By integrating Cognitive Radio (CR) functionality into the Internet of Things (IoT), the CR-based IoT network offers a promising solution to the spectrum scarcity problem faced by traditional IoT systems. However, accurate and fair Cooperative Spectrum Sensing (CSS) faces many challenges, such as malicious nodes’ presence, sensor behaviour reliability, and IoT devices’ limited energy. In this paper, we propose a lightweight blockchain-enabled CSS system that enhances transparency and reliability in sensing report exchange and fusion. Specifically, we introduce a comprehensive trust evaluation algorithm and a fair sensor selection method to assess the reliability of sensors and fairly assign spectrum sensing tasks. To ensure the decentralization and security of IoT networks, we propose a lightweight consensus mechanism which utilizes trust values and deposits to determine voting weights during producer elections while removing malicious nodes by blocklist mechanism. A smart contract is also designed to automate the entire CSS process, including spectrum sensing, deposit management, and trust management. Finally, security analysis and numerical simulations are conducted to demonstrate the effectiveness and robustness of the proposed blockchain-enabled CSS system.

Cooperative spectrum sensing (CSS) is a promising approach to improve the detection of primary users (PUs) using multiple sensors. However, there are several challenges for existing combination methods, i.e., performance degradation and ceiling effect for hard-decision fusion (HDF), as well as significant uploading latency and non-robustness to noise in the reporting channel for soft-data fusion (SDF). To address these issues, an integrated communication and computation (ICC) framework is proposed in this paper. Specifically, distributed semantic communication (DSC) jointly optimizes multiple sensors and the fusion center to minimize the transmitted data without degrading detection performance. Moreover, over-the-air computation (AirComp) is utilized to further reduce spectrum occupation in reporting channel, taking advantage of characteristics of wireless channel to enable data aggregation. Under the ICC framework, a particular system, namely ICC-CSS, is designed and implemented, which is theoretically proved to be equivalent to the optimal estimator-correlator (E-C) detector with equal gain SDF when the PU signal samples are independent and identically distributed. Extensive simulations verify the superiority of ICC-CSS compared with various conventional CSS schemes in terms of detection performance, robustness to SNR variations in both sensing and reporting channels, as well as scalability with respect to the number of samples and sensors.

The proliferation of synthetic media generation technologies, such as generative AI, has led to a surge of media content generation and consumption. While this progress opens new opportunities, especially in creative industries, it also causes challenges, including piracy, fake media distribution, and concerns about trust and privacy. In the creative sector, media modifications are often part of the production pipelines and in many application domains, creators need or want to declare the type of modifications that were performed on the media asset. The cryptographically signed association of provenance information with the media asset itself provides a trust link between the owner or editor of a media asset and its consumers. The absence of such assertions may reveal the lack of trustworthiness in media assets or worse, the intention to hide the existence of manipulations. This paper describes the JPEG Trust framework (ISO/IEC 21617) that aims to establish trust in digital media creation, modification, annotation, distribution and consumption. The framework provides standardized protocols to extract indicators to assess trustworthiness, means to annotate media provenance, and securely link the assets and associated annotations together.

Abstract In most threshold Elliptic Curve Digital Signature Algorithm (ECDSA) signatures using additively homomorphic encryption, the zero-knowledge (ZK) proofs related to the ciphertext or the message space are the bottleneck in terms of bandwidth as well as computation time. In this paper, we propose a compact ZK proof for relations related to the Castagnos–Laguillaumie (CL) encryption, which is 33% shorter and 29% faster than the existing work in PKC 2021. We also give new ZK proofs for relations related to homomorphic operations over the CL ciphertext. These new ZK proofs are useful to construct a bandwidth-efficient universal composable-secure threshold ECDSA without compromising the proactive security and the non-interactivity. In particular, we lowered the communication and computation cost of the key refresh algorithm in the Paillier-based counterpart from $O(n^3)$ to $O(n^2)$. Considering a 5-signer setting, the bandwidth is better than the Paillier-based counterpart for up to 99, 95 and 35% for key generation, key refreshment and pre-signing, respectively.

This paper investigates master-auxiliary unmanned aerial vehicles (UAVs)-enabled wireless-powered Internet-of-Things (WPIoT) networks, which overcome the inflexibility and site selection issues caused by traditional fixed-point intelligent reflecting surface (IRS). Specifically, multiple rechargeable Master UAVs (MUAVs) and IRS-integrated Auxiliary UAVs (AUAVs) are applied in pairs to cooperatively charge and collect data from IoT devices clustered in different subareas under cloud scheduling. Given the constraints of limited onboard battery capacity and complete data collection, we formulate a system energy minimization problem, which is then divided into three sub-problems. We first utilize a pair of U-nets with quantization layers trained by deep unsupervised learning (DUL) to output discrete downlink (DL) and uplink (UL) IRS phases separately. Gradient functions with specific features are also proposed to solve non-differentiable issue during training. Given the optimized IRS phase policies, off-policy deep reinforcement learning (DRL) is exploited to optimize intra-subarea data collection policy and inter-subarea multi-UAV scheduling scheme. Two assistive techniques, positive transition initialization (PTI) and action mask, are proposed to guide the learning of the agent and alleviate the burden of optimization. Numerical results indicate that our proposed approach combining DUL with DRL can improve performance by more than 90% compared to the pure DRL method. Furthermore, our proposed Master-Auxiliary-UAV collaborative data collection scheme (MACS) can achieve better energy performance than homogeneous MUAV data collection scheme (HMCS) in cases with low onboard battery capacity while halving task completion time.

Deep learning-empowered semantic communication is regarded as a promising candidate for future 6G networks. Although existing semantic communication systems have achieved superior performance compared to traditional methods, the end-to-end architecture adopted by most semantic communication systems is regarded as a black box, leading to the lack of explainability. To tackle this issue, in this paper, a novel semantic communication system with a shared knowledge base is proposed for text transmissions. Specifically, a textual knowledge base constructed by inherently readable sentences is introduced into our system. With the aid of the shared knowledge base, the proposed system integrates the message and corresponding knowledge from the shared knowledge base to obtain the residual information, which enables the system to transmit fewer symbols without semantic performance degradation. In order to make the proposed system more reliable, the semantic self-information and the source entropy are mathematically defined based on the knowledge base. Furthermore, the knowledge base construction algorithm is developed based on a similarity-comparison method, in which a pre-configured threshold can be leveraged to control the size of the knowledge base. Moreover, the simulation results have demonstrated that the proposed approach outperforms existing baseline methods in terms of transmitted data size and sentence similarity.

