Spectral Efficiency Evaluation for Selection Combining Diversity Schemes under Worst Case of Fading Scenario

The results of spectral efficiencies for optimum rate adaptation with constant power (ORA) and channel inversion with fixed rate (CIFR) schemes over uncorrelated diversity branch with Selection Combining (SC) available so far in literature are applicable only for m?1.This paper derived closed-form expressions for the spectral efficiency of dual-branch SC over uncorrelated Nakagami-0.5(m<1) fading channels. This spectral efficiency is evaluated under ORA and CIFR schemes. Since, the spectral efficiency expression under ORA scheme contains an infinite series, hence bounds on the errors resulting from truncating the infinite series have been derived The corresponding expressions for Nakagami-0.5 fading are called expressions under worst fading condition with severe fading. Finally, numerical results are presented, which are then compared to the spectral efficiency results which have been previously published for ORA and CIFR schemes. It has been observed that by employing SC, spectral efficiency improves under ORA, but does not improve under CIF

Performance of Asynchronous MC-CDMA Systems with Maximal Ratio Combining in Frequency-Selective Fading Channels

The bit error rate (BER) performance of the asynchronous uplink channel of multicarrier code division multiple access (MC-CDMA) systems with maximal ratio combining (MRC) is analyzed. The study takes into account the effects of channel path correlations in generalized frequency-selective fading channels. Closed-form BER expressions are developed for correlated Nakagami fading channels with arbitrary fading parameters. For channels with correlated Rician fading paths, the BER formula developed is in one-dimensional integration form with finite integration limits, which is also easy to evaluate. The accuracy of the derived BER formulas are verified by computer simulations. The derived BER formulas are also useful in terms of computing other system performance measures such as error floor and user capacity

Receive Combining vs. Multi-Stream Multiplexing in Downlink Systems with Multi-Antenna Users

In downlink multi-antenna systems with many users, the multiplexing gain is strictly limited by the number of transmit antennas $N$ and the use of these antennas. Assuming that the total number of receive antennas at the multi-antenna users is much larger than $N$, the maximal multiplexing gain can be achieved with many different transmission/reception strategies. For example, the excess number of receive antennas can be utilized to schedule users with effective channels that are near-orthogonal, for multi-stream multiplexing to users with well-conditioned channels, and/or to enable interference-aware receive combining. In this paper, we try to answer the question if the $N$ data streams should be divided among few users (many streams per user) or many users (few streams per user, enabling receive combining). Analytic results are derived to show how user selection, spatial correlation, heterogeneous user conditions, and imperfect channel acquisition (quantization or estimation errors) affect the performance when sending the maximal number of streams or one stream per scheduled user---the two extremes in data stream allocation. While contradicting observations on this topic have been reported in prior works, we show that selecting many users and allocating one stream per user (i.e., exploiting receive combining) is the best candidate under realistic conditions. This is explained by the provably stronger resilience towards spatial correlation and the larger benefit from multi-user diversity. This fundamental result has positive implications for the design of downlink systems as it reduces the hardware requirements at the user devices and simplifies the throughput optimization.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 11 figures. The results can be reproduced using the following Matlab code: https://github.com/emilbjornson/one-or-multiple-stream