Johanna Ketonen

Performance—Complexity Comparison of Receivers for a LTE MIMO–OFDM System

Implementation of receivers for spatial multiplexing multiple-input multiple-output (MIMO) orthogonal-frequency- division-multiplexing (OFDM) systems is considered. The linear minimum mean-square error (LMMSE) and the -best list sphere detector (LSD) are compared to the iterative successive interference cancellation (SIC) detector and the iterative -best LSD. The performance of the algorithms is evaluated in 3G long-term evolution (LTE) system. The SIC algorithm is found to perform worse than the -best LSD when the MIMO channels are highly correlated, while the performance difference diminishes when the correlation decreases. The receivers are designed for 22 and 4 4 antenna systems and three different modulation schemes. Complexity results for FPGA and ASIC implementations are found. A modification to the -best LSD which increases its detection rate is introduced. The ASIC receivers are designed to meet the decoding throughput requirements in LTE and the -best LSD is found to be the most complex receiver although it gives the best reliable data transmission throughput. The SIC receiver has the best performance-complexity tradeoff in the system but in the 4 4 case, the -best LSD is the most efficient. A receiver architecture which could be reconfigured to using a simple or a more complex detector as the channel conditions change would achieve the best performance while consuming the least amount of power in the receiver.

ASIC implementation comparison of SIC and LSD receivers for MIMO-OFDM

MIMO-OFDM receivers with horizontal encoding are considered in this paper. The successive interference cancellation (SIC) algorithm is compared to the K-best list sphere detector (LSD). A modification to the K-best LSD algorithm is introduced. The SIC and K-best LSD receivers are designed for a 2 times 2 antenna system with 64-quadrature amplitude modulation (QAM). The ASIC implementation results for both architectures are presented. The K-best LSD outperforms the SIC receiver in bad channel conditions but the SIC receiver performs better in channels with less correlated MIMO streams. The latency of the K-best LSD is large due to the high modulation order and list size. The throughput of the SIC receiver is more than 6 times higher than that of the K-best LSD.

Implementation of LS, MMSE and SAGE channel estimators for mobile MIMO-OFDM

The use of decision directed (DD) channel estimation in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) downlink receiver is studied in this paper. The 3GPP long term evolution (LTE) based pilot structure is used as a benchmark. The space-alternating generalized expectation-maximization (SAGE) algorithm is used to improve the performance from that of the pilot symbol based least-squares (LS) channel estimator. The DD channel estimation improves the performance with high user velocities, where the pilot symbol density is not sufficient. It can also be used to reduce the pilot overhead without any performance degradation. Minimum mean square error (MMSE) filtering can also be used in estimating the channel in between pilot symbols. The pilot based LS, MMSE and the SAGE channel estimators are implemented and the performance-complexity trade-offs are studied.

Decision directed channel estimation for improving performance in LTE-A

The use of decision directed (DD) channel estimation in a MIMO-OFDM downlink receiver with a LTE pilot structure is studied in this paper. The space-alternating generalized expectation-maximization (SAGE) algorithm is used to improve the receiver performance from the one obtained by using the pilot symbol based least-squares (LS) channel estimator. The DD channel estimation improves the performance with high user velocities, where the pilot symbol density is not sufficient. The DD channel estimation can also be used to reduce the pilot overhead without any performance degradation by transmitting data instead of pilot symbols. The complexity of the SAGE channel estimator is discussed as well.

Receiver implementation for MIMO-OFDM with AMC and precoding

Receivers for horizontally encoded LTE based MIMO-OFDM systems are considered in this paper. Adaptive modulation and coding (AMC) is used as well as precoding. The linear minimum mean square error (LMMSE), successive interference cancellation (SIC) and K-best list sphere detectors (LSD) are compared. The receivers were designed and implemented for 2×2 and 4×4 antenna systems and meet the decoding rate requirement in LTE, i.e, 210 Mb/s in 2×2 and 405 Mb/s in 4×4 antenna systems. The results show that the performance of the receivers is similar in low SNR but the performance difference increases when a higher rank transmission is used. The K-best LSD has the highest performance and complexity. A simpler receiver could be used in the low SNRs to save power and a more complex receiver in the high SNRs when a higher goodput is needed.

Decision-Directed Channel Estimation Implementation for Spectral Efficiency Improvement in Mobile MIMO-OFDM

Channel estimation algorithms and their implementations for mobile receivers are considered in this paper. The 3GPP long term evolution (LTE) based pilot structure is used as a benchmark in a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) receiver. The decision directed (DD) space-alternating generalized expectation-maximization (SAGE) algorithm is used to improve the performance from that of the pilot symbol based least-squares (LS) channel estimator. The performance is improved with high user velocities, where the pilot symbol density is not sufficient. Minimum mean square error (MMSE) filtering is also used in estimating the channel in between pilot symbols. The pilot overhead can be reduced to a third of the LTE pilot overhead with DD channel estimation, obtaining a ten percent increase in data throughput. Complexity reduction and latency issues are considered in the architecture design. The pilot based LS, MMSE and the SAGE channel estimators are implemented with a high level synthesis tool, synthesized with the UMC 0.18 μ

Satellite Uplink Transmission with Terrestrial Network Interference

\mu

Receiver implementations for co-channel interference suppression in MIMO-OFDM

m CMOS technology and the performance-complexity trade-offs are studied. The MMSE estimator improves the performance from the simple LS estimator with LTE pilot structure and has low power consumption. The SAGE estimator has high power consumption but can be used with reduced pilot density to increase the data rate.

VLSI Implementations of Sphere Detectors

The impact of the terrestrial network interference to a satellite uplink transmission is studied in this paper. In a system, where an LTE terrestrial network is extended by a satellite communication, the terrestrial interference can become a problem. The interference levels are studied via infield measurements and link budget calculations and the interference tolerance of the satellite receiver are studied via computer simulations. The results show that in spite of the long satellite communication link, strictly power limited system and terrestrial interference the satellite link can operate on the same frequency band as the terrestrial network.

MIMO detection in single carrier systems

Suppression of co-channel interference in the receiver of a MIMO-OFDM system is considered. The interference and noise spatial covariance matrix measured on the pilot subcarriers is used in data detection and channel estimation. The impact of the accuracy of the matrix decomposition on the structure of the covariance matrix is studied. An algorithm to adapt the accuracy of the matrix decomposition and the use of interference suppression is proposed. The complexity and performance of eigenspace tracking is also considered. The different interference mitigation methods are implemented and the performance