360-degree panoramic video provides users with an unprecedented immersive experience and is rapidly evolving with the support of virtualized devices. Effective viewport prediction is crucial for alleviating high-speed bandwidth constraints and enhancing user quality of service. However, most existing research relies on saliency maps derived from multi-user viewport trajectories, often neglecting the rich multi-scale information inherent in individual user viewport trajectory. Inspired by signal decomposition theory, we propose an Empirical Mode Decomposition-based LSTM-MLP (EMD-ML) model. The EMD-ML extracts robust spatiotemporal representations from user viewport trajectory at multiple scales. Leveraging multi-scale representations and a hybrid learning framework, the model achieves accurate long-term viewport prediction from limited short-term viewport trajectory. The EMD-ML model achieves nearly a 50% reduction in orthodromic distance compared to state-of-the-art methods across four publicly available datasets. Additionally, experiments on different video types and training datasets show that our method generalizes well and can be applied in real-world scenarios.

User behavior threat detection is important for the protection of network system security. Traditional supervised modeling methods and unbalanced sample data lead to a high false positive rate in user behavior detection. In addition, network user behaviors are complex, changeable, and difficult to predict, and existing detection methods are facing ever greater challenges. Effectively detecting user behavior remains a challenge. In this paper, we propose a user behavior threat detection method based on an Adaptive Sliding Window Generative Adversarial Network(ASW-GAN). This method designs an adaptive sliding window mechanism to process behavior data and uses the GAN model to detect threat behavior, finally uses the maximum interclass variance algorithm Otsu to optimize test detection result. Compared with other typical methods, the proposed method achieves a higher accuracy rate and a markedly lower false positive rate, and can effectively evaluate user threat behaviors.

In vehicular ad hoc networks (VANET), the cross-domain identity authentication of users is very important for the development of VANET due to the large cross-domain mobility of vehicle users. The Public Key Infrastructure (PKI) system is often used to solve the identity authentication and security trust problems faced by VANET. However, the PKI system has challenges such as too centralized Authority of Certification Authority (CA), frequent cross-domain access to certificate interactions and high authentication volume, leading to high certificate management costs, complex cross-domain authentication paths, easy privacy leakage, and overburdened networks. To address these problems, this paper proposes a lightweight blockchain-based PKI identity management and authentication architecture that uses smart contracts to reduce the heavy burden caused by CAs directly managing the life cycle of digital certificates. On this basis, a trust chain based on smart contracts is designed to replace the traditional CA trust chain to meet the general cross-domain requirements, to effectively avoid the communication pressure caused by a mass of certificate transmissions. For the cross-domain scenario with higher privacy and security requirements the identity attribute authentication service is provided directly while protecting privacy by using the Merkle tree to anchor identity attribute data on and off the blockchain chain. Finally, the proposed scheme was comprehensively analyzed in terms of cost, time consumption and security.

The safe storage and sharing of medical data have promoted the development of the public medical field. At the same time, blockchain technology guarantees the safe storage and sharing of medical data. However, the consensus algorithm in the current medical blockchain cannot meet the requirements of low delay and high throughput in the large-scale network, and the identity of the primary node is exposed and vulnerable to attack. Therefore, this paper proposes an efficient consensus algorithm for medical data storage and sharing based on a master–slave multi-chain of alliance chain (ECA_MDSS). Firstly, institutional nodes in the healthcare alliance chain are clustered according to geographical location and medical system structure to form a multi-zones network. The system adopts master–slave multi-chain architecture to ensure security, and each zone processes transactions in parallel to improve consensus efficiency. Secondly, the aggregation signature is used to improve the practical Byzantine fault-tolerant (PBFT) consensus to reduce the communication interaction of consensus in each zone. Finally, an efficient ring signature is used to ensure the anonymity and privacy of the primary node in each zone and to prevent adaptive attacks. Meanwhile, a trust model is introduced to evaluate the trust degree of the node to reduce the evil done by malicious nodes. The experimental results show that ECA_ MDSS can effectively reduce communication overhead and consensus delay, improve transaction throughput, and enhance system scalability.

