Large language models (LLMs) can effectively handle outdated information through knowledge editing.However, current approaches face two key limitations: (I) Poor generalization: Most approaches rigidly inject new knowledge without ensuring that the model can use it effectively to solve practical problems.(II) Narrow scope: Current methods focus primarily on structured fact triples, overlooking the diverse unstructured forms of factual information (e.g., news, articles) prevalent in real-world contexts.To address these challenges, we propose a new paradigm: teaching LLMs to edit knowledge via Chain of Thoughts (CoTs) reasoning (CoT2Edit).We first leverage language model agents for both structured and unstructured edited data to generate CoTs, building high-quality instruction data.The model is then trained to reason over edited knowledge through supervised finetuning (SFT) and Group Relative Policy Optimization (GRPO).At inference time, we integrate Retrieval-Augmented Generation (RAG) to dynamically retrieve relevant edited facts for real-time knowledge editing.Experimental results demonstrate that our method achieves strong generalization across six diverse knowledge editing scenarios with just a single round of training on three open-source language models.The codes are available at https:// github.com/FredJDean/CoT2Edit.

Yanxiao Zhao, Yaqian Li, Zi-Hao Bo, Rinyoichi Takezoe, Haojia Hui, Mo Guang, Renlei, Xiaolin Qin, Kaiwen Long. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Deep learning (DL)-based channel estimation models are capable of processing large-scale wireless data and have shown strong performance in modeling complex reconfigurable intelligent surface (RIS) channels. However, their vulnerability to adversarial attacks remains underexplored. To address this gap, this paper analyzes vulnerabilities in DL-based channel estimation models for RIS-assisted communications. We propose a novel adaptive adversarial training framework based on projected gradient descent (PGD) as an effective defense strategy learning from large-scale data. Unlike standard adversarial training, the proposed framework employs a progressive adversarial schedule that incrementally increases both perturbation strength and PGD iteration-depth during training. In addition, a balanced composition of clean and adversarial samples is maintained within each mini-batch, preserving baseline accuracy while systematically enhancing robustness against strong adversarial attacks. Extensive simulations are conducted to evaluate our proposed strategy compared with other existing defense techniques. The results show that our approach significantly enhances the robustness of DL-based channel estimation models against adversarial attacks.

Knowledge diversity and knowledge forgetting are two major issues in sustaining collaborative learning within heterogeneous vehicle networks. These issues become especially severe when vehicles possess varying sensing capabilities, computational resources, and domain expertise, leading to fragmented learning and unstable knowledge retention over time. To address these challenges, we propose a dual-level game-theoretic approach. We first formulate a new metric, Utility-of-Information (UoI), to characterize the features of knowledge learning, retention, and consolidation. Based on this metric, we design a game-theoretic dual-level approach, which comprises a lower-level coalition formation game where vehicles self-organize into “teacher-student” coalitions based on their UoI profiles, and an upper-level UAV resource allocation game where vehicle coalitions compete for limited communication resources. To optimize both levels of the game, we design a unified reinforcement learning-based framework that enables adaptive searching for optimization under dynamic network conditions. Experimental results demonstrate that our approach effectively addresses knowledge diversity and significantly mitigates the effects of knowledge forgetting in UAV-assisted heterogeneous vehicle networks.

Deep reinforcement learning (DRL) has achieved remarkable success in solving sequential decision-making problems across various domains. However, a critical challenge is sample inefficiency, especially in real-world environments with high-dimensional solution spaces due to continuous state and action spaces. Although off-policy actor-critic algorithms have been proposed to mitigate this issue, the gains in sample efficiency remain limited, as decision-making in these algorithms relies solely on the policy function that might not always yield optimal actions. To bridge the gap, we design a novel feedback-based decision-making mechanism (FADA) that incorporates a feedback mechanism into the actor-critic framework to enhance decision-making robustness. Specifically, FADA utilizes feedback from the value function (critic) to calibrate the decisions produced by the policy function (actor). More concretely, FADA comprises four integrated modules: a decision-space expansion module (DEM) to produce a pool of candidate actions, a critic-guided evaluation module (CGEM) that estimates the efficacy of the candidate actions, an adaptive selection module (ASM) that adaptively selects a set of elite actions based on the estimated efficacy and samples the final action, and an iterative refinement module (IRM) that improves the quality of elite actions. We evaluate our approach on multiple tasks in the DeepMind Control Suite, and the experimental results demonstrate a significant improvement in sample efficiency.

