In this article, a nonstationary 3-D wideband geometry-based stochastic model (GBSM) is proposed for low-altitude unmanned aerial vehicle (UAV) multiple-input–multiple-output (MIMO) communication systems. The proposed GBSM is a combination of Line-of-Sight (LoS) components, local multipath components (MPCs) scattering from the scatterers around the receiver (Rx), named as local scatterers, and far MPCs scattering from far scatterers, defined as not local scatterers, and uses 2-D one-ring and 3-D cylinder to mimic local scatterers as well as 3-D multiple confocal elliptic cylinders to mimic far scatterers. In this article, two-state continuous-time Markov chains (CTMCs) are introduced to model appearances or disappearances of the LoS components, local MPCs at the transmitter (Tx) installed on UAV, and far MPCs at the Tx and Rx, and the evolution process of the far MPCs is also investigated. The concept of the visibility region (VR) is introduced to model the birth and death processes of the local MPCs at the Rx, and the effect of the size of the VR on channel statistics is also considered. In the proposed GBSM, the inherited nature of the LoS components, the local MPCs and far MPCs, is considered, and the corresponding statistical properties are derived. The proposed nonstationary 3-D GBSM is validated by the measured results in terms of temporal correlation, and the numerical results show that the proposed 3-D GBSM is suitable for describing nonstationarity of the 3-D UAV-MIMO channel.

Path fading and non-line-of-sight (NLOS) signals constitute serious problems in the wireless localization process. These problems cause unpredictable degradation in the localization precision. In this paper, a novel wireless localization approach, which is based on twice receiving array signal spectra fusions and asymmetric second-order stochastic resonance (ASSR) networks, is proposed. By combining and repetitively processing the above two techniques, the receiving signal-to-noise ratio (SNR) can be enhanced. Additionally, the receiving array signal without the line-of-sight (LOS) component can be determined and removed from the spectra fusion process. The theoretical analyses presented verify the unbiasedness and asymptotic efficiency of the proposed twice receiving spectra fusion approach. Computer simulations demonstrate that the fused spectra can significantly improve the wireless localization precision compared with conventional and up-to-date localization methods, especially under low SNR conditions.

The fifth generation mobile network(5G) is coming, and the base station network carries more and more pressure due to the higher rate demand of users. Load balancing traffic is crucial for high link utilization and low latency. Most of the research on load balancing algorithms focuses on data center scenarios and works based on TCP. Traditional algorithm used in 5G base station network is Equal Cost Multipath Routing(ECMP), which has poor performance due to its congestion agnostic nature. Other existing cogestion-aware algorithms work well in data center networks. However, the transport protocol and the traffic type is different in 5G base station network. It will change the performance of the original algorithms and cause the challenges to implement the schemes. In this paper, we propose RavenFlow, an effective load balancing algorithm working on layer 2 while using UDP. It makes use of different congestion information and eliminates the dependence on reverse packets. We evaluate the performance of RavenFlow in ns-3 simulator under typical 5G traffic. The result shows that RavenFlow achieves shorter Flow Completion Time(FCT) and smaller difference of utilization for different links.

In this letter, a radio frequency (RF) precoding method is put forward for massive multiple-input multiple-output (MIMO) systems with limited RF chains on high altitude platform (HAP). In the proposed method, we formulate the RF precoder design as an average signal-to-leakage-plus-noise ratio (SLNR) maximization problem, where the RF precoder is composed of some columns of a specific discrete fourier transform (DFT) matrix. First, we obtain the lower bound of the average SLNR and transform it into a trace problem. Then, for ease of handling, signal-to-leakage ratio (SLR) is adopted to achieve the initial RF precoder. Finally, a greedy algorithm for RF precoding is proposed to update the initial RF precoder for better performance. Numerical simulations demonstrate that the design proposed in this letter has better performance than other RF precoding schemes.

In the wireless positioning, the performances of most traditional methods are influenced by the low signal-to-noise (SNR) and none-line-of-sight (NLOS) problems seriously. So in this study, a kind of nonlinear stochastic-resonance (SR) signal enhancement technique combined with the distributed receiving array power spectrum fusion approach is proposed. By utilizing the signal power improvement property, the fitness of distributed SR system with the array structure, and the information fusion of the spectrum, the corresponding wireless positioning can be realized satisfactorily. Computer simulations also show the advantages over conventional methods in the direction of arrival (DoA) estimation and positioning estimation.

With the coming fifth generation (5G) network, effective congestion control mechanism is needed for increasing requirements of high quality of service (QoS). Quantized Congestion Notification (QCN) is a standard layer 2 congestion control mechanism providing low latency and preventing packet loss. However, it cannot provide fairness when several flows share one bottle link. Meanwhile, QCN performs poorly in rate convergence and queue occupancy stability under bursty traffic which is normal in 5G scenario. In this paper, we address these issues and propose an enhanced QCN congestion notification algorithm called stable and fair QCN (SFQCN). SFQCN senses congestion through joint queue occupancy and incoming traffic estimation, then feedbacks individual congestion notification message with information of expected share rate to each flow. We evaluate the performance of SFQCN in terms of rate convergence and queue occupancy under typical 5G traffic using NS3 simulator. The results of simulation validate that SFQCN achieves fast and fair rate allocation while ensuring a low and stable queue occupancy at congestion point.

In the computer network intrusion detection system, data objects, mapped from original space, are analyzed based on kernel clustering algorithm. During the process of kernel clustering, some representative points are introduced to represent a cluster. In just one iteration, the distance between a data object and representative points of a cluster is computed to partition the data objects. The clustering results contain normal data and abnormal data, which achieve the goal of intrusion detection. At the same time, KDD CUP 1999 dataset is used to make simulations. The results show that the proposed algorithm has higher detecting probability under the condition of low constant false alarm rate compared with K-Means clustering algorithm and SVM.

A kind of uplink power control approach of asynchronous code division multiple access (CDMA) mobile communication system based on the adaptive chaotic simulated annealing (CSA) is proposed. The particular influence of near-far effect can be solved by using the global optimal searching function of CSA method, and the multiple access interference (MAI) problem can be overcome by introducing adaptive MAI cancellation network in corresponding close loop control strategy. Computer simulations show the comparison results between proposed approach and conventional method with heterogeneous signal-to-interference ratio (SIR) threshold decision. It shows that this new approach can achieve higher channel capability and lower average outrage probability with some small increase of iteration steps, which can improve the performance of CDMA mobile communication system effectively.