With the wide deployment of Internet of Things (IoT), blockchain systems have been playing a crucial role to establish a trusted computing environment among potentially mistrusting agents without depending on a centralized server. Different from previous blockchain consensus protocols adopted in IoT, which rely on efficient and stable transmissions, in this article, we consider how to reach blockchain consensus in wireless networks without reliable network support. Specifically, a realistic signal to interference plus noise ratio (SINR) model is adopted to depict the unreliable transmissions in wireless channels. Based on the SINR model, a distributed and randomized consensus algorithm is proposed to reach$k$-times consensus among$n$devices within$O(k+\log n)$time steps with high probability. Note that the time complexity of our algorithm is asymptotically optimal since$\Omega (k+\log n)$is a lower bound to achieve$k$-times consensus in a distributed environment. We conduct both rigorous theoretical analysis and extensive simulations to validate our method. It is believed that our work can facilitate the implementation of blockchains in many wireless scenarios in which the reliable and fast transmissions cannot be guaranteed.

Known as a distributed ledger technology (DLT), blockchain has attracted much attention due to its properties such as decentralization, security, immutability and transparency, and its potential of servicing as an infrastructure for various applications. Blockchain can empower wireless networks with identity management, data integrity, access control, and high-level security. However, previous studies on blockchain-enabled wireless networks mostly focus on proposing architectures or building systems with popular blockchain protocols. Nevertheless, such existing protocols have obvious shortcomings when adopted in wireless networks where nodes may have limited physical resources, may fall short of well-established reliable channels, or may suffer from variable bandwidths impacted by environments or jamming attacks. In this paper, we propose a novel consensus protocol named Proof-of-Channel (PoC) leveraging the natural properties of wireless communications, and develop a permissioned BLOWN protocol (BLOckchain protocol for Wireless Networks) for single-hop wireless networks under an adversarial SINR model. We formalize BLOWN with the universal composition framework and prove its security properties, namely persistence and liveness, as well as its strengths in countering against adversarial jamming, double-spending, and Sybil attacks, which are also demonstrated by extensive simulation studies.

Pilot pollution and limited power have become two important factors limiting the throughput of massive multi-input–multioutput (MIMO) systems. To improve system performance, we consider device-to-device (D2D) communication underlay massive MIMO (denoted “massive MIMO-D2D” for short) cellular networks and then perform pilot allocation and power optimization under this network. To begin with, the closed-form spectrum efficiency (SE) expressions for different types of users are derived in the massive MIMO-D2D cellular network. Then, we analyze the deficiencies of the existing pilot allocation schemes and propose a new pilot allocation problem, i.e., the SE product is maximized for enhancing the system SE and ensuring fairness among the users simultaneously. To solve the maximum SE product problem, we develop a pilot gray wolf prey (PGWO) algorithm by designing the fitness value used to measure pilot pollution and the global objective function used to evaluate the quality of pilot allocation. The PGWO algorithm can find the optimal SE accurately through a global search, and it is suitable for the pilot allocation of different models from single cell to multicell. Besides, we formulate the maximum–minimum fairness problem for power optimization and prove that the power objective function conforms to linear programming, and a bisection algorithm is provided to optimize the power. Simulation results show that the proposed SE product problem with the proposed PGWO algorithm promotes fairness for users while further enhancing SE compared to the existing pilot allocation schemes, and joint pilot allocation and power optimization achieves great sum SE over only pilot allocation.

This paper presents$\mathit {wChain}$, a blockchain protocol specifically designed for multihop wireless networks that deeply integrates wireless communication properties and blockchain technologies under the realistic SINR model. We adopt a hierarchical spanner as the communication backbone to address medium contention and achieve fast data aggregation within$O(\log N\log \Gamma)$slots where$N$is the network size and$\Gamma $refers to the ratio of the maximum distance to the minimum distance between any two nodes. Besides,$\mathit {wChain}$employs data aggregation and reaggregation as well as node recovery mechanisms to ensure efficiency, fault tolerance, persistence, and liveness. The worst-case runtime of$\mathit {wChain}$is upper bounded by$O(f\log N\log \Gamma)$, where$f=\lfloor \frac {N}{2} \rfloor $is the upper bound of the number of faulty nodes. To validate our design, we conduct both theoretical analysis and simulation studies. The results not only demonstrate the nice properties of$\mathit {wChain}$, but also point to a large new space for the exploration of blockchain protocols in wireless networks.