This paper investigates heterogeneous unmanned aerial vehicles (UAVs)-enabled wireless-powered Internet-of-Things (WPIoT) networks, where a Master UAV (MUAV) and an intelligent reflecting surface (IRS)-integrated Auxiliary UAV (AUAV) cooperatively collect data from terrestrial IoT devices. Specifically, the MUAV leverages a successive hover-and-fly strat-egy for energy broadcasting and data collection, while the AUAV accompanies MUAV and assists in both wireless power transfer (WPT) and wireless information transmission (WIT) processes through adaptive adjustment of IRS. Given the stringent re-quirement of complete data collection, we divide the energy-efficiency maximization problem into two sub-problems: discrete phase control and joint trajectory design and time allocation. To address these issues, we propose combining deep unsupervised learning (DUL) and artificial replay buffer initialization (ARBI)-enhanced off-policy deep reinforcement learning (DRL). Numeri-cal results demonstrate that our proposed scheme achieves better performance in terms of energy-efficiency, task complete time and reward convergence.

This article investigates a master unmanned aerial vehicle (MUAV)-powered Internet of Things (IoT) network, in which we propose using a rechargeable auxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) to enhance the communication signals from the MUAV and also leverage the MUAV as a recharging power source. Under the proposed model, we investigate the optimal collaboration strategy of these energy-limited UAVs to maximize the accumulated throughput of the IoT network. Depending on whether there is charging between the two UAVs, two optimization problems are formulated. To solve them, two multi-agent deep reinforcement learning (DRL) approaches are proposed, which are centralized training multi-agent deep deterministic policy gradient (CT-MADDPG) and multi-agent deep deterministic policy option critic (MADDPOC). It is shown that the CT-MADDPG can greatly reduce the complexity of optimization, and the proposed MADDPOC is able to support low-level multi-agent cooperative learning in the continuous action domains, which has great advantages over the existing option-based hierarchical DRL that only supports single-agent learning and discrete actions.

Blockchain is a promising technology for future dynamic spectrum access (DSA) management due to its decentralization, immutability, and traceability. However, many challenges need to be addressed to integrate the blockchain to DSA, such as the trustworthiness of participating nodes’ spectrum sensing results, privacy protection of sensing nodes’ identities, and affordable lightweight consensus algorithms for IoT devices. In this article, we propose a trust-centric privacy-preserving blockchain for DSA in IoT networks. To be specific, we propose a trust evaluation mechanism to evaluate the trustworthiness of sensing nodes and design a Proof-of-Trust (PoT) consensus mechanism to build a scalable blockchain with high transaction-per-second (TPS). Moreover, a privacy protection scheme is proposed to protect sensors’ real-time geolocation information when they upload sensing data to the blockchain. Two smart contracts are designed to make the whole procedure (spectrum sensing, spectrum auction, and spectrum allocation) run automatically. Simulation results demonstrate the expected computation cost of the PoT consensus algorithm for reliable nodes is low, and the cooperative sensing performance is improved with the help of the trust evaluation mechanism. In addition, incentivization and security are also analyzed, which show that our system can not only encourage nodes’ participation, but also resist many kinds of attacks which are frequently arise in the trust management mechanism and blockchain-based IoT systems.

In this paper, we propose a novel Intelligent Reflected Surface (IRS)-aided Master-Auxiliary-Unmanned Aerial Vehicle (UAV)-powered Internet of Things (IoT) Network (IRS-MAIN). Compared to the conventional terrestrial or aerial IRS-assisted communication networks, the IRS-MAIN not only benefits from wide communication range due to the mobility of UAVs, but also enjoys enhanced channel condition brought by the IRS. To be specific, let the Auxiliary UAV (AUAV) carry an IRS to enhance signals from the Master UAV (MUAV) served as a radio frequency (RF) transmitter. We focus on the problem to maximize the total throughput by jointly optimizing the trajectories and transmit power of the MUAV. A modified multi-agent deep reinforcement learning (MADRL) based algorithm, named as Pre-activation Penalty Multi-agent Deep Deterministic Policy Gradient (PP-MADDPG), is proposed to solve the formulated problem in an accurate and efficient way. Simulation results are provided to demonstrate that PP-MADDPG outperforms the baseline method in terms of the throughput as well as the convergence rate.

This article investigates a full-duplex orthogonal-frequency-division multiple access (OFDMA)-based multiple unmanned-aerial-vehicles (UAVs)-enabled wireless-powered Internet-of-Things (IoT) networks. In this paper, a swarm of UAVs is first deployed in 3-D to simultaneously charge all devices, i.e., a downlink (DL) charging period, and then flies to new locations within this area to collect information from scheduled devices in several epochs via OFDMA due to potential limited number of channels available in IoT during an uplink (UL) communication period. To maximize the UL throughput of IoT devices, we jointly optimize the UL-and-DL 3-D deployment of the UAV swarm, including the device-UAV association, the scheduling order, and the UL-DL time allocation. In particular, the DL energy harvesting threshold of devices and the UL signal decoding threshold of UAVs are taken into consideration when studying the problem. Besides, both line-of-sight and non-line-of-sight channel models are studied depending on the position of sensors and UAVs. The influence of potential limited number of channels in IoT is also considered. Guidelines on the 3-D placement of UAVs in the DL charging and the UL communications are also given. Finally, simulation results show that the proposed optimal time allocation OFDMA-UAV scheme achieves significant throughput gains compared with conventional schemes.

In this paper, we investigate a large intelligent surface (LIS) assisted symbiotic radio (SR) system, in which a LIS device, operating as an Internet-of-Things (IoT) device, exploits the signal from a primary transmitter (PT) as its communication carrier to achieve its own information transmission, and concurrently serves as a desirable additional link to aid the primary transmission from the PT to a primary receiver (PR). A cooperative beamforming scheme (i.e., active transmit beamforming at the PT and passive reflecting beamforming at the LIS device) is proposed to minimize PT's transmit power under quality-of-service (QoS) constraints of both the primary and LIS device transmissions. Both continuous and discrete phase shift setups of the LIS device are considered. For the continuous phase shift setup, a closed-form solution is derived, analytically showing that by smartly configuring the phase shifts, the signals from primary link and backscatter link can add coherently at the PR; while for the discrete phase shift setup, a near-optimal solution for the 1-bit phase shifter is obtained via the semi-definite relaxation (SDR) technique, and a general successive refinement algorithm (SRA) is developed for any-bit phase shifter. Simulation results demonstrate that cooperative beamforming design can adaptively adjust beamformers to strike a balance between the primary and LIS device transmissions.