GPS spoofing is a great threat to the safety of transportation systems as well as other systems that rely on GPS for navigation. This paper proposes a novel computer vision based approach for GPS spoofing detection, termed SEquential dashcam-based vEhicle localization frameworK (SEEK). SEEK utilizes vehicle dashcam images to identify a vehicle's true location and detects possible GPS spoofing attacks through verifying if the reported GPS locations of the vehicle are correct. However, it is nontrivial to use dashcam images for vehicle localization due to multiple challenges caused by real-world driving, including the complicated lighting/weather conditions, season/timing variations of the images, large blockage ratio in the images, and varying driving speeds. SEEK features a unique design with novel schemes to address complicated lighting/weather conditions, transform images to align with season changes, reduce blockage, and adopt a sequential image matching scheme. The performance evaluation shows that SEEK significantly outperforms the previous GPS spoofing detection scheme, and achieves a detection accuracy of up to 94%.

Although the concept of digital twin technology has been in existence for nearly half a century, its application in healthcare is a relatively recent development. In healthcare, the utilization of digital twin and data-driven models has proven to enhance clinical decision support, particularly in the treatment and assessment of chronic wounds, leading to improved clinical outcomes. This article proposes the implementation of a digital twin in the domain of healthcare, specifically in the management of chronic wounds, by leveraging artificial intelligence techniques. The digital twin is composed of data collection, data processing, and AI models dedicated to wound healing. A novel AI pipeline is utilized to track the healing of chronic wounds. The digital twin, serving as a virtual representation of the actual wound, simulates and replicates the healing process. Furthermore, the proposed wound-healing prediction model effectively guides the treatment of chronic wounds. Additionally, by comparing the actual wound with its digital twin, the system enables early identification of non-healing wounds, facilitating timely adjustments and modifications to the treatment plan. By incorporating a digital twin in healthcare, the proposed system enables personalized and tailored treatments, potentially playing a crucial role in proactive problem identification.

Intelligent Reflecting Surface (IRS) has been demonstrated as a promising and innovative technology for next-generation wireless communications. It can be utilized to flexibly re-configure the fundamental communication environment to realize low-cost, energy-saving, and low-interference wireless communications. On the other hand, malicious users may also utilize the powerful capability of the IRS to re-configure the communication environment to achieve an advantageous position to launch security attacks such as eavesdropping and jamming wireless networks. Therefore, while the integration of IRS into wireless communications brings promising new opportunities, it also raises significant concerns from the security perspective. This issue has not been thoroughly studied in the literature. In this paper, we first introduce the recent works of using IRS in wireless communications by grouping them into two categories: 1) securing communication via IRS and 2) launching attacks using IRS. We then derive a critical performance metric, the Signal-to-Noise Ratio (SNR), for evaluating IRS-assisted wireless communication systems. Next, we present four typical scenarios of utilizing IRS for security or threats to wireless communications. At last, we evaluate the IRS-assisted system with regard to the SNR performance affected by the IRS in those four scenarios toward a deeper understanding of the potential of IRS-assisted wireless communication systems in terms of security and threats.

The transmission scheduling is a critical problem in radio frequency (RF) energy harvesting communications. Existing transmission strategies are mainly based on a conventional model, in which the amount of harvested energy is modeled as predetermined random variables and the data transmission is arranged in a fixed feasible energy tunnel. In this paper, we show through the theoretical analysis and experimental results that due to the nonlinear battery charging characteristics, the harvested energy will largely depend on the transmission strategy. The bounds of feasible energy tunnel become dynamic. To describe a practical ambient energy harvesting process more accurately, a new nonlinear recursive model is proposed by adding a feedback loop that reflects the real-time influence of the data transmission on the energy harvesting process. In addition, to improve communication performance, we redesign the optimal transmission scheduling strategy based on the new model. In order to cope with the challenge of the endless loop in the new model, a recursive algorithm is developed. The simulation results reveal that the new transmission scheduling strategy can balance the efficiency of energy harvest and energy utilization regardless of the length of energy packets, thus improving the throughput performance of RF energy harvesting wireless communications.

