One of the key technologies of orthogonal frequency division multiplexing (OFDM) systems is its large peak to average power ratio (PAPR) of the channel signals. Partial transmit sequences (PTS) is a kind of promising method that improves PAPR performance effectively with not distortion processing, the complexity of PTS algorithm increases quickly with the divided number of signal blocks. Therefore, a novel sub-optimal algorithm is proposed to reduce computational complexity in this paper, which use the bacterial foraging optimization to realize the search process of optimal phase factors. Simulation results demonstrate that the proposed method can obtain better balance between PAPR performance and computational complexity.

Cross layer congestion control algorithm based on compressed sensing (CS) is developed and designed in order to relieve congestion in Wireless Sensor Networks (WSNs).The main idea of this paper is to reduce congestion of sensor nodes by compressed transmission signal and allocated channel. In order to make an ideal distribution for sensor node, original signal is compressed at the network bottleneck, prevented high levels of data flow. The channel weights and the maximum effective set of containing channel least are calculated, and we allocate the appropriate channel in order to avoid channel contention and balance the network loading among the sensor nodes. Simulation results indicate the superior performance of our proposed algorithm to strike the appropriate performance in the congestion control, energy consumption and network lifetime for the wireless sensor networks.

This paper presents a cross-layer congestion control algorithm based on compressed sensing (CS) in wireless sensor networks (WSNs), in which node-congestion and link-congestion are occurred simultaneously. WSNs model is provided in the first group. The second group includes controller designing: sparse signal is projected compressively in bottleneck node, which reduces the data transmission; then the compressed signal is reconstructed with convex optimization method, whose target is reconstruct original signal. Via Lyapunov function the validity of the proposed algorithm is verified. NS2 and MATLAB simulation results show that there are improvements in the dropped packets and throughput as compared to the other congestion control protocols. The proposed scheme also perfects the quality of service for the whole network.

High instantaneous peak power of the transmitted signals is the main obstacle of orthogonal frequency division multiplexing (OFDM) systems for its application, therefore, the peak to average power ratio (PAPR) reduction has been one of the most important technologies. Among all the existing methods, partial transmit sequences (PTS) is a distortionless phase optimization technique that significantly improves PAPR performance to with a small amount of redundancy. However, the computational complexity in conventional PTS increases exponentially with the number of subblocks. In this paper, an intelligent optimization method is proposed for PTS technique to obtain good balance between computational complexity and PAPR performance. Simulation results show that the proposed method can achieve better performance compared with conventional algorithms.

On the base of the analysis of the electricity production and consumption characteristics of iron and steel enterprises, this paper has established a optimization model of electricity production in iron and steel enterprises which comprehensive considered the electricity, gas, and steam. It can do joint optimization scheduling from electricity production and outsourcing, gas distribution, and steam production three sides, so that it can provide a reasonable electricity production and outsourcing strategy for iron and steel enterprises to reduce the system operation costs. Where, the optimized allocation of surplus gas and steam production scheduling is the means, the optimization scheduling of electricity production and outsourcing is ultimate goal.

With the rapid growing of traffic, backbone routers encounter low-efficient route matching, unbalanced link utilization and queue waiting time waist problems. In order to improve the forwarding efficiency, a novel Queue Extended Forwarding (QEF) algorithm is proposed. Using extended queue and programmable router, QEF can make routing decision for queuing packet and forward more than one packets in one time. Theoretic and performance analysis show that QEF can obtain a reasonable performance.

A low-complexity estimation method is presented to estimate the parameter of coherently distributed source. The central direction of arrival (DOA) is estimated by proposed second-order statistics based on Schur-Hadamard product steering vector, and then angular spread are given by beamforming method. The method is a low-complex estimator that does not need a joint search in two-dimensional parameter space. And angular spread is included in the constraint, so it owns more robustness in estimating angular spread. The simulation results prove the effectiveness of our method.

In this work, we study the traffic adjustment over multipath network in the presence of both inelastic and elastic traffic flows. The characteristics of these two types of traffic differ significantly. Hence, earlier approaches that focus on homogeneous scenarios with a single traffic type are not directly applicable. We formulate a new traffic adjustment problem based on time series prediction and HPSO (hybrid particle swarm optimization) that incorporates the performance requirements of inelastic and elastic traffic flows. Simulations results show that our adjustment approach not only achieve the maximization utility gained by elastic traffic but also satisfy the reliability demand of inelastic traffic. Our optimal algorithm is extended to provide better packet loss rate performance for the inelastic traffic.

