Greedy Bit Research Articles

Optimal Energy Efficient Bit and Power Loading for Multicarrier Systems

The energy efficient bit and power loading problem is studied for the multicarrier communication systems aiming to maximize the system energy efficiency (EE) under the total transmit power constraint, the per subcarrier bit error rate (BER) constraint and the minimum transmission rate constraint. To solve the non-convex combinatorial EE optimization problem, an equivalent one-dimensional problem is constructed and an optimal greedy bit and power loading algorithm is proposed. Furthermore, a simplified version of the bit and power loading algorithm is proposed as well to achieve the same optimal solution but with significant lower complexity.

Discrete rate maximisation power allocation with enhanced bit error ratio

This study aims to maximise the rate over a multiple-in multiple-out (MIMO) link using incremental power and bit allocation. Two different schemes, greedy power allocation (GPA) and greedy bit allocation (GBA), are addressed and compared with the standard uniform power allocation (UPA). The design is constrained by the target bit error ratio (BER), the total power budget and fixed discrete modulation orders. The authors demonstrate through simulations that GPA outperforms GBA in terms of throughput and power conservation, whereas GBA is advantageous when a lower BER is beneficial. Once the design constraints are satisfied, remaining power is utilised in two possible ways, leading to improved performance of GPA and UPA algorithms. This redistribution is analysed for fairness in BER performance across all active subchannels using a bisection method.

Low Complexity Precoded Greedy Power Allocation Algorithms for OFDM Communication Systems

In this paper, an enhanced greedy bit and power allocation algorithms for orthogonal frequency division multiplexing (OFDM) communication systems are introduced. These algorithms combine low complexity greedy power allocation algorithms with a simplified maximum ratio combining (MRC) precoding technique at the transmitter for maximizing the average data throughput of OFDM communication systems. Results of computer simulations show that precoding is an effective technique for improving the throughput performance of the proposed bit and power allocation algorithms.

Efficient Two-Stage Discrete Bit-Loading Algorithms for OFDM Systems

Aiming at minimizing the transmit power of orthogonal frequency-division multiplexing (OFDM) systems subject to the target data rate, the target bit error rate (BER), the maximum allowable power per subcarrier, and the maximum allowable size of constellations, we present a couple of new discrete bit-loading algorithms, named efficient bit filling (EBF) and efficient bit removal (EBR), which are based on our new idea of group-by-group bit filling. Both EBF and EBR perform bit loading in two stages and are able to converge to the optimal bit-allocation profile. In the first stage, an intermediate bit-allocation profile, which is tightly close to the final bit-allocation profile, is obtained by using our proposed group-by-group bit filling (for EBF) or group-by-group bit removal (for EBR). In the second stage, the well-known greedy bit filling (for EBF) or greedy bit removal (for EBR) is performed for the remaining bits (usually of a small size). Analytical and numerical results reveal that the proposed algorithms converge to the optimal bit allocation with much lower computational complexities than existing optimal algorithms. It is also shown that the proposed algorithms outperform typical suboptimal algorithms in power efficiency with relatively higher computational complexities.

WiMAX parameters adaptation through a baseband processor using discrete particle swarm method

The measurements of physical level parameters can become the area where decisions about cognitive radio will have the most striking effect. Field-programmable gate array (FPGA) enables real-time analyses of physical layer data to satisfy constraints like dynamic spectrum allocations, data throughput, and the coding rate. Cognitive radio will be based on simple network management techniques, using remote procedure calls. Intelligent knowledge-base system (IKBS) techniques will be used to search the parameter space in selecting changes to the system. Worldwide Interoperability for Microwave Access (WiMAX) PHY-layer functions will be managed cognitively by a FPGA-based controller to optimize the performance of the system. Instead of simple bit loading methods, the global multi-criteria optimization promise possibility to adapt more parameters with respect to several objectives. In this paper, the application of particle swarm optimization to fixed WiMAX-OFDM (Orthogonal Frequency Division Multiplexing) parameter adaptation is presented and compared with the greedy bit loading algorithm.

Cross-layer optimization for OFDM wireless networks-part II: algorithm development

We have established a theoretical framework for cross-layer optimization in orthogonal frequency division multiplexing (OFDM) wireless networks. In this paper, we focus on effective and practical algorithms for efficient and fair resource allocation in OFDM wireless networks. We have taken various conditions into account and developed a variety of efficient algorithms, including sorting-search dynamic subcarrier assignment, greedy bit loading, and power allocation, as well as objective aggregation algorithms. We have also modified those algorithms for a certain type of nonconcave utility functions. To further improve performance by exploiting time diversity, a low-pass time filter can be easily incorporated into all of the algorithms. Simulation results have confirmed that the utility-based cross-layer optimization can significantly enhance the system performance and guarantee fairness. The gains come from multiuser diversity, frequency diversity, as well as time diversity. The fairness is automatically achieved by the behavior of marginal utility functions.