This paper addresses the energy efficiency issue for content sharing with collaborative mobile clouds (CMC). We start by maximizing the data rate of cellular transmissions under the maximum transmit power constraint of the cellular users, to obtain the optimal beamforming vectors. Using these vectors, we propose a water filling based data segmentation approach for content distribution. Furthermore, within the CMC, we design cost-effective resource allocation and power control mechanisms for device-to-device communications. Through performance comparisons, we disclose that our proposed scheme outperforms some previous study in terms of delay and energy consumption per mobile terminal, which further validates the effectiveness of our design.

The transmission scheduling is a critical problem in radio frequency (RF) energy harvesting communications. Existing transmission strategies are mainly designed based on a classic model, in which the harvested energy is assumed pre-determined and considered as prior knowledge in offline approaches. In this extended abstract, we challenge this assumption showing that the harvested energy is affected by the transmission scheduling and becomes unknown and not pre-determined. In the new model, we add a feedback line from the data transmission to the harvested energy. It properly indicates the interplay between the energy harvest and the data transmission but challenges the transmission scheduling in the meantime. We formulated the optimal transmission scheduling based on the new model and advocate a recursive solution.

We propose a new energy harvesting (EH) strategy that uses a dedicated energy source (ES) to optimally replenish energy for radio frequency EH powered wireless devices. Specifically, we develop a two-step dual tunnel energy requesting (DTER) strategy that minimizes the energy consumption on both the EH device and the ES. Besides the causality and capacity constraints that are investigated in the existing approaches, DTER also takes into account the overhead issue and the nonlinear charge characteristics of an energy storage component to make the proposed strategy practical. Both offline and online scenarios are considered in the second step of DTER. To solve the nonlinear optimization problem of the offline scenario, we convert the design of offline optimal energy requesting problem into a classic shortest path problem and thus a global optimal solution can be obtained through dynamic programming algorithms. The online suboptimal transmission strategy is developed as well. Simulation study verifies that the online strategy can achieve almost the same energy efficiency as the global optimal solution in the long term.

One grand challenge in software defined networking is to select appropriate locations for controllers to shorten the latency between controllers and switches in wide area networks. In the literature, the majority of approaches are focused on the reduction of packet propagation latency, but propagation latency is only one of the contributors of the overall latency between controllers and their associated switches. In this paper, we explore and investigate more possible contributors of the latency, including the end-to-end latency and the queuing latency of controllers. In order to decrease the end-to-end latency, the concept of network partition is introduced and a clustering-based network partition algorithm (CNPA) is then proposed to partition the network. The CNPA can guarantee that each partition is able to shorten the maximum end-to-end latency between controllers and switches. To further decrease the queuing latency of controllers, appropriate multiple controllers are then placed in the subnetworks. Extensive simulations are conducted under two real network topologies from the Internet Topology Zoo. The results verify that the proposed algorithm can remarkably reduce the maximum latency between controllers and their associated switches.

In this paper, we investigate malicious detection and avoidance of black hole attack for smart meter network. In terms of routing protocol, the reactive routing protocol Ad hoc On-Demand Distance Vector (AODV) is commonly adopted for smart meter network and is considered in our paper. However, the default AODV is vulnerable to black hole attacks. To detect and avoid black hole attacks, we propose a new routing protocol, termed Enhanced Ad hoc On-Demand Distance Vector (E-AODV) by modifying the Route Reply (RREP) system based on AODV. The RREP in E-AODV updates the destination sequence number corresponding to a fresh route request. By comparing sequence numbers from multiple replies, it detects the existence of black hole behavior because the attacker usually sends a much higher sequence number, compared to the actual one generated from the destination. After detection, the receiving meter sends a deny reply to the destination. Upon receipt of the deny reply, the destination regenerates RREP with an updated sequence number, which is kept to itself. Since attackers only respond when a new request is initiated, this time the attacker will not act because no request is initiated. Therefore, it is able to avoid the effects of malicious meter. Extensive simulations are carried out in NS3 to evaluate the performance of E-AODV. Results show that E-AODV obtains considerately higher performance in terms of packet delivery ratio and throughput compared with the default AODV routing protocol.

A smart meter Neighborhood Area Network (NAN) is a significant component for smart grid. The delay- sensitive communication in NAN, such as the interaction of power system control signal, usually requires the maximum allowed delay in the order of a few milliseconds. Therefore, it is crucial to investigate how to shorten the latency and guarantee real-time communications. Since the location of Data Aggregation Points (DAPs) significantly affects the propagation latency between DAPs and their associated smart meters, in this paper, we aim at tackling the DAP placement problem in a delay-sensitive smart meter NAN. Specifically, the DAP placement problem is formulated first. Then, a network partition approach, termed Clustering-based DAP Placement (CDP), is proposed to solve the problem. Extensive simulations are conducted based on an actual neighborhood topology. The simulation results demonstrate that the proposed CDP is able to remarkably reduce the maximum propagation latency of data between DAP and their associated smart meters.