A User Equipment (UE) beamforming method is proposed for massive MIMO based stratospheric communications with multiple High Altitude Platforms (HAPs). The method leverages multiple-antenna UEs to mitigate interference from neighboring HAPs. No additional channel information exchange between UEs and HAPs is needed. The beamforming vector is obtained at the UE side by using the semi-definite relaxation based method. Both the Rician channel model and the HAP- MIMO channel model based on the dynamic evolution of scatters are adopted. Simulations under various environmental configurations demonstrate the performance improvement of the proposed method.

In this study, the authors consider a decode‐and‐forward (DF) relaying protocol with full‐duplex wireless information and power transfer, and investigate its outage performance. In this protocol, a cooperative relay can transmit the information to the destination and harvest the energy from the source simultaneously. Thus, part of the energy used by the relay can be recycled through the self‐loop channel that exists in the relay's full‐duplex operation. An approximated expression for the outage probability is derived under the scenario where the self‐loop channel is fading, and then the authors derive an exact closed‐form expression for the outage probability under a scenario where the self‐loop channel is non‐fading. Moreover, to optimise the outage performance, a power allocation scheme is also provided. The authors analyse the effect of self‐loop channel on the outage performance, and show that, benefiting from the full‐duplex operation and self‐energy recycling, the considered protocol is superior to the existing power splitting‐based and time switching‐based DF relaying protocols, in terms of outage performance. Simulations are provided to validate the authors' results.

With Massive MIMO installed on High Altitude Platforms (HAPs), capacity analysis is conducted for both sparse users and hotspot users. Sparse users are assumed to follow the Poisson Point Process and their capacities are obtained via the random geometry theorem. For hotspot users, Massive MIMO combined with HAP-based communication is shown to be able to achieve the multiplexing gain thus increase the hotspot capacity. The channel correlation model of UPA under LOS propagations is obtained for users within the hotspot. An upper bound of the correlation function is further derived to show that the correlation between hotspot users can be sufficiently low, resulting in hotspot capacity improvement. The hotspot capacity is affected by location distributions of the scheduled users. Four user location distribution models are considered, which leads to the capacity upper bound, the practical schemes to implement and the capacity estimation method respectively.

This paper proposes a three-dimensional (3-D) multiuser channel model for stratospheric multipath-fading channel. The birth-death process is used to model the non-stationary properties that scatterers “appear” and “disappear” on the array axes. The spatial correlation of multiuser High Altitude Platform (HAP) multiple-input multiple-output (MIMO) channel is investigated in this paper. This model can be used to estimate the acquired antenna separation at Stratospheric Base Station (SBS) and user to achieve uncorrelated channel matrix between two adjacent users. Numerical results show that the proposed model is able to capture characteristics as observed in measurements.

In this paper, we model and analyze the resource management problem of energy harvesting-based underlaying device-to-device (D2D) communication in uplink cellular networks (CNs). D2D transmitter can harvest energy from ambient inter- ference and reuse the spectrum of the cellular users (CUs) to com- municate with corresponding receiver. The energy of transmitters comes form the mix of the harvested energy and its battery. To improve the spectrum utilization, one D2D pair should be enable to reuse the spectrum of multiple CUs. In our analysis, dynamic spectrum allocation is applied to minimize the total energy cost of the system with QoS guarantees for both cellular link and D2D link. We first propose a sub- optimal resource allocation scheme while the spectrum bands are static. Then by applying efficient iteration method, we propose the optimal resource management algorithm with dynamic spectrum allocation. Numerical results validate the significant advantages of our proposed schemes in energy harvesting-based D2D underlaying CNs.

In massive multiple-input multiple-output (MIMO) systems, the effect of small-scale fading and noise disappear when the number of base station antennas goes to infinity. The only remaining impairment is pilot contamination. In this paper, interference mitigation method is proposed by leveraging the dual antenna user equipments. Given the antenna correlation matrix at the user side, the optimal beamforming weight is obtained explicitly. Using the proposed beamforming weight, the interference are reduced during both the uplink pilot transmission and the downlink data transmission, thus the cell capacity is improved. Simulations under various environmental configurations verify the capacity improvements.

In this paper, considering the scenario that the available channel state information (CSI) used for relay selection (RS) is outdated, we propose a RS scheme for full-duplex (FD) cooperative networks in an interference-limited environment, where the residual loop interference (RLI) at the relay and the interference comes from the direct link both affect the cooperative transmission. Specifically, based on the statistical CSI, power allocation under the sum power constraint is performed firstly according to the signal-to-interference-ratio (SIR) balancing condition, and then with the known outdated CSI and the correlation coefficient between the outdated CSI and the real one, the conditional outage probability (COP) associated with each relay is derived in a closed form. The relay with the minimum COP is selected to participate in transmission. It is shown that the proposed scheme can provide a significant performance improvement over the conventional RS schemes for both symmetric and asymmetric network configurations. Simulations are provided to validate our results.

In the area of interference management, many works have used interference alignment (IA) to tackle interference in heterogeneous networks (HetNets). However, few work has been done from the energy‐efficiency (EE) aspect. In this study, the authors address the joint EE resource allocation and IA problem for HetNets where several femtocells underlay within the coverage of a macrocell, and take into account the service requirements of macro cell user (MU). A mixed integer and non‐convex optimisation framework is adopted to investigate the EE maximisation problem which is a multi‐dimensional resource management problem. To solve the problem, the authors propose a user scheduling scheme to determine the transmission sequence as well as encoding and decoding matrices for each user. Then an iterative power control algorithm is proposed to optimise the EE of the networks. Simulation results validate the convergence and performance of proposed scheme.