Hybrid precoding is widely used in millimeter wave (mmWave) massive multiple‐input multiple‐output (MIMO) systems. However, most prior work on hybrid precoding focused on the fully connected hybrid architectures and the subconnected but fixed architectures in which each radio frequency (RF) chain is connected to a specific subset of the antennas. The limited work shows that dynamic subarray architectures address the tradeoff between achievable spectral efficiency and energy efficiency of mmWave massive MIMO systems. Nevertheless, in the multiuser hybrid precoding systems, the existing dynamic subarray schemes ignore the fairness of users and the problem of user selection. In this paper, we propose a novel multiuser hybrid precoding scheme for dynamic subarray architectures. Firstly, we select a multiuser set among all users according to the analog effective channel information of the base station (BS) and then design the subset of the antennas to each RF by the fairness antenna‐partitioning algorithm. Finally, the optimal analog precoding vector is designed according to each subarray, and the digital precoding is designed by the minimum mean‐squared error (MMSE) criterion. The simulation results show that the performance advantages of the proposed multiuser hybrid precoding scheme for dynamic subarray architectures.

Internet of Things (IoT) brought great convenience to people’s daily lives. Meanwhile, the IoT devices are facing severe attacks from hackers and malicious attackers. Hackers and malicious attackers use various methods to invade the Internet of Things system, causing the Internet of Things to face a large number of targeted, concealed, and penetrating potential threats, which makes the privacy problem of the Internet of Things suffers serious challenges. But the existing methods and technologies cannot fully identify the attacker’s attack process and protect the privacy of the Internet of Things. Alarm correlation method can construct a complete attack scenario and identify the attacker’s intention by alarming the alarm data which provides an effective protection for user privacy. However, the existing alarm correlation methods still have the disadvantages of low correlation accuracy, poor correlation efficiency, and strong dependence on the knowledge base. To address these issues, we propose an alarm correlation method based on Affinity Propagation (AP) clustering algorithm and causal relationship. Our method considers that the alarm data triggered by the same attack process has high similarity characteristics, adopts the AP algorithm to improve the correlation efficiency, and at the same time constructs a complete attack process based on the causal correlation idea. The new alarm correlation method has a high correlation effect and builds a complete attack process to help managers identify attack intentions and prevent attacks.

In mmWave massive MIMO systems, traditional digital precoding is difficult to be implemented because of the high cost and energy consumption of RF chains. Fortunately, the hybrid precoding which combines digital precoding and analog precoding not only solves this problem successfully, but also improves the performance of the system effectively. However, due to the constant mode constraint introduced by the phase shifter in the analog domain, it is difficult to solve the hybrid precoding directly. There is a solution which divides the total optimization problem into two stages to solve, that is, first fix the digital precoding matrix, solve the analog precoding matrix, and then optimize the digital precoding matrix according to the obtained analog precoding matrix. In this paper, a high energy‐efficient hybrid precoding scheme is proposed for the subconnection structure. In the first stage, the optimization problem can be decomposed into a series of subproblems by means of the independent submatrix structure of the analog precoding matrix. When the optimized analog precoding matrix is obtained, the digital precoding matrix can be solved by the minimum mean error (MMSE). Finally, the digital precoding matrix is normalized to satisfy the constraint conditions. The simulation results demonstrate that the performance of the proposed algorithm is close to that of fully digital precoding based on subconnection structure and better than that of the existing algorithms. In addition, this paper presents the simulation analysis of the algorithm performance under imperfect channel state information. Simulation results show that when the estimation accuracy of channel state information is 0.8, the spectral efficiency of the proposed algorithm can already be maintained at a good level.

The fast penetration of Intelligent Connected Vehicles (ICVs) has become the primary growth engine of the automotive industry in recent years. Urban vehicular network consisting of ICVs is evolving towards a distributed intelligent platform for pervasive sensing, connecting and computing in Intelligent Transportation System (ITS) and smart cities. In this paper, we propose that parked vehicles (PVs) could be exploited for environment perception and model inference. We describe the system architecture and its typical application scenarios of distributed environment perception for city roads, parking lots, as well as for commercial and residential buildings. PVs are motivated to assist in deep learning model inference for the captured image data in such applications. Regarding the diversity of PVs in deep learning capability, a differential incentive mechanism is elaborately designed based on contract theory to emulate PVsparticipation. The experiment on the dataset of German Traffic Sign Recognition Benchmark is conducted to verify the effectiveness and efficiency of the proposed approach.

