To enhance the anti-interference capability of unmanned aerial vehicle (UAV) networks, many UAV nodes are now equipped with directional antennas. However, their narrow beamwidth significantly increases the latency of neighbor discovery. In conventional neighbor discovery protocols, each node scans independently, leading to redundant discoveries as adjacent nodes repeatedly scan the same regions. To optimize this process, we propose Co-ND, a collaborative neighbor discovery protocol that enables a discovering node to offload part of its scanning workload to neighboring collaborators. This protocol not only accelerates the discovery process while preserving discovery completeness. To analyze the performance of Co-ND, we first model neighbor discovery as a state-transition process and derive a recursive expression for the delay. Then, by applying the additive drift principle, we calculate the upper and lower bounds and a closed-form approximation for the delay, theoretically proving its advantages in delay reduction. Finally, we conduct extensive simulations under various settings. The results show that Co-ND significantly outperforms existing schemes in neighbor discovery latency, especially in narrow-beam scenarios.

Diffusion models (DMs) have recently achieved significant success in wireless communications systems due to their denoising capabilities. The broadcast nature of wireless signals makes them susceptible not only to Gaussian noise, but also to unaware interference. This raises the question of whether DMs can effectively mitigate interference in wireless semantic communication systems. In this paper, we model the interference cancellation problem as a maximum a posteriori (MAP) problem over the joint posterior probability of the signal and interference, and theoretically prove that the solution provides excellent estimates for the signal and interference. To solve this problem, we develop an interference cancellation diffusion model (ICDM), which decomposes the joint posterior into independent prior probabilities of the signal and interference, along with the channel transition probability. The log-gradients of these distributions at each time step are learned separately by DMs and accurately estimated through deriving. ICDM further integrates these gradients with advanced numerical iteration method, achieving accurate and rapid interference cancellation. Extensive experiments demonstrate that ICDM significantly reduces the mean square error (MSE) and enhances perceptual quality compared to schemes without ICDM. For example, on the CelebA dataset under the Rayleigh fading channel with a signal-to-noise ratio (SNR) of 20 dB and signal to interference plus noise ratio (SINR) of 0 dB, ICDM reduces the MSE by 4.54 dB and improves the learned perceptual image patch similarity (LPIPS) by 2.47 dB. The code is available at https://github.com/Wireless3C-SJTU/ICDM.

This work proposes a Dimension-Reduced Koopman Model Predictive Control (Dr-KMPC) framework for discrete-time nonlinear systems. The main challenges in existing Koopman-based control approaches arise from the curse of dimensionality associated with dictionary-based lifting and the computational burden of deep learning-based alternatives. Particularly, standard Extended Dynamic Mode Decomposition (EDMD) often requires a high-dimensional lifted space to ensure accuracy, which renders the subsequent controller design computationally expensive. To address these issues, we introduce a trainable linear encoding map to project high-dimensional observables into a lower-dimensional latent space. The resulting non-convex joint parameter learning problem is solved by using a novel Block Coordinate Descent (BCD) algorithm, which decomposes the optimization into sequentially solvable, strictly convex linear least-squares sub-problems. The resulting Dr- KMPC formulation allows standard Quadratic Programming (QP) solvers to determine optimal control inputs efficiently. Rigorous theoretical analysis establishes recursive feasibility and asymptotic stability of the proposed MPC method. Simulation results show that the proposed method can effectively handle nonlinear systems and achieve high control performance.

For massive grant-free access, maximum a posterior (MAP) estimation-based device activity detection is known to achieve superior detection accuracy in the scenario of known activity probabilities. Unfortunately, no comparable Bayesian statistical estimation method exists in the scenario of unknown activity probabilities. This paper investigates MAP estimation-based device activity detection in this scenario. First, we formulate the hierarchical MAP estimation problem for device activities, which is a very challenging non-convex problem. For tractability, we obtain its approximate non-convex problem and prove its equivalence to the MAP estimation problem of device activities and activity probabilities. Next, we propose a block coordinate descent (BCD)-based algorithm to obtain the approximate MAP estimation problem's stationary points. Finally, we numerically show that the proposed algorithm achieves a superior detection accuracy and short computation time tradeoff compared with the state-of-the-art device activity detection methods.

Integrated Sensing and Communications (ISAC) is emerged as one of the key technologies in next generation wireless systems. However, ISAC systems have been commonly explored neglecting mutual coupling. This paper investigates the mutual coupling exploitation to further improve the performance of ISAC systems. We aim to maximize the sensing beampatern gain by optimizing the tunable loads and the dual-functional beamforming while satisfying the minimum signal-to-interference-plus-noise (SINR) per user and the hardware constraint of the tunable loads. To solve the non-convex problem, we propose a penaltybased iterative algorithm to obtain a stationary point. Specifically, in each iteration we adopt the block coordinate descent (BCD) method where the dual-functional beamforming is obtained by using Lagrange duality, and the tunable loads are solved with closed forms. Numerical results demonstrate the notable gains and effectiveness of the proposed algorithms compared to the baseline schemes. To the best of our knowledge, this is the first study utilizing the MC effect to improve system performance in an ISAC system with tunable loads.

