For ultra-dense vehicular networks, dynamic resource optimization among a large number of heterogeneous agents is rather challenging. This paper proposes a learning-based resource allocation scheme in a vehicle-assisted mobile edge computing (MEC) network, where the uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs) are equipped with the MEC servers to provide the computational offloading services to the ground users with time-varying computing demands. Each self-interested vehicle jointly optimizes its trajectory planning and data offloading policies to maximize the expectation of its locally cumulative energy efficiency under the collision and energy constraints. We model the non-cooperative interactions among the massive co-channel vehicles as a multi-population mean-field game (MPMFG), where each vehicle constructs two types of mean-field terms to model the UAVs’ and UGVs’ population distributions, respectively. We propose a multi-population mean-field parameterized deep Q network (MPMF-PDQN) algorithm to solve the equilibrium among the vehicular servers in a discrete-continuous hybrid action space. The simulation results demonstrate that the proposed algorithm significantly enhances the average energy efficiency of the vehicles compared with the baseline algorithms.

Unmanned aerial vehicles (UAVs) are widely deployed as aerial base stations to provide flexible communication coverage for ground users (GUs), yet the air-ground communications remain highly vulnerable to the jamming attacks. Unlike conventional fixed-policy jammers, the intelligent jammers dynamically adapt their jamming strategies based on the observed UAV communication policies, creating significant anti-jamming challenges particularly under asymmetric information. In this paper, we formulate the strategic interactions between a UAV-mounted server and a jammer as a partially observable pursuit-evasion game, where the UAV aims to maximize the GUs' uplink rates through dynamic evasion while the jammer strategically pursues to maximize the jamming effect. The information asymmetry is explicitly modeled by considering both the jammer's hidden location from the UAV and the jammer's inability to observe the UAV's remaining energy state. To optimize the UAV's anti-jamming policy under these challenges, we propose a novel opponent-modeling based reinforcement learning algorithm, named neural fictitious self-play with dueling double deep recurrent Q network (NFSP-D3RN). This algorithm optimizes the UAV's anti-jamming policy through reinforcement learning, while maintaining robustness against non-stationarity induced by the jammer's adaptive behavior through implicit opponent modeling. Extensive simulations demonstrate that our proposed algorithm achieves superior anti-jamming performance compared with the benchmarks under unknown jammer locations, with results approaching the upper bound of perfect location knowledge.

Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks. However, RIS-assisted communications have to continuously tune phase-shifts relying on accurate channel state information (CSI) that is generally difficult to obtain due to the large number of RIS channels. The joint design of CSI acquisition and subsection RIS phase-shifts remains a significant challenge in dynamic environments. In this paper, we propose a diffusion-enhanced decision Transformer (DEDT) framework consisting of a diffusion model (DM) designed for efficient CSI acquisition and a decision Transformer (DT) utilized for phase-shift optimizations. Specifically, we first propose a novel DM mechanism, i.e., conditional imputation based on denoising diffusion probabilistic model, for rapidly acquiring real-time full CSI by exploiting the spatial correlations inherent in wireless channels. Then, we optimize beamforming schemes based on the DT architecture, which pre-trains on historical environments to establish a robust policy model. Next, we incorporate a fine-tuning mechanism to ensure rapid beamforming adaptation to new environments, eliminating the retraining process that is imperative in conventional reinforcement learning (RL) methods. Simulation results demonstrate that DEDT can enhance efficiency and adaptability of RIS-aided communications with fluctuating channel conditions compared to state-of-the-art RL methods.