With the repaid development of Internet of Vehicles (IoV), more available resources and energy-efficient optimizations in resources scheduling are exactly required for large-scale network implementation for sustainable development. We observe that parked vehicles (PVs) have rich and underutilized resources for task execution. By scheduling them as general computing nodes to undertake computation tasks, we introduce a new computing paradigm, named by parked vehicular computing (PVC). There exists some challenging issues to be addressed for the facilitation of PVC. In particular, an incentive mechanism is needed to offer optimized rewards for PVs with the consideration of their parking time and energy consumption. In this paper, we investigate an energy-efficient PVC paradigm, and we design a contract-based incentive mechanism to motivate PVs to contribute their idle on-board resources. The PVs are classified into different types according to their parking time. Then, the designed contracts are assigned to different types of PVs. To realize the incentive mechanism, the optimization problem with the contract design is formulated to maximize the utility of the service provider. For optimal contract design, we solve the simplified problem by using Lagrangian multiplier method. Numerical results indicate that the proposed PVC with optimal contract design outperforms existing work in improving social welfare of resource scheduling, which takes quality-of-service and overall energy consumption into consideration. We also demonstrate that the contract-based incentive mechanism is energy-efficient and effective.

Cloud workflow scheduling is significantly challenging due to not only the large scale of workflow but also the elasticity and heterogeneity of cloud resources. Moreover, the pricing model of clouds makes the execution time and execution cost two critical issues in the scheduling. This paper models the cloud workflow scheduling as a multiobjective optimization problem that optimizes both execution time and execution cost. A novel multiobjective ant colony system based on a co-evolutionary multiple populations for multiple objectives framework is proposed, which adopts two colonies to deal with these two objectives, respectively. Moreover, the proposed approach incorporates with the following three novel designs to efficiently deal with the multiobjective challenges: 1) a new pheromone update rule based on a set of nondominated solutions from a global archive to guide each colony to search its optimization objective sufficiently; 2) a complementary heuristic strategy to avoid a colony only focusing on its corresponding single optimization objective, cooperating with the pheromone update rule to balance the search of both objectives; and 3) an elite study strategy to improve the solution quality of the global archive to help further approach the global Pareto front. Experimental simulations are conducted on five types of real-world scientific workflows and consider the properties of Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than both some state-of-the-art multiobjective optimization approaches and the constrained optimization approaches.

Potable water distribution networks (WDNs) are important infrastructures of modern cities. A good design of the network can not only reduce the construction expenditure but also provide reliable service. Nowadays, the scale of the WDN of a city grows dramatically along with the city expansion, which brings heavy pressure to its optimal design. In order to solve the large-scale WDN optimization problem, a cooperative co-evolutionary algorithm is proposed in this paper. First, an iterative trace-based decomposition method is specially designed by utilizing the information of water tracing to divide a large-scale network into small subnetworks. Since little domain knowledge is required, the decomposition method has great adaptability to multiform networks. Meanwhile, during optimization, the proposed algorithm can gradually refine the decomposition to make it more accurate. Second, a new fitness function is devised to handle the pressure constraint of the problem. The function transforms the constraint into a part of the objective to punish the infeasible solutions. Finally, a new suite of benchmark networks are created with both balanced and imbalanced cases. Experimental results on a widely used real network and the benchmark networks show that the proposed algorithm is promising.

This paper focuses on the secure transmission of wireless-powered relay systems with the imperfect eavesdropper channel state information. For efficient energy transfer and information relaying, a novel two-phase protocol is proposed, in which the relay operates in a full-duplex (FD) mode to achieve the simultaneous wireless power and information transmission. Compared with those existing protocols, the proposed design possesses two main advantages: 1) it fully exploits the available hardware resource (antenna element) of relay and can offer a higher secrecy rate and 2) it enables the self-energy recycling (S-ER) at relay, in which the loopback interference generated by the FD operation is harvested and reused for information relaying. To maximize the worst-case secrecy rate (WCSR) through jointly designing the source and relay beamformers coupled with the power allocation ratio, an optimization problem is formulated. This formulated problem is proved to be non-convex and the challenge to solve it is how to concurrently solve out the beamformers and the power allocation ratio. To cope with this difficulty, an alternative approach is proposed by converting the original problem into three subproblems. By solving these subproblems iteratively, the closed-form solutions of robust beamformers and power allocation ratio for the original problem are achieved. Simulations are done and results reveal that the proposed S-ER -based secure transmission scheme outperforms the traditional time-switching based relaying scheme at a maximum WCSR gain of 80%. Results also demonstrate that the WCSR performance of the scheme reusing all antennas for information reception is much better than that of schemes exploiting only one antenna.