With the development of the space-air-ground integration technology, the satellites are armed with a certain on-board computing resource. To fully offload communication tasks to each satellite, we investigate a multiuser multi-satellite cooperative communication system without a central processing unit (CPU), where the satellites are equipped with tunable load antennas leveraging the mutual coupling effect to reconfigure the wireless channel. First, we formulate the sum spectral efficiency (SE) maximization problem with respect to the beamforming and the tunable loads under the power constraint and the constraints of the tunable loads. Afterwards, we propose a cooperative algorithm with closed-form updates to obtain a stationary point based on parallel successive convex approximation (SCA). Furthermore, we propose an efficient information exchange strategy for the satellites based on the ring all-reduce method, which significantly reduces the information exchange overhead of each satellite. Lastly, numerical results verify the proposed design's notable gain over the baselines. As far as we know, this is the first work to study the multi-satellite cooperative communication system with tunable load antennas.

The costs of channel estimation, reflection adjustment, and computation have severe impacts on intelligent reflection surface (IRS)-aided physical layer security (PLS) wireless communication systems in practice but are usually overlooked for simplicity in most existing works. This paper considers a multi-antenna base station serving a single-antenna legitimate user with the assistance of a multi-element IRS under the surveillance of a single-antenna eavesdropper. Firstly, we introduce a partial instantaneous CSI and statistical CSI (PICSI-SCSI)-adaptive beamforming and SCSI-adaptive reflection design. Secondly, we maximize the achievable ergodic secrecy rate (ESR) with respect to the PICSI-SCSI-adaptive beamforming and SCSI-adaptive reflection design, resulting in a two-timescale stochastic non-convex problem. Thirdly, we present two stochastic iterative algorithms to reach stationary and approximate stationary points. Moreover, we show that the two proposed designs achieve lower computational complexities and adjustment costs for reflection than the existing PICSI-SCSI-adaptive beamforming and reflection design. Lastly, we numerically demonstrate the two proposed designs’ notable gains over baselines. To our knowledge, this is the first work providing an optimization-based PICSI-SCSI-adaptive beamforming and SCSI-adaptive reflection design in an IRS-aided PLS wireless communication system, achieving promising secure performance at the minimum adjustment cost for reflection.

The utilization of unmanned aerial vehicles (UAVs) as base stations is an essential case in air-to-ground communication. To balance the equipment cost and performance of directional antennas, the ground terminal can be equipped with a servo phased antenna, which requires the careful design of the access mechanisms. In this paper, we consider the application in which the UAV uses an omnidirectional antenna while the ground terminals use a servo-phased array. We first proposed two access approaches, i.e., stop-and-scan and continuous scan schemes. Then, we established theoretical models for the two schemes and analyzed the performance and the factors impacting it. After that, we conducted a Monte Carlo simulation and compared the performance with numerical results. Results show that the access delay in the stop-and-scan scheme is inversely proportional to the beam width, servo acceleration, and the number of access time slots. The access delay of the continuous scan scheme is significantly lower than the other. However, in the continuous scan scheme, the beam width and the servo acceleration should satisfy a specific constraint to avoid access failure.

Most existing studies on massive grant-free access, proposed to support massive machine-type communications (mMTC) for the Internet of things (IoT), assume Rayleigh fading and perfect synchronization for simplicity. However, in practice, line-of-sight (LoS) components generally exist, and time and frequency synchronization are usually imperfect. This paper systematically investigates maximum likelihood estimation (MLE)-based device activity detection under Rician fading for massive grant-free access with perfect and imperfect synchronization. We assume that the large-scale fading powers, Rician factors, and normalized LoS components can be estimated offline. We formulate device activity detection in the synchronous case and joint device activity and offset detection in three asynchronous cases (i.e., time, frequency, and time and frequency asynchronous cases) as MLE problems. In the synchronous case, we propose an iterative algorithm to obtain a stationary point of the MLE problem. In each asynchronous case, we propose two iterative algorithms with identical detection performance but different computational complexities. In particular, one is computationally efficient for small ranges of offsets, whereas the other one, relying on fast Fourier transform (FFT) and inverse FFT, is computationally efficient for large ranges of offsets. The proposed algorithms generalize the existing MLE-based methods for Rayleigh fading and perfect synchronization. Numerical results show that the proposed algorithm for the synchronous case can reduce the detection error probability by up to 50.4% at a 78.6% computation time increase, compared to the MLE-based state-of-the-art, and the proposed algorithms for the three asynchronous cases can reduce the detection error probabilities and computation times by up to 65.8% and 92.0%, respectively, compared to the MLE-based state-of-the-arts.

The impacts of channel estimation, beamforming adjustment, phase shift adjustment, and computation costs on an intelligent reflecting surface (IRS)-assisted secure wireless communication system are severe in practice but are usually ignored for simplicity. In this paper, we consider a multi-antenna BS serving a single-antenna legitimate user with the help of a multi-element IRS in the presence of an eavesdropper. To maximally reduce the implementation cost, we investigate the no-instantaneous channel state information (ICSI) case with the legitimate user’s and eavesdropper’s statistical CSI (SCSI). First, we present a SCSI-adaptive (quasi-static) beamforming and phase shift design, also referred to as a quasi-static design, which has low channel estimation, beamforming adjustment, and phase shift adjustment costs. Then, we formulate the maximization of the achievable ergodic secrecy rate with respect to the quasi-static design as a challenging stochastic non-convex problem. Next, we propose two parallel iterative algorithms to obtain a stationary point and an approximate stationary point and present their respective quasi-static designs. Furthermore, we show that the quasi-static designs derived from the stationary point and approximate stationary point achieve lower implementation costs than existing designs. Finally, we numerically verify the analytical results and demonstrate notable gains of the two proposed quasi-static designs over existing designs.