This paper proposes a peer-assisted power supply approach for a wireless powered Internet of Things (IoT) non-orthogonal multiple access (NOMA) network in which passive user equipments (UEs) without battery harvest energy from active UEs equipped with power supply. Specifically, passive UEs harvest energy from active UEs during their uplink transmission using NOMA. They then upload information along with the active UEs. Particularly, considering the combination of time division multiple access (TDMA) and NOMA, under the assumption that the power of active UEs is fixed, we study different transmission modes (non-stand-alone/stand-alone) and different operations (NOMA/NOMA-plus-TDMA). Taking into account the practical applications, we re-investigate the above schemes in the scenario where active UEs' energy is limited, i.e., the power of active UEs is not fixed and is affected by time allocation. We maximize the sum-throughput of each proposed model. We prove that the optimization problems for all cases are convex, and we obtain closed-form solutions for most cases. Finally, we show by simulations that, in all cases, the transmit power of active UEs and the number of UEs have a positive effect on the sum-throughput. Besides, in terms of maximizing the sum-throughput, the NOMA-plus-TDMA operation outperforms the NOMA operation. If active UEs' power is fixed, the non-stand-alone transmission outperforms the stand-alone transmission, and vice versa.

In this paper, we propose a series of novel coordination schemes for multi-cell downlink communication. Starting from full base station (BS) coordination, we first propose a fully-coordinated scheme to exploit beneficial effects of both inter-cell and intra-cell interference, based on sharing both channel state information (CSI) and data among the BSs. To reduce the coordination overhead, we then propose a partially-coordinated scheme where only intra-cell interference is designed to be constructive while inter-cell is jointly suppressed by the coordinated BSs. Accordingly, the coordination only involves CSI exchange and the need for sharing data is eliminated. To further reduce the coordination overhead, a third scheme is proposed, which only requires the knowledge of statistical inter-cell channels, at the cost of a slight increase on the transmission power. For all the proposed schemes, imperfect CSI is considered. We minimize the total transmission power in terms of probabilistic and deterministic optimizations. Explicitly, the former statistically satisfies the users' signal-to-interference-plus-noise ratio (SINR) while the latter guarantees the SINR requirements in the worst case CSI uncertainties. Simulation verifies that our schemes consume much lower power compared to the existing benchmarks, i.e., coordinated multi-point (CoMP) and coordinated-beamforming (CBF) systems, opening a new dimension on multi-cell coordination.

This paper investigates the rotary-wing unmanned aerial vehicle (UAV)-enabled full-duplex wireless-powered Internet-of-Things (IoT) networks, in which a rotary-wing UAV equipped with a full-duplex hybrid access point (HAP) serves multiple sparsely-distributed energy-constrained IoT sensors. The UAV broadcasts energy when flying and hovering, and collects information only when hovering. It is assumed that the transmission range of the UAV is limited and the sensors are sparsely distributed in the IoT network. Under these practical assumptions, we formulate three optimization problems: a sum-throughput maximization (STM) problem, a total-time minimization (TTM) problem, and a total-energy minimization (TEM) problem. For the TEM problem, we further take into consideration that the power needed for hovering, flying, and transmitting are different. For the STM, TTM and TEM problems, optimal solutions are obtained. Finally, numerical results show that the performance achieved by the proposed optimal time allocation schemes outperform existing time allocation schemes. It is also observed that i) the time allocation between hovering and flying time has different trends for different goals; ii) there is an optimal UAV transmit power range that minimizes the energy consumed by the UAV during the entire cycle.

In this paper, we study the secrecy throughput in a massive Multiple-Input-Multiple-Output (MIMO) full-duplex wireless powered communication network (WPCN). The network consists of a massive MIMO base station (BS) and two groups of single-antenna sensor nodes which harvest energy from the BS. The first group, referred to as information transmitters (ITs), use the harvested energy to transmit information back to the BS; the second group, referred to as energy receivers (ERs), use the harvested energy for non-transmission related operations. We consider a two-slot protocol. In the first time slot, all nodes harvest energy from the BS. In the second slot, ITs transmit information to the BS, while BS use one set of its antennas to receive the signals and the other set of antennas to transmit artificial noise (AN) to the ERs. The AN serves two purposes: wireless power transfer to the ERs, and information security for the ITs with ERs, which are considered as potential eavesdroppers. We aim to maximize the total secrecy throughput of ITs subject to the ERs' received energy constraints. The problem is shown to be non-convex. To tackle the problem, we propose a twostage suboptimal approach, referred to as Maximizing Received AN aided Secrecy Throughput Maximization (MRAN-STM). In the first stage, the transmitted AN is optimized to maximize the minimum received AN of all the ERs. Then, in the second stage, the power allocation and time slot duration are optimized to maximize the total secrecy throughput. Numerical results show the improvement of the proposed algorithm.

This study investigates time-division duplex (TDD) orthogonal-frequency-division multiple access (OFDMA) unmanned aerial vehicles (UAVs)-aided wireless-powered Internet-of-Things (IoT) networks. Here, a swarm of UAVs simultaneously charge all IoT devices with constant power during a downlink (DL) phase. Using the harvested energy, each IoT device transmits data to an UAV during an uplink (UL) phase via OFDMA. We propose a novel framework to maximize the UL throughput by formulating and solving a joint optimization problem to find the optimal DL and UL time portions, device-UAV association, and the 3D placement of the UAVs. Using our proposed framework, it is shown that the 3D position of the UAVs will have different trends during the UL communications and the DL charging. The proposed TDD-OFDMA UAVs- aided can significantly improve the sum throughput of the IoT devices compare to the fixed base stations schemes.

In this paper, we investigate the secrecy throughput for a full-duplex wireless powered communication network. A multi-antenna base station (BS) transmits energy towards nodes all the time and each node harvests energy prior to its transmission time slot. Nodes sequentially transmit their confidential information to the BS in presence of other nodes which are considered as potential eavesdroppers. We derive the secrecy rate and formulate the sum secrecy throughput optimization of all nodes. The optimization variables are the time slot duration and the BS beamforming during different transmission phases. The problem is non-convex and non-trivial. We propose a suboptimal approach in which the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during the information transmission phase, with which we then obtain the optimum beamforming in each time slot and its duration. We compare our algorithm with uniform time slot and uniform beamforming in different settings and demonstrate its superior performance.

This paper investigates the rotary-wing unmanned aerial vehicle (UAV)-aided full-duplex wireless powered Internet-of-Things (IoT) networks, in which a rotary-wing UAV equipped with a full-duplex hybrid access point (HAP) serves multiple sparsely distributed energy constrained IoT sensors. The UAV broadcasts energy while flying and hovering. On the other hand, the UAV collects information while hovering. It is assumed that the transmission range of the UAV is limited and the sensors are sparsely distributed in the IoT networks. Thus, the energy broadcasted from the UAV is only available for the adjacent sensor. Here, we propose a new line model for UAV-aided IoT networks. With the proposed line model, we investigate the optimal time allocation to maximize the network throughput subject to a total time constant and a UAV maximum flight speed. The formulated throughput maximization problem is proved to be a convex optimization problem and the optimal solution is obtained by the mutual coupling of the convex optimization conditions. We further propose a simple algorithm under a specific condition. Finally, the numerical results verify that the performance achieved by the proposed optimal time allocation scheme outperforms the existing time allocation schemes. The maximum communication distance of the UAV at different heights and different transmission powers can be obtained through the comparison of algorithms.

