Pareto boundary with colored Gaussian noise and DIA achieves almost the same performance as DDA with white Gaussian noise at a lower cost to implement.

In this paper, a cloud radio access network (Cloud-RAN) based collaborative edge AI inference architecture is proposed. Specifically, geographically distributed devices capture real-time noise-corrupted sensory data samples and extract the noisy local feature vectors, which are then aggregated at each remote radio head (RRH) to suppress sensing noise. To realize efficient uplink feature aggregation, we allow each RRH receives local feature vectors from all devices over the same resource blocks simultaneously by leveraging an over-the-air computation (AirComp) technique. Thereafter, these aggregated feature vectors are quantized and transmitted to a central processor (CP) for further aggregation and downstream inference tasks. Our aim in this work is to maximize the inference accuracy via a surrogate accuracy metric called discriminant gain, which measures the discernibility of different classes in the feature space. The key challenges lie on simultaneously suppressing the coupled sensing noise, AirComp distortion caused by hostile wireless channels, and the quantization error resulting from the limited capacity of fronthaul links. To address these challenges, this work proposes a joint transmit precoding, receive beamforming, and quantization error control scheme to enhance the inference accuracy. Extensive numerical experiments demonstrate the effectiveness and superiority of our proposed optimization algorithm compared to various baselines.

To facilitate the data collection process, simultaneous wireless information and power transfer utilizes the same signal for powering the devices and delivering the information, while the integrated sensing and communication utilizes the same signal for data transmission and radar sensing. In next generation networks, the sensing, communication, and power transfer functionalities are expected to be integrated together to enhance the radio resource efficiency and enable the data collection by massive low-power devices, which leads to the new research direction namely integrating sensing, communication, and power transfer (ISCPT). The ISCPT beamforming design for multiple users is investigated in this paper to improve the sensing performance while guaranteeing the communication and power transfer requirements. The resultant non-convex optimization problem is solved by the approach based on semidefinite relaxation and rank reduction methods. Simulations are further conducted to verify the effectiveness of the proposed design.

Integrated sensing and communication (ISAC) has the advantages of efficient spectrum utilization and low hardware cost. It is promising to be implemented in the fifth-generation-advanced (5G-A) and sixth-generation (6G) mobile communication systems, having the potential to be applied in intelligent applications requiring both communication and high-accurate sensing capabilities. As the fundamental technology of ISAC, ISAC signal directly impacts the performance of sensing and communication. This article systematically reviews the literature on ISAC signals from the perspective of mobile communication systems, including ISAC signal design, ISAC signal processing, and ISAC signal optimization. We first review the ISAC signal design based on 5G, 5G-A, and 6G mobile communication systems. Then, radar signal processing methods are reviewed for ISAC signals, mainly including the channel information matrix method, spectrum lines estimator method, and super-resolution method. In terms of signal optimization, we summarize peak-to-average power ratio (PAPR) optimization, interference management, and adaptive signal optimization for ISAC signals. This article may provide the guidelines for the research of ISAC signals in 5G-A and 6G mobile communication systems.

Achieving seamless global coverage is one of the ultimate goals of space-air-ground integrated network, as a part of which High Altitude Platform (HAP) network can provide wide-area coverage. However, deploying a large number of HAPs will lead to severe congestion of existing frequency bands. Spectrum sharing improves spectrum utilization. The coverage performance improvement and interference caused by spectrum sharing need to be investigated. To this end, this paper analyzes the performance of spectrum sharing between HAP network and terrestrial network. We firstly generalize the Poisson Point Process (PPP) to curves, surfaces and manifolds to model the distribution of terrestrial Base Stations (BSs) and HAPs. Then, the closed-form expressions for coverage probability of HAP network and terrestrial network are derived based on differential geometry and stochastic geometry. We verify the accuracy of closed-form expressions by Monte Carlo simulation. The results show that HAP network has less interference to terrestrial network. Low height and suitable deployment density can improve the coverage probability and transmission capacity of HAP network.