Energy harvesting is emerging as a promising alternative source to power the next generation of wireless networks. This paper introduces a new energy harvesting strategy that uses a dedicated energy source to optimally replenish energy for radio frequency (RF) based wireless communication systems. Specifically, we develop a two-step dual tunnel energy requesting (DTER) strategy that allows an energy harvesting device to effectively obtain energy from a dedicated energy source. While minimizing the system energy consumption, DTER takes into account the practical constraints on both the energy source and the energy harvesting device. Additionally, the overhead issue and the charge characteristics of an energy storage component are examined to make the proposed strategy practical. To solve the nonlinear optimization problem in DTER, we convert the design of optimal energy requesting problem into a classic shortest path problem and thus enable us to find a global optimal solution through dynamic programming algorithms. Theoretical analysis and simulation study verify that DTER outperforms two other schemes in the literature.

Multicast routing that meets multiple quality of service constraints is important for supporting multimedia communications in the Internet of Things (IoT). Existing multicast routing technologies for IoT mainly focus on ad hoc sensor networking scenarios; thus, are not responsive and robust enough for supporting multimedia applications in an IoT environment. In order to tackle the challenging problem of multicast routing for multimedia communications in IoT, in this paper, we propose two algorithms for the establishing multicast routing tree for multimedia data transmissions. The proposed algorithms leverage an entropy-based process to aggregate all weights into a comprehensive metric, and then uses it to search a multicast tree on the basis of the spanning tree and shortest path tree algorithms. We conduct theoretical analysis and extensive simulations for evaluating the proposed algorithms. Both analytical and experimental results demonstrate that one of the proposed algorithms is more efficient than a representative multiconstrained multicast routing algorithm in terms of both speed and accuracy; thus, is able to support multimedia communications in an IoT environment. We believe that our results are able to provide in-depth insight into the multicast routing algorithm design for multimedia communications in IoT.

Software Defined Networking (SDN), the novel paradigm of decoupling the control logic from packet forwarding devices, has been drawing considerable attention from both academia and industry. As the latency between a controller and switches is a significant factor for SDN, selecting appropriate locations for controllers to shorten the latency becomes one grand challenge. In this paper, we investigate multi-controller placement problem from the perspective of latency minimization. Distinct from previous works, the network partition technique is introduced to simplify the problem. Specifically, the network partition problem and the controller placement problem are first formulated. An optimized K-means algorithm is then proposed to address the problem. Extensive simulations are conducted and results demonstrate that the proposed algorithm can remarkably reduce the maximum latency between centroid and their nodes compared with the standard K-means. Specifically, the maximum latency can reach 2.437 times shorter than the average latency achieved by the standard K-means.

Spectrum prediction plays a critical role in cognitive radio networks because it is promising to significantly speed up the sensing process and hence save energy as well as improve resource utilization. However, most existing spectrum prediction models are not able to fully explore the hidden correlation among adjacent observations or appropriately describe the channel behavior. In this paper, we propose a novel prediction approach termed high-order hidden bivariate Markov model (H^2BMM), by leveraging the advantages of both HBMM and high-order. H^2BMM applies two dimensional parameters, i.e., hidden process and underlying process, to more accurately describe the channel behavior. In addition, the current channel state is predicted by observing multiple previous states. Extensive simulations are conducted and results verify that the prediction accuracy is significantly improved using the proposed H^2BMM compared with traditional Hidden Markov Model (HMM) and Hidden Bivariate Markov Model (HBMM).

On Demand Frame Contention (ODFC) is designated as a solution to exclusive self-coexistence in wireless regional area networks. According to ODFC, contention winners are selected in a random manner regardless of the users' delay constraints and frame demands. As a result, ODFC may freeze some users due to that their delay constraints are not satisfied. Moreover, it may lead to a unfair resource distribution in terms of frame demands. To fully consider various delay constraints and frame demands, in this paper we formulate the resource allocation optimization problem as an integer programming problem and present a new approach termed \emph{On Demand Delay-constrained Fair Distribution} (ODDFD). ODDFD utilizes an iterative approach to solve the resource allocation problem considering delay constraints and frame demands. The distinguished feature of ODDFD is that it is able to deal with both delay sensitive networks and delay insensitive networks. Specifically, for a delay sensitive network, ODDFD minimizes jitter variance and average unexpected delay. For a delay insensitive network, the resource is allocated based on their frame demands and achieve a fair distribution in terms of their demands. Extensive simulations are conducted and verify that the jitter variance and average unexpected delay are decreased in a delay sensitive network, and the fairness of frame demands is increased in a delay insensitive network.

