Coverage optimization and power reduction in SFN using a hybrid PSO algorithm

In this work, the usefulness and limitations of a planning tool that combines a propagation prediction method and a modern heuristic technique when applied to improve the coverage area in single frequency networks (SFN) is presented. The propagation prediction method uses 3D digital terrain maps along with a propagation model to estimate the contribution of any transmitter of the SFN at each location within the region in which the SFN is intended to be deployed, given in terms of parameters such as the received power and propagation delay. Furthermore, a particle swarm optimization (PSO) algorithm is used to optimize the transmission parameters of any of the transmitters within the SFN, including transmitters delay or gain for their sector antennas, in order to exploit the multipath tolerance in OFDM receivers. Preliminary results comparing the performance of different PSO schemes considering as the canonical problem a SFN consisting of eighteen transmitters, in which transmitters and receivers have been simplified, are reported and discussed. Moreover, results considering sector and directive antennas at the transmitter and receiver sides, respectively, are also included.

In a Single Frequency Network (SFN) for Advanced Television Systems Committee (ATSC) terrestrial digital television (DTV) system, the interferences induced by the multiple transmitters and/or repeaters using same frequency are unavoidable. To ease the interference handing in the SFN for ATSC DTV system, transmitter identification (TxID) signal is recommended in the ATSC terrestrial DTV system. The TxID signal is embedded in the ATSC DTV signal and transmitted from each transmitter and repeater within the SFN and in ATSC recommended practice (RP) A/111, Kasami sequence is recommended as the TxID signal. In this paper, we propose an additional data transmission scheme using the TxID signal. In the proposed scheme, the Kasami sequence not only acts as the conventional TxID of the SFN for ATSC terrestrial DTV system but also carriers the additional data bits on its polarity and the code phase.

In a single frequency network (SFN) for the Advanced Television Systems Committee (ATSC) terrestrial digital television (DTV) system, co-channel interference induced by the multiple transmitters and/or repeaters which use the same output channel is inevitable. Since the presence of interference results in the performance degradation of the SFN, it is crucial to manipulate the interference by adjusting the transmit power and the emission time of each transmitter and repeater within the SFN. In the ATSC terrestrial DTV system, in order to facilitate the interference manipulation process, a transmitter identification (TxID) signal which is embedded in the transmit signal from each transmitter and repeater is recommended. However, such an embedded signal may degrade the DTV signal quality even though the injection level of the TxID signal is very low compared to the DTV signal. Hence, the effect of the TxID signal on the DTV signal needs to be investigated before deploying the SFN with TxID signal. In this paper, the effect of the TxID signal on the performance of legacy DTV receivers is investigated not only with extensive computer simulation but also with laboratory and field tests. According to the various test results, the average performance loss of the conventional DTV receiver due to the TxID signal is less than 0.2 dB when the TxID signal is injected at 30 dB below the DTV signal power.

The broadcasting of TV programmes to mobile phones can be enabled by the newly developed technology called Digital Video Broadcasting-Handheld (DVB-H). Because of the scarcity and cost of frequency resources, frequency reuse needs to be considered when rolling out DVB-H networks. By simulcasting the same content from several transmitters, a Single Frequency Network (SFN) can provide good coverage and good frequency efficiency. In this paper, the performance of densified DVB-H SFN networks is analysed in terms of the coverage probability under different coverage requirements with and without frequency reuse. A dichotomy searching approach is used to determine the optimal cell radius for a cell in a densified DVB-H SFN for a given network topology. Based on the optimal cell radius map and a SFN gain map generated from the simulation results, guidelines are proposed on how to avoid the potential pitfalls in configuring the parameters of a densified DVB-H SFN network and optimise its parameters in terms of minimising the cost of the network for a range of predefined network parameters.

Single frequency networks (SFN) are usually considered to deploy OFDM modulation based digital broadcast systems, exploiting the multipath tolerance of the OFDM receivers, which are capable of combining signals coming from several transmitters with different propagation delays. In this work, a planning tool is proposed to improve the coverage area in SFN networks, optimizing transmission parameters such as the transmitters delay or power, using 3D digital terrain maps along with a propagation prediction tool to estimate the power received at each location inside the coverage area. A heuristic particle swarm optimization algorithm is used (PSO) to optimize transmission parameters. The approach proposed makes it possible to consider several configurations regarding both transmitters and receivers. Preliminary test results are shown for a SFN with eighteen transmitters and omnidirectional radiators at both sides that demonstrate the usefulness of the approach. Furthermore, results considering receivers with directive antennas as well as transmitters with sectored antennas are also included and discussed.