In the upcoming next-generation (5G-Advanced and 6G) wireless networks, sensing as a service will play a more important role than ever before. Recently, the concept of perceptive network is proposed as a paradigm shift that provides sensing and communication (S&C) services simultaneously. This type of technology is typically referred to as Integrated Sensing and Communications (ISAC). In this paper, we propose the concept of sensing quality of service (QoS) in terms of diverse applications. Specifically, the probability of detection, the Crámer-Rao bound (CRB) for parameter estimation and the posterior CRB for moving target indication are employed to measure the sensing QoS for detection, localization, and tracking, respectively. Then, we establish a unified framework for ISAC resource allocation, where the fairness and the comprehensiveness optimization criteria are considered for the aforementioned sensing services. The proposed schemes can flexibly allocate the limited power and bandwidth resources according to both S&C QoSs. Finally, we study the performance trade-off between S&C services in different resource allocation schemes by numerical simulations.

The unprecedented growth of mobile data traffic has fueled the deployment of artificial intelligence (AI) at the network edge, while distilling the intelligence from raw data by machine learning requires tremendous labelling effort. To overcome this challenge, wireless crowd labelling (WCL) is proposed for efficient data labelling by exploiting billions of available mobile annotators and the multicasting property of wireless channels. A WCL system is considered in this paper where unlabelled data (objects) are multicast via fading channels to different clusters of annotators for repetition labelling to improve the accuracy. Given the desired labelling accuracy, the superposition coding technique together with the repetition labelling scheme give rise to a new tradeoff between radio-and-annotator resource consumption. Building on such tradeoff, the annotator clustering and transmit power control are jointly optimized to maximize the labelling throughput (i.e., the number of labelled objects) or minimize the power consumption, resulting in NP-hard integer programming problems. To solve these problems, the optimal structure of annotator clustering is derived by exploiting the property that the power allocation for multicasting objects tends to compensate for the worst channel among the annotators in each cluster. Based on such structure, the throughput maximization problem can be recognized as a longest-path problem and solved by means of branch-and-bound, while the power minimization problem can be recasted to a shortest-path problem and solved by means of forward dynamic programming. The solution approaches can be further simplified when the channels are symmetric by merging the same nodes and cutting the identical paths in the path graph. In addition, exact polices are derived for the special cases where either the annotators or power are constrained. Last, simulation results are presented to demonstrate the performance of our proposed joint designs.

Spatial beamforming is an efficient way to realize dual-functional radar-communication (DFRC) for integrated sensing and communications towards 6G network. In this paper, we study the DFRC design for a general scenario, where the dual-functional base station simultaneously detects the target as a MIMO radar while communicating with multiple multi-antenna communication users (CUs). This necessitates a joint transceiver beamforming design for both MIMO radar and multi-user MIMO communication. In order to avoid iterative optimization with high complexity, two low-complexity beamforming designs based on CU-selection and zero-forcing are proposed, where the closed-form expressions of the low-complexity beamforming designs are derived. Simulation results are provided to verify the effectiveness of the proposed low-complexity designs.

To achieve 360ºcoverage, we investigate a simulta-neous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS) aided downlink non-orthogonal multiple access (NOMA) network with randomly deployed users. For different scenarios, we first derive two STAR- RIS-aided channel models, namely the central limit model and the curve fitting model. More specifically, the central limit model fits the scenarios with numerous RIS elements while the curve fitting model can be extended to multi-cell scenarios. The analytical results reveal that 1) the central limit model has closed-form expressions calculated as the error functions, and 2) the curve fitting model can be closely modeled as a Gamma distribution. We then derive the closed-form outage probability expressions for the NOMA users. Numerical results indicate that 1) the two channel models match the simulation results well in low signal-to-noise-ratio (SNR) regions and perform as boundaries in high SNR regions, 2) the central limit model performs as an upper bound of the simulation results, while a lower bound can be obtained by the curve fitting model, and 3) the both users in the NOMA pair have no error floor.

