The rise of 6G networks brings ultra-fast communication and broad connectivity, enabling intelligent applications such as IoT, smart cities, and autonomous vehicles. However, training AI models in these environments often faces privacy concerns and limited labeled data. Federated Learning (FL) provides a privacy-preserving solution by allowing clients to collaboratively train models without sharing raw data. Yet, FL still struggles with the scarcity of labeled data due to privacy constraints. Semi-Supervised Learning (SSL) can alleviate this issue by leveraging both labeled and unlabeled data. While combining SSL and FL (FSSL) offers promise, it also introduces new challenges, such as confirmation bias and degraded performance under non-IID data distributions. Most existing FSSL methods assume uniform labeling capabilities across clients, which is rarely the case in practice. This leads to a new challenge only occur in FSSL called annotation heterogeneity, where some clients have many labeled samples while others have few or none. When combined with data heterogeneity, this dual-data heterogeneity (data and annotation heterogeneity) severely affects global model performance. Therefore, FSSL must learn under label scarcity while preserving privacy, remain stable when non-IID data is compounded by annotation heterogeneity, and stay communication-efficient for mobile deployment. In this work, we propose Federated Fisher Focus Filtering (FedF3), an enhanced framework built upon our previous SynFMPL method, to address the combination effect of dual-data heterogeneity. FedF3 introduces a two-stage strategy, Adaptive Loss Filtering stabilizes early training by suppressing unreliable contributions under dual heterogeneity, and Fisher Focus Selection preserves accuracy with Fisher-guided sparsity to meet communication budgets. Our method demonstrates robust performance improvements across diverse and heterogeneous FL settings, mitigating the negative effects of dual-data heterogeneity while preserving personalization and privacy.

With the rapid advancement of intelligent IoT services, the need for high-quality and efficient wireless communication has become increasingly critical for information exchanges and collaborations among intelligent edge devices such as autonomous mobile robots (AMRs), robots in smart factories, and surveillance cameras, etc., Federated Learning (FL) is a promising distributed learning framework by exploring the computation capability on edge devices while protecting data privacy of users. On the other hand, Over-the-Air (OTA) computation techniques is a new paradigm of integrated computation and communication. OTA based FL (OTA-FL) can enhance spectrum efficiency by leveraging the superposition properties of wireless channels for model aggregations. The implementation of OTA-FL on edge devices can further protect user data privacy for clients, improve the throughput of inter-device communications, and enhance the distributed learning performance. However, despite these advantages, the convergence of OTA-FL systems remain vulnerable to model poisoning attacks, presenting significant challenges. In this study, we address these vulnerabilities by proposing a two-phase detection mechanism that secures OTA-FL system while preserving high spectral efficiency. Our simulation results demonstrate that under varying conditions, our approach can make distributed learning more secure and communication-efficient.

The over-the-air (OTA) computation which utilizes the waveform-superposition property of wireless signals has been considered as a promising approach to simultaneously accomplish communication and computing tasks in multiple access channels. Equipping federated learning (FL) with the OTA computation allows distributed Artificial Intelligence of Things (AIoT) devices to collaboratively train machine learning models over wireless environment, while preserving data privacy without excessive bandwidth consumption. In fact, the appearance of jamming attacks in OTA-FL systems can severely disrupt the convergence. This paper studies the problem of jamming attacks and its countermeasures in over-the-air federated learning (OTA-FL). To this end, we propose the coordinated dropout strategy (CoDrop), which enables AIoT devices to collaboratively drop out (i.e., to refrain from transmitting) part of their gradients so that the jamming signals aggregated in received signals can be accurately measured and mitigated. Our simulation results reveal that CoDrop is effective in alleviating the negative impacts of jamming signals with significantly low dropout rates, and it is shown to converge well as compared to existing solutions under various parameter settings.