In the resource constrained unmanned aerial vehicle (UAV) assisted communication networks, a major challenge is how to achieve efficient multi-UAV cooperation with minimal communication overhead. This paper proposes a teammate modeling based resource allocation scheme for a multi-UAV network, where multiple co-channel UAVs cooperatively provide the downlink communication services to the ground users (GUs) with temporal-correlated task demands. We formulate the cooperative resource allocation as a decentralized partially observable Markov decision process (Dec-POMDP), in which the UAVs jointly optimize their trajectory planning, power allocation, and user association policies to maximize the system's cumulative achievable sum rate. To address the non-stationarity introduced by parallel decision-making among the partially observable UAV agents, we propose a teammate modeling based multi-agent reinforcement learning algorithm, named teammate-advisory advantage actor-critic (TAA2C). This algorithm facilitates the exchange of low-dimensional advisory information among the UAV agents to enhance the inter-Uavcollaboration while maintaining low communication overhead. Simulation results demonstrate that, our proposed TAA2C algorithm achieves a better trade-off between communication overhead and cooperation efficiency, compared with the baseline algorithms of Independent A2C (IA2C), Federated A2C (FA2C), and Multi-Agent A2C (MAA2C). As the network scale increases, TAA2C even surpasses the centralized training baseline MAA2C in terms of cumulative sum rate, at only 10% of the communication overhead.

Blockchain-assisted federated learning (BFL) can achieve decentralized storage and management of model data without relying on a central server. However, security issues caused by deliberate attacks in distributed systems and efficiency issues induced by heterogeneous computing consumption in resource-limited systems need to be urgently addressed in BFL. To address these issues, we propose a decentralized reputation management (DRM) mechanism for a trustworthy BFL (T-BFL) network, that explores, stores, and utilizes the endogenous reputation of distributed nodes to promote system security and efficiency. The proposed DRM includes three core modules, i.e., decentralized reputation evaluation, reputation-based model aggregation, and reputation-based blockchain consensus. Specifically, in the off-chain phase of T-BFL, the reputation value of each node is evaluated based on model quality, which other peer nodes can verify. This reputation value further determines the weight of global aggregation at each node. In the on-chain phase, the reputation of each node serves as the stake to dynamically adjust its consensus difficulty. Furthermore, we investigate the convergence rate of the T-BFL network under the poisoning attack, and dynamically optimize the energy allocation of local training, consensus, and communications by minimizing the upper bound of the global loss function. Extensive experiments are conducted to evaluate the performance of T-BFL on MNIST, Fashion-MNIST, and Cifar-10 datasets. The experimental results demonstrate that, compared with traditional BFL, T-BFL can achieve up to 56.12% accuracy improvement and$8.6\times $acceleration for reaching the target learning accuracy under the poisoning attack.

Traditional federated learning (FL) frameworks rely on a central server for model coordination among distributed mobile terminals (MTs). The centralization faces two critical challenges, i.e., single point of failure and potential privacy leakage. Differentially private decentralized FL (DP-DFL) has been proposed to address these challenges, wherein the MTs exchange models in a decentralized manner and maintain the differential privacy (DP) guarantee by adding noise to local models before model interaction. However, existing DP-DFL frameworks confront difficulty in achieving the expected privacy and convergence performance, simultaneously. To address this issue, we propose a novel DP-DFL framework (called randomized DP-DFL) that employs a randomized model interaction scheme to lower the model exposure frequency and hence reduce privacy budget consumption. Specifically, the scheme includes two sequential steps, i.e., randomized terminal assignment and randomized model transmission. In Step 1), the model interaction phase of DFL is further divided into several sequential substages. MTs are randomly assigned to each sub-stage. In Step 2), each MT sequentially transmits either a model previously received from its neighbors or its own local model according to the assigned sub-stage order. The proposed scheme enhances the MTs' privacy of DFL since the exposure probabilities of the MTs' local models are significantly reduced via these two randomized steps. Besides, we theoretically analyze the convergence and privacy performance of randomized DP-DFL. In particular, properly tuning the number of sub-stages in randomized DP-DFL can achieve an optimal balance between privacy and convergence. Experimental results show that randomized DP-DFL consistently outperforms traditional frameworks. Compared with baselines, randomized DP-DFL reduces 40.9% privacy loss under the same target accuracy while improving 9.5% learning accuracy under the same privacy loss on EMNIST and CIFAR-10, respectively