A novel reconfigurable intelligent surfaces (RISs)-based transmission framework is proposed for downlink non-orthogonal multiple access (NOMA) networks. We propose a quality-of-service (QoS)-based clustering scheme to improve the resource efficiency and formulate a sum rate maximization problem by jointly optimizing the phase shift of the RIS and the power allocation at the base station (BS). A model-agnostic meta-learning (MAML)-based learning algorithm is proposed to solve the joint optimization problem with a fast convergence rate and low model complexity. Extensive simulation results demonstrate that the proposed QoS-based NOMA network achieves significantly higher transmission throughput compared to the conventional orthogonal multiple access (OMA) network. It can also be observed that substantial throughput gain can be achieved by integrating RISs in NOMA and OMA networks. Moreover, simulation results of the proposed QoS-based clustering method demonstrate observable throughput gain against the conventional channel condition-based schemes.

Driven by the vision of intelligent connection of everything and digital twin towards 6G, a myriad of new applications, such as immersive extended reality, autonomous driving, holographic communications, intelligent industrial internet, will emerge in the near future, holding the promise to revolutionize the way we live and work. These trends inspire a novel technical design principle that seamlessly integrates two originally decoupled functionalities, i.e., wireless communication and sensing, into one system in a symbiotic way, which is dubbed symbiotic sensing and communications (SSaC), to endow the wireless network with the capability to “see” and “talk” to the physical world simultaneously. Noting that the term SSaC is used instead of ISAC (integrated sensing and communications) because the word “symbiotic/symbiosis” is more inclusive and can better accommodate different integration levels and evolution stages of sensing and communications. Aligned with this understanding, this article makes the first attempts to clarify the concept of SSaC, illustrate its vision, envision the three-stage evolution roadmap, namely neutralism, commensalism, and mutualism of SaC. Then, three categories of applications of SSaC are introduced, followed by detailed description of typical use cases in each category. Finally, we summarize the major performance metrics and key enabling technologies for SSaC.

Next-generation wireless networks will provide users ubiquitous low-latency computing services using devices at the network edge, called mobile edge computing (MEC). The key operation of MEC is to offload computation intensive tasks from users. Since each edge device comprises an access point (AP) and a computer server (CS), a MEC network can be decomposed as a radio access network (RAN) cascaded with a CS network (CSN). Based on the architecture, we investigate network constrained latency performance, namely communication latency (comm- latency) and computation latency (comp-latency) under the constraints of RAN coverage and CSN stability. To this end, a spatial random network is constructed featuring random node distribution, parallel computing, non-orthogonal multiple access, and random computation-task generation. Based on the model and the network constraints, we derive the scaling laws of comm-latency and comp-latency with respect to network-load parameters and network-resource parameters. Essentially, the analysis involves the interplay of stochastic geometry, queueing, and parallel computing. Combining the derived scaling laws quantifies the tradeoffs between the latency, network coverage and network stability.

This paper presents a technology of sensing hidden vehicles by exploiting multi-path vehicle-to-vehicle (V2V) communication. This overcomes the limitation of existing RADAR technologies that requires line-of-sight (LoS), thereby enabling more intelligent manoeuvre in autonomous driving and improving its safety. The proposed technology relies on transmission of orthogonal waveforms over different antennas at the target (hidden) vehicle. Even without LoS, the resultant received signal enables the sensing vehicle to detect the position, shape, and driving direction of the hidden vehicle by jointly analyzing the geometry (AoA/AoD/propagation distance) of individual propagation path. The accuracy of the proposed technique is validated by realistic simulation including both highway and rural scenarios.