The analysis and optimization of single intelligent reflecting surface (IRS)-assisted systems have been extensively studied, whereas little is known regarding multiple-IRS-assisted systems. This paper investigates the analysis and optimization of a double-IRS cooperatively assisted downlink system (D-IRS-C), where a multi-antenna base station (BS) serves a single-antenna user with the help of two multi-element IRSs, connected by an inter-IRS channel. The channel between any two nodes is modeled with Rician fading. The BS adopts the instantaneous CSI-adaptive maximum-ratio transmission (MRT) beamformer, and the two IRSs adopt a cooperative quasi-static phase shift design. The goal is to maximize the average achievable rate, which can be reflected by the average channel power of the equivalent channel between the BS and user at low channel estimation and phase adjustment costs and computational complexity. First, we obtain tractable expressions of the average channel power of the equivalent channel in the general (Rician factor), pure line of sight (LoS), and pure non-line of sight (NLoS) regimes, respectively. Then, we jointly optimize the phase shifts of the two IRSs to maximize the average channel power of the equivalent channel in these regimes. The optimization problems are challenging non-convex problems. We obtain globally optimal closed-form solutions for some cases and propose computationally efficient iterative algorithms to obtain stationary points for the other cases. Next, we compare the computational complexity for optimizing the phase shifts and the optimal average channel power of D-IRS-C with those of a counterpart double-IRS non-cooperatively assisted system (D-IRS-NC) and a counterpart single-IRS-assisted system (S-IRS) at a large number of reflecting elements in the three regimes. Finally, we numerically demonstrate notable gains of the proposed solutions over the existing solutions at different system parameters. To our knowledge, this is the first work that optimizes the quasi-static phase shift design of D-IRS-C and characterizes its advantages over the optimal quasi-static phase shift design of the counterpart D-IRS-NC and S-IRS.

Grant-free access is recently proposed as an essential technique for supporting massive machine-type communications (mMTC) for the Internet of Things (IoT). However, high accuracy and low complexity device activity detection under imperfect frequency synchronization remains open. To address this issue, this paper proposes a maximum likelihood estimation (MLE)- based device activity detection method for the frequency asynchronous case. First, we formulate the estimation of device activities together with the device carrier frequency offsets (CFOs) as an MLE problem. Then, to tackle this challenging nonconvex problem, we propose a computationally efficient iterative algorithm using the block coordinate descent (BCD) method and fast computation enabled by fast Fourier transform (FFT). Analytical and numerical results demonstrate the notable gains of the proposed method over the existing solutions and offer important design insights into practical massive grant-free access for mMTC.

Most existing random access schemes for machine-type communications (MTC) simply adopt a uniform preamble selection distribution, irrespective of the underlying device activity distributions. Hence, they may yield unsatisfactory access efficiency. In this paper, we model device activities for MTC as multiple Bernoulli random variables following an arbitrary multivariate Bernoulli distribution which can reflect both dependent and independent device activities. Then, we optimize preamble selection and access barring for random access in MTC according to the underlying joint device activity distribution. Specifically, we investigate three cases of the joint device activity distribution, i.e., the cases of perfect, imperfect, and unknown joint device activity distributions, and formulate the average, worst-case average, and sample average throughput maximization problems, respectively. The problems in the three cases are challenging nonconvex problems. In the case of perfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain stationary points of the original problem and an approximate problem, respectively. In the case of imperfect joint device activity distribution, we develop an iterative algorithm and a low-complexity iterative algorithm to obtain a Karush-Kuhn-Tucker (KKT) point of an equivalent problem and a stationary point of an approximate problem, respectively. Finally, in the case of unknown joint device activity distribution, we develop an iterative algorithm to obtain a stationary point. The proposed solutions are widely applicable and outperform existing solutions for dependent and independent device activities.

The broadcast scheme is a basic and important data dissemination mechanism in FANET. In this paper, we propose an efficient neighbor quality based mMultil- Ppoint Rrelay (MPR) broadcast scheme based on local neighbor status. The local neighbor status includes two aspects, i.e., the physical link status and the congestion status. The physical link status is indicated by the expected successful packet delivery ratio and link expiration time calculated from the Received Signal Strength Indication (RSSI). The congestion status is determined by the queue length of each neighboring nodes. Then an efficient MPR set can be selected from one-hop neighbors by local neighbor status. Each node uses its MPR set to rebroadcast messages. We use RSSI to calculate the packet successful expectation and link expiration time, and MAC layer queue length to indicate how busy the node is. Simulation result shows that our method performs better in terms of end-to-end delay and packet delivery rate than probability methods and other deterministic methods.