In this paper, we study the secrecy throughput of a full-duplex wireless powered communication network (WPCN) for Internet of Things (IoT). The WPCN consists of a full-duplex multiantenna base station (BS) and a number of sensor nodes. The BS transmits energy all the time, and each node harvests energy prior to its transmission time slot. The nodes sequentially transmit their confidential information to the BS, and the other nodes are considered as potential eavesdroppers. We first aim to optimize the sum secrecy throughput of the nodes. The optimization variables are the duration of the time slots and the BS beamforming vectors in different time slots. The optimization problem is shown to be nonconvex. To tackle the problem, we propose a suboptimal two stage approach, referred to as sum secrecy throughput maximization (SSTM). In the first stage, the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during information transmission time slots. Then, the optimal beamforming vector in the initial noninformation transmission time slot and the optimal time slots are derived. We then consider secrecy throughput fairness among the nodes and propose max-min fair (MMF) and proportional fair (PF) algorithms. The MMF algorithm maximizes the minimum secrecy throughput of the nodes, while the PF achieves a good tradeoff between the sum secrecy throughput and fairness among the nodes. Through the numerical simulations, we first demonstrate the superior performance of the SSTM to uniform time slotting and beamforming in different settings. Then, we show the effectiveness of MMF and PF algorithms.

Internet-of-Things (IoT) is a promising technology to connect massive machines and devices in the future communication networks. In this paper, we study a wireless-powered IoT network (WPIN) with short packet communication (SPC), in which a hybrid access point (HAP) first transmits power to the IoT devices wirelessly, then the devices in turn transmit their short data packets achieved by finite blocklength codes to the HAP using the harvested energy. Different from the long packet communication in conventional wireless network, SPC suffers from transmission rate degradation and a significant packet error rate. Thus, conventional resource allocation in the existing literature based on Shannon capacity achieved by the infinite blocklength codes is no longer optimal. In this paper, to enhance the transmission efficiency and reliability, we first define effective-throughput and effective-amount-of-information as the performance metrics to balance the transmission rate and the packet error rate, and then jointly optimize the transmission time and packet error rate of each user to maximize the total effective-throughput or minimize the total transmission time subject to the users' individual effective-amount-of-information requirements. To overcome the non-convexity of the formulated problems, we develop efficient algorithms to find high-quality suboptimal solutions for them. The simulation results show that the proposed algorithms can achieve similar performances as that of the optimal solution via exhaustive search, and outperform the benchmark schemes.

The Random Access (RA) procedure is used to request channel resources for the uplink data transmission in the cellular-based massive Internet of Things (mIoT). To ease the RA failure and the network congestion, power ramping (PR) technique is used to step up the preamble transmit power after each unsuccessful RA attempt. In this paper, we develop a traffic aware spatio-temporal model to analyze the PR scheme in the mIoT network, where the Signal-to-Interference-and-Noise Ratio (SINR) outage and collision events jointly determine the traffic evolution and the RA success probability. Compared with existing literature only modelled collision from single cell perspective, we model both the SINR outage and the collision from the network perspective. Based on this analytical model, we derive the exact expression for the RA success probability to show the effectiveness of the PR scheme. Our results show that the geometry PR scheme with smooth increased transmission power is effective in heavy traffic scenario in terms of increasing the RA success probability.

Massive Internet of Things (mIoT) has provided an auspicious opportunity to build powerful and ubiquitous connections that face a plethora of new challenges, where cellular networks are potential solutions due to their high scalability, reliability, and efficiency. The random access channel (RACH) procedure is the first step of connection establishment between IoT devices and base stations in the cellular-based mIoT network, where modeling the interactions between static properties of the physical layer network and dynamic properties of queue evolving in each IoT device are challenging. To tackle this, we provide a novel traffic-aware spatio-temporal model to analyze RACH in cellular-based mIoT networks, where the physical layer network is modeled and analyzed based on stochastic geometry in the spatial domain, and the queue evolution is analyzed based on probability theory in the time domain. For performance evaluation, we derive the exact expressions for the preamble transmission success probabilities of a randomly chosen IoT device with different RACH schemes in each time slot, which offer insights into the effectiveness of each RACH scheme. Our derived analytical results are verified by the realistic simulations capturing the evolution of packets in each IoT device. This mathematical model and the analytical framework can be applied to evaluate the performance of other types of RACH schemes in the cellular-based networks by simply integrating its preamble transmission principle.

The cellular-based infrastructure is regarded as one of the potential solutions for massive Internet of Things (mIoT), where the random access (RA) procedure is used for requesting channel resources in the uplink data transmission. Due to the nature of the mIoT network with the sporadic uplink transmissions of a large amount of IoT devices, massive concurrent channel resource requests lead to a high probability of RA failure. To relieve the congestion during the RA in mIoT networks, we model RA procedure and analyze as well as evaluate the performance improvement due to different RA schemes, including power ramping (PR), back-off (BO), access class barring (ACB), hybrid ACB and back-off schemes, and hybrid power ramping and back-off (PR&BO). To do so, we develop a traffic-aware spatio-temporal model for the contention-based RA analysis in the mIoT network, where the signal-to-noise-plus-interference ratio (SINR) outage and collision events jointly determine the traffic evolution and the RA success probability. Compared to existing literature that only models collision from the single-cell perspective, we model both SINR outage and the collision from the network perspective. Based on this analytical model, we derive the analytical expression for the RA success probabilities to show the effectiveness of different RA schemes. We also derive the average queue lengths and the average waiting delays of each RA scheme to evaluate the packets accumulation status and packets serving efficiency. Our results show that our proposed PR&BO scheme outperforms other schemes in heavy traffic scenarios in terms of the RA success probability, the average queue length, and the average waiting delay.

In this paper, a new spectrum sharing model referred to as riding on the primary (ROP) is proposed for wireless-powered IoT devices with ambient backscatter communication capabilities. The key idea of ROP is that the secondary transmitter harvests energy from the primary signal, then modulates its information bits to the primary signal, and reflects the modulated signal to the secondary receiver without violating the primary system's interference requirement. Compared with the conventional spectrum sharing model, the secondary system in the proposed ROP not only utilizes the spectrum of the primary system but also takes advantage of the primary signal to harvest energy and to carry its information. In this paper, we investigate the performance of such a spectrum sharing system under fading channels. To be specific, we maximize the ergodic capacity of the secondary system by jointly optimizing the transmit power of the primary signal and the reflection coefficient of the secondary ambient backscatter. Different (ideal/practical) energy consumption models, different (peak/average) transmit power constraints, different types (fixed/dynamically adjustable) reflection coefficient, and different primary system's interference requirements (rate/outage) are considered. Optimal power allocation and reflection coefficient are obtained for each scenario.