In this paper, the priority-aware interference-avoidance scheduling for multi-user coexisting wireless networks with heterogeneous traffic demands is addressed. Both admission control and throughput maximization for admitted users are studied. These problems are addressed by a proposed sequential solution framework where at each step a large-scale linear program with a large number of variables is required to be solved. To efficiently solve the large-scale program, an accelerated column generation based method is proposed. In the proposed method, an efficient greedy initialization algorithm is first put forward by exploiting the proposed solution structure. After that, both upper and lower bounds on the optimal objective function of each optimization problem are derived, which are used to significantly alleviate the dependence of the whole solution procedure on deriving optimality of problems. Simulation results show that the proposed algorithm can effectively and efficiently handle the coexistence of multiple users with heterogeneous priorities and traffic demands.

Spanner construction is one of the most important techniques for topology control in wireless networks. A spanner can help not only to decrease the number of links and to maintain connectivity but also to ensure that the distance between any pair of communication nodes is within some constant factor from the shortest possible distance. Due to the non-locality, constructing a spanner is especially challenging under the physical interference model signal-to-interference-and-noise-ratio (SINR). In this paper, we develop two localized randomized algorithms SINR-directed-YG and SINR-undirected-YG to construct a directed Yao graph (YG) and an undirected YG in O(log n) (n is the number of wireless nodes) time slots with a high probability, in which each node is capable of performing successful local broadcasts to gather neighborhood information within a certain region and the SINR constraint is satisfied at all the steps of the algorithms. The resultant graph of SINR-undirected-YG, which is based on SINR-directed-YG, possesses a constant stretch factor 1/1-2 sin(π/c), where c > 6 is a constant. To the best of our knowledge, SINR-undirected-YG is the first spanner construction algorithm under SINR. We also obtain Yao-Yao graph under SINR. Extensive theoretical performance analysis and simulation study are carried out to verify the effectiveness and the efficiency of our proposed algorithms.

In order to meet the demand of explosive data traffic, ultradense base station (BS) deployment in heterogeneous networks (HetNets) as a key technique in 5G has been proposed. However, with the increment of BSs, the total energy consumption will also increase. So, the energy efficiency (EE) has become a focal point in ultradense HetNets. In this paper, we take the area spectral efficiency (ASE) into consideration and focus on the tradeoff between the ASE and EE in an ultradense HetNet. The distributions of BSs in the two-tier ultradense HetNet are modeled by two independent Poisson point processes (PPPs) and the expressions of ASE and EE are derived by using the stochastic geometry tool. The tradeoff between the ASE and EE is formulated as a constrained optimization problem in which the EE is maximized under the ASE constraint, through optimizing the BS densities. It is difficult to solve the optimization problem analytically, because the closed-form expressions of ASE and EE are not easily obtained. Therefore, simulations are conducted to find optimal BS densities.

Abstract Network coding has emerged some exciting future because of its smart technology in wireless sensor networks. At the same time, it is facing security attacks, especially conspiracy Attack. Most existing security strategies are concentrated on coding design, there has been almost no consideration from topological structure. In this background, a weakly‐secure scheme is proposed from the perspective of topology. Considering the performance of this scheme, an advanced scheme is put forward later. Simulations show that the two strategies can prevent cooperative eavesdroppers from acquiring any useful information transmitted from source node to sink node and the performance of advanced scheme is better. Copyright © 2013 John Wiley & Sons, Ltd.

This paper proposes a power allocation scheme to maximize the sum capacity of all users for signal-to-leakage-and-noise ratio SLNR precoded multiuser multiple-input single-output downlink. The designed scheme tries to explore the effect of the power allocation for the SLNR precoded multiuser multiple-input single-output system on sum capacity performance. This power allocation problem can be formulated as an optimization problem. With high signal-to-interference-plus-noise ratio assumption, it can be converted into a convex optimization problem through the geometric programming and hence can be solved efficiently. Because the assumption of high signal-to-interference-plus-noise ratio cannot be always satisfied in practice, we design a globally optimal solution algorithm based on a combination of branch and bound framework and convex relaxation techniques. Theoretically, the proposed scheme can provide optimal power allocation in sum capacity maximization. Then, we further propose a judgement-decision algorithm to achieve a trade-off between the optimality and computational complexity. The simulation results also show that, with the proposed scheme, the sum capacity of all the users can be improved compared with three existing power allocation schemes. Meanwhile, some meaningful conclusions about the effect of the further power allocation based on the SLNR precoding have been also acquired. The performance improvement of the maximum sum capacity power allocation scheme relates to the transmit antenna number and embodies different variation trends in allusion to the different equipped transmit antenna number as the signal-to-noise ratio SNR changes.Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we propose an extensible and flexible truthful auction framework that is individual-rational and self-collusion resistant. By properly setting one simple parameter, this framework can yield efficient auctions (like VCG) and (sub)optimal auctions (like Myerson's Optimal Mechanism (MOM)) with a more computationally-efficient procedure compared to VCG and MOM; by carefully choosing virtual valuation functions for the bidders, it can produce attribute-aware auctions that take the channel diversity into consideration. The framework adopts a novel procedure that can prevent bidder self-collusion resulted from the bid diversity. Theoretical analysis and case studies demonstrate the strength of our auction framework in handling various considerations in a practical heterogeneous spectrum market.