In this paper, we focus on a dual-functional communication-navigation system. First, we consider the ranging accuracy and take Cramer-Rao lower bound(CRLB) as the metrics. Based on the derived CRLB, we analyze the effect of adding the pulse shaping filter on it and the influence of rolloff factor. Moreover, we compare the CRLB of different single-carrier modulation formats. As for communication performance, we consider a case where the maximum transmitting power is the same, which means lower Peak to Average Power Ratio(PAPR) can achieve higher SNR and channel capacity. On this basis, we analyze the PAPR of pulse shaping filters and modulation formats. Considering these two metrics together, we select appropriate pulse shaping filter and modulation formats.

Industrial Wireless Sensor Networks (IWSNs) are mainly used for critical monitoring and control applications in industrial automation systems. The applications require realtime data delivery within strict delay constraints. Exceeding the required delay bound for emergency traffic could result in economic losses or even safety incidents. In this paper, we propose a Priority-enhanced slot Allocation Medium Access Control protocol (PriAlloc-MAC), which can guarantee the transmission of different traffic categories of IWSNs in given time constraints. In this protocol, the nodes with emergency traffic are allowed to preempt slots to reduce the channel access delay. Multiple nodes with emergency traffic access the channel without conflicts according to their access sequence numbers (ASNs). In addition, the protocol utilizes a short time slot design and allocates a different number of time slots to each node according to the length of the data packet to be transmitted to improve channel utilization. In this paper, the performance of PriAlloc-MAC is evaluated and compared with WirelessHART. Results show that PriAlloc-MAC is significantly better than WirelessHART in terms of traffic delay and channel utilization.

In Aeronautical Ad Hoc Network (AANET), attacks are often diverse, and a lack of preventive measures may cause serious losses. Therefore, compared with traditional MANET, AANET has higher security requirements. In this paper, we propose a trust-based partially distributed key management scheme to consider both capability and integrity trust of network. Capability trust reflects the link state of the routing path and the metric is used to choose reliable intermediate nodes for transmission. The integrity trust distinguishes compromised nodes from well-behaving nodes. The key management scheme only issues certificates to the well-behaving nodes to verify their legitimacy. We perform simulations and compare the proposed scheme with a composite trust-based public key management scheme (CTPKM). The simulation results demonstrate the performance advantages of our scheme in terms of overhead, packet delivery ratio and evaluation accuracy.

In this paper, we firstly establish the model of inductive power transfer (IPT) through metal barriers by using the electromagnetic model. Then we theoretically analyze the operating frequency range of IPT through metal barriers, which includes the optimal operating frequency. After that, the theoretical results are proved by simulation in MAXWELL and SIMPLORER.

Orthogonal frequency division multiplexing (OFDM) systems suffer from a large peak-to-average power ratio (PAPR), leading to lower power efficiency and nonlinear distortion at the transmit power amplifier. Tone reservation (TR) techniques can effectively reduce PAPR, but may also cause data rate reduction if the TR ratio is not carefully designed. In this paper, we propose a low-complexity TR approach for PAPR reduction in OFDM systems, which is parametrized by the TR ratio. Then, under certain approximations, we characterize the PAPR reduction and the data rate increase of the proposed TR approach using order statistics. Next, we formulate the TR ratio optimization problem to maximize the data rate. The optimization problem is very challenging, as the data rate cannot be explicitly expressed as a function of the TR ratio. To reduce the computation complexity, we propose a near optimal solution by solving an approximate optimization problem of the original one. Finally, numerical results verify the analysis and demonstrate the performance of the proposed near optimal solution.

Caching and multicasting are two promising methods to support massive content delivery in multi-tier wireless networks. In this paper, we consider a random caching and multicasting scheme with caching distributions in the two tiers as design parameters, to achieve efficient content dissemination in a two-tier large-scale cache-enabled wireless multicasting network. First, we derive tractable expressions for the successful transmission probabilities in the general region as well as the high signal-to-noise ratio (SNR) and high user density region, respectively, utilizing tools from stochastic geometry. Then, for the case of a single operator for the two tiers, we formulate the optimal joint caching design problem to maximize the successful transmission probability in the asymptotic region, which is nonconvex in general. By using the block successive approximate optimization technique, we develop an iterative algorithm, which is shown to converge to a stationary point. Next, for the case of two different operators, one for each tier, we formulate the competitive caching design game where each tier maximizes its successful transmission probability in the asymptotic region. We show that the game has a unique Nash equilibrium (NE) and adopt an iterative algorithm, which is shown to converge to the NE under a mild condition. Finally, by numerical simulations, we show that the proposed designs achieve significant gains over existing schemes.

Detection probability is one of the most important criteria in the satellite-based automatic identification system. It represents how many vessels can be detected in the monitoring area of the system. However, existing research often neglects practical factors in S-AIS (satellite-based AIS) and cannot assess the performance of a practical system. In this paper, we propose a much detailed collision model by considering both the effects of SINR (Signal to Interference plus Noise Ratio) caused by antenna patterns and the demodulation performance. Through this model, we can construct much more accurate and reasonable detection probability calculation method. Simulation results show that the detection probability from the proposed practical model is much larger than that from the legacy model.