Millimeter-wave (mm-wave) communication opens up tens of giga-hertz spectrum in the mm-wave band for use by next-generation wireless systems, thereby solving the problem of spectrum scarcity. Maintaining connectivity stands out as a key design challenge for mm-wave networks deployed in urban regions due to the blockage effect characterizing mm-wave propagation. In this paper, we set out to investigate the blockage effect on the connectivity of mm-wave networks in a Manhattan-type urban region modeled using a random regular lattice, while base stations (BSs) are Poisson distributed in the plane. In particular, we analyze the connectivity probability that a typical user is within the transmission range of a BS and connected by a line-of-sight. First, we consider a single-tier network. By jointly applying the random lattice and stochastic geometry theories, a lower bound on the connectivity probability is derived as a function of building parameters (e.g., size and site occupancy probability) and BS parameters (e.g., transmission range and BS density). For the case of dense buildings, the bound is derived in a simpler form. Next, the preceding lower bounds are tightened based on the geometric technique of partitioning the irregular blockage-free region around the typical user. Moreover, the analysis is generalized to mm-wave channels with both LoS and NLoS paths. Finally, the results are extended to a K-tier heterogeneous network (HetNet), where building heights are random, and depending on its height, a building can block the signals transmitted by a subset of BS tiers but not all. The analysis shows that the connectivity probability of the K-tier HetNet increases linearly with the number of tiers. In general, our work quantifies the relation between the coverage of an mmwave network and the parameters of building and BS processes, providing useful guidelines for deploying practical networks in a Manhattan-type region.

Next-generation wireless networks will provide users ubiquitous low-latency computing services using devices at the network edge, called mobile edge computing (MEC). The key operation of MEC is to offload computation intensive tasks from users. Since each edge device comprises an access point (AP) and a computer server (CS), an MEC network can be decomposed as a radio access network cascaded with a CS network. Based on the architecture, we investigate network-constrained latency performance, namely communication latency and computation latency, under the constraints of radio-access connectivity and CS stability. To this end, a spatial random network is modeled featuring random node distribution, parallel computing, non-orthogonal multiple access, and random computation-task generation. Given the model and the said network constraints, we derive the scaling laws of communication latency and computation latency with respect to network-load parameters (density of mobiles and their task-generation rates) and network-resource parameters (bandwidth, density of APs/CSs, and CS computation rate). Essentially, the analysis involves the interplay of the theories of stochastic geometry, queueing, and parallel computing. Combining the derived scaling laws quantifies the tradeoffs between the latencies, network connectivity, and network stability. The results provide useful guidelines for MEC-network provisioning and planning by avoiding either of the cascaded radio access network or CS network being a performance bottleneck.

The future Internet-of-things (IoT) is motivating the development of our daily services as well as revolutionizing the way we interplay with our life. The ubiquitous visible light communication (VLC) technique has been seamlessly combined into the energy-efficient backscatter communication system, called the visible-light backscatter communication (VL-BackCom), for powering the massive number of IoT devices and prolonging their working-time. In the VL-BackCom system, the tag can modulate and backscatter the visible light signal (by switching the liquid crystal display (LCD) shutter) illuminated from light source to its nearby receiver. However, few work focuses on modeling and analyzing the performance of the large-scale VL-BackCom network. To this end, this paper makes the first attempt to model and investigate the network performance, namely the success VL-BackCom probability and network capacity, by borrowing the analytical tractable tool from stochastic geometry. The network topology is modeled using the analytical tractable generalized Gauss-Poisson process (GPP) under some justifiable assumptions, yielding a lower bound for practical VL-BackCom network with irregular deployment. The expressions of the success VL-BackCom probability and network capacity are clearly derived to characterize the VL-BackCom link's reliability as well as the spatial success transmission density, respectively. Moreover, the effects of backscatter parameters, say duty cycle and reflection coefficient, on network performance are also studied.

The millimeter-wave (mmWave) communication exploits tens-of-GHz of available spectrum in the mmWave band for solving the problem of spectrum scarcity in next-generation wireless networks. Maintaining connectivity in urban mmWave networks is one key design challenge because of the blockage effect characterizing mmWave propagation. Specifically, mmWave signals can be blocked by buildings and other large urban objects. Thus, the type of urban model affects the performance of mmWave networks. In this paper, we make the first attempt to study the connectivity of mmWave networks in a Manhattan-type region modeled using a random lattice while base stations (BSs) are Poisson distributed in the plane. In particular, we define and analyze the connectivity probability that a typical user is within the transmission range of a BS and connected by a line-of-sight. By jointly applying random-lattice and stochastic-geometry theories, different lower bounds on the connectivity probability are derived as functions of the buildings' size and distribution as well as the BSs' transmission range and density. We also investigate the asymptotic connectivity probability for the case of dense buildings. Our study yields closed-form relations between the parameters of the building process and the BS process, providing useful guidelines for practical mmWave network deployment and opening up many directions for future extensions.