In ultra-dense autonomous aerial vehicle (AAV) networks, it is challenging to coordinate the resource allocation and interference management among large-scale AAVs, for providing flexible and efficient service coverage to the ground users (GUs). In this paper, we propose a learning-based resource allocation scheme in an ultra-dense AAV communication network, where the GUs’ service demands are time-varying with unknown distributions. We formulate the non-cooperative game among multiple co-channel AAVs as a stochastic game, where each AAV jointly optimizes its trajectory, user association, and downlink power control to maximize the expectation of its locally cumulative energy efficiency under the interference and energy constraints. To cope with the scalability issue in a large-scale network, we further formulate the problem as a mean-field game (MFG), which simplifies the interactions among the AAVs into a two-player game between a representative AAV and a mean-field. We prove the existence and uniqueness of the equilibrium for the MFG, and propose a model-free mean-field reinforcement learning algorithm named maximum entropy mean-field deep Q network (ME-MFDQN) to solve the mean-field equilibrium in both fully and partially observable scenarios. The simulation results reveal that the proposed algorithm improves the energy efficiency compared with the benchmark algorithms. Moreover, the performance can be further enhanced if the GUs’ service demands exhibit higher temporal correlation or if the AAVs have wider observation capabilities over their nearby GUs.

In an intelligent reflecting surface (IRS)-assisted wireless communication system, obtaining the real-time channel state information (CSI) through channel estimation (CE) is crucial for achieving the IRS’s passive beamforming gain, which however shortens the effective data transmission time due to the CSI feedback overhead. It is of utmost importance to decide how often to estimate the channels in an IRS-assisted system. In this paper, we propose an integrated CE and beamforming scheme to jointly optimize the adaptive CE interval and passive beamforming strategy, based on the past observation sequences composed of imperfect CSI and data rate feedback. We formulate the two-stage optimization problem as a bilevel partially observable Markov decision process (POMDP), aiming to maximize the expectation of cumulative throughput of the system. We propose two bilevel deep reinforcement learning (DRL) algorithms, namely recurrent neural network (RNN) based proximal policy optimization (PPO) algorithm and Belief-based PPO algorithm, to solve this problem. In these two algorithms, the CSI features from the past observation sequences are implicitly extracted by the RNN network or explicitly inferred by the belief network, which then serve as the inputs for the two-stage policy networks to determine the necessity of CE and the IRS beamforming vector based on the PPO algorithm. Simulation results demonstrate the superiority of the proposed adaptive CE scheme over the periodic counterpart in terms of throughput. Moreover, the results show that it is profitable to estimate the channels less frequently if the channels exhibit a higher correlation across time.

A critical security issue in the blockchain-aided cooperative spectrum sensing (B-CSS) network is that, blockchain cannot guarantee the reliability of off-chain data source, even though the data has been recorded on the chain. Furthermore, the performance optimization of the B-CSS networks is constrained by an underlying tradeoff between throughput and security. Driven by these issues, we first develop a novel B-CSS framework with a decentralized reputation management (DRM) mechanism, wherein nodes not only collaborate to detect the availability of target spectrum off the chain, but also act as the blockchain nodes to maintain global decisions on the chain. In the off-chain phase, the DRM mechanism can enhance the trustworthiness of CSS by evaluating each node's reputation according to its contribution to the global detection. Furthermore, in light of the on-chain throughput-and-security tradeoff, verifiable reputation can be utilized as the consensus stake to adjust the difficulty level of block generation. Then, given the on-chain reputation consensus, we maximize the average throughput of the proposed framework by jointly optimizing the block size, sensing time, and block generation time. Simulation results demonstrate the optimized performance of the proposed framework. Moreover, compared with the baseline schemes, our proposal is more robust to the threat of malicious attacks such as data-tampering attack and collusion attack.

This paper proposes a dynamic resource trading scheme in unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network. A UAV-assisted MEC server adaptively adjusts its trajectory to sell the computation offloading services to the mobile users (MUs), where the MUs have stochastic task arrivals. In this context, we formulate the sequential resource trading problem as a stochastic Stackelberg game, which is composed of two stages for each trading round. In the first stage, the self-interested UAV jointly optimizes its trajectory and service price to maximize its long-term profits. In the second stage, the non-cooperative MUs optimize their binary offloading decisions to minimize the average task processing delay and service payment. However, it is challenging to obtain the equilibrium across the fully decentralized agents with constantly evolving and tightly coupled policies, where each agent is confronted with a non-stationary environment. To solve this problem, we propose an opponent modeling based double deep Q learning (OM-DDQN) algorithm, where each agent adopts opponent modeling to effectively predict the trading strategies of other agents in the network. Simulation results demonstrate that, compared with the baseline algorithms, the proposed algorithm can achieve a win-win resource trading outcome that not only enhances the UAV's profit but also reduces the MUs' costs.