Space-based Automatic Identification System (AIS) uses Gaussian minimum shift keying modulation (GMSK) as the modulation scheme. Thus, Viterbi Algorithm (VA) is commonly implemented considering the tradeoff between performance and complexity. For the further improvement of the performance of Viterbi decoding, we propose a Soft-Output Viterbi Algorithm for AIS receiver (SOVA-AIS) in this paper. The proposed SOVA-AIS introduces soft values to quantify the error probability of demodulated bits and use them to update the survivor path in VA to get more precise estimation of the signal phase. Simulation results show that, compared with VA, SOVA-AIS can provide 0.5-1.5dB gain in the case with only one AIS signal and 1-3dB gain in the case with two collided AIS signals.

Single Carrier-Frequency Division Multiple Access (SC-FDM) and Tone Reservation (TR) are used independently to reduce Peak to Average Power Ratio (PAPR) in wireless communications. In this paper, we jointly consider TR and SC-FDM to achieve much lower PAPR. We first present the PAPR of SC-FDM system with different DFT/IDFT size, and then theoretically analyze the PAPR gain of TR in SC-FDM. By considering the impact of TR on transmission power, we propose a novel metric, the effective signal power, to measure the performance of TR in SC-FDM. Afterwards, the TR optimization problem is formulated and solved by TR gradient algorithm. Finally, the performance of TR with SC-FDM is evaluated.

Because of the wide field of view (FOV) to monitor vessel movements, satellite-based automatic identification system (S-AIS) has been promoted in recent years. However, synchronization is a tough work in S-AIS receiver because of the large Doppler shift and serious signal collision resulting from simultaneous transmission in the FOV of a satellite. In this paper, we propose a pipelined synchronization approach with good performance and low complexity. It consists of three parts, i.e., packet detection, windowing and timing recovery, and frequency and timing offset estimation, which can be implemented in feedforward structure with low complexity. Simulation results show that the proposed synchronization approach has significant performance gain compared with existing algorithms and provides an acceptable estimation range to cope with large Doppler shift and message collision.

This paper studies the throughput capacity of wireless networks with social characteristics. We propose a simple model to reflect both the social relations between nodes and power-law node degree distribution, and then examine their impact on capacity. We show the fact that two features above lead to traffic locality and improve capacity. Moreover, multicasting may be employed to further enhance performance when information is desired to be published from the source to all its contacts, of which the number follows power-law distribution. In addition, we propose the corresponding capacity-achieving communication schemes, which optimally exploit the underlying structure. Our study is an attempt to understand how social relations may impact on network capacity from a theoretical perspective, and provides fundamental insight on the design and analysis of real wireless networks.

Traffic offloading over Delay Tolerant Networks (DTNs) is a promising paradigm to alleviate the network congestion caused by explosive traffic demands. As we all know, in mobile networks, the delay profile for traffic is remarkable due to user's mobility. How to exploit delay tolerance to improve the profit of the operator as well as mobile users becomes a big challenge. In this paper, we investigate the problem of the interrelation of delay and user QoS. Inspired by contract theory, we model the delayed offloading process as a monopoly market where the operator makes pricing by considering statistical information about user satisfaction. In addition, we propose an incentive framework to motivate users to leverage their delay and price sensitivity in exchange for service cost. To capture the heterogeneity of user satisfaction, we classify users into different types. Each user chooses an appropriate quality-price contract item according to its type. Moreover, we derive an optimal contract which is feasible and maximizes the operator's profit as well. Numerical results validate the effectiveness of our incentive framework for traffic offloading over DTNs.

Multiple-Input Multiple-Output (MIMO) technology has become an efficient way to improve the capacity and reliability of wireless networks. Traditional MIMO schemes are designed mainly for the scenario of contiguous spectrum ranges. However, in cognitive radio networks, the available spectrum is discontiguous, making traditional MIMO schemes inefficient for spectrum usage. This motivates the design of new MIMO schemes that apply to networks with discontiguous spectrum ranges. In this paper, we propose a scheme called VSMC MIMO, which enables MIMO nodes to transmit variable numbers of streams in multiple discontinuous spectrum ranges. This scheme can largely improve the spectrum utilization and meanwhile maintain the same spatial multiplexing and diversity gains as traditional MIMO schemes. To implement this spectral-efficient scheme on cooperative MIMO relays in cognitive radio networks, we propose a joint relay selection and spectrum allocation algorithm and a corresponding MAC protocol for the system. We also build a testbed by the Universal Software Radio Peripherals (USRPs) to evaluate the performances of the proposed scheme in practical networks. The experimental results show that VSMC MIMO can efficiently utilize the discontiguous spectrum and greatly improve the throughput of cognitive radio networks.