The Internet of Vehicles (IoV) enables vehicles to communicate with each other and infrastructure through basic safety messages (BSMs), thereby reducing accidents by means of early warnings and collision avoidance. In fact, IoV networks are vulnerable to positional attacks induced by malicious vehicles, which can severely compromise traffic safety and system reliability. In spite of the existing works devoted to misbehavior detection for certain types of attacks, extracting statistical features from BSMs to defend against all positional attacks in IoV networks has been largely overlooked. In this paper, we extract six types of positional attacks from the VeReMi-Extension dataset and discover hidden attack patterns. Based on these discovered patterns and our data analysis, we categorize them into the statistical features of Gaussian-like, Lévy-like sparse, and Lévy-like ordinary positional attacks. Then, we propose a three-feature misbehavior detection mechanism to distinguish between normal and malicious vehicles, where the first feature detection employs the Jarque-Bera test to identify Gaussian-like positional attacks, the second feature detection identifies Lévy-like sparse positional attacks by evaluating data sparsity, and the third feature detection uses Isolation Forest to detect Lévy-like ordinary positional attacks. Simulation results show that the proposed solution achieves promising false alarm and miss detection rates, and acts robustly across diverse parameter settings.

The over-the-air (OTA) computation which utilizes the waveform-superposition property of wireless signals has been considered as a promising approach to simultaneously accomplish communication and computing tasks in multiple access channels. Equipping federated learning (FL) with the OTA computation allows distributed Artificial Intelligence of Things (AIoT) devices to collaboratively train machine learning models over wireless environments, while preserving data privacy without excessive bandwidth consumption. However, the appearance of wardens in OTA-FL can result in model eavesdropping and the risk of information leakage. In this paper, we investigate how OTAFL can be fulfilled among AIoT devices while ensuring the covertness (i.e., the prevention of eavesdropping) against wardens. To this end, we formulate an optimization problem to maximize achievable data rates subject to covertness and transmit power constraints. Then, we first derive optimal detection thresholds to simplify the covertness constraints, and then propose the covertness-aware transmit power control (CAMEO) algorithm, which iteratively explores transmit powers that yield high target signal-to-interference-plus-noise ratios while satisfying both the covertness and transmit power constraints. Simulation results demonstrate that the CAMEO algorithm outperforms comparison schemes in terms of test accuracy, and it is also shown to converge well under various parameter settings.

Orthogonal delay-Doppler division multiplexing (ODDM) has been proposed as a novel modulation scheme for high-mobility environments. Since it is a multi-carrier modulation scheme, ODDM may be subject to concerns regarding high peak-to-average power ratio, which is a practically important issue but remains to be thoroughly studied. In this paper, we propose a method for estimating the instantaneous power distribution of ODDM using its wideband filtered OFDM-based approximate implementation. Specifically, the output of the inverse discrete Fourier transform in this implementation is treated as the signal alphabet of a single-carrier modulation scheme. Analytical results show good agreement with simulations, validating the effectiveness of the proposed approach.

Federated learning (FL) has emerged as a promising technique to realize distributed machine learning (ML) in practice. FL enables multiple clients to collaboratively train a common ML model without the need to collect raw data from clients, which therefore has merit in the protection of data privacy. On the other hand, it is known that quantum computing excels in solving specific problems that are computationally prohibitive on classical computers due to its ability to harness quantum superposition and entanglement. However, current noisy intermediate-scale quantum (NISQ) computers have difficulties in dealing with many-qubit computation due to the lack of reliable error-correction schemes, which renders hybrid quantum-classical computing with few qubits a promising alternative. In this paper, we investigate how hybrid quantum-classical computing can be applied to FL while considering the data heterogeneity among clients. To this end, we propose the two-qubit quantum circuit-embedded convolutional neural network (2QCNN) for each client, which incorporates parallel two-qubit variational quantum circuits (VQCs) between the fully connected layers of the CNN. Simulation results show that 2QCNN outperforms the comparison schemes in terms of test accuracies. In addition, the impacts of the number and depth of parallel two-qubit VQCs on the performance of 2QCNN under various degrees of data heterogeneity are further evaluated.

Federated learning (FL) is an emerging distributed machine learning (ML) paradigm in the Artificial Intelligence of Things (AIoT). FL enables AIoT devices to collaboratively train an ML model on the network edge, while protecting data privacy and solving the problem of isolated data islands. Contemporary FL is typically realized through the model aggregation of locally trained models and the model dissemination of a globally averaged model; such a procedure iteratively proceeds until a predefined convergence criterion is met. However, FL necessitates frequent information exchanges between AIoT devices and a parameter server, which inevitably induces tremendous communication costs. Therefore, this article proposes a new design for efficient one-shot FL for AIoT systems, so that the model aggregation and dissemination can be completed within a single communication round. To this end, we leverage optimal transport (OT) theory to design the coupled model averaging (CODE) algorithm to fuse the model weights of the neural networks (NNs) on AIoT devices. The CODE algorithm initially performs OT-based layer-by-layer model averaging (MA) over two NNs to form a fused NN, which will then be averaged with another NN. The CODE algorithm progressively determines a pair of NNs, and continues until all NNs have been examined to achieve one-shot FL. In addition, we provide a detailed convergence analysis for the proposed solution. Our simulation results show that the proposed solution outperforms other one-shot MA mechanisms under various parameter settings.

