Discrete Multitone Modulation for Maximizing Transmission Rate in Step-Index Plastic Optical Fibres

The use of standard 1-mm core-diameter step-index plastic optical fiber (SI-POF) has so far been mainly limited to distances of up to 100 m and bit-rates in the order of 100 Mbit/s. By use of digital signal processing, transmission performance of such optical links can be improved. Among the different technical solutions proposed, a promising one is based on the use of discrete multitone (DMT) modulation, directly applied to intensity-modulated, direct detection (IM/DD) SI-POF links. This paper presents an overview of DMT over SI-POF and demonstrates how DMT can be used to improve transmission rate in such IM/DD systems. The achievable capacity of an SI-POF channel is first analyzed theoretically and then validated by experimental results. Additionally, first experimental demonstrations of a real-time DMT over SI-POF system are presented and discusse

A new low-cost discrete bit loading using greedy power allocation

In this paper we consider a low cost bit loading based on the greedy power allocation (GPA). Compared to the standard GPA, which is optimal in terms of maximising the data throughput, three suboptimal schemes are suggested, which perform GPA on subsets of subchannels only. We demonstrate how these schemes can reduce complexity. Two of the proposed algorithms can achieve near optimal performance by including a transfer of residual power between subsets at the expense of a very small extra cost. By simulations, we show that the two near optimal schemes perform best in two separate and distinct SNR regions

Suboptimal greedy power allocation schemes for discrete bit loading

In this paper we consider low cost discrete bit loading based on greedy power allocation (GPA) under the constraints of total transmit power budget, target BER, and maximum permissible QAM modulation order. Compared to the standard GPA, which is optimal in terms of maximising the data throughput, three suboptimal schemes are proposed, which perform GPA on subsets of subchannels only. These subsets are created by considering the minimum SNR boundaries of QAM levels for a given target BER. We demonstrate how these schemes can significantly reduce the computational complexity required for power allocation, particularly in the case of a large number of subchannels. Two of the proposed algorithms can achieve near optimal performance including a transfer of residual power between subsets at the expense of a very small extra cost. By simulations, we show that the two near optimal schemes, while greatly reducing complexity, perform best in two separate and distinct SNR regions

Suboptimal greedy power allocation schemes for discrete bit loading

In this paper we consider low cost discrete bit loading based on greedy power allocation (GPA) under the constraints of total transmit power budget, target BER, and maximum permissible QAM modulation order. Compared to the standard GPA, which is optimal in terms of maximising the data throughput, three suboptimal schemes are proposed, which perform GPA on subsets of subchannels only. These subsets are created by considering the minimum SNR boundaries of QAM levels for a given target BER. We demonstrate how these schemes can significantly reduce the computational complexity required for power allocation, particularly in the case of a large number of subchannels. Two of the proposed algorithms can achieve near optimal performance including a transfer of residual power between subsets at the expense of a very small extra cost. By simulations, we show that the two near optimal schemes, while greatly reducing complexity, perform best in two separate and distinct SNR regions

Greedy power allocation for multicarrier systems with reduced complexity

In this paper we consider a reduced complexity discrete bit loading for Multicarrier systems based on the greedy power allocation (GPA) under the constraints of transmit power budget, target BER, and maximum permissible QAM modulation order. Compared to the standard GPA, which is optimal in terms of maximising the data throughput, three suboptimal schemes are proposed, which perform GPA on subsets of subcarriers only. These subsets are created by considering the minimum SNR boundaries of QAM levels for a given BER. We demonstrate how these schemes can reduce complexity. Two of the proposed algorithms can achieve near optimal performance by including a transfer of residual power between groups at the expense of a very small extra cost. It is shown that the two near optimal schemes,while greatly reducing complexity, perform best in two separate and distinct SNR regions

Suboptimal greedy power allocation schemes for discrete bit loading

In this paper we consider low cost discrete bit loading based on greedy power allocation (GPA) under the constraints of total transmit power budget, target BER, and maximum permissible QAM modulation order. Compared to the standard GPA, which is optimal in terms of maximising the data throughput, three suboptimal schemes are proposed, which perform GPA on subsets of subchannels only. These subsets are created by considering the minimum SNR boundaries of QAM levels for a given target BER. We demonstrate how these schemes can significantly reduce the computational complexity required for power allocation, particularly in the case of a large number of subchannels. Two of the proposed algorithms can achieve near optimal performance including a transfer of residual power between subsets at the expense of a very small extra cost. By simulations, we show that the two near optimal schemes, while greatly reducing complexity, perform best in two separate and distinct SNR regions

Competent genetic-evolutionary optimization of water distribution systems

A genetic algorithm has been applied to the optimal design and rehabilitation of a water distribution system. Many of the previous applications have been limited to small water distribution systems, where the computer time used for solving the problem has been relatively small. In order to apply genetic and evolutionary optimization technique to a large-scale water distribution system, this paper employs one of competent genetic-evolutionary algorithms - a messy genetic algorithm to enhance the efficiency of an optimization procedure. A maximum flexibility is ensured by the formulation of a string and solution representation scheme, a fitness definition, and the integration of a well-developed hydraulic network solver that facilitate the application of a genetic algorithm to the optimization of a water distribution system. Two benchmark problems of water pipeline design and a real water distribution system are presented to demonstrate the application of the improved technique. The results obtained show that the number of the design trials required by the messy genetic algorithm is consistently fewer than the other genetic algorithms

Intersystem soft handover for converged DVB-H and UMTS networks

Digital video broadcasting for handhelds (DVB-H) is the standard for broadcasting Internet Protocol (IP) data services to mobile portable devices. To provide interactive services for DVB-H, the Universal Mobile Telecommunications System (UMTS) can be used as a terrestrial interaction channel for the unidirectional DVB-H network. The converged DVB-H and UMTS network can be used to address the congestion problems due to the limited multimedia channel accesses of the UMTS network. In the converged network, intersystem soft handover between DVB-H and UMTS is needed for an optimum radio resource allocation, which reduces network operation cost while providing the required quality of service. This paper deals with the intersystem soft handover between DVB-H and UMTS in such a converged network. The converged network structure is presented. A novel soft handover scheme is proposed and evaluated. After considering the network operation cost, the performance tradeoff between the network quality of service and the network operation cost for the intersystem soft handover in the converged network is modeled using a stochastic tree and analyzed using a numerical simulation. The results show that the proposed algorithm is feasible and has the potential to be used for implementation in the real environment