In this paper, we study the throughput and delay scaling laws for cognitive radio network with static primary users and heterogeneous mobile secondary users coexist in the unit planar region for both without base-station case and with base-station case. The primary network consists of $n$ randomly and uniformly distributed static primary users with higher priority to access the spectrum. The secondary network consists of $m=(h+1)n^{1+\epsilon}$ heterogeneous mobile secondary users with $h=O(\log n)$ We use standard asymptotic notations in our paper. Consider two nonnegative function $f(\cdot)$ and $g(\cdot)$ : (1) $f(n)=o(g(n))$ means $\lim_{n\rightarrow\infty}f(n)/\break g(n)= 0$ . (2) $f(n)=O(g(n))$ means $\lim_{n\rightarrow\infty}f(n)/g(n) . (3) $f(n)=\omega(g(n))$ means $\lim_{n\rightarrow\infty}f(n)/g(n)=\infty$ . (4) $f(n)=\Omega(g(n))$ means $\lim_{n\rightarrow\infty}f(n)/g(n) > 0$ . (5) $f(n)=\Theta(g(n))$ means $f(n)=O(g(n))$ and $g(n)=O(f(n))$ . (6) $f(n)=\mathtilde{\Theta}(g(n))$ means that $f(n)=\Theta(g(n))$ when poly-logarithmic factors are ignored.

In this paper,1we investigate the throughput and delay in heterogeneous cognitive radio networks (HCRN), where the data source and the destination (S-D) is heterogeneously distributed following a rank based model and secondary users (SUs) provide relay service for primary users (PUs). We consider two scenarios: 1) PUs and SUs are both static; and 2) PUs are static and SUs are mobile. For scenario 1, we show that the primary network throughput is the same for different heterogeneous extents of S-D distribution owing to the flexible assistance of SUs, while the throughput of secondary networks is proven to be changing with the S-D heterogeneity exponent α, which depicts the variation of different heterogeneous extents of S-D distribution. In addition, the delay of both primary and secondary networks are shown to be altering with α. Further, we reveal that the number of SUs required to assist PUs can be dramatically reduced when considering the S-D heterogeneity, while achieving the same primary network throughput. For scenario 2, we utilize a modified uniform mobility (MUM) model to depict the motion of SUs and mainly focus on the analysis of throughput and delay for primary networks. It shows that the primary network throughput is also free of the heterogeneous extent of S-D distribution, while the delay changes with α. Due to the mobility of SUs, a better delay-throughput tradeoff of primary networks is achieved compared with that in scenario 1.

In this paper, we study the problem of data gathering with compressive sensing (CS) in wireless sensor networks (WSNs). Unlike the conventional approaches, which require uniform sampling in the traditional CS theory, we propose a random walk algorithm for data gathering in WSNs. However, such an approach will conform to path constraints in networks and result in the non-uniform selection of measurements. It is still unknown whether such a non-uniform method can be used for CS to recover sparse signals in WSNs. In this paper, from the perspectives of CS theory and graph theory, we provide mathematical foundations to allow random measurements to be collected in a random walk based manner. We find that the random matrix constructed from our random walk algorithm can satisfy the expansion property of expander graphs. The theoretical analysis shows that a k-sparse signal can be recovered using `1 minimization decoding algorithm when it takes m = O(k log(n=k)) independent random walks with the length of each walk t = O(n=k) in a random geometric network with n nodes. We also carry out simulations to demonstrate the effectiveness of the proposed scheme. Simulation results show that our proposed scheme can significantly reduce communication cost compared to the conventional schemes using dense random projections and sparse random projections, indicating that our scheme can be a more practical alternative for data gathering applications in WSNs.

In this paper, we focus on the capacity and delay tradeoff for multicast traffic pattern in cognitive radio mobile ad hoc networks (MANETs). In our system model, the primary network consisting of n primary nodes overlaps with the secondary network consisting of m secondary nodes in a unit square. Assume that all nodes move according to an independent and identically distributed mobility model, and each primary node serves as a source that multicasts its packets to kp primary destination nodes, whereas each secondary source node multicasts its packets to ks secondary destination nodes. Under the cell partitioned network model, we study the capacity and delay for the primary networks under two communication schemes, i.e., noncooperative scheme and cooperative scheme. The communication pattern considered for the secondary network is cooperative scheme. Given that m = nβ(β > 1), we show that per-node capacities O(1/kp) and O(1/ks) are achievable for the primary network and the secondary network, with average delays Θ(n log kp) and Θ(m log ks), respectively. Moreover, to reduce the average delay in the secondary network, we employ a redundancy scheme and prove that a per-node capacity O(1/ks√m log ks) is achievable with average delay Θ(√m log ks). We find that the fundamental delay-capacity tradeoff in the secondary network is delay/capacity ≥ O(mkslog ks) under both cooperative and redundancy schemes.

Network throughput and packet delay are both important performance metrics in Ad-Hoc networks. While uncorrelated mobility has been proved to be able to increase the network capacity at the cost of delay, the influence of correlated mobility on network performance has not been well studied. In correlated mobility model, nodes in the same group are constrained to lie in a disc area, whose center moves uniformly according to the i.i.d. model; however, it is still an open issue how to evaluate the performance of capacity and delay tradeoff in hybrid Ad-Hoc networks. In this paper, we propose a theoretical framework to examine such issue when relay nodes are under correlated mobility and static nodes are acting as transmitters and receivers. Simulation results shows that our algorithm provides better throughput delay tradeoff than i.i.d. model.