Massive Internet of Things (mIoT) has provided an auspicious opportunity to build powerful and ubiquitous connections that faces a plethora of new challenges, where cellular networks are potential solutions due to their high scalability, reliability, and efficiency. The contention-based random access procedure (RACH) is the first step of connection establishment between IoT devices and Base Stations (BSs) in the cellular-based mIoT network, where modelling the interactions between static properties of physical layer network and dynamic properties of queue evolving in each IoT device are challenging. To tackle this, we provide a novel traffic-aware spatio- temporal model to analyze RACH in cellular-based mIoT networks, where the physical layer network are modelled and analyzed based on stochastic geometry, and the queue evolution are analyzed based on probability theory. For performance evaluation, we derive the exact expressions for the preamble transmission success probabilities of a randomly chosen IoT device with baseline scheme in each time slot. Our derived analytical results are verified by the realistic simulations capturing the evolution of packets in each IoT device.

In this paper, a new spectrum sharing model referred to as riding on the primary (RoP) is proposed for wireless- powered IoT devices with ambient backscatter communication capabilities. The key idea of RoP is that the secondary transmitter harvests energy from the primary signal, then modulates its information bits to the primary signal, and reflects the modulated signal to the secondary receiver without violating the primary system's interference requirement. Compared with the conventional spectrum sharing model, the secondary system in the proposed RoP not only utilizes the spectrum of the primary system but also takes advantage of the primary signal to harvest energy and to carry its information. In this paper, we investigate the performance of such a spectrum sharing system under fading channels. To be specific, we maximize the ergodic capacity of the secondary system by jointly optimizing the transmit power of the primary signal and the reflection coefficient of the secondary ambient backscatter. Different (ideal/practical) energy consumption models, different (peak/average) transmit power constraints, different types (fixed/dynamically adjustable) reflection coefficient are considered. Optimal power allocation and reflection coefficient are obtained for each scenario.

In a multi-antenna time-division duplex (TDD) communication system, due to channel reciprocity, the downlink channel state information can be obtained by conducting uplink training. In a wire-tap channel, an active eavesdropper can perform active eavesdropping by pilot spoofing attack. In such an attack, the eavesdropper, during the uplink training phase, transmits the identical pilot sequence as that of the legitimate receiver to the transmitter. As a result, the estimated channel by the transmitter is a weighted sum of the legitimate channel and the eavesdropping channel. Motivated by the seriousness of pilot spoofing attack, in this paper, we propose a two-stage uplink training method for pilot spoofing attack detection and secure transmission. Using the new training method, the legitimate channel and the eavesdropping channel can be correctly estimated separately. Then we propose a pilot spoofing attack detector followed by a beamforming scheme for secure data transmission. Simulation results have shown that our proposed method achieves higher detection probability, and larger secrecy rate than previously proposed anti-pilot spoofing methods.

With the recent advances in radio frequency (RF) energy harvesting (EH) technologies, wireless powered cooperative cognitive radio network (CCRN) has drawn an upsurge of interest for improving the spectrum utilization with incentive to motivate joint information and energy cooperation between the primary and secondary systems. Dedicated energy beamforming is aimed at remedying the low efficiency of wireless power transfer, which nevertheless arouses out-of-band EH phases and thus low cooperation efficiency. To address this issue, in this paper, we consider a novel CCRN aided by full-duplex (FD)-enabled energy access points (EAPs) that can cooperate to wireless charge the secondary transmitter while concurrently receiving primary transmitter's signal in the first transmission phase, and to perform decode-and-forward relaying in the second transmission phase. We investigate a weighted sum-rate maximization problem subject to transmitting power constraints as well as a total cost constraint using successive convex approximation techniques. A zero-forcing-based suboptimal scheme that requires only local channel state information for the EAPs to obtain their optimum receiving beamforming is also derived. Various tradeoffs between the weighted sum-rate and other system parameters are provided in numerical results to corroborate the effectiveness of the proposed solutions against the benchmark ones.

Cognitive radio (CR) has been advocated to improve the network spectrum efficiency for decades, and the cooperation between the primary and secondary systems has become a new paradigm to further improve the spectrum utilization. However, in practice, secondary transmitters (STs) are usually power constrained, which limits the application of cooperative cognitive radio networks (CCRN). In this paper, to tackle this, we consider a novel spectrum sharing CCRN powered by energy access points (EAPs) that can charge users wirelessly, in which a multi-antenna secondary user (SU) solely powered by its harvested energy seeks cooperation with a single-antenna primary user (PU) by serving as a deocde-and-forward (DF) relay. We investigate a payoff maximization problem from the SU's perspective, who gets paid by offering data relaying service for PU but has to pay for WEH, and obtain its optimal DF relay and WEH strategy. A greedy-based algorithm that can assign the ST to right EAPs is also proposed for the ease of implementation. The proposed scheme is shown to be effective by simulations with a negligible gap to the optimal solution.

In this paper, we propose an incentive mechanism to motivate WiFi Access Points (APs) to provide data offloading service for the mobile network operator (MNO). Particularly, we propose using both salary and bonus to attract WiFi APs to participate in data offloading. Under the proposed incentive scheme, WiFi APs are rewarded not only based on the amount of offloaded data but also based on the quality of the offloading service. We investigate the interactions between theWiFi APs and the MNO using Stackelberg game. We derive the best response functions for WiFi APs (i.e. the optimal amount of data to offload), and show that pure strategy Nash Equilibrium (NE) always exists for the subgame. Then, given WiFi APs' strategies, we investigate the optimal strategy (i.e. the optimal salary and the optimal bonus) for the MNO to maximize its utility. It is shown that the proposed incentive mechanism is effective in stimulating WiFi APs to offload more data and provide higher quality of offloading service.

With high scalability, high video streaming quality, and low bandwidth requirement, peer-to-peer (P2P) systems have become a popular way to exchange files and deliver multimedia content over the internet. However, current P2P systems are suffering from “free-riding” due to the peers' selfish nature. In this paper, we propose a credit-based incentive mechanism to encourage peers to cooperate with each other in a heterogeneous network consisting of wired and wireless peers. The proposed mechanism can provide differentiated service to peers with different credits through biased resource allocation. A Stackelberg game is formulated to obtain the optimal pricing and purchasing strategies, which can jointly maximize the revenue of the uploader and the utilities of the downloaders. In particular, peers' heterogeneity and selfish nature are taken into consideration when designing the utility functions for the Stackelberg game. It is shown that the proposed resource allocation scheme is effective in providing service differentiation for peers and stimulating them to make contribution to the P2P streaming system.