Resource allocation plays a critical role in implementing D2D communications underlaying a cellular network. Game-based approaches are recently proposed to address the resource allocation issue. Most existing approaches employ deterministic game models while implicitly assuming that each player in the game is completely willing to exchange transmission parameters with other players. Thus each player knows the complete information of all others. However, this assumption may not be satisfied in practice. For example, users may be reluctant to disclose all their parameters to peers. In this paper, we fully consider this scenario, i.e., players have incomplete information of others, and investigate the resource allocation problem for multicell D2D communications where a D2D link utilizes common resources of multiple cells. To attack this problem, a game-theoretic approach under the incomplete information condition is proposed. Specifically, we characterize the Base Stations (BSs) as players competing for resource allocation quota from the D2D demand, formulate the utility of each player as payoff from both cellular and D2D communications leasing the resources, and design the strategy for each player that is determined based on prior probabilistic payoff information of other players. We conduct extensive simulations to examine the proposed approach and the results demonstrate that the utility, sum rate, and sum rate gain of each player under the incomplete information condition are surprisingly higher than the counterparts under the complete information condition.

The proliferation of the mobile Internet and social networks reshapes the proportion of uploaded data in the entire Internet traffic. IFOM (IP Flow Mobility) technology, which offloads the cellular data to the WiFi or Femtocells or other complementary networks, plays a crucial role in improving the throughput of cellular systems. Although there have been many studies on the IFOM technology, most of them are done from a technical perspective, and the issues related the dissemination and utilization of the IFOM technology are largely overlooked. Unlike prior research works, this paper addresses issues involved with the IFOM technology from an economic perspective. Specifically, we model the competitions among multiple service providers supporting or not supporting the IFOM technology by leveraging the Game Theory, and then analyze the Nash Equilibrium for the ensuing game model. We also conduct extensive simulations to determine the factors that affect the market share and profit of the service providers. We believe that this research work will provide valuable guidance to service providers for the promotion and utilization of the IFOM technology.

Spectrum sensing plays a critical role in cognitive radio networks. Most of existing works on spectrum sensing adopted energy detection which takes samples on a band and then compares the summation with a threshold to determine the state of the band. However, if a licensed band is subdivided by the primary users, such as in the unlicensed WiFi band, the energy detection faces a challenge. The threshold used to decide if there is a PU signal on the band now depends on the number of sub-bands that are being used by primary users, since the received signal power on the band is now dependent on the number of used sub-bands. In this work, we propose a wavelet based spectrum sensing approach that does not depend on the number of used sub-bands and adaptively detects PU signals on a licensed band. We use the measured real world signals to test the approach. The simulation results indicate that the proposed approach can effectively detect the PU signal on a licensed band without needing the knowledge of band subdivision. In addition, the comparative study with the existing techniques is performed to evaluate two performance metrics, true detection and false alarm, for primary users signal detection.

Spectrum sensing is an essential process in cognitive radio networks. The majority of existing sensing approaches aim to detect the existence of a signal on a busy channel without differentiating whether a signal originates from a primary user or not. In this paper, we address this issue and propose to employ an Angle of Arrival (AoA) based sensing approach to effectively distinct a primary user's signal from a secondary user' signal. Multiple Signal Classification (MUSIC), a classical AoA algorithm, is selected due to easy implementation and high resolution. Unlike the previous works on AoA based sensing, we thoroughly investigate its sensing performance based on a practical model which captures typical characteristics in cognitive radio networks. Two performance metrics named false alarm probability and miss detection probability are theoretically analyzed. Closed-form analytical expressions are derived for both metrics. Extensive simulations are carried out under various scenarios to evaluate AoA based sensing approach.