Federated learning (FL) has received explosive research attention in that it enables multiple clients to collaboratively train a global model without sharing raw data in between, thereby facilitating the protection of data privacy. Typically, FL can converge well after a couple of communication rounds, but its convergence is vulnerable to the model poisoning attacks induced by fake clients. Existing works have been devoted to designing various post-processing techniques to alleviate the adverse effects of the model poisoning attacks, they, however, fail to accurately trim off the local models of fake clients while keeping those of benign clients intact during model averaging. In this paper, we investigate the problem of model poisoning attacks in federated learning (FL). To cope with this problem, we design the federated adaptive trimmed mean (FedATM) algorithm, where the clients are sorted in accordance with the distances between local models, and a distance-based threshold is designed to detect the presence of fake clients, thereby preventing the fake local models from destroying the accuracy of model averaging. Simulation results show that the proposed FedATM algorithm is robust to model poisoning attacks as compared to several comparison schemes under various data heterogeneities.

Federated learning (FL) enables edge devices (EDs) to collaboratively train a single machine learning (ML) model maintained by an edge server (ES) without sharing their raw data contents, thereby facilitating distributed ML while protecting data privacy. In fact, FL can be implemented in wireless environments by means of the over-the-air (OTA) computation, which takes advantage of the waveform-superposition property of wireless signals to receive local gradients from EDs without the needs of increased bandwidth. Despite the existing works devoted to power control and client selection in OTA-FL systems, they mostly neglect how to construct cohorts (i.e., a subset of EDs that have similar channel coefficients) to elevate the quality of the aggregated local gradients. In this paper, we investigate the problem of gradient aggregation and recovery in OTA-FL systems. To cope with this problem, we propose the cohort-based power scaling and gradient recovery (CRAIC) algorithm, where we first construct cohorts based on uplink channel coefficients, and then we adjust the transmit powers of EDs and recover the aggregated local gradients in a cohort basis. Simulation results show that our proposed solution outperforms several comparison schemes, and we further evaluate how it performs under various parameter settings.

Federated learning (FL) has been widely regarded as a promising distributed machine learning technology that utilizes on-device computation while protecting clients' data privacy. To adapt FL to wireless networks, the over-the-air (OTA) computation, which employs the superposition nature of wireless waveforms, can prevent excessive consumption of the communication resources. However, energy harvesting technology can overcome the energy limitation of clients to realize durable computation. Despite the existing works devoted to OTA FL from various aspects, they mostly neglect jointly performing client selection and energy management for energy harvesting devices. In this paper, we investigate the combined problem of client selection and energy management for OTA FL and formulate it as a nonlinear integer programming (NIP) problem to minimize the optimality gap. To solve the NIP problem, we propose a client selection scheme that jointly considers channel state information, residual battery capacities, and dataset size. Our simulation results show that the proposed solution outperforms other comparison schemes within various parameter settings.

The performance of an orthogonal frequency division multiplexing (OFDM) system can be largely restricted without taking precautions against phase noise (PN) in receivers. In this paper, we focus on a time-domain PN estimation and compensation problem in OFDM systems. Despite the existing works devoted to the estimation of PN with the aid of pilot signals, they mostly select a set of orthogonal basis to assemble PN, which may lead to inaccurate estimation and compensation of PN. To alleviate the adverse impacts of PN in OFDM systems, we propose a data-driven approach that leverages historical PN samples to extract a set of non-orthogonal frequency-oriented (NOFO) basis for PN mitigation. To show the practicability of the proposed data-driven approach, we employ a 5G new radio (NR) like simulation setting and our numerical results show that the proposed approach can mitigate PN effectively, thereby attaining improved bit error rate (BER) performance.