Single frequency network (SFN) enables efficient use of spectrum by allocating the same frequency among multiple transmitters. In order to efficiently design and manage the SFN, the Advanced Television Systems Committee (ATSC) 3.0 physical layer standard has adopted transmitter identification (TxID) technology which allows to measure the individual and/or whole network channel response, such as power and time delay of the broadcast signal from transmitters. Since TxID is embedded into the host ATSC 3.0 frame as radio frequency (RF) watermark, it is transparent to legacy ATSC 3.0 receivers and only a specially designed TxID analyzer is able to detect it. This paper presents new TxID detection methods that improve the detection performance of ATSC 3.0 TxID signal by resolving the problems and limitation of the conventional TxID detection method. The superiorities of the proposed detection methods are verified through computer simulation, laboratory test, and field trial.

In single-frequency network (SFN) broadcasting systems, the presence of several transmitters translates into an equivalent channel with relevant sparsity in the delay domain, which may significantly impair the error performance. In DVB-H systems this channel sparsity, coupled with the time variation due to mobile users, renders the channel estimation task very challenging, especially when only a moderate complexity is tolerated at the receiver side. To solve this problem, this work proposes a new low-complexity multistage channel estimation technique that exploits the data at the output of the channel decoder, as well as a simple parallel intercarrier interference (ICI) canceller. The proposed scheme iteratively estimates data and channel in a joint fashion, and also includes a regularization-based preprocessing stage to specially deal with severe channel sparsity. Simulation results show that our low-complexity scheme is adequate for practical implementations of fast-mobility DVB-H receivers in all SFN scenarios.

The learning algorithm of fuzzy cognitive map(FCM) is mainly based on expert intervention or effective historical data to learn the weight value in weight matrix W. With the increase of the number of nodes, the number of weights need to be learned will also increase square, the performance of the learning algorithm will decline sharply. Therefore, it is an important task to design a high-performance learning algorithm which depends on expert knowledge as little as possible. A single learning algorithm has some limitations, such as easy local optimization, slow learning rate and so on. Among them, particle swarm optimization algorithm has strong global optimization ability, which can quickly optimize the weight matrix to a certain accuracy, but the follow-up optimization time is too long. For the gradient descent algorithm, when the initial value is chosen properly, its can approach the global optimal solution quickly. In this paper, we design particle swarm optimization (PSO) and gradient descent algorithm (GDA) to learn FCM respectively. According to the characteristics of the two algorithms, a hybrid algorithm(HA) is proposed: first, the PSO algorithm is used to optimize weight matrix to a certain accuracy, then the gradient descent method is added, which makes up for the disadvantage that the error of single PSO algorithm decreases slowly in the later stage. Experimental results show that the hybrid algorithm proposed in this paper has a significant improvement in both accuracy and speed.

In our previous research applied to the job shop scheduling problem (JSSP) for machine tool production, the multi-objective optimisation model based on the particle swarm optimization (PSO) research had an imbalance performance between the convergence rate and the convergence precision. In this article, a new hybrid algorithm using PSO and generic algorithm (GA) is proposed to solve this particular problem. In this new algorithm, named hybrid PSO-GA algorithm (HPGA), the PSO algorithm is redefined and modified by introducing genetic operators, i.e. the crossover operator and the mutation operator, to update the particles in the population. The HPGA is then applied in heavy machinery company with minimising machines’ makespan and minimising jobs’ tardiness as the two optimal objectives. The comparisons with actual application report have illustrated that the proposed HPGA can obtain higher quality of schedule solution for machine tool production. Furthermore, with solution quality and convergence rate as the two estimation measurements metrics, some comparisons are performed in order to illustrate that the HPGA has superiority over the PSO, GA and simulated annealing algorithm (SA). Results have indicated that, with the combination of the merits of PSO and GA, the proposed HPGA approach can achieve not only better solution quality but also faster convergence rate than the PSO, GA and SA, within a reasonable computation time for high dimensions JSSP.