Recent studies show that the 60GHz wireless technology could help resolving the hotspot issue in data center networks (DCNs). However, transmissions over 60GHz suffer from limitations of short transmission range and blockage, which makes it a new challenge how to appropriately establish wireless links in the DCN. In this paper, we propose to integrate wireless links into the DCN with the wireless neighborway scheme. Each top-of-rack switch (ToR) has multiple 60GHz wireless links connecting to its neighboring ToRs, which are termed as neighborways. The elephant flow from the sending ToR can be partially offloaded through neighborways, which are then delivered to the ToRs around the receiving ToR through wired links of the DCN, and finally converged to the receiving ToR. The fundamental challenge for the design is how to prevent the offloaded traffics from forming new hotspots in the network fabric. To this end, we developed a wireless network planning solution with corresponding IP address assignment and traffic engineering scheme, which can leverage the potential underutilized wired links in the DCN and meanwhile avoid forming new hotspots. The simulation results show that the proposed scheme can notably relieve the hotspots in the DCN.

Offloading data from cellular networks to WiFi or femtocell networks is an efficient way to alleviate the network congestion caused by rapidly increasing demands for mobile data. To this end, an Internet Service Provider (ISP) is willing to deploy WiFi/femtocell networks. With the advent of such networks, it is necessary to analyze how the ISP sets data plans to improve its profit while mobile data offloading is supported. In this paper, we develop a contract-based scheme to deal with data plan setting problem in two-tier networks. The contract offered by the ISP is a set of data plans which provide different combinations of data volume and price. We classify consumers into different types according to their percentages of data traffic offloaded to WiFi/femtocell networks. Each consumer can choose its own data plan based on its type, which is private information. Under asymmetric information scenario, we provide the necessary and sufficient conditions for the feasibility of a contract and then we derive the optimal contract which maximizes the ISP's profit. Numerical results validate the effectiveness of our scheme and indicate that the ISP can improve its profit by raising the throughput of its WiFi/femtocell networks and/or lowering its energy cost.

Recently, security of cognitive radio (CR) is becoming a severe issue. There is one kind of threat, which we call greedy spectrum occupancy threat (GSOT) in this paper, has long been ignored in previous work. In GSOT, a secondary user may selfishly occupy the spectrum for a long time, which makes other users suffer additional waiting time in queue to access the spectrum and leads to congestion or breakdown. In this paper, a queueing model is established to describe the system with greedy secondary user (GSU). Based on this model, the impacts of GSU on the system are evaluated. Numerical results indicate that the steady-state performance of the system is influenced not only by average occupancy time, but also by the number of users as well as number of channels. Since a sudden change in average occupancy time of GSU will produce dramatic performance degradation, the greedy second user prefers to increase its occupancy time in a gradual manner in case it is detected easily. Once it reaches its targeted occupancy time, the system will be in steady state, and the performance will be degraded. In order to detect such a cunning behavior as quickly as possible, we propose a wavelet based detection approach. Simulation results are presented to demonstrate the effectiveness and quickness of the proposed approach.

We study the relay-based communication schemes for cooperative spectrum sharing among multiple primary users (PUs) and multiple secondary users (SUs) with incomplete information. Inspired by the matching theory, we model the network as a matching market. In this market, each PU proposes a certain proposal representing a combination of relay power and spectrum access time to attract the SUs, while each SU maximizes its utility by selecting the most suitable PU. We derive the sufficient and necessary conditions for a stable matching in which none of the PUs or SUs would like to change its decision. We further establish a distributed matching algorithm (DMA) and a DMA with utility increasing (DMA-UI) to achieve the equilibria in partially incomplete and incomplete information scenarios, respectively. Moreover, we provide detailed discussions on the implementation of the distributed algorithms in practical networks. Simulation results show that the losses of PUs' total utilities caused by incomplete information are diminished when the number of SUs increases. Specifically, the effects of the incomplete information are reduced as the competition among SUs (PUs) is more intensive than that among PUs (SUs).

In this paper, we develop a truthful and efficient combinatorial auction scheme under a novel spectrum allocation model that can achieve a worst-case approximation ratio \sqrt{m} in social welfare. We propose to tackle the dynamic spectrum access problem in cognitive radio (CR) networks with time-frequency flexibility requirements. We model the spectrum opportunity in a time-frequency division manner and the spectrum allocation as a combinatorial auction. Then we design an auction mechanism to reach the upper bound in polynomial time and propose a combined approach to improve the bound in the cost of increasing computational complexity. A truthful payment that gives incentive to the SUs for revealing the truthful valuation of the desirable bundle of slots is presented. In order to reduce the complexity, we simplify the general model to a modified model that only allows frequency flexibility, and then present a truthful, optimal and computationally efficient auction mechanism. Extensive simulation results of the social welfare and spectrum ratio show that the performance of the combined approximation algorithm is better than the sorting based greedy algorithm.

In this paper, we explore methods to generate optimal network topologies for wireless sensor networks (WSNs) with and without obstacles. Specifically, we investigate a dense network with n sensor nodes and m=nb(0v) (0 <; v ≤ 1) arbitrarily or randomly distributed obstacles, which block cells they are located in, i.e., sensor nodes cannot be placed in these cells and nodes' communication cannot cross them directly. We find that the overall throughput capacity is bounded by the transmission burden in areas around these blocked cells and introduce a novel algorithm of complexity O(M) to generate optimal sensor nodes' topologies for any given obstacles' distributions. We further analyze its performance for regularly distributed obstacles, which can be taken to estimate the lower bound of the algorithm's performance.