The proliferation of the Internet of Things (IoT) applications has resulted in the ever-increasing research attention in recent years. In light of the sensitivity to latency in various IoT applications, the age of information (AoI) has been widely regarded as a promising performance metric to quantify the timeliness (i.e., freshness or age) of information updates from IoT devices. In addition, serverless computing (also known as function as a service (FaaS)) evolves as a highly scalable and flexible computing architecture that can facilitate timely IoT analytics. In fact, the execution of serverless functions may rely on the data collected from IoT devices, therefore the freshness of information updates of IoT devices has prompt impacts on the age of service (AoS) of serverless functions. Although existing works have been devoted to various aspects of serverless computing, the issues regarding how information updates affect the AoS of serverless functions are rarely investigated. In this paper, we address the information update delivery and acquisition (IUDA) problem for IoT with serverless computing and formulate it an integer linear program (ILP), the objective of which is to minimize a weighted sum of AoS of serverless functions. To cope with the IUDA problem, we propose an offline and an online algorithm for scheduling information updates with and without the knowledge of the arrivals of serverless functions, respectively. Our simulation results demonstrate that the proposed solutions outperform existing solutions in terms of the AoS performance and effectively provide serverless functions with timely information updates under various parameter settings.

The proliferation of Internet of Things (IoT) applications has prompted the continuous increase of research efforts in recent years. In light of the diversified use cases and service requirements, the information freshness [or Age of Information (AoI)] of IoT data is key for latency-sensitive IoT applications (e.g., industrial automation and intelligent transportation) because stale information may lead to delayed responses and catastrophic outcomes. In addition, various types of IoT applications require the analytics of IoT data collected from their constituent IoT devices. While previous AoI-related works have analyzed or optimized the information freshness of various communication systems, the problem that theinformation cofreshness[or Coage of Information (CoI)] of an IoT application is determined by the maximum AoI of the constituent IoT devices has been rarely investigated. In this article, we address the grant assignment and transmission scheduling (GATS) problem for IoT and formulate it as an integer linear program (ILP) to minimize a weighted sum of CoI. Due to the intractability of the original GATS problem, we transform it to an equivalent problem of the maximization of the number of the eliminated age blocks. Then, we propose the CoI-aware age block elimination (CABEL) algorithm in which information updates are selected progressively according to their coage efficiency (CE) values and prove that the achieved approximation factor depends on the relative service costs and uplink delays. Our simulation results demonstrate that the proposed solution can effectively perform information updates and utilize service budgets, thereby achieving low CoI compared with the existing solutions under various parameter settings.

Vehicle-based visual crowdsourcing is an emerging paradigm where the visual data collected from dash cameras are analyzed with the aim of measuring phenomena of common interest. To ensure the efficiency in vehicle-based visual crowdsourcing, there remain at least two technical challenges. First, to maximize the Quality of Information (QoI), which measures the amount of information extracted from the collected data, the context of data collection (e.g., camera position and orientation) must be taken into account in the process of task allocation. Second, intensive data collection from dense measurement points is key to ensure timely and accurate sensing of the targets of interest, whereas there exists a trade-off between the amount and rate of data collection and the computing and communication resources required to fulfill the latency constraint. To solve these challenges, we propose gathering and processing the collected data at the edge of the network and design a context-aware task allocation scheme, called FlexSensing, to jointly optimize the QoI and processing latency. We target application scenarios where commercial vehicles are turned into vehicular fog nodes (VFNs). These nodes gather and process the visual data collected from other vehicles within their coverage areas. The key idea of FlexSensing is to determine the rate of data collection for each sensing vehicle in the targeted area and to assign processing tasks to VFNs based on the estimated QoI and the workload of the VFNs. Given the excessive computational complexity of task allocation in this context, we formulate task allocation as a Markov decision process and apply a deep Q-network (DQN) to learn the optimized task allocation strategies for increasing the QoI of collected data while reducing the processing latency. To evaluate the effectiveness of FlexSensing, we simulate the mobility of different vehicles involved in the scenario at different times of the day based on real-world traffic data collected from the city of Helsinki and select a real-time object detection application for a case study. As compared with the existing task allocation strategies, the DQN-based task allocation strategies reduce the average processing latency by up to 51% and increase the QoI of the collected data by up to 34%.