Spectrum sensing is a critical component for cognitive radio networks. Most of the spectrum sensing algorithms and performance analysis, however, assume that the secondary users are stationary. In this paper, we investigate the performance analysis of spectrum sensing by mobile secondary users. Two performance metrics, false alarm probability and miss detection probability, are thoroughly investigated. In addition, a new performance metric, expected transmission time, is designed to factor the secondary users' mobility. The random waypoint based mobility model is adopted for secondary users. For spectrum sensing by mobile secondary users, a critical variable is the distance between the primary user and mobile secondary users. We mathematically model this distance, and derive its probability distribution. At last, the expressions are derived for all three performance metrics, the false alarm probability, the miss detection probability, and the expected transmission time. Extensive simulations are performed, and the results are consistent with the theoretical analysis. It is concluded that the mobility of secondary users has significant impact on miss detection probability, but not on false alarm probability.

Device-to-Device (D2D) communication is envisioned as a promising technology to significantly improve the performance of current cellular infrastructures. Allocating resources to the D2D link, however, raises an enormous challenge to the co-existing D2D and cellular communications due to mutual interference. While there have been many resource allocation solutions proposed for D2D underlaying cellular network, they have primarily focused on the intracell scenario while leaving the intercell settings untouched. In this paper, we investigate the resource allocation problem for intercell D2D communications underlaying cellular networks, where D2D link is located in the overlapping area of two neighboring cells. We present three inter-cell D2D scenarios regarding the resource allocation problem. To address this problem, we develop a repeated game model under these scenarios. Distinct from existing works, we characterize the communication infrastructure, namely Base Stations (BSs), as players competing resource allocation quota for D2D demand, and define the utility of each player as the payoff from both cellular and D2D communications using radio resources. We also propose a resource allocation algorithm and protocol based on the equilibrium derivations. Numerical results indicate that the developed model not only significantly enhances the system performance including sum rate and sum rate gain, but also sheds lights on resource configurations for intercell D2D scenarios.

The IEEE 802.22 cognitive Wireless Regional Area Networks (WRAN) uses cognitive radio technique to allow Secondary Users (SUs) to opportunistically share the TV bands with TV broadcast service on a non-interfering basis and hence improve the spectrum utilization. WRAN employs Orthogonal Frequency Division Multiplexing (OFDM) technology for transmission and thus is sensitive to inter-carrier interference. So one of the challenging issues in WRAN performance analysis is the interference analysis from SUs. Existing research on interference primarily focuses on the co-channel interference. The study of inter-carrier interference in WRAN has not received considerable attention. In this paper, we design a new interference model for IEEE 802.22 cognitive WRAN that incorporates both the co-channel interference and inter-carrier interference. We first examine the interference in the context of one SU, and then study the aggregate interference in the context of multiple SUs. Existing work on studying the aggregate interference is to derive its probability density function, which is known computationally intensive. In this paper, we investigate the aggregate interference from a new perspective. Instead of deriving the probability density function, we calculate the aggregate interference by estimating the maximum number of interfering SUs. Our results suggest that the maximum number typically ranges from 6 to 10 depending on the network configurations. This finding significantly simplifies the interference estimation process. To verify the new interference model, comprehensive simulations are performed. Results confirm that the inter-carrier interference cannot be ignored especially when a high frequency offset is present. The maximum number of SUs and its effectiveness are also validated.

Extreme physical conditions in the deep space environment result in the severe challenge of unreliable deep-space data communications. Moreover, high data rate services such as Earth-Mar multimedia communication in space exploration applications consume significant amount of energy, while the limited battery energy storage forms another communication bottleneck. To overcome these challenges, we propose an algorithm to adaptively control transmission power in a two-hop relay Mars-Earth communication link, to improve end-to-end throughput performance while minimizing the energy consumption at different transmission stations. In the proposed approach, transmission power of each communication station is adaptively adjusted according the energy storage leftover at each node. To achieve such optimal power adaptation, energy budget constraint is considered in the overall optimization process. Simulation results demonstrate the proposed power control methodology can bring significant improvement of throughput performance to support traffic intensive multimedia applications by exploring the limited energy budget.