In this paper, we consider a wireless communication network with a full-duplex hybrid energy and information access point and a set of wireless users with energy harvesting capabilities. The hybrid access point (HAP) implements full-duplex through two antennas: one for broadcasting wireless energy to users in the downlink and the other for simultaneously receiving information from the users via time division multiple access (TDMA) in the uplink. Each user can continuously harvest wireless power from the HAP until it transmits, i.e., the energy causality constraint is modeled by assuming that energy harvested in the future cannot be used for the current transmission. This leads to the causal dependence of each user's harvesting time on the transmission time of earlier users, e.g., the second user scheduled to transmit can harvest more energy if the first user has longer transmission time. Under this setup, we investigate the sum-throughput maximization (STM) problem and the total-time minimization (TTM) problem for the proposed full-duplex wireless-powered communication network. For the STM problem, the optimal solution is obtained as a closed-form expression, which can be computed with linear complexity. For the TTM problem, by exploiting the properties of the coupled constraints, we propose a two-step algorithm to obtain an optimal solution. Then, low-complexity suboptimal solutions are proposed for each problem by exploiting the characteristics of the optimal solutions. Finally, simulation studies on the effect of user scheduling show that different scheduling strategies should be adopted for STM and TTM.

In this paper, the sum-rate of a cognitive multiple access channel (C-MAC) is studied, where a secondary network (SN) with multiple secondary users (SUs) transmitting to a secondary base station (SBS) shares the spectrum band with a primary user (PU). An interference power constraint (IPC) is imposed on the SN to protect the PU. Under the IPC and the individual transmit power constraint (TPC) imposed on each SU, we investigate the power allocation strategies to maximize the sum-rate of the C-MAC without successive interference cancellation (SIC). We prove that the optimal solution must be at the extreme points of the feasible region. We show that Dynamic Time Division Multiple Access (D-TDMA) is optimal with high probability when the number of SUs is large. Furthermore, we show through simulations that the optimal power allocation to maximize the sum-rate of the C-MAC with SIC is optimal or near-optimal for our setting when D-TDMA is not optimal.

In this paper, we consider a wireless communication network with a hybrid access point (HAP) and a set of wireless users with energy harvesting capabilities. The HAP is assumed to have two antennas: one for transferring wireless energy in the downlink and one for receiving wireless information in the uplink. Without fixed energy sources, users first have to harvest energy from the wireless signals broadcast by the HAP, and then using the harvested energy to transmit their individual collected data to the HAP through dynamic-time-division-multiple-access (D-TDMA). All users can harvest the wireless energy prior to its transmission, and thus latter users can harvest more energy. We investigate the optimal time allocation to minimize the completion time of charging and transmitting all data subject to the constraints that all users have to send back some minimum amount of data. A high-efficient algorithm is then proposed to obtain an optimal time allocation of the formulated problem by exploring the properties of the constraints. It is also shown by simulations that the users with higher SNR should be scheduled to transmit first. Then, a suboptimal algorithm is also proposed based on the optimal time allocation. It is then shown by simulations that the suboptimal time allocation can achieve a close-to-optimal performance.

In this paper, we consider a wireless communication network with a hybrid access point (HAP) and a set of wireless users with energy harvesting capabilities. The HAP is assumed to have two antennas: one for transferring wireless energy in the downlink and one for receiving wireless information in the uplink. Without fixed energy sources, users first have to harvest energy from the wireless signals broadcast by the HAP, and then using the harvested energy to transmit their individual information to the HAP through dynamic-time-division-multiple-access (D-TDMA). We investigate the optimal time allocation to maximize the throughput of the proposed system subject to a total time constant. The formulated throughput maximization problem is proved to be a convex optimization problem. By using convex optimization techniques, the optimal time allocation strategy is obtained in closed-form expression. We show that the optimal time allocation can be obtained with linear complexity. It is then shown by simulations that the total throughput of the network increases with the number of users. It is also shown by simulations that the users with low SNR should be scheduled to transmit first.

In this paper, we investigate resource allocation strategies for a point-to-point wireless communications system with hybrid energy sources consisting of an energy harvester and a conventional energy source. By assuming that the non-causal information of the energy arrivals and the channel power gains is known, a mixed integer programming problem is formulated to minimize the total energy cost of such a system over N fading slots under the energy harvesting constraints and a proposed outage constraint. The outage constraint requires that a minimum fraction of slots to be reliably decoded. This constraint is useful if, for example, an outer code is used to recover that all data bits. Optimal linear time algorithms are obtained for two extreme cases: when the number of outage slots is 1 or N -1. For the general case, a lower bound based on linear programming relaxation, and two suboptimal algorithms are proposed. Numerical simulations indicate that the proposed suboptimal algorithms exhibit only a small gap from the lower bound for a wide range of given parameters.

In this paper, we investigate resource allocation strategies for a point-to-point wireless communications system with hybrid energy sources consisting of an energy harvester and a conventional energy source. In particular, as an incentive to promote the use of renewable energy, we assume that the renewable energy has a lower cost than the conventional energy. Then, by assuming that the non-causal information of the energy arrivals and the channel power gains are available, we minimize the total energy cost of such a system over N fading slots under a proposed outage constraint together with the energy harvesting constraints. The outage constraint requires a minimum fixed number of slots to be reliably decoded, and thus leads to a mixed-integer programming formulation for the optimization problem. This constraint is useful, for example, if an outer code is used to recover all the data bits. Optimal linear time algorithms are obtained for two extreme cases, i.e., the number of outage slot is 1 or N - 1. For the general case, a lower bound based on the linear programming relaxation, and two suboptimal algorithms are proposed. It is shown that the proposed suboptimal algorithms exhibit only a small gap from the lower bound. We then extend the proposed algorithms to the multi-cycle scenario in which the outage constraint is imposed for each cycle separately. Finally, we investigate the resource allocation strategies when only causal information on the energy arrivals and only channel statistics is available. It is shown that the greedy energy allocation is optimal for this scenario.

WiFi offloading is regarded as one of the most promising techniques for dealing with the explosive data increase in cellular networks due to its high data transmission rate and low requirement on devices. In this paper, we investigate the mobile data offloading problem through a third-party WiFi access point (AP) for a cellular mobile system. From the cellular operator's perspective, by assuming a usage-based charging model, we formulate the problem as a utility maximization problem. In particular, we consider three scenarios: 1) successive interference cancellation (SIC) available at both the base station (BS) and the AP; 2) SIC available at neither the BS nor the AP; and 3) SIC available at only the BS. For scenario 1, we show that the utility maximization problem can be solved by considering its relaxation problem, and the proposed data offloading scheme is near-optimal when the number of users is large. For scenario 2, we prove that with high probability the optimal solution is One-One-Association, i.e., one user connects to the BS and one user connects to the AP. For scenario 3, we show that with high probability there is at most one user connecting to the AP, and all the other users connect to the BS. By comparing these three scenarios, we prove that SIC decoders help the cellular operator maximize its utility. To relieve the computational burden of the BS, we propose a threshold-based distributed data offloading scheme. We show that the proposed distributed scheme performs well if the threshold is properly chosen.