The authors focus on robust beamforming design for multiple‐input multiple‐output (MIMO) interference channels with imperfect channel state information (CSI) which is described as a bounded CSI error model. With all transmissions restricted to single data stream, the problem of maximising the minimum signal‐to‐inference‐plus‐noise ratio (SINR) subject to individual power constraint at each transmitter is considered as the max–min fairness transceiver design. Specifically, the authors propose a robust beamforming algorithm named as block diagonalisation assisted (BDA) approach for solving the max–min problem. First, block diagonalisation precoding is applied to simplify the original max–min SINR problem. Then, an iterative algorithm is proposed with alternating process to solve the simplified problem, and the convergence behaviour and computational complexity are analysed. Simulation results show the convergence of the BDA algorithm and demonstrate the robustness of the proposed transceiver design through the bit error rate and the minimum SINR.

This paper considers the Base Station (BS) clustering in a downlink heterogeneous network with finite backhaul capacity. We consider a tree structure network where each BS has only one incoming link and several outgoing links. The objective is to maximize the minimum rate among all users while satisfying the backhaul capacity constraint and the per-BS power constraint. We propose an algorithm that combines the bisection and the Alternating Direction Method of Multipliers (ADMM). The bisection search is conducted for the minimum rate. When it is given, we use ADMM to check the feasibility of the network while obeying the backhaul and power constraints. There are two steps involved in ADMM: i) a second order conic programming is used to calculate the beamformer; ii) a closed-form rule is used to determine the BS clustering. Due to the non-convexity and the non-smoothness, ADMM is not guaranteed to solve the problem. Therefore, we propose a revise step to further improve the performance. The simulation results show that the proposed algorithm outperforms the heuristic method and the revise step does improve the performance in all considered scenarios.

To offload the traffic from macrocell systems and improve the spectrum efficiency, device-to-device(D2D) communications and femtocell systems are introduced in the 4G LTE-A as an underlay to the cellular systems by reusing the cellular resources. This paper aims to optimize the transmit power and resource allocation for D2D transmitter, femtocell users and cellular users to better utilize uplink resources in a cellular system. We first consider a scenario where there is only one D2D pair which can reuse frequency resources of multiple cellular users and derive an optimal power assignment strategy to achieve the highest throughput of D2D pair. Furthermore we propose a joint power and resource allocation strategy in a multi-femtocell system to maximize the throughput of D2D pairs with the smallest possible total power consumption. Through simulations, we show that the proposed intelligent allocation strategy can both increase D2D throughput by exploring spatial reuse gain and reduce total power consumption.

This paper studies the linear transceiver design for multi-stream multiple-input-multiple-output (MIMO) interference channels with imperfect channel state information (CSI). Two CSI error models are considered. The same coding and modulation scheme is assumed for all users and data streams. In the first model, where the CSI error is statistical, we minimize the average maximum per-stream mean square error (MSE). In the second model, where the CSI error is norm bounded, the worst-case maximum per-stream MSE is optimized. Since the problems are not jointly convex, the alternate optimization approach is adopted to obtain suboptimal transceivers. For the statistical CSI error, a low-complexity algorithm utilizing Lagrangian duality is proposed, which has a semi-closed form solution and can be implemented distributively. Compared with the classical algorithm using the second-order-cone programming, the proposed algorithm significantly reduces the computational complexity without sacrificing the performance. For the bounded CSI error, we present two approaches. One uses S-procedure to obtain an equivalent problem and the other one solves an approximated problem. Simulation results demonstrate the robustness of the proposed algorithms and their improved performance over the sum-MSE and the maximum per-user MSE based methods. The lower complexity of the Lagrangian duality based algorithm is also verified numerically.

This paper investigates the price-based power control problem in the spectrum sharing cognitive radio networks. The primary user (PU) can admit secondary users (SUs) to access by pricing their interference power under the interference power constraint. We model the interaction between the PU and the SUs as a Stackelberg game. The revenue function of the PU is expressed as a nonconcave function of SU's transmit power by backward induction. Variable substitution is used to transform the nonconcave maximization problem into a concave maximization problem. Based on the equivalent concave maximization problem, a novel algorithm is proposed to find the optimal price for the PU to maximize its revenue. The optimal price of each SU is given as a closed-form expression with one parameter, which can be determined with the complexity of$O(K)$, where$K$is the number of the SUs. Furthermore, asymptotic analysis is exploited to derive the number of the admitted SUs at low and high interference-to-noise ratio. Simulation results show the effectiveness of the proposed algorithm in comparison with the nonuniform pricing algorithm.

This paper considers a distributed approach in designing the downlink beamformers in a multiple-input-single-output (MISO) wireless system. The base stations (BSs) aim to minimize transmit power subject to a set of signal-to-interference-plus-noise ratio (SINR) targets of users. To avoid using a central processor (CP), a distributed iteration framework is adopted in which each BS announces a SINR pricing term to limit the interference it generates at non-local users. The pricing term is announced on these users' SINR constraints by using interior-point method. With a second-order Taylor approximation the pricing term can be locally built only with a few parameters exchanged between BSs. The main contribution of this paper is to propose an original SINR pricing mechanism that achieves a performance quite close to the optimum with limited backhaul signaling. Simulation is presented, which compares the algorithm with the non-cooperative game and the existing interference pricing algorithm. Numerical results show that the proposed SINR pricing algorithm closely approaches the optimal performance with a desirable convergence speed.

This paper considers a game theoretical approach in designing the multiuser downlink beamformers in practical multi-cell mutiple-input-single-output (MISO) systems, where a channel state information (CSI) model consisting both CSI error and delay is adopted. Under this consideration, each base station (BS) takes the average mean square error (AMSE), which indicates the average difference between the users' expected signal and the real received one, as its optimization object. By regarding the inter-cell interference as colored additional noise, a BS can locally derive a closed-form beamforming scheme by minimizing its AMSE. In this way, the whole system performs a non-cooperative robust game where each BS determines its optimal beamformer in a totally distributed manner. Then the condition guaranteeing the existence and uniqueness of a Nash Equilibrium (NE) in the game is theoretically examined and numerically verified in this paper. Numerical results also verify the advantage in robustness and performance over the non-robust game based on the conventional MSE-based beamformer.