Cognitive radio is a promising technology for sharing the underutilized frequency bands that have been licensed to primary users. However, due to the uncertainty in detecting the existence of the primary user, the secondary user may interfere with the primary users when both primary and secondary users are active simultaneously. Therefore, understanding the interference and its consequences on the cognitive network is critical. Unlike the statistical models previously reported in the literature that aim at approximation of the interference, based on the solid mathematical analysis, we propose an accurate model for describing the co-channel interference with probability density function, cumulative distribution function, mean, and variance of the interference suffered by the primary users. The proposed model not only takes into account a number of factors, such as the spectrum-sensing scheme, the spatial distribution of secondary users, and the channel conditions, including shadowing and Nakagami fading, but also gives an exact mathematical expression of the influences from these factors. The developed framework supports practical applications such as evaluating the cognitive network of any spatial shape and density of the secondary users and the methods of power control and spectrum sensing used by the secondary users. Simulation results are provided to verify the effectiveness of the analytical model.

In this paper, we proposed a scheme, with which the transmitted signals at the eavesdropper node can be nulled out to avoid intercepting in the case of one eavesdropper. The amplify-and-forward (AF) based cooperative protocol is considered to form the transmission link. Assuming the globe channel state information is available at all the transmission nodes, we simulate the transmission system and show the secrecy rate of the proposed scheme. The simulation results show that the performance of the secrecy capacity of the proposed scheme is better than the bound method and the direct transmission.

In this paper, we present interference models for cognitive radio (CR) networks employing various interference management mechanisms including power control, contention control or hybrid power/contention control schemes. For the first case, a power control scheme is proposed to govern the transmission power of a CR node. For the second one, a contention control scheme at the media access control (MAC) layer, based on carrier sense multiple access with collision avoidance (CSMA/CA), is proposed to coordinate the operation of CR nodes with transmission requests. The probability density functions (PDFs) of the interference received at a primary receiver from a CR network are first derived numerically for these two cases. For the hybrid case, where power and contention controls are jointly adopted by a CR node to govern its transmission, the interference is analyzed and compared with that of the first two schemes by simulations. Then, the interference PDFs under the first two control schemes are fitted by log-normal PDFs to reduce computation complexity. Moreover, the effect of a hidden primary receiver on the interference experienced at the receiver is investigated. It is demonstrated that both power and contention controls are effective approaches to alleviate the interference caused by CR networks. Some in-depth analysis of the impact of key parameters on the interference of CR networks is given as well.

This paper proposes a novel three-dimensional (3D) two-sphere regular-shaped geometry-based stochastic model (RS-GBSM) with only double-bounced rays for non-isotropic scattering narrowband multiple-input multiple-output (MIMO) mobile-to-mobile (M2M) channels. The proposed 3D model has the ability to investigate the joint impact of both the azimuth angle and elevation angle on channel statistics. Based on the proposed model, the space-time (ST) correlation function (CF) is derived and the impact of some important parameters on the resulting ST CF is investigated. Numerical results show that the 3D model results in lower ST correlations than the corresponding 2D model.

A novel peak to average power ratio (PAPR) reduction method, threshold method, for wavelet based multicarrier modulation (WMCM) systems is proposed and presented in this paper. Theory analysis and simulation results both prove that our proposed PAPR reduction method to be a feasible and efficient one for multicarrier modulation system (MCM)

In this paper, the influences of iterations and the length of the interleaver on the performance of turbo codes in regions of low signal to noise ratio over Rayleigh fading channels are studied on the wavelet based orthogonal frequency division multiplexing (WOFDM). The results show that to improve the performance of turbo codes in WOFDM systems you have two ways to choose: increase the iteration number or enlarge the length of your interleaver. But, both the ways can bring side effects to your systems, it is also the case on coded OFDM systems. According to the demand of concrete system there must be a trade-off.