This paper proposes a dynamic spectrum sharing scheme in an unmanned aerial vehicle (UAV) assisted cognitive radio network. The UAV serves as a secondary base station to provide communication services to multiple secondary users (SUs) by adaptively utilizing the spatio-temporal spectrum opportunities of multiple device-to-device primary users (PUs), where each PU’s spectrum occupancy follows a two-state Markov process. We jointly optimize the UAV’s trajectory and user association to maximize the expectation of its cumulative energy efficiency subject to the interference constraint of the PUs. We formulate this problem as a partially observable Markov decision process (POMDP), where the UAV can only observe the spectrum occupancy status of the adjacent PUs. Due to the lack of the PUs’ spectrum occupancy statistics, we propose a model-free reinforcement learning algorithm named partially observable double deep Q network (PO-DDQN) to obtain the near-optimal spectrum sharing policy. Simulation results show that our proposed algorithm outperforms the baseline policy gradient (PG) algorithm in terms of convergence speed and the UAV’s energy efficiency. Additionally, the spectrum utilization efficiency can be further enhanced when the UAV has wider observation radius, or if the PUs’ spectrum occupancy exhibits stronger temporal correlation.

Building upon the prevailing concept of edge computing (EC), a distributed EC market requires decentralized and verified transaction management to trade computing resources. Towards this goal, we study a blockchain-aided EC market wherein each data service operator (DSO) rents a group of edge computing nodes (ECNs) and leases the ECNs to the user terminals (UTs) to provide computation offloading services. A trustworthiness model is introduced to evaluate the quality of each network entity throughout the transactions. We develop a two-level trading mechanism over smart contract to enable the automatic and efficient transactions among the network entities and provide high quality services. First, we propose a smart contract based matching mechanism to establish the renting association between the DSOs and ECNs with the aim of maximizing the social welfare. Second, we propose a social welfare improved double auction (SWIDA) mechanism to build up the leasing association between the DSOs and UTs, and determine the pricing of the winners. We show that the proposed double auction mechanism can achieve individual rationality, balanced budget, truthfulness in expectation, and an improved social welfare than the benchmark mechanisms. Moreover, we put forth a trustworthiness driven Proof-of-Stake (PoS) consensus mechanism to enable verified transaction and fair allocation of block generation reward. Following the principle of PoS, we formulate the block generation as a coalitional game, wherein each stakeholder votes according to its trustworthiness and coinage, and shares the reward among the coalition according to the Shapley values. The simulation results show that the proposed PoS consensus mechanism can reduce the wealth inequality among the network entities compared with the conventional consensus mechanisms.

Unmanned aerial vehicle (UAV) technology is a promising solution for rapidly providing wireless communication services to ground users, where a UAV has limited service coverage and needs to fly through users at different locations for serving them locally. The existing UAV deployment studies largely assume the users’ demands do not change during UAV deployment. When the users’ demands dynamically change over time, the key challenge is how to adapt the UAV deployment strategy to the partial and even outdated observations on the users’ activities given the UAV's flying speed limit. In this paper, we study dynamic UAV deployment to learn and adapt to the time-varying user activities, where the activity pattern of a user (if out of the UAV service coverage) is hidden from the UAV and follows a time-slotted Markov chain that switches between active and idle states. We formulate the learning-and-adaption based UAV deployment problem as a partially observable Markov decision process (POMDP) to maximize the total discounted hit rate of active users, where the UAV decides for itself whether to chase an active user in a distant location (with delayed reward) or to wait for the idle user in the current location to return to the active state (with smaller service probability) over time. We show there is a fundamental delay-reward tradeoff, and prove that the UAV will optimally follow a threshold-based policy by waiting at an idle user for a time threshold before moving to another user. We also show the UAV is more likely to move if the temporal correlation of each user's idling pattern is stronger or the travel distance between users is shorter. Furthermore, we extend to a more general scenario where the UAV does not even know the parameters of each user's temporal activity distribution, and apply Q-learning to develop another threshold-based deployment policy for a multi-user scenario.