In wireless sensor networks (WSNs), keeping all sensor nodes connected to the sink for collecting data is very important. Nonetheless, when some sensor nodes fail due to emergencies, the connection may be lost and the network may be partitioned into several isolated segments. To restore the connections between each segment and the sink in order to gather the measured data, relay nodes need placing properly in the gap regions. Besides, we usually would like to keep the connectivity for a certain period of time in the real scene. However, obtaining the minimal number, the optimal positions and the optimal transmission power of the placed relay nodes subjected to the network lifetime requirement is NP-hard and heuristics are thus pursued. In this paper, we propose a distributed and energy efficient algorithm, Relay node Placement with Guaranteed Lifetime (RPGL), to reconnect the network while demanding minimal relay nodes subjected to that the network will not be partitioned again due to the failure of the relay nodes within the required time. Simulation results not only verify the theoretical analysis but also show that the number of the relay nodes required is less than the other efficient algorithms in the references.

This paper focuses on the transceiver design for multiple-input-multiple-output (MIMO) interference channel. The optimization objective is to minimize the maximum per-stream mean square error (MSE) which greatly influences the bit error rate (BER) performance. Considering the imperfect channel state information (CSI) in reality, we propose two robust algorithms based on statistical and bounded CSI error models, respectively. For statistical CSI error model, the maximum average per-stream MSE is minimized while the worst-case per-stream MSE is optimized for bounded CSI error model. We utilize an alternate optimization approach to iteratively update the precoding and receiving matrices since the robust min-max per-stream MSE problems are not jointly convex. The objective is monotonically decreased and lower bounded, so the convergence of the algorithms is guaranteed. The improved performance and robustness of the proposed algorithms are verified by simulations.

Energy efficiency is a critical issue in Wireless Sensor Networks (WSN) and data gathering should be carefully designed to ensure high energy efficiency. In this work, we pay attention to two parts that are related to energy efficiency in WSN, which are transmission cost and network lifetime. In this work, network lifetime is defined as the time until the first node depletes its energy in the network. There is a tradeoff between transmission cost and network lifetime. When we reduce transmission cost, we usually increase the number of bottleneck nodes which are likely to soon deplete their energy as well, and thus reduce network lifetime. We prolong network lifetime at the cost of slightly increasing transmission cost to make a compromise between transmission cost and network lifetime. We apply compressive sensing (CS) to data gathering and take advantage of the zero elements in the sampling basis of CS to further reduce the transmission cost. We introduce a new concept of transmission vector and propose a novel method of constructing minimum transmission data gathering tree with low computation complexity. We also propose a high energy efficiency (HEE) algorithm to construct the data gathering tree with both low transmission cost and long network lifetime in this work. The simulation demonstrates that our algorithm has a good performance on the tradeoff between transmission cost and network lifetime.

In this paper, we investigate the subchannel assignment and power allocation algorithm for downlink OFDM-based cognitive network where the Channel-State Information(CSI) between the secondary system and the primary system is imperfect. We formulate an optimization problem under both the interference-power outage constraint and maximum transmit-power constraint to maximize the spectrum utility and provide proportional fairness to the SUs. Lagrangian duality method is used to solve the problem. Simulation results show that the proposed algorithm achieve good performance in terms of fairness and spectrum efficiency.

In this paper, we investigate a decode-and-forward (DF) relaying protocol for a three-node half-duplex single-antenna network, consisting of a single source-destination pair and a relay. The proposed relaying protocol, called DF with time allocation (DF-TA), can switch its transmission mode according to the partial channel state information at the transmitter (CSIT), which is obtained through a one bit feedback from the destination. We derive the diversity multiplexing tradeoff (DMT) of DF-TA in a closed-form, and develop an adaptive time allocation strategy to achieve its optimal DMT. We show that the DF-TA significantly improves the DMT of existing time allocation schemes with or without CSIT in DF relaying channel. Moreover, in contrast to the power control schemes based on CSIT, which are performed with the assumption of long-term power constraint, the DF-TA can be generalized to the practical scenarios where a strict short-term power constraint is imposed on, due to the environmental safety and interference prevention.

A relay-based mechanism is proposed in a multi-channel cognitive radio network, while the multiple primary users (PUs) are considered in each channel. This mechanism guarantees that PUs achieve rates increment due to the assistance of the secondary users (SUs) in their transmission, while the SUs acquire reserved leasing timeslot to access the spectrum. We carefully investigate the issue on how to assign SUs to assist PUs during primary timeslot, which can be formed as a binary combinatorial optimization problem. Furthermore, we propose a matching algorithm to solve this problem. Simulation results show that, the proposed matching algorithm can solve this problem efficiently. Besides, the proposed relay-based mechanism performs significantly better than the traditional underlay-based mechanism.

In this paper,we concentrate on the design, implementation and performance evaluation of the medium access control (MAC) for the multi-hop sensor networks. Being operated in unlicensed bands, the battery-operated sensor nodes need to sense available spectrum and consume precious energy. Taking account of the cognitive radio as well as the traditional characters of the wireless sensor networks, we design a MAC protocol for the cognitive radio sensor network (CRSN) that supplies schemes for solution. We propose a cluster-based protocol (KoN-MAC) for the battery-operated sensor nodes in multi-hop networks, which allows sensor nodes to dynamically select an interference free channel for data communication. We evaluate the performance of the proposed protocol by NS-2 and the simulation results show that our protocol can achieve better energy gains as well as higher throughput, less delay and lower packet loss radio.

In this paper, we consider a dual-hop amplify-and-forward (AF) relay system, where the base station (BS) equipped with multiple antennas communicates with the mobile station (MS) via a relay station (RS). We derive the exact closed-form outage probability for two cases: i) interference only at the MS and ii) interference at both the RS and the MS. It is validated that our analyses agree with the corresponding Monte Carlo simulations.

In the spectrum underlay model, cognitive radios can access the spectrum owned by primary users if they can limit their interference power below a certain threshold, called interference temperature limit (ITL). In this paper, we study the power control problem under ITL. By considering both efficiency and fairness, the power control problem is modeled as a cooperative game. A power control scheme based on Nash bargaining solution (NBS) is proposed. We present a distributed power control (DPC) algorithm based on NBS. The convergence of DPC algorithm is proven by theoretic analysis. Simulation results show that proposed DPC algorithm achieves an effective tradeoff between fairness and efficiency.