Future Internet-of-Things (IoT) will connect billions of small computing devices embedded in the environment and support their device-to-device (D2D) communication. Powering the massive number of embedded devices is a key challenge of designing IoT, since batteries increase the devices' form factors and battery recharging/replacement is difficult. To tackle this challenge, we propose a novel network architecture that enables D2D communication between passive nodes by integrating wireless power transfer and backscatter communication, which is called a wirelessly powered backscatter communication (WP-BackCom) network. In this network, standalone power beacons (PBs) are deployed for wirelessly powering nodes by beaming unmodulated carrier signals to targeted nodes. Provisioned with a backscatter antenna, a node transmits data to an intended receiver by modulating and reflecting a fraction of a carrier signal. Such transmission by backscatter consumes orders-of-magnitude less power than a traditional radio. Thereby, the dense deployment of low-complexity PBs with high transmission power can power a large-scale IoT. In this paper, a WP-BackCom network is modeled as a random Poisson cluster process in the horizontal plane where PBs are Poisson distributed and active ad hoc pairs of backscatter communication nodes with fixed separation distances form random clusters centered at PBs. The backscatter nodes can harvest energy from and backscatter carrier signals transmitted by PBs. Furthermore, the transmission power of each node depends on the distance from the associated PB. Applying stochastic geometry, the network coverage probability and transmission capacity are derived and optimized as functions of backscatter parameters, including backscatter duty cycle, reflection coefficient, and the PB density. The effects of the parameters on network performance are quantified.

Future Internet-of-Things (IoT) will connect billions of small computing devices embedded in the environment and support their device-to-device (D2D) communication. Powering this massive number of embedded devices is a key challenge of designing IoT since batteries increase the devices' form factors and their recharging/replacement is difficult. To tackle this challenge, we propose a novel network architecture that integrates wireless power transfer and backscatter communication, called wirelessly powered backscatter communication (WP-BC) networks. In this architecture, power beacons (PBs) are deployed for wirelessly powering devices; their ad-hoc communication relies on backscattering and modulating incident continuous waves from PBs, which consumes orders-of-magnitude less power than traditional radios. Thereby, the dense deployment of lowcomplexity PBs with high transmission power can power a largescale IoT. In this paper, a WP-BC network is modeled as a random Poisson cluster process in the horizontal plane where PBs are Poisson distributed and active ad-hoc pairs of backscatter communication nodes with fixed separation distances form random clusters centered at PBs. Furthermore, by harvesting energy from and backscattering radio frequency (RF) waves transmitted by PBs, the transmission power of each node depends on the distance from the associated PB. Applying stochastic geometry, the network coverage probability and transmission capacity are derived and optimized as functions of the backscatter reflection coefficient and duty cycle as well as the PB density. The effects of the parameters on network performance are characterized.

In the heterogeneous networks (HetNets) powered by hybrid energy sources, it is imperative to reduce the total on-grid energy consumption as well as minimize the peak-to-average on-grid energy consumption ratio, since the large peak-to-average on-grid energy consumption ratio will translate into the high operational expenditure (OPEX) for mobile network operators. In this paper, we propose a joint user association and green energy allocation algorithm which aims to lexicographically minimize the on-grid energy consumption in HetNets, where all the base stations (BSs) are assumed to be powered by both the power grid and renewable energy sources. The optimization problem involves both the user association optimization in space dimension, and the green energy allocation in time dimension. The independence nature of this two-dimensional optimization allows us to decompose the problem into two sub-problems. We first formulate the user association optimization in space dimension as a convex optimization problem to minimize total energy consumption via balancing the traffic across different BSs in a certain time slot. We then optimize the green energy allocation across different time slots for an individual BS to lexicographically minimize the on-grid energy consumption. Simulation results indicate the proposed algorithm achieves significant on-grid energy saving, and substantially reduces peak-to-average on-grid energy consumption ratio.