To improve the simulation accuracy of disease prediction model, a modified hybrid algorithm combining BP neural network (BPNN) with particle swarm optimization (PSO) algorithm based on chaos theory optimization is proposed considering BPNN is easy to fall into the local extremum. The chaos theory is used to optimize PSO algorithm to overcome the premature convergence of the traditional PSO algorithm. Then, the improved CAPSO algorithm is used to train the BPNN, to make full use of the global search characteristic of PSO algorithm and the local search ability of BPNN. The fitness function of the PSO algorithm is used as the energy function, and the optimization method of the improved hybrid algorithm is selected according to the specified number of evolution. An optimized network model was used to predict the prevalence of coronary heart disease. Compared with other algorithms such as BP neural network, the results show that the proposed algorithm has high accuracy and can significantly improve the quality of prediction.

An improved chaos particle swarm optimization (CPSO) algorithm is proposed on path planning for unmanned aerial vehicle (UAV) to overcome the inadequacy of particle swarm optimization (PSO) algorithm, which falls into local optimum easily and converges slowly in process with poor precision. Through the in-depth analysis of PSO algorithm, the chaos optimization (CO) algorithm principle is introduced into it based on the traditional update operations on the particles’ velocity and position; as a result, the diversity of particles is increased, the suboptimal search on path planning is avoided and the quickness accompanied with accuracy of convergence is improved. Combined with digital map for modeling the UAV’s flight environment, the 3-D path planning is achieved. As the simulation results demonstrated, this hybrid algorithm is superior to the traditional PSO algorithm on path searching, especially in the 3-D environment.

In this paper, a hybrid algorithm based on Particle Swarm Optimization (PSO) and Cuckoo Search (CS) is detailed with the application of Optimal Reactive Power Dispatch (ORPD). The Levy flight and the elimination mechanism are introduced to the traditional PSO algorithm. Benchmark tests show that the hybrid algorithm has better optimization efficiency than original PSO and CS algorithms. In the application of the ORPD, the fitness function is the total real loss of the distribution network. Inequality constrains are handled by the limitation of searching space. The simulation results on the IEEE-14 bus standard system and a real distribution network illustrate the feasibility and effectiveness of the algorithm. The total real power losses decrease 7.1% and 8% in two applications. The mathematical tests and engineering applications demonstrate that the hybrid algorithm has better searching ability than the standard PSO algorithm, and it is suitable for the solution of ORPD problem.

The particle swarm optimization algorithm (PSO) is not good at dealing with discrete optimization problems, and for the krill herd algorithm (KH), the ability of local search is relatively poor. In this paper, we optimized PSO by quantum behavior and optimized KH by simulated annealing, so a new hybrid algorithm, named the annealing krill quantum particle swarm optimization (AKQPSO) algorithm, is proposed, and is based on the annealing krill herd algorithm (AKH) and quantum particle swarm optimization algorithm (QPSO). QPSO has better performance in exploitation and AKH has better performance in exploration, so AKQPSO proposed on this basis increases the diversity of population individuals, and shows better performance in both exploitation and exploration. In addition, the quantum behavior increased the diversity of the population, and the simulated annealing strategy made the algorithm avoid falling into the local optimal value, which made the algorithm obtain better performance. The test set used in this paper is a classic 100-Digit Challenge problem, which was proposed at 2019 IEEE Congress on Evolutionary Computation (CEC 2019), and AKQPSO has achieved better performance on benchmark problems.

Composite plate low energy impact localization system based on FBG sensing network and hybrid algorithm

The scattering effect of turbid media can lead to optical wavefront distortion. Focusing light through turbid media can be achieved using wavefront shaping techniques. Intelligent optimization algorithms and neural network algorithms are two powerful types of algorithms in the field of wavefront shaping but have their advantages and disadvantages. In this paper, we propose a new hybrid algorithm that combines the particle swarm optimization algorithm (PSO) and single-layer neural network (SLNN) to achieve the complementary advantages of both. A small number of training sets are used to train the SLNN to obtain preliminary focusing results, after which the PSO continues to optimize to the global optimum. The hybrid algorithm achieves faster convergence and higher enhancement than the PSO, while reducing the size of training samples required for SLNN training. SLNN trained with 1700 training sets can speed up the convergence of the PSO by about 50% and boost the final enhancement by about 24%. This hybrid algorithm will be of great significance in fields such as biomedicine and particle manipulation.