In the long term evolution (LTE) time division duplex (TDD) systems, base stations use uplink sounding reference signals (SRSs) to estimate downlink channel state information (CSI) for downlink beamforming transmissions. Because SRSs sent by up to 8 users are multiplexed on the same time-frequency resources, there exist mutual interferences among these SRSs over fading channels. Thus this scheme inevitably impacts the estimation accuracy of CSI and thereby degrades the beamforming performance. In this paper, we propose a novel sounding scheme to improve the quality of CSI, in which according to the CQIs (channel quality indications) periodically reported by each user and the bandwidth requirement of each user, BS dynamically schedules each user to send SRSs only on the requisite bandwidth with best CQIs instead of on full bandwidth or specified sub-bandwidth as in the current LTE TDD systems. Simulations show that the proposed scheme obtains much better estimation accuracy than the current scheme for various frequency-selective and/or time-selective fading channels, and is robust to frequency selective channels. Therefore the proposed scheme is applicable to the uplink SRS design in LTE-Advanced and other MIMO-OFDM TDD systems.

In the 3rdgeneration partnership project (3GPP) long term evolution (LTE) time division duplex (TDD) systems, base stations use uplink sounding reference signals (SRSs) to estimate downlink channel state information (CSI) for downlink beamforming transmissions. Because SRSs sent by up to 8 users are multiplexed on the same time-frequency resources, there exist mutual interferences among these SRSs over fading channels. Thus the current scheme inevitably impacts the estimation accuracy of CSI and thereby degrades the beamforming performance. Based on the characteristics of TDD systems, we propose a novel sounding scheme to improve the quality of CSI, in which according to the CQIs (channel quality indications) periodically reported by each user and the bandwidth requirement of each user, BS schedules each user to send SRSs only on the requisite bandwidth with best CQIs instead of on full bandwidth or specified sub-bandwidth as in the current LTE TDD systems. Simulations show that the proposed scheme outperforms the current scheme in estimation accuracy for various frequency-selective and/or time-selective fading channels, and is especially robust to frequency selective channels. The proposed scheme has been adopted as a candidate scheme for 3GPP LTE-Advanced.

In the 3rdgeneration partnership project (3GPP) long term evolution (LTE) time division duplex (TDD) systems, base stations use uplink sounding reference signals (SRSs) to estimate downlink channel state information (CSI) for downlink beamforming transmissions. Because SRSs sent by up to 8 users are multiplexed on the same time-frequency resources, there exist mutual interferences among these SRSs over fading channels. Thus the current scheme inevitably impacts the estimation accuracy of CSI and thereby degrades the beamforming performance. Based on the characteristics of TDD systems, we propose a novel sounding scheme to improve the quality of CSI, in which according to the CQIs (channel quality indications) periodically reported by each user and the bandwidth requirement of each user, BS dynamically schedules each user to send SRSs only on the requisite bandwidth with best CQIs instead of on full bandwidth or specified sub-bandwidth as in the current LTE TDD systems. Simulations show that the proposed scheme outperforms the current scheme in estimation accuracy for various frequency-selective and/or time-selective fading channels, and is especially robust to frequency selective channels. Therefore the proposed scheme is applicable to the uplink SRS design in LTE-Advanced and has been adopted as a candidate scheme for 3GPP LTE-Advanced.

In the long-term evolution (LTE) time division duplex (TDD) systems, base stations (BSs) use uplink sounding reference signals (SRSs) to estimate downlink channel state information (CSI) for downlink beamforming transmissions. In the current SRS pattern scheme, because SRSs of up to eight users are multiplexed on the same time-frequency resources, there exist mutual interferences among these SRSs over fading channels. Thus, the current scheme impacts the estimation accuracy of CSI and degrades the beamforming performance. Based on the characteristics of TDD systems, the authors propose a SRS pattern scheme, in which each user sends SRSs only on the allocated bandwidth instead of on full bandwidth as in the current LTE TDD systems, to eliminate the interferences among SRSs. Simulations show that the proposed scheme outperforms the current scheme in the estimation accuracy for various frequency-selective and time-selective fading channels. In addition, compared with the current SRS pattern scheme, the proposed SRS pattern scheme is robust to frequency-selective channels, and does not be impacted by the timing offset, and provides the flexibility for the choice of channel estimator at the BS side.

Focusing on a time-division duplex (TDD) multiple-input-single-output (MISO) beamforming system, this study investigates the robust uplink pilot time interval (UPTI) design to overcome the impact of delay and channel estimation error. In TDD beamforming systems, the base station estimates the downlink (DL) channel state information (CSI) by exploiting the received uplink (UL) pilots and the channel reciprocity. Then, utilising the estimated DL CSI, the beamforming vector in the DL transmission is generated. Owing to the constraints of the TDD frame structure and the UL pilot overhead, there inevitably exist delay and estimation error between the estimated and the actual DLCSI, which would degrade system performance. In this study, the upper bound is first derived on ergodic rate for TDD MISO beamforming systems with channel estimation error and delay. Then, with any one given setting of the normalised UL pilot overhead, the optimal robust UPTI is designed, which maximises the upper bound on ergodic rate in the worst case of the CSI delay (i.e. in the case of maximum delay). Simulation results validate that the designed optimal robust UPTI can not on I y maximise the upper bound on ergodic rate but also be adaptive to the variation of channel conditions very well.

In this paper, we propose a new spectrum handoff scheme with spectrum admission control (SAC). In the proposed scheme, the secondary users (SUs) make up secondary user groups (SUGs) to achieve appointed detection probability of primary user signals, and perform spectrum handoff to an available spectrum when the primary users (PUs) reuse the spectrum in cognitive radio networks (CRNs). A simple Markov model is adopted to analyze the performance of spectrum handoff in terms of blocking probability, forced termination probability and throughput of the cognitive radio system. Numerical results show that the new handoff strategy is suitable for multi-user cognitive radio systems, and that spectrum admission control and cooperative sensing can effectively increase the efficiency of spectrum handoff.

A cooperative spectrum sensing technique in cognitive radio (CR) networks is proposed in this paper, which is based on the data fusion of different stochastic resonance (SR) energy detectors. Due to the noisy uncertainty in the unpredictable wireless communication channel, the detection performance of the traditional energy detector cannot be guaranteed. By introducing the SR system with different SR noise types, and with the fusion on these SR-based energy detectors, the detection probability can be improved even under low signal-to-noise ratio (SNR) circumstances. Computer simulation results validate the efficiency of the proposed novel cooperative spectrum sensing approach.