Credit-based incentive mechanisms are widely adopted in today's peer-to-peer (P2P) multimedia streaming systems due to their effectiveness in fighting against “free-riding” and stimulating the cooperation between peers. In this paper, we investigate the optimal and suboptimal credits allocation strategies for peers to maximize their viewing experience in such systems. Especially, the dynamic changing feature of credits is taken into consideration when we formulate the problem, and the optimal credits allocation is shown to be a staircase-like function over time. Then, based on the special features of the optimal credits allocation strategy, an effective double-loop iterative algorithm is proposed. For the consideration of practical implementation, three low-complexity suboptimal credits allocation strategies are proposed. It is shown that each of the suboptimal strategies has its own feature and is applicable to different scenarios.

With high scalability, high video streaming quality, and low bandwidth requirement, peer-to-peer (P2P) streaming systems are gradually replacing their server-client-based counterparts in the realm of delivering multimedia content over the internet. However, current P2P streaming systems are suffering from “free-riding” due to the peers' selfish nature. In this paper, we propose a credit-based incentive mechanism to encourage peers to cooperate with each other. The proposed mechanism provides service differentiation for peers with different credits and connection types through biased resource allocation. A Stackelberg game is formulated to obtain the optimal resource allocation strategy, which can jointly maximize the revenue of the uploader and the utilities of the downloaders. Especially, the selfish nature of peers is taken into consideration when designing the utility functions of the Stackelberg game. It is shown that the proposed resource allocation scheme is effective in providing service differentiation for peers and stimulating them to make contribution to the P2P streaming system.

This paper investigates price-based resource allocation strategies for two-tier femtocell networks, in which a central macrocell is underlaid with distributed femtocells, all operating over the same frequency band. Assuming that the macrocell base station (MBS) protects itself by pricing the interference from femtocell users, a Stackelberg game is formulated to study the joint utility maximization of the macrocell and femtocells subject to a maximum tolerable interference power constraint at the MBS. Two practical femtocell network models are investigated: sparsely deployed scenario for rural areas and densely deployed scenario for urban areas. For each scenario, two pricing schemes: uniform pricing and non-uniform pricing, are proposed. The Stackelberg equilibriums for the proposed games are characterized, and an effective distributed interference price bargaining algorithm with guaranteed convergence is proposed for the uniform-pricing case. Numerical examples are presented to verify the proposed studies. It is shown that the proposed schemes are effective in resource allocation and macrocell protection for both the uplink and downlink transmissions in spectrum-sharing femtocell networks.

This paper investigates price-based resource allocation strategies for the uplink transmission of a spectrum-sharing femtocell network, in which a central macrocell is underlaid with distributed femtocells, all operating over the same frequency band as the macrocell. Assuming that the macrocell base station(MBS) protects itself by pricing the interference from the femtocell users, a Stackelberg game is formulated to study the joint utility maximization of the macrocell and the femtocells subject to a maximum tolerable interference power constraint at the MBS. In particular, two pricing schemes: uniform pricing and non-uniform pricing, are investigated. Then, the Stackelberg equilibriums for the proposed games are studied, and the relationship between the two pricing schemes is examined. It is shown that the nonuniform pricing scheme maximizes the revenue of the MBS, while the uniform pricing scheme maximizes the sum-rate of the femtocell users.

In this paper, we investigate the distributed power allocation strategies for a spectrum-sharing femtocell network, where a central macrocell underlaid with several femtocells. Assuming that the macrocell protects itself by pricing the interference from the femtocells, a Stackelberg game is formulated to jointly consider the utility maximization of the macrocell and the femtocells. Then, the Stackelberg equilibrium for the proposed game is studied, and an effective distributed interference price bargaining algorithm with guaranteed convergency is proposed to achieve the equilibrium. Numerical examples are then presented to verify the proposed studies. It is shown that the algorithm is effective in distributed power allocation and macrocell protection requiring minimal network overhead for spectrum-sharing-based two-tier femtocell networks.

In this paper, we consider a cognitive radio (CR) network where a secondary (cognitive) user shares the spectrum for transmission with a primary (non-cognitive) user over block-fading (BF) channels. It is assumed that the primary user has a constant-rate, constant-power transmission, while the secondary user is able to adapt transmit power and rate allocation over different fading states based on the channel state information (CSI) of the CR network. We study a new type of constraint imposed over the secondary transmission to protect the primary user by limiting the maximum transmission outage probability of the primary user to be below a desired target. We derive the optimal power allocation strategies for the secondary user to maximize its ergodic/outage capacity, under the average/peak transmit power constraint along with the proposed primary user outage probability constraint. It is shown by simulations that the derived new power allocation strategies can achieve substantial capacity gains for the secondary user over the conventional methods based on the interference temperature (IT) constraint to protect the primary transmission, with the same resultant primary user outage probability.

This paper is concerned with a spectrum sharing cognitive radio network. In particular, the individual outage capacity region for a M-user fading cognitive multiple access network sharing the same spectrum with an existing primary network is first characterized. The primary network's transmission is assumed to be protected by the interference power constraint. Then, under the interference power constraint and the individual transmit power constraint of each user, the individual outage capacity region is implicitly obtained by characterizing the boundary points on the individual usage probability region for a given rate vector. The optimal power allocation and decoding strategy is then derived by the Lagrange dual decomposition method. It is proved that the optimal decoding strategy is the successive decoding strategy, and the decoding order is determined by dual variables together with the channel fading gains of the primary and secondary links. Finally, several numerical examples are given to validate the proposed studies.

This paper considers a spectrum underlay network, where an OFDM-based cognitive radio (CR) system is allowed to share the subcarriers of an OFDMA-based primary system for simultaneous transmission. Instead of using the conventional interference power constraint (IPC) to protect the primary users (PUs) in the primary system, a new criterion referred to as rate loss constraint (RLC), in the form of an upper bound on the maximum rate loss of each PU due to the CR transmission, is proposed for primary transmission protection. Assuming the channel state information (CSI) of the PU link, the CR link, and their mutual interference links is available to the CR, the optimal power allocation strategy to maximize the achievable rate of the CR system is derived under RLC together with CR¿s transmit power constraint. It is shown that the CR system can achieve a significant rate gain under RLC as compared to IPC. Furthermore, the relationship between RLC and IPC is investigated, and it is shown that the rate gain is obtained by exploiting the additional CSI of the PU link. A more general case referred to as hybrid protection to PUs is then studied, by taking into account that some PU links¿ CSI is not available at CR.