Directional wireless power transfer (WPT) technology provides a promising energy solution to remotely recharge the Internet of things sensors using directional antennas. Under a harvest-then-transmit protocol, the access point can adaptively allocate the transmit power among multiple energy directions to maximize the social welfare of the sensors, i.e., downlink sum received energy or uplink sum rate, based on full or quantized channel gains reported from the sensors. However, such power allocation can be challenged if each sensor belongs to a different agent and works in a competitive way. In order to maximize their own utilities, the sensors have the incentives to falsely report their channel gains, which unfortunately reduces the social welfare. To tackle this problem, we design the strategy-proof mechanisms to ensure that each sensor’s dominant strategy is to truthfully reveal its channel gain regardless of other sensors’ strategies. Under the benchmark full channel gain reporting (CGR) scheme, we adopt the Vickrey-Clarke-Groves (VCG) mechanism to derive the price functions for both downlink and uplink, where the truthfulness is guaranteed by asking each sensor to pay the social welfare loss of all other sensors attributable to its presence. For the 1-bit CGR scheme, the problem is more challenging due to the severe information asymmetry, where each sensor has true valuation of full channel gain but may report the false information of quantized channel gain. We prove that the classic VCG mechanism is no longer truthful and then propose two threshold-based price functions for both downlink and uplink, where the truthfulness is ensured by letting each sensor pay its own achievable utility improvement due to its participation. The numerical results validate the truthfulness of the proposed mechanism designs.

Unmanned aerial vehicle (UAV) as an aerial base station is a promising technology to rapidly provide wireless connectivity to ground users. Given UAV's agility and mobility, a key question is how to adapt UAV deployment to the best cater to instantaneous wireless traffic in a territory. In this paper, we propose an adaptive deployment scheme for a UAV-aided communication network, where the UAV adapts its displacement direction and distance to serve randomly moving users' instantaneous traffic in the target cell. In our adaptive scheme, the UAV does not need to learn users' exact locations in real time, but chooses its displacement direction based on a simple majority rule by flying to the spatial sector with the greatest number of users in the cell. To balance the service qualities of the users in different sectors, we further optimize the UAV's displacement distance in the chosen sector to maximize the average throughput and the successful transmission probability, respectively. We prove that the optimal displacement distance for average throughput maximization decreases with the user density: the UAV moves to the center of the chosen sector when the user density is small and the UAV displacement becomes mild when the user density is large. In contrast, the optimal displacement distance for success probability maximization does not necessarily decrease with the user density and further depends on the target signal-to-noise ratio (SNR) threshold. The extensive simulations show that the proposed adaptive deployment scheme outperforms the traditional non-adaptive scheme, especially when the user density is not large.

Unmanned aerial vehicle (UAV) can be used as an aerial base station to provide rapid wireless connectivity to ground users. Given UAV's agility and mobility, a key problem is how to adapt UAV deployment to best cater to the instantaneous wireless traffic in a territory. In this paper, we propose a traffic-aware adaptive UAV deployment scheme in a UAV-aided communication network, where the UAV initiated at the cell center adapts its displacement direction and distance to the spatial randomness of the Poisson distributed mobile users within its target cell. In each realization, the UAV chooses its displacement direction based on a simple majority rule, i.e., to fly to the sector that has the greatest number of users. To balance the service for the users in different sectors, we further optimize the UAV's displacement distance in the chosen sector to maximize the average throughput. We show that the optimal displacement distance under the proposed scheme decreases with the user density. Extensive simulations illustrate that the proposed adaptive deployment scheme outperforms the traditional non-adaptive scheme, where the performance gain is especially significant for small user density.