A new censoring cooperative spectrum sensing scheme in cognitive radio (CR) network is proposed in this paper, which is based on stochastic resonance (SR) technique and optimal censoring interval. The selection strategy for the optimal censoring interval is derived. The observations of the cooperative secondary users (SUs) whose statistics fall into the censoring interval are processed by SR system. Theoretical analyses and simulation results show that the proposed cooperative spectrum sensing scheme has the same detection performance and lower computational complexity compared with the method that each cooperative performs spectrum sensing using SR-based energy detection, and the detection performance of both above method is superior than that of the conventional method that each cooperative performs spectrum sensing using energy detection.

A novel queuing model in this paper is proposed to provide a tool for performance evaluation to IEEE 802.15.4 Medium Access Control (MAC) protocol with sleep mode enabled. IEEE 802.15.4 node with sleep mode enabled behaves in a different way from other queuing models because the node goes to sleep periodically and thus packets arriving in sleep period accumulate in the beginning of the active portion, which makes a heavier load at the beginning than any other time. This model analyzes this behavior by using an embedded discrete-time Markov chain model that includes the slot number in the states to obtain the time-varying queue length. To obtain the service time, we introduce the virtual service time into this model which makes the proposed queuing model different from models in any previously published works. The accuracy of the proposed model is validated by Monte Carlo simulations. The proposed model can accurately evaluate the performance of IEEE 802.15.4.

In this paper, we propose a new downlink multi-user multiple-input multiple-output (MIMO) precoding scheme based on maximizing the successive signal to leakage plus noise ratio (SSLNR). The work is motivated by the precoding scheme developed by Sadek, Tarighat and Sayed by maximizing the signal to leakage plus noise ratio (SLNR), which does not impose any restriction on the number of antennas at the transmitter and receiver. In our proposed scheme, dirty paper coding (DPC) is applied to cancel the known leakages, so SLNR can be calculated only using the unknown leakages. The proposed scheme has no restriction of the number of antennas as the conventional SLNR scheme. In addition, the proposed scheme decreases the complexity of the receivers compared with the conventional SLNR scheme. Simulations show that the proposed scheme has superior bit error rate (BER) performance over the conventional SLNR scheme.

This paper proposes an optimization scheme on uplink pilots time interval (UPTI) in terms of maximum average post-processing SNR (signal to noise ratio) for a time division duplex (TDD) multiple input single output (MISO) beamforming system with channel estimation error and delay. In TDD system, the base station estimates the channel state information (CSI) at transmitter based on uplink pilots and then uses it to generate the beamforming vector in the downlink transmission. Because of the constraints of the TDD frame structure and the uplink pilot overhead, there inevitably exists delay and channel estimation error between CSI estimation and its use. In this paper, we first derive average post-processing SNR for TDD MISO beamforming system with channel estimation error and delay. We then obtain the optimal UPTI, which maximizes average post-processing SNR, given the normalized pilot overhead (the number of pilot symbols per data symbol). The simulation results validate that the optimal UPTI not only maximizes the average post-processing SNR but also maximizes the system ergodic rate. Especially our research is valuable for the uplink sounding reference signal design in LTE-Advanced system.

This paper examines the capacity of a time division duplex (TDD) multiple input single output (MISO) beamforming system with channel estimation error and delay over the time varying fading channel. In TDD system, the base station estimates the channel state information (CSI) at transmitter based on uplink pilots and then uses it to generate the beamforming vector in the downlink transmission. Because of the constraints of the TDD frame structure and the uplink pilot overhead, there inevitably exists delay and channel estimation error between CSI estimation and its use. In this paper, we first derive the capacity upper bound for TDD MISO beamforming system with channel estimation error and delay. We then obtain the optimal uplink pilot time interval (UPTI), which maximizes the capacity upper bound, given the normalized pilot overhead (the number of pilot symbols per data symbol). The simulation results validate that the optimal UPTI not only maximizes the capacity upper bound but also maximizes the system ergodic capacity. Especially our research is valuable for the uplink sounding reference signal design in LTE-Advanced system.

In Multiuser Multiple-Input Multiple-Output (MU-MIMO) downlink systems, the zero-forcing algorithm is a simple and effective technique for separating users and data streams of each user at the transmitter side, but its performance depends greatly on the accuracy of the available channel state information (CSI) at the transmitter. Due to the existence of the CSI delay and channel estimation error in practical systems, CSI is always imperfect, which degrades system performance significantly. In this paper, by using the correlation between the actual channel and the estimated one as well as the channel's time-correlation, we develop a novel bit error rate (BER) expression for M-QAM (quadrature amplitude modulation) signal in time division duplex (TDD) downlink MU-MIMO system with channel estimation error and CSI delay. We find that channel estimation error causes array gain loss while CSI delay causes diversity gain loss. Moreover, CSI delay causes more performance degradation than channel estimation error at high signal to noise ratio (SNR) for time varying channel. Especially our research is valuable for adaptive modulation and coding scheme and the optimization of MU-MIMO system. Numerical simulations show accurate agreement with the analytical expressions.

An optimal scheme on uplink pilot time interval (UPTI) to maximize average post-processing SNR (signal to noise ratio) is proposed in order to overcome the impact of channel estimation error and delay on a time division duplex (TDD) multiple input single output (MISO) beamforming system. In TDD system, the base station estimates the channel state information (CSI) at transmitter based on uplink pilots and then uses it to generate the beamforming vector in the downlink transmission. Because of the constraints of the TDD frame structure and the uplink pilot overhead, there inevitably exists delay and channel estimation error between CSI estimation and its use. In this paper, we first derive average post-processing SNR for TDD MISO beamforming system with channel estimation error and delay. We then obtain the optimal UPTI, which maximizes average post-processing SNR, given the normalized pilot overhead (the number of pilot symbols per data symbol). The simulation results validate that the optimal UPTI not only maximizes the average post-processing SNR but also minimizes the BER. Especially our research is valuable for the uplink sounding reference signal design in LTE- Advanced system.