The concept of femtocell access points underlying existing macrocell network has recently emerged as a key technology that provides better coverage and improves wireless system capacity. Among all three access control mechanism adopted by Femtocell Access Points (FAPs), Hybrid access is regarded as the most desirable because this mechanism makes it possible to allow both femto and macro end users to access femtocell network, which significantly enhances overall network performance. In this paper, we propose an incentive mechanism for the two-tier macro-femtocell network under hybrid access control, in which FAPs expect to lease licensed spectrum from the coexisting Macrocell Service Provider (MSP) to serve their end users (FUs), in return FAPs grant the MSP a fraction of Access Permission (ACP) so that macro end users (MUs) can access FAPs to increase the Quality of Service and macrocell network capacity. We introduce the concept of contract to specify the relationship between FAPs and the MSP. We study how the MSP can obtain the optimal contract by designing different ACP-spectrum combinations (i.e., the amount of spectrum sold from the MSP to FAPs and the corresponding quantity of ACP sold from FAPs to the MSP) for different types of FAPs. Numerical results show that both MSP and FAPs can benefit from this incentive mechanism in term of their utilities.

In this paper, we propose a Differential Overlap Decoding (DOD) algorithm to solve the hidden-terminal problems in OFDM based WLAN systems. DOD exploits the retransmission and random-jitter features of WLAN to decode the collided packets as a whole. DOD provides with new methods and views to jointly separate and decode collided packets in OFDM based system. In DOD, we formulate IFFT/FFT, channel influence and packet collision as linear processes. Thus we can express the received collided packets as linear equations, and simplify this problem into solving linear equations. We evaluate the performance of DOD through simulation, and results show that when DOD is applied, hidden-terminal problems can be viewed as a 3-5 dB BER performance degradation rather than network contention.

The problem of jointly estimating clock skew and offset for wireless sensor networks (WSNs) in a pairwise broadcast synchronization (PBS) protocol is considered. The random part of the delay is supposed to be an exponential random variable. We consider two estimators, i.e., joint maximum-likelihood estimator (JMLE) and generalized ML-like estimator (GMLLE) proposed by Leng and Wu . For both estimators, the corresponding algorithms are explicitly derived and presented. For the GMLLE, the corresponding performance bound based on the reduced set of observations is derived and the optimal value of a user-defined parameter is identified accordingly. At last, analytical results are corroborated by numerical experiments. We observe that: (i) JMLE usually outperforms GMLLE at the cost of larger computational complexity; (ii) JMLE, while achieving the same estimation accuracy as that of the LP method presented in , enjoys significantly lower computational complexity than that of the latter.

In Cognitive Radio Networks (CRNs), Primary Users (PUs) can share their idle spectra with Secondary Users (SUs) under certain mechanisms. In this paper, we propose a distributed-centralized and incentive-aware spectrum sharing scheme for the multiple-PU scenario, which introduces a Random Leader who is elected randomly from SUs or PUs. The distributed aspect of our scheme lies in that it requires no central control entities, which can be independently implemented within a distributed spectrum market. The centralized aspect is that the leader draws up and assigns the socially optimal contracts for all PUs and SUs in a centralized manner, which maximizes the throughput of the whole network and attains the economic robustness (including Incentive Compatibility and Individual Rationality). Analysis shows that the proposed scheme takes in the advantages of both centralized and distributed schemes but overcomes their weaknesses. We use the proposed scheme to study two sharing scenarios: the short-term and the long-term spectrum sharing. The Short-Term Sharing (STS) focuses on distributing PUs' idle spectra within one time slot while the Long-Term Sharing (LTS) considers multiple slots, where the spectrum mobility must be investigated. As an integrated design of both STS and LTS, our scheme not only fulfils SUs' heterogeneous spectrum requirements but also obtains the socially optimal throughput while accounting for both SUs' and PUs' incentives.

Cyclostationary feature detection is capable of improving sensing performance in Cognitive Radio systems. The maximum over spectral on cyclostationary domain is called α-domain. α-domain feature detection will greatly reduce computational complexity with respect to prior-knowledge of carrier frequency. To select the threshold for α-domain feature detection under a given noise level and miss detection probability Pf, we need the expression of α-domain noise distribution. In this paper, Generalized Extreme Value (GEV) distribution is introduced and found as the noise distribution on α-domain. Maximum likelihood estimation is applied to estimate the parameters of GEV distribution. Based on GEV distribution, close form of detection threshold λth in terms of false alarm probability Pf is obtained. The validation of α-domain noise distribution is confirmed by the Monte-Carlo simulation.

This paper proposes a novel channel estimation method to further improve the performance of the optimal pilot sequences in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We first assume that the virtual pilot tones superimposed at the data locations over the specific sub-carriers are transmitted from all transmit antennas. We then obtain the virtual received pilot signals at the corresponding locations at the receive antennas by interpolation in the time domain. Finally, the channel parameters are obtained from the combination of the virtual and real received pilot signals over one OFDM symbol based on the least squares (LS) channel estimation. Simulation results show that the proposed channel estimation method provides much better performance than the previous method for the optimal pilot sequences over multiple OFDM symbols, especially in fast time- varying channels.