Partial transmit sequence (PTS) is an effective technique to reduce the peak-to-average power ratio (PAPR) for orthogonal frequency division multiplexing (OFDM) transmitter. However, selecting the optimal parameters for the PTS is very complex since it requires an exhaustive search of all possible weighting factors whose number grows exponentially with the number of subblocks. An improved particle swarm optimization (PSO) based PTS algorithm is proposed to reduce the complexity by choosing the weighting factors suboptimally. The evaluation shows that the proposed algorithm performs very closely to the optimal PTS in many cases with much lower complexity.

In the Internet today, multipath routing is proved a popular technique that improves reliability and robustness of data delivery; however, current forwarding algorithm over multipath network have problems such as lacking of preparing for sudden spikes. This paper proposes a new forwarding algorithm, which is based on time series prediction and PSO (particle swarm optimization). We describe how the algorithm dynamically predicts loss levels over all available network paths and adjust the allocation proportion of packets for next prediction period by the adaptive PSO. We present our simulation work using NS-2 to show the superiority of our algorithm over equality splitting algorithm.

For cognitive radar, it should have the basic function which is learning. The radar needs to manage its resources dynamically and interactively between the setting of radar parameters to optimize the tasks the setting of radar parameters to optimize the tasks to be carried out and perceive environment highlights the role in which knowledge and intelligence will be central in cognitive radar performance. The problem discussed here is the time allocation of cognitive radars in a multitarget environment. Radars are used to detect, to locate and to identify target. In this paper, we develop the optimization criterion based on the detection probabilities

Cognitive radar can be aware of its environment, utilize intelligent signal processing, provide feedback from the receiver to the transmitter for adaptive illumination and preserve the information contents of radar returns. In this paper, based on the analysis of the parameters of radar mesurements, range-Doppler resolution cell is built up. Then stochastic dynamic programming model of waveform selection in cognitive radar is proposed, which is viewed as an important part of cognitive radar. In simulation, the importance of adaptive waveform, selection in cognitive radar is shown.

We consider the problem of estimating the central direction of arrival (DOA) of multiple coherently distributed sources. This problem is encountered due to the presence of local scatters in the vicinity of a transmitter or due to signals propagating through a random inhomogeneous medium. Since the integral steering vector of coherently distributed source can be deduced to be a Schur-Hadamard product comprised of point source steering vector and a real vector, a rotational eigenstructure is showed to exist accurately for two identical closely spaced subarrays. And then the central DOA can be obtained analytically without any peak-finding searching, which significantly reduces the computational complexity. In addition, the proposed algorithm needs little prior knowledge about angular signal intensity. The simulation results illustrate the better performance of the proposed method.

We consider the problem of estimating the parameters-the central direction of arrival (DOA) and angular spread of a coherently distributed source. This problem is encountered due to the presence of local scatterers in the vicinity of a transmitter or due to signals propagating through a random inhomogeneous medium. Since the computational complexity of the parameter estimation is normally highly demanding, we first decouple the central DOA from angular spread. And then the preliminary estimate of the central DOA can be obtained at a signal subspace. Angular spread is estimated by beamspace propagator method sequently. So a two-dimensional maximization problem is replaced by two one-dimension problem. The proposed algorithm has improved precision, low computational cost. Simulations clearly demonstrate that the algorithm is not only effective, but also enjoys better performance.

A new two-dimensional direction of arrival (2-D DOA) estimation method is proposed, in this paper. The presented method makes using of blind source separation method based on the second-order identification to estimate the array response matrix. Via the rotational invariance techniques, we can estimate the 2-D DOA from the array response matrix estimation. The estimated elevation angles and azimuth angles is automatically determined. Simulation results are presented verifying the efficiency of the proposed method.

Because of enabling to provide high-rate and high-quality mobile communication service in mobile wireless communication, Orthogonal Frequency Division Multiplexing (OFDM) system has become the focus of study recently. Estimating and equalizing channels in OFDM system is one of the technical difficulties. In this paper, aiming at decreasing the high computing complexity degree of LMMSE algorithm, one simplified way is proposed that the LMMSE algorithm be simplified through the self-correlation matrix of channel impulse response. In other words, channel correlation is neglected. It can be seen from the simulation results that the method can simplify the computing complexity degree to some extent and offer relatively better bit error performance.