Energy saving in cellular systems has attracted the extensive attention of various studies due to ever-deteriorating global warming. In addition to network greenness, various emerging mobile applications (e.g., mixed reality and automated vehicles) further necessitate timely service provision. Recently, the age of information (AoI) has been regarded as a promising performance metric for quantifying the freshness (i.e., timeliness) of information updates in communication systems. Despite the existing works devoted to energy saving in cellular systems, how to leverage information freshness to ensure timely information updates while achieving network greenness is rarely investigated. In this paper, we investigate the problem of freshness-aware energy saving in cellular systems, where active base stations (BSs) can cooperatively update information to target devices (TDs). To address this problem, we formulate a mixed-integer nonlinear program (MINLP) to minimize average power consumption. Due to its intractability, we propose decomposing the MINLP into two subproblems by means of constraint reinterpretation and spatiotemporal decoupling. Then, we propose a two-stage solution that leverages LP techniques to sequentially determine update scheduling and BS activeness. Our simulation results show that the proposed solution can adequately perform update scheduling and BS activeness control, thereby effectively saving energy for cellular systems under various parameter settings.

Multi-access edge computing (MEC) has emerged as a promising platform to provide user equipment (UEs) with timely computational services through the deployed edge servers. Typically, the size of an uplink task data (e.g., images or videos) required for processing is more pronounced than that of a downlink task result, and hence MEC offloading (MECO) plays a decisive role in the efficiency of MEC systems. In the light of an unprecedented growth of UEs in next-generation mobile networks, the reception of uplink signals at base stations (BSs) can be corrupted due to potential inter-user interference. To address this issue, coordinated multi-point (CoMP) reception which enables BSs to cooperatively receive uplink signals has evolved as an effective approach to enhance the received signal qualities. In this paper, we investigate a resource allocation problem for MECO with CoMP reception and formulate it as a mixed-integer non-linear program (MINLP). To solve this problem, we leverage the concept of interference graphs to characterize uplink inter-user interference, based on which we propose a resource allocation algorithm that consists of three phases: 1) computing resource allocation, 2) subcarrier allocation and cell clustering, and 3) subcarrier reuse and cell re-clustering. The simulation results show that our proposed solution can effectively enhance the delay performance of MECO through CoMP reception as compared with existing solution approaches under various system settings.

Multiaccess edge computing (MEC) systems provide low-latency computing services for Internet of Things (IoT) applications by processing IoT data on edge servers. In the era of heterogeneous IoT environments, the success of IoT applications hinges on the processing of diversified IoT data. To leverage MEC systems to enable timely IoT services, we characterize IoT applications as cotasks, where each cotask is completed only if all its constituent subtasks (e.g., IoT data processing) are finished. Existing works have been devoted to the design of task offloading and scheduling decisions for MEC-enabled IoT applications, but they mostly neglect the cotask feature. In this article, we investigate the problem of cotask processing in MEC systems, and we formulate it as a nonlinear program (NLP) to minimize total cotask completion time (TCCT). In the light of uncertain communication latency, we transform the NLP to a parameterized and unconstrained version, based on which we propose the deep dual learning (DDL) method, where the learner keeps updating primal and dual variables based on randomly perturbed samples. Furthermore, we provide the duality gap and time complexity analyses for the DDL method. Our simulation results demonstrate that the proposed solution can gradually converge over iterations, and its TCCT performance outperforms other comparison schemes under various system settings.

The ever-evolving mobile applications need more and more computing resources to smooth user experience and sometimes meet delay requirements. Therefore, mobile devices (MDs) are gradually having difficulties to complete all tasks in time due to the limitations of computing power and battery life. To cope with this problem, mobile edge computing (MEC) systems were created to help with task processing for MDs at nearby edge servers. Existing works have been devoted to solving MEC task offloading problems, including those with simple delay constraints, but most of them neglect the coexistence of deadline-constrained and delay- sensitive tasks (i.e., the diverse delay sensitivities of tasks). In this paper, we propose an actor-critic based deep reinforcement learning (ADRL) model that takes the diverse delay sensitivities into account and offloads tasks adaptively to minimize the total penalty caused by deadline misses of deadline-constrained tasks and the lateness of delay-sensitive tasks. We train the ADRL model using a real data set that consists of the diverse delay sensitivities of tasks. Our simulation results show that the proposed solution outperforms several heuristic algorithms in terms of total penalty, and it also retains its performance gains under different system settings.