In downlink Multiuser Multiple-Input Multiple-Output (MU-MIMO) systems, the zero-forcing (ZF) transmission is a simple and effective technique for separating users and data streams of each user at the transmitter side, but its performance depends greatly on the accuracy of the available channel state information (CSI) at the transmitter. Due to the existence of the CSI delay and channel estimation error in practical systems, CSI is always imperfect, which degrades system performance significantly. In this paper, by characterizing CSI inaccuracies due to channel delay and channel estimation error, we develop a novel bit error rate (BER) expression for M-QAM (quadrature amplitude modulation) signal in time division duplex (TDD) downlink MU-MIMO system where each user is equipped with one antenna. By simulation, we find that channel estimation error causes array gain loss while CSI delay causes diversity gain loss. Moreover, CSI delay causes more performance degradation than channel estimation error at high signal to noise ratio (SNR) for time varying channel. Especially our research is valuable for adaptive modulation and coding scheme and the optimization of MU-MIMO system. Numerical simulations show accurate agreement with the analytical expressions.

For iterative mean-square error (MSE) quantizers with alphabet size M=2Kusing low-density generator-matrix (LDGM) code constructions, an efficient recovery algorithm is proposed, which adjusts the priors used in belief propagation (BP) to limit the impact of previous non-ideal decimation steps. Based on an analysis of the BP process under ideal or non-ideal decimation, the algorithm first estimates the conditional probability distributions describing the effect of non-ideal decimation, then adjusts the priors to make the distributions match the ideal situation. As shown in simulation results, the recovery algorithm can improve quantization performance greatly, reducing the shaping loss to as low as 0.012 dB, while the increase in computational complexity is modest thanks to the use of FFT techniques.

In this paper, we consider the range-based sensor network localization with inaccurate anchor position information. First, a novel optimization algorithm named sequential greedy optimization (SGO) algorithm is proposed, and then two distributed localization algorithms are obtained: the first is obtained by applying the SGO algorithm to a convex formulation of the localization problem, named CSGLA; while the second is obtained by applying the SGO algorithm to a nonconvex formulation of the localization problem, named NCSGLA. The CSGLA must converge globally while the NCSGLA may converge locally. Both algorithms are partially asynchronous and can be implemented in a distributed fashion in networks. We demonstrate the localization performance via simulations. Simulation results show that, 1) the CSGLA algorithm works faster than the synchronous algorithm with the same localization accuracy; 2) with a reasonably good initialization, the NCSGLA can work much better than the CSGLA.

The problem of network coding over a multiple-access channel (MAC) is considered in this paper. Lattice strategies of encoding indexes of messages are proposed, ensuring that if the sum of any two lattice points is another point over a finite field, then the sum of the two indexes mapped to these two points is equal to the index mapped to another point. In this way, the interference caused by other transmitters can effectively be canceled when the sum of the two indexes as a codeword of network coding is estimated. It is shown that the lattice strategies obtain more information for reconstructing sources than the existing methods in a modified version of the classical butterfly network.

The closure of the set of entropy functions associated with n discrete variables, ¿¿¿¿¿¿n*, is a convex cone in (2n- 1)-dimensional space, but its full characterization remains an open problem. In this paper, we map ¿¿¿n* to an n-dimensional region ¿¿n* by averaging the joint entropies with the same number of variables, and show that the simpler ¿¿n* can be characterized solely by the Shannon-type information inequalities.

In this study, the noise variance and channel estimation problems are investigated over the UWB channel with narrowband interference (NBI). We first give two ways to construct the samples of noise variance using the least square (LS) channel estimates, by which we model the estimation problem of noise variance as parameter estimation of an exponential distribution and turn the NBI detection in the LS channel estimates into a general constant false alarm rate (CFAR) problem. Then, a robust noise variance estimator based on order statistic is presented to avoid the impact of NBI, and its estimation variance is also analyzed. In addition, a low complexity robust channel estimation (RCE) scheme is provided without requiring any knowledge of channel statistics including finite delay spread. The RCE scheme includes three new steps, namely the identification of the NBI signal by a CFAR detector, the recovery of channel estimates at the jammed frequency bins by either utilizing the conjugate symmetry property of the UWB channel or median filtering, and finally noise reduction through threshold filtering. Simulation results demonstrate the superior performance of the proposed schemes compared to the existing methods.

Energy efficiency is a critical issue in wireless sensor networks. Energy consumption can be saved by using a lower transmission power, but this also increases the channel error rate and the energy consumption on packet retransmission, as well as the packet delay. In this paper, taking retransmission in physical layer into account, we propose a cross-layer strategy to explore the tradeoff between energy efficiency and packet timeliness in time division multiple access (TDMA) based sensor networks, by transmission power allocation and routing path selection schemes. We divide the problem into two optimization sub-problems of power allocation and path selection in each layer, and propose lower-complexity algorithms for each sub-problem. Numerical results show that a tradeoff exists between energy efficiency and packet timeliness, and energy consumption can be significantly reduced while satisfying the packet timeliness constraint by using the proposed strategy.

Knowing the positions of nodes is essential to many wireless sensor network applications. In contrast to range-based localization methods, range-free methods are more appealing since they do not need additional expensive hardware for ranging. A range-free localization problem is a generalization of unit disk graph coordinates realization problem and in essence a feasibility problem with quadratic inequalities, which is NP- hard. In this paper, we formulate the range-free localization problem as a nondifferentiable optimization problem solved by a polynomial-time algorithm, normalized incremental subgradient (NIS) algorithm. Extensive simulations have been conducted. The simulation results show that the NIS-based localization algorithm significantly outperforms the MDS-MAP method and the SDP method, whether the network is regular or not.

In this paper, we present a new analytical model to accurately evaluate the throughput, service delay and the energy consumption of S-MAC protocol. Our model takes into account the impact of several factors together, including periodic listen and sleep cycle, various incoming traffic loads, the backoff mechanism in S-MAC, the queuing behavior at the MAC layer, and the dependency of service delay distributions among the nodes. Simulations show that our analytical results are highly accurate. Based on our model, we study how the listen and sleep cycle effects the trade-off between energy consumption and quality of service (QoS) requirement, and further derive the optimal value for duty cycle under different traffic conditions.