This paper considers a spectrum sharing based cognitive radio network where a M-user fading multiple access network shares the same spectrum with an existing primary network. The primary network's transmission is assumed to be protected by the interference power constraint. Under this interference power constraint together with the individual transmit power constraint of each user, the outage capacity regions for the fading cognitive multiple access channel (C-MAC) are defined for two different scenarios, i.e., an outage must be declared simultaneously for all users (common outage) and outages are declared individually for each user (individual outage). Then, optimal power allocation strategies to achieve the boundary points of these outage capacity regions are derived by considering their equivalent problems, i.e., the common/individual usage probability maximization for given rate vectors. It is rigorously proved that the optimal decoding strategy is the successive decoding strategy, and the decoding order is determined by the dual variables and the channel power gains of the involved channels. Then, a modified ellipsoid method is proposed to obtain the optimal dual variables. Finally, several numerical examples are given to validate the proposed studies.

This paper considers a spectrum sharing wireless environment, where an OFDM-based cognitive radio system is allowed to access the spectrum originally licensed to an OFDMA primary system. A new criterion referred to as the rate loss constraint, in the form of an upper bound on the maximum rate loss of the primary user due to the secondary transmission, is proposed for primary transmission protection. In addition, assuming that some PUs are protected by the rate loss constraint, and some PUs are protected by the interference power constraint, the optimal power allocation strategy to maximize the rate of the cognitive radio system under such a hybrid protection to PUs together with a transmit power constraint is derived. Then, the relationship between the rate loss constraint and the interference power constraint is investigated, and it is shown by simulation that the cognitive radio system can achieve a significant rate gain under the proposed constraint compared with that under the conventional interference power constraint.

This paper considers a cognitive radio network where a secondary user shares the same narrow band with a primary user for transmission. Instead of adopting the conventional interference-power/interference-temperature constraint, this paper proposes a new type of constraint for the secondary user to protect the primary transmission, which limits the maximum outage probability of the primary transmission subject to the secondary user's interference to be below a prescribed target. Under this newly proposed constraint along with the average/peak transmit power constraint, the paper derives the optimal power allocation strategies over block-fading channels for the secondary user to achieve its outage capacity. It is shown by simulations that the derived power allocation strategies achieve substantial outage capacity gains for the secondary user over the conventional power control policies based upon the interference-temperature constraint, given the same primary user's outage probability constraint.

In this paper, we consider a secondary link sharing the spectrum with a primary link in a fading cognitive radio (CR) network. Instead of applying the conventional interference power constraint at the primary user (PU) receiver for the secondary user (SU) to protect the primary transmission, we propose a new constraint on the maximum tolerable outage probability for the PU due to the SU transmission. Under the assumption that perfect instantaneous channel state information (CSI) on the SU channel, the channel from the SU transmitter to PU receiver, and the PU channel is available at the SU transmitter, we derive the optimal power allocation strategies to achieve the ergodic capacity of the SU fading channel. It is shown by simulations that the proposed power allocation strategies can achieve substantial capacity gain for the SU over that based on the conventional interference power constraint, for the same PU outage probability loss.

This paper considers spectrum sharing between a cognitive radio (CR) and a primary radio (PR) where the CR protects the PR transmission by regulating the resultant interference power level at the PR receiver to be below some predefined threshold. The interference-power constraint at the PR receiver is usually one of the following two types: average interference power (AIP) constraint that regulates the average power level over different fading states and peak interference power (PIP) constraint that limits the peak power level at each fading state. From CR's perspective, AIP constraint is more favorable than PIP constraint because of its more flexibility for dynamic power allocations. On the contrary, from the perspective of protecting the PR, the more restrictive PIP constraint appears at a first glance to be a better option. Some surprisingly, this paper shows that in terms of various forms of capacity limits achievable for the PR fading channel, namely, ergodic and outage capacities, AIP constraint is also superior over PIP constraint. This result is based upon an interesting interference diversity phenomenon, i.e., variable interference power levels at the PR receiver in the AIP case are more advantageous over constant ones in the PIP case for minimizing the resulted PR capacity losses. Therefore, AIP constraint leads to larger fading channel capacities over PIP constraint for both CR and PR transmissions.

A cognitive radio network (CRN) is formed by either allowing the secondary users (SUs) in a secondary communication network (SCN) to opportunistically operate in the frequency bands originally allocated to a primary communication network (PCN) or by allowing SCN to coexist with the primary users (PUs) in PCN as long as the interference caused by SCN to each PU is properly regulated. In this paper, we consider the latter case, known as spectrum sharing, and study the optimal power allocation strategies to achieve the ergodic capacity and the outage capacity of the SU fading channel under different types of power constraints and fading channel models. In particular, besides the interference power constraint at PU, the transmit power constraint of SU is also considered. Since the transmit power and the interference power can be limited either by a peak or an average constraint, various combinations of power constraints are studied. It is shown that there is a capacity gain for SU under the average over the peak transmit/interference power constraint. It is also shown that fading for the channel between SU transmitter and PU receiver is usually a beneficial factor for enhancing the SU channel capacities.

In this paper, a new spectrum sharing model called sensing-based spectrum sharing is proposed for cognitive radio networks. This model consists of two phases: in the first phase, the secondary user (SU) listens to the spectrum allocated to primary user (PU) to detect the state of PU; in the second phase, SU adapts its transit power based on the sensing results. If the PU is inactive, the SU allocates the transmission power based on its own benefit. However, if the PU is active, interference power constraint is imposed in order to protect the PU. By studying the ergodic capacity of SU, we show that this spectrum sharing model can achieve a higher capacity of SU link and improve the spectrum utilization compared to conventional opportunistic spectrum access or simple spectrum sharing. Using the dual decomposition method, we find the optimal power allocation policies and the optimal sensing time for fading channels to achieve the ergodic capacity of the SU link considering both transmit and interference power constraints. Finally, the numerical results are presented to validate the analytical results.

In this paper, we study the optimal power control policies for fading channels in cognitive radio networks considering both the transmit and the interference power constraints. For each of the constraints, the peak power and the average power are investigated. We derive the optimal power allocation strategies in terms of maximizing the ergodic capacity of the secondary user when the channel state information is available to the transmitter and the receiver. It is shown that the optimal power adaption for each case has its own features, and is quite different from one to another. Finally, the numerical results are presented to validate the proposed power allocation methods for various power constraint region.

In this paper, we study the optimal power allocation strategies to achieve the delay-limited capacity and outage capacity for fading channels in cognitive radio networks considering interference power constraint. The optimal power allocation to achieve delay-limited capacity is shown to be similar to channel inversion, and the derived delay-limited capacity is related to the channel statistics. Moreover, the optimal power allocation strategy to achieve the outage capacity is derived, and the corresponding minimum outage probability is evaluated for each fading scenario under the peak interference power constraint and the average interference power constraint respectively. Finally, the simulation results are presented to validate the proposed power allocation methods.