Energy saving in cellular systems is increasingly important due to the ever- deteriorating global warming. Heterogeneous networks (HetNets) can attain energy savings thanks to the lower operational and transmit power consumption of small base stations (BSs). To address inter-cell interference problem yet achieving network energy conservation, green multicell clustering facilitating BS sleeping and coordinated multipoint (CoMP) clustering paves a way toward future green HetNets. To further alleviate the backhaul power consumption induced by content requests from users, caching popular contents at BSs in a shareable manner is regarded as a viable solution. In this paper, we investigate the problem of green multicell clustering in HetNets with shareable caching (RELISH), and show its NP-hardness. By observing that BS sleeping plays a pivoting role in the RELISH problem, we propose the clustering- then-caching strategy to decompose the RELISH problem, and then design the dual- ascending clustering algorithm followed by the zero-replica caching algorithm for solving the sub-problems. The simulation results demonstrate that our proposed solution is effective in reducing total power consumption, and we also show how the power savings vary with system parameters.

Interactive applications that come along with 360- degree (or 360) videos have brought immersive experiences to users thanks to the elevated machine computability. In fact, the provision of such high quality of experience (QoE) hinges on the persistent delivery of 360 videos, potentially consuming an excessive need of network bandwidth. To prevent the delivery of entire 360 videos from adversely affecting QoE, tile-based viewport adaptive streaming that divides 360 video chunks into tiles and conveys streams with differentiated quality levels to viewport and non-viewport areas has been regarded as a promising solution. Existing works have been devoted to the design of 1) viewport prediction (VPP) to predict users' viewport orientation due to head movements, and 2) tile bitrate selection (TBS) to determine tile-based bitrates for viewport and non-viewport areas. Despite the heuristic solutions proposed by the existing works, there is lack of knowledge of whether QoE can be enhanced by learning from historical data. In this paper, we propose the system-Plato, to leverage machine learning to tile-based viewport adaptive streaming for 360 videos. In particular, Plato applies long short term memory (LSTM) model to VPP, and uses part of non-viewport areas to help resist prediction errors. In addition, Plato uses real-world traces to train a TBS agent based on reinforcement learning to determine tile bitrates for both viewport and non-viewport areas. Our simulation results show that Plato outperforms existing schemes in various QoE metrics.

The introduction of small cells has displayed its energy-saving potentials in heterogeneous networks (HetNets) for the low operational and transmit power consumptions. To cope with the severity of inter-cell interference induced by the deployed small cells, existing research has been investigating base station (BS) sleeping incorporated with coordinated multipoint (CoMP) transmissions for the greenness of HetNets. Unfortunately, the fundamentals of multicell sleeping control and transmit power adaptation (MST) in green HetNets are in essence an NP-hard problem, which motivates us to find approximate solutions with provable performance guarantees. In this paper, we formulate the MST problem as a mixed integer linear program (MILP) and show its NP-hardness. By applying linear programming (LP) relaxation to the MST problem, we propose the progressive sleeping control with LP-based transmit power adaptation (PSLA) algorithm. We further prove that the achieved total power consumption can be upper-bounded, and show that the tightness of the upper bounds hinges on the factors of user satisfiability and network heterogeneity. Finally, the simulation results demonstrate the energy-saving performance of our proposed solution, as well as the impacts of the number of users and small cells in the network.

Cloud radio access networks (C-RAN) has been regarded as a promising solution to the next generation communication system, but the massive fronthaul bandwidth required to aggregate baseband samples from remote radio head (RRH) to the baseband unit (BBU) pool has a significant impact on the performance of C-RAN. Existing baseband compression algorithms can hardly solve this problem. So, in this paper we consider a new flexible C-RAN architecture with two types of RRHs with different degrees of centralization in the network, namely, primitive RRH and RRH with Layer 1 functions. The objective is then to determine where and how many nodes of each type to be deployed in the target service region so that the deployment cost is minimized, under the condition that the fronthaul capacity is limited and the total traffic demand in the system is satisfied. We prove that the problem is NP-hard and propose an efficient algorithm with polynomial time complexity to solve the problem. The simulation results show that the proposed solution is indeed better than existing solutions and also adaptive to different types of traffic distributions and demands.