Cognitive radio is viewed as a disruptive technology innovation to improve spectrum efficiency. The deployment of coexisting cognitive radio networks, however, raises a great challenge to the medium access control (MAC) protocol design. While there have been many MAC protocols developed for cognitive radio networks, most of them have not considered the coexistence of cognitive radio networks, and thus do not provide a mechanism to ensure fair and efficient coexistence of cognitive radio networks. In this paper, we introduce a novel MAC protocol, termed fairness-oriented media access control (FMAC), to address the dynamic availability of channels and achieve fair and efficient coexistence of cognitive radio networks. Different from the existing MACs, FMAC utilizes a three-state spectrum sensing model to distinguish whether a busy channel is being used by a primary user or a secondary user from an adjacent cognitive radio network. As a result, secondary users from coexisting cognitive radio networks are able to share the channel together, and hence to achieve fair and efficient coexistence. We develop an analytical model using two-level Markov chain to analyze the performance of FMAC including throughput and fairness. Numerical results verify that FMAC is able to significantly improve the fairness of coexisting cognitive radio networks while maintaining a high throughput.

Spectrum sensing plays a critical role in cognitive radio networks (CRNs). The majority of spectrum sensing algorithms aim to detect the existence of a signal on a channel, i.e., they classify a channel into either busy or idle state, referred to as a two-state sensing model in this paper. While this model works properly when there is only one CRN accessing a channel, it significantly limits the potential and fairness of spectrum access when there are multiple co-existing CRNs. This is because if the secondary users (SUs) from one CRN are accessing a channel, SUs from other CRNs would detect the channel as busy and hence be starved. In this paper, we propose a three-state sensing model that distinguishes the channel into three states: idle, occupied by a primary user, or occupied by a secondary user. This model effectively addresses the fairness concern of the two-state sensing model, and resolves the starvation problem of multiple co-existing CRNs. To accurately detect each state of the three, we develop a two-stage detection procedure. In the first stage, energy detection is employed to identify whether a channel is idle or occupied. If the channel is occupied, the received signal is further analyzed at the second stage to determine whether the signal originates from a primary user or an SU. For the second stage, we design a statistical model and use it for distance estimation. For detection performance, false alarm and miss detection probabilities are theoretically analyzed. Furthermore, we thoroughly analyze the performance of throughput and fairness for the three-state sensing model compared with the two-state sensing model. In terms of fairness, we define a novel performance metric called all-level fairness for all(ALFA) to characterize fairness among CRNs. Extensive simulations are carried out under various scenarios to evaluate the three-state sensing model and verify the aforementioned theoretical analysis.

Cognitive radio networking is emerging as a promising paradigm for future wireless networks. In this paper, the delay performance of cognitive radio networks supporting heterogeneous traffic is analyzed. In order to guarantee primary users' (PUs) licensed membership, packets from PUs are distinguished from secondary users (SUs) by employing an absolute priority scheme. Meanwhile, various delay requirements over the packets from SUs are fully considered. The packets from SUs are classified into either delay-sensitive packets or delay-insensitive packets. Moreover, a novel relative priority strategy is designed between these two types of traffic by proposing a “transmission window” strategy. The delay performance of both a single-PU scenario and a multiple-PU scenario is thoroughly investigated employing queueing theory. In the multiple-PU scenario, a dynamic and adaptive channel selection scheme based on learning automata is developed with the objective of reducing the average delay for all SU packets. Numerical experiments are conducted and the results demonstrate the delay performance with respect to varied transmission window sizes. The results in the multiple-PU scenario verify that the proposed learning automata channel selection scheme significantly improves the delay performance of SU packets.

Wireless mesh networks (WMNs) have recently emerged as a promising broadband access infrastructure for next-generation wireless networking. Several approaches that exploit directional antennas have been proposed in the literature to increase the performance of WMNs. In this paper, we study the interference optimization multicast problem in WMNs where nodes are equipped with directional antennas. Interference can make a significant impact on the performance of multi-hop wireless networks. Directional transmissions can greatly reduce radio interference, increase spatial reuse, and enable more efficient MAC designs. We first present the definition of interference with directional transmissions that are suitable for designing multicast algorithms, and formulate minimum interference multicast problems using a linear programming model. We then propose a heuristic algorithm to solve the problem. Our model and algorithm are good for both single multicast session and multiple multicast sessions. Multicast routing found by our interference-aware algorithm tends to have less channel collisions.

Achieving efficient bandwidth utilization in multi-channel sensor networks is a challenging research problem. In this paper, we present a cognitive load balance algorithm for single-hop multi-channel sensor networks. Based on the load distribution of all base stations, our algorithm dynamically alternates the communication channels. As a result, the extra load from over-loaded channels is directed to under-loaded channels with a computed switch probability. In this paper, we also prove that a high throughput can be achieved if the load is balanced. The performance of the load balance algorithm is evaluated through both theoretical analysis and simulation study.