To conserve energy, periodical active/sleep dynamics is adopted in the design of medium access protocol (MAC) in wireless sensor networks. At the same time, the QoS requirements (e.g. packet delay, packet loss rate and throughput) need to be satisfied. In synchronous wakeup patterns of contention-based sensor networks, nodes wake up simultaneously and compete to access a shared channel for data transmission. We develop a finite queuing model for the sensor nodes and derive the network performance of contention-based sensor networks with the synchronous wakeup patterns. Furthermore, the impact of active/sleep duty cycle, active time scale and node buffer size on the tradeoff between power efficiency and QoS requirements is studied based on the model. Simulation results match well with our analysis results and validate the accuracy of our model and approach.

In this paper, a lattice-reduction-aided (LRA) minimum mean square error (MMSE) vector precoding (VP) is proposed for multiple input multiple output (MIMO) systems. Three schemes are provided for the perturbation vector design by Babai's approximation procedures based on lattice reduction method to reduce the complexity. Performance and complexity analysis are provided. Simulation results show that the proposed schemes significantly outperform the conventional MMSE Tomlinson-Harashima precoding (THP) and the zero forcing (ZF) VP. Compared with the MMSE VP via closest-point search, our LRA approach provides a simple alternative with little performance loss.

In contention-based sensor networks, nodes compete to access a shared channel for data transmission and collision is a common challenge. For power conservation, periodical active/sleep dynamics is adopted in the design of medium access protocol (MAC). At the same time, the quality of service (QoS) requirements (e.g. packet delay, packet loss rate and throughput) need to be satisfied. We develop a finite queuing model for the sensor node and derive the network performance in contention-based sensor networks. Furthermore, the impact of active/sleep dynamics and node buffer size on the tradeoff between power efficiency and QoS requirements is studied based on the model. Simulation results match well with our analysis results and validate the accuracy of our model and approach.

We propose a novel vertical handoff decision algorithm for overlay wireless networks consisting of cellular and wireless local area networks (WLANs). The target network is selected using a fuzzy logic-based normalized quantitative decision algorithm which, in addition to usual parameters such as the current received signal strength (RSS) and the available bandwidth, also takes a prediction of the RSS into account, resulting in a more accurate handoff. The RSS prediction is obtained using a differential prediction algorithm that has good accuracy. Furthermore, to reduce system load, a pre-decision method is employed before actual handoff decision to filter out users with high mobility or low RSS from using the WLAN. Simulation results show that the proposed algorithm can reduce the call-dropping probability as well as unnecessary handoffs in heterogeneous network environments.

Few watermarking systems are designed to resolve the problem of joint ownership verification. This paper proposes a new watermarking system for proving joint ownership of digital images, where a TTP is needed. The watermarking key is shared by all co-owners using Shamir's secret sharing scheme as presented by Shamir (1979). Furthermore, each owner is distributed a detection watermark for individual verification. The simulation results demonstrate that the proposed system is effective, and it can resist malicious attacks performed by the co-owners and some common image processing operations.

A modified decision feedback equalizer (DFE) is proposed for efficient equalization of sparse channels with large delay echoes. This new structure can both avoid noise accumulation in large number of filter taps and reduce hardware implementation complexity effectively. Brief analysis about noise accumulation effects in large-scale equalizer and working mechanism description of this effective NRCR-DFE algorithm exhibit us where the performance gain lies. Simulation results show that this modified DFE has higher performance than the conventional ones and is quite fit for applications in high-speed systems like high definition television (HDTV) and broadband mobile communication.

We propose a novel blind non-additive robust watermarking scheme in this paper. The energy of the watermark is spread to all regions of the host data instead of some individual elements, which entitles the watermark with imperceptibility and high robustness. A class of 1-D Markov chaotic maps employed to perform host data element classification and watermark encryption ensures the security of the system. To prove the validity of this proposed scheme, some analyses and brief objective comparisons with the popular spread spectrum (SS) scheme are also presented. Simulation results show that our scheme can survive severe processing such as high-ratio JPEG compression, Gaussian noise pollution and histogram equalization.

In this paper, a novel algorithm is proposed to embed and detect digital watermarks. It is different from other algorithms, because the algorithm embeds digital bits in the relationship among pixels, whereas most other algorithms embed digital bits in terms of pixel position. In the embedding phase one discrete chaotic map is used to create random sequences to encrypt watermarking bits, the other discrete chaotic map is used to permute the original image. Watermarking detection resorts to looking for a path with the minimal distance like the Viterbi algorithm, and it is performed without the original image. Experimental results show that the algorithm can effectively resist the influence of Gaussian noise, JPEG compression, cropping operation, and can recover the original watermarks well.

The 4G wireless communication system is envisioned to integrate cellular network (e.g. UMTS) and wireless LAN (WLAN), in virtue of the individual coverage and capacity complementary characteristics. This paper describes a possible architecture integrating UMTS and 802.11 WLAN at SGSN. A functional entity, network interwork unit (NIU), is placed between the SGSN and WLAN access network side to hide the WLAN particularities. In this integrated architecture, mobile IP based vertical handover management increases the heterogeneous system signaling cost as well as handover latency, so one major task in the handover design is to reduce the unnecessary handover probability. This paper presents an analytical vertical handover initiation model based on the criteria of received signal strength (RSS) and distance. The handover performance evaluation criterion of interest is the handover probability. The numerical results show that our model is in line with the real cases when the distance between the base station (BS) of UMTS cell and the access point (AP) of WLAN is long or not.

MIMO-OFDM is one of the promising scheme for achieving high data rate and large system capacity over wireless networks. In this paper, we investigate the blind channel estimation in MIMO-OFDM systems based on the noise subspace method. We exploit the cyclostationarity induced by OFDM with cyclic prefix. The sufficient identifiability conditions were established, and the noise subspace algorithm was developed for MIMO-OFDM channel estimation. The algorithm only requires knowledge of the upper bound on the channel length, it does not impose restrictions on channel zeros and it exhibits low sensitivity to stationary noise. Finally, we demonstrate validity of the algorithm presented in this paper by Monte Carlo simulations.