8140126
Joint Optimization of Bit and Power Loading for Multicarrier Systems
In this letter, a novel low complexity bit and power loading algorithm is formulated for multicarrier communication systems. The proposed algorithm jointly maximizes the throughput and minimizes the transmit power through a weighting coefficient α, while meeting constraints on the target bit error rate (BER) per subcarrier and on the total transmit power. The optimization problem is solved by the Lagrangian multiplier method if the initial α causes the transmit power not to violate the power constraint; otherwise, a bisection search is used to find the appropriate α. Closed-form expressions are derived for the close-to-optimal bit and power allocations per subcarrier, average throughput, and average transmit power. Simulation results illustrate the performance of the proposed algorithm and demonstrate its superiority with respect to existing allocation algorithms. Furthermore, the results show that the performance of the proposed algorithm approaches that of the exhaustive search for the discrete optimal allocations.
7083448
Bit loading with BER-constraint for multicarrier systems
We present discrete adaptive bit loading algorithms for multicarrier systems with uniform (nonadaptive) power allocation operating in a frequency selective fading environment. The algorithms try to maximize the overall throughput of the system while guaranteeing that the mean bit error rate (BER) remains below a prescribed threshold. We also study the impact of imperfect subcarrier signal-to-noise ratio information on throughput performance. Results show that the proposed algorithms have approximately the same throughput and mean BER as the optimal allocation while having a significantly lower computational complexity relative to other algorithms with near-optimal allocations. Moreover, when compared with algorithms that employ approximations to water filling, the computational complexity is comparable while the overall throughput is closer to the optimum.
8201266
Optimization and performance evaluation of multicarrier transmission
Optimization of the performance of multicarrier transmission over a linear dispersive channel is presented. The optimum data and power assignment to the subcarriers are derived for both the conventional error probability criterion, and a new criterion based on the normalized mean-square error. The assignments and algorithms hold for channels where performance is degraded by additive noise, intersymbol and interchannel interference. Lower bounds on throughput are derived and are used to compare multicarrier performance with conventional single-carrier quadrature amplitude modulation (QAM) with both linear and decision feedback equalization. It is seen that multicarrier transmission can provide a significant improvement at low and intermediate channel signal-to-noise ratios. As an example, the optimization is applied to the high-speed digital subscriber loop, and multicarrier transmission is demonstrated to be superior to decision feedback equalized single-carrier QAM.
22021118
Adaptive power loading based on unequal-ber strategy for OFDM systems
An adaptive power loading algorithm with uniform (nonadaptive) bit allocation is proposed for constant data rate OFDM systems in this letter. This algorithm aims to minimize the transmit power while guaranteeing the target mean bit error rate (BER). The power loading is based on the unequal-BER (UBER) strategy which permits unequal mean BERs on different subcarriers. The closed-form expressions for optimal BER and power distributions are derived. Simulation results indicate the superiority of the proposed algorithm.
17596112
An efficient algorithm for optimal discrete bit-loading in multicarrier systems
An efficient algorithm for bit-loading in multicarrier systems is proposed based upon an allocation rhythm/order underlying the classical optimal greedy bit-allocation procedure. An accurate complexity comparison in terms of the exact number of execution cycles over a processor is made with the classical Greedy solution along with another recently proposed optimal bit-loading algorithm. Simulation results verify the significant complexity advantage of our algorithm with no loss in performance.
62702238
Nonlinear multiobjective optimization
Preface. Acknowledgements. Notation and Symbols. Part I: Terminology and Theory. 1. Introduction. 2. Concepts. 3. Theoretical Background. Part II: Methods. 1. Introduction. 2. No-Preference Methods. 3. A Posteriori Methods. 4. A Priori Methods. 5. Interactive Methods. Part III: Related Issues. 1. Comparing Methods. 2. Software. 3. Graphical Illustration. 4. Future Directions. 5. Epilogue. References. Index.