Ultra-dense network is considered a promising solution for high network capacity. It is favored for its scalability and cost effectiveness. However, when a user equipment (UE) is in a long discontinuous reception cycle and at high speeds, it may suffer from poor mobility performance and high power consumption. While handover algorithms have been widely studied, the impact of mobility measurements on mobility performance is largely ignored. In this paper, we investigate how to perform mobility measurements intelligently. We analyze the radio link failure rate and the timing of handover. We then propose a mechanism to adjust measurement frequency based on the analysis to minimize the power consumption. Our simulation results demonstrate the mechanism can save considerable amount of energy in ultra-dense networks.

Energy saving in cellular systems is increasingly important due to ever-deteriorating global warming. Heterogeneous networks (HetNets) composed of various tiers of cells can attain energy savings thanks to the lower operational and transmit power consumptions of small cells. To address the inter-cell interference problem yet achieving network energy conservation, multicell cooperation facilitating cooperative transmission (also known as coordinated multipoint or CoMP) and sleep mode operation paves a way toward future green HetNets. To further alleviate the induced backhaul energy consumption caused by cooperative transmission, content caching which proactively caches popular files at local storages is regarded as a viable solution. In this paper, we investigate how energy-aware multicell cooperation in HetNets with content caching (ENCORE) can be achieved. On proving that the ENCORE problem is decomposable into two sub-problems, we claim that the place-then-transmit strategy is optimal to the ENCORE problem. Then, we design algorithms for the sub-problems and prove that the total energy consumption achieved by the proposed solution is upper-bounded. Our simulation results demonstrate that the proposed solution outperforms various dynamic clustering approaches in terms of energy savings, and show the impacts of content popularity and cache size on the backhaul energy consumption.

Concerns with global warming have prompted much research effort in energy-related issues for cellular systems. Heterogeneous networks (HetNets) incorporated with small cells save energy effectively due to the lower transmit and operational power consumptions of small cells. Multicell cooperation employing both cooperative transmission to tackle inter-cell interference and sleep mode operation to attain network energy conservation plays a decisive role in future green HetNets. Energy harvesting is another trend to achieve network energy savings. In this paper, we investigate how green multicell cooperation (GMC) can be achieved in HetNets facilitated with hybrid energy sources. Due to the lack of efficient solution approaches in coping with the spatial and temporal characteristics of the GMC problem simultaneously, we propose a greedy decomposition to solve the GMC problem via two sub-problems. We prove that the achieved grid energy consumption can be upper bounded, and observe that the tightness of the upper bounds hinges on two clustering-related factors, which may guide the scheduling design in future green HetNets. Our simulation results show that the proposed solution outperforms the existing approaches in terms of grid energy savings, and also demonstrate the cluster formation results and the dynamics of the clustering-related factors.

The increasing severity of the inter-cell interference problem due to universal frequency reuse has prompted many research studies on coordinated multipoint (CoMP) transmission. In addition to the enhancement of the received signal strength, CoMP transmission can be performed in an energy-efficient manner. However, existing works on CoMP transmission do not investigate the potentials of energy saving with the incorporation of renewable energy. In this paper, we formulate a renewable energy aware cluster formation (REAL) problem to minimize the energy consumption in electric grid, with the support of hybrid energy supply in each cell site. Due to the difficulties in solving the REAL problem, we propose to decompose it into two stages and design polynomial-time algorithms for the decomposed problems. In our simulation results, we show the achieved SINR by the harvested energy in cluster formation. Then, we demonstrate that our proposed solution can better utilize the harvested energy and effectively save the energy consumption in electric grid.

To enhance the utilization of base stations (BSs) and face the challenge of the upcoming mobile data tsunami, energy saving and traffic offloading are two important issues to address in green cellular networks. In this paper, we design an optimal green policy, called Genie, to strike a balance between energy saving and traffic offloading in the heterogeneous cellular network, so that the system can either activate hotspot cells for traffic offloading or to deactivate the hotspot cells for energy saving. We prove that the optimal green policy is monotone hysteretic, and our system can thus be realized by simple switch-up and switch-down thresholds, while avoiding the ping-pong effect suffered by existing works. We show via simulations that our optimal green policy can significantly reduce total energy cost and perform energy saving and traffic offloading intelligently under all traffic conditions.