Wireless power transfer (WPT) prolongs the lifetime of wireless sensor network by providing sustainable power supply to the distributed sensor nodes (SNs) via electromagnetic waves. To improve the energy transfer efficiency in a large WPT system, this paper proposes an adaptively directional WPT (AD-WPT) scheme, where the power beacons (PBs) adapt the energy beamforming strategy to SNs' locations by concentrating the transmit power on the nearby SNs within the efficient charging radius. With the aid of stochastic geometry, we derive the expressions of the distribution metrics of the aggregate received power at a typical SN. To design the charging radius for the optimal AD-WPT operation, we exploit the tradeoff between the power intensity of the energy beams and the number of SNs to be charged. Depending on different SN task requirements, the optimal AD-WPT can maximize the average received power or the active probability of the SNs, respectively. It is shown that both the maximum average received power and the maximum sensor active probability increase with the increased deployment density and transmit power of the PBs, and decrease with the increased density of the SNs and the energy beamwidth. Finally, we show that the optimal AD-WPT can significantly improve the energy transfer efficiency compared with the traditional omnidirectional WPT.

Wireless power transfer (WPT) prolongs the lifetime of wireless sensor network by providing sustainable power supply to the distributed sensor nodes (SNs) via electromagnetic waves. To improve the energy transfer efficiency in a large WPT system, this paper proposes an adaptively directional WPT (AD-WPT) scheme, where the power beacons (PBs) adapt the energy beamforming strategy to SNs' locations by concentrating the transmit power on the nearby SNs within the efficient charging radius. With the aid of stochastic geometry, we derive the closed-form expressions of the distribution metrics of the aggregate received power at a typical SN. We analyze the optimal charging radius that maximizes the average received power. It is shown that both the optimal charging radius and maximized average received power decrease with the increased density of the SNs and the energy beamwidth. Finally, we show that the optimal AD-WPT can significantly improve the energy transfer efficiency compared to the traditional omnidirectional WPT.

In this paper, we propose a limited feedback based underlay spectrum sharing scheme in a Poisson cognitive network. Both primary and secondary transmitters are Poisson distributed nodes with limited feedback of channel quality information from their local receivers. The primary and secondary transmitters are elected to transmit if their local channel gains are above certain thresholds. Closed-form optimal node density of primary user is obtained analytically by maximizing the primary area spectral efficiency under the primary outage constraint. In addition, closed-form optimal node density of the secondary user is derived by maximizing the secondary area spectral efficiency under the secondary outage constraint and primary efficiency loss constraint, where the primary efficiency loss constraint guarantees the degradation of the area spectral efficiency of primary user is within a tolerable threshold. Numerical results show the maximized area spectral efficiency of secondary user increases as the increase of the secondary outage constraint before it is flattened out by the primary efficiency loss constraint.

In this paper, we propose a limited feedback based underlay spectrum sharing scheme where a secondary decode-and-forward opportunistic relay network shares the spectrum with a pair of primary users. The primary destination and each of the N secondary relays send 1-bit feedback of channel quality information to their corresponding transmitters. By overhearing the primary feedback and receiving the secondary feedback, one of the secondary relays is scheduled at the secondary transmitter in each fading block. By maximizing the average secondary rate under the average primary rate loss constraint and average secondary power constraint, the asymptotically optimal solutions of secondary thresholds and power allocation are derived analytically for large N. With the increase of the number of secondary relays, the average achievable rate of secondary user grows as 1/2 log log N.

In this paper, we propose a limited feedback based underlay spectrum sharing scheme where a downlink secondary network shares the spectrum with a pair of primary users. The primary receiver and N secondary receivers each sends 1-bit feedback of channel quality information to their corresponding transmitters. By overhearing the primary feedback and receiving the secondary feedback, one of the secondary receivers is scheduled at the secondary transmitter in each fading block. The optimal channel quantization thresholds and power allocation are jointly determined by maximizing the average throughput of the secondary user under the average secondary power constraint and the average primary rate loss constraint. The average throughput of the secondary user grows as log log N.

In this paper, we propose two limited feedback based underlay spectrum sharing schemes in Poisson cognitive radio networks without and with primary exclusive regions, respectively. Both primary and secondary transmitters are Poisson distributed nodes with limited feedback of channel quality information from their local receivers. The primary transmitters are active if their local primary channels are above a certain threshold. In the opportunistic spectrum sharing scheme without primary exclusive region, the secondary transmitters are elected to transmit if their local channels are above the required threshold. In the opportunistic spectrum sharing scheme with a primary exclusive region, the secondary transmitters transmit if their local channels are above the threshold and they are outside the exclusive regions of the active primary receivers. For both schemes, the optimal secondary node density is analytically derived by maximizing the secondary area spectral efficiency subject to the secondary outage constraint and the primary efficiency loss constraint. Numerical results show that, for a tight secondary outage constraint, it is more beneficial to use the scheme with no primary exclusive region. For relatively loose secondary outage constraint, the scheme with primary exclusive regions is recommended.

In this paper, we propose an underlay spectrum sharing scheme where a secondary broadcast system shares the spectrum with a pair of primary users. Both primary and secondary users are discrete rate-power adaptive systems equipped with limited feedback of the channel quality information. By overhearing the primary feedback and receiving the feedback from each secondary receivers, the best secondary receiver is scheduled at the secondary transmitter which chooses an appropriate power-rate pair from the pre-designed quantization codebook. The secondary quantization codebook is designed with the aim of finding the optimal power and rate allocation which maximize the average secondary rate under the average secondary power constraint and the primary rate loss constraint. Numerical results illustrate that, the secondary performance improves with the increase of secondary feedback load. In addition, for a fixed total feedback load, increasing the number of the secondary receivers brings more benefits than increasing the number of feedback bits at each secondary receiver.

In this paper, an underlay spectrum sharing scheme is proposed where both primary and secondary users are discrete power-rate adaptive systems with limited feedback from receivers. By receiving secondary and overhearing primary quantized channel quality information from the secondary receiver and primary receiver, respectively, the secondary transmitter adapts its resource allocation to the current channel quality by selecting a proper power-rate pair from a pre-designed secondary quantization codebook. The secondary quantization codebook is designed to maximize the secondary average rate subject to the primary rate loss constraint and the average secondary transmit power constraint, which is discussed in three cases when different amount of side information of the primary codebook and the cross interference link are available at the secondary receiver side. Differential Evolution algorithm is employed to provide the global optimal solutions to the proposed secondary quantization codebook optimization problems. Numerical results show that, by introducing the secondary feedback, the secondary throughput is greatly improved. Furthermore, more secondary feedback bits or more primary side information result in better secondary performance.

In this paper, an underlay spectrum sharing scheme is proposed where both primary and secondary users are discrete power-rate adaptive systems with limited feedback from receivers. By receiving secondary and overhearing primary quantized channel quality information from the secondary receiver and primary receiver, respectively, the secondary transmitter adapts its resource allocation to the current channel quality by selecting a proper power-rate pair from the pre-designed secondary quantization codebook. The secondary quantization codebook is designed to maximize the secondary average achievable rate subject to the long term power constraint of the secondary transmitter and the rate loss constraint at the primary receiver. Numerical results show that, by introducing the secondary feedback, the secondary throughput is greatly improved. More secondary feedback bits result in better secondary performance.

In this paper, we propose a relay assisted spectrum sharing (RASS) scheme based on the mixed sharing strategy in cognitive radio networks. Mixed sharing is a more general sharing strategy which provides a higher spectrum utilization efficiency than underlay sharing and interweave sharing. Compared to conventional mixed sharing, the proposed approach enhances the throughput of secondary users while not causing harmful interference to the primary receiver. The optimal time allocation which maximizes the achievable capacity of the secondary system is derived. At the same time, the existence of the optimal sensing time is proved and the optimal sharing time allocation between the two-hop relay links is presented. Numerical results show that there exists global optimal time allocation for sensing and sharing in the proposed RASS scheme.

In this paper we investigate opportunistic spectrum access in cognitive radio networks when a decode and forward relay is employed. To better exploit the white space in primary networks, we propose two cognitive spectrum access approaches based on white space modeling, referred to as successive sensing based spectrum access and simultaneous sensing based spectrum access. We further study the optimal time allocation for the two-hop relay to minimize the outage probability of the cognitive data transmission. Numerical results show that the optimal time allocation heavily depends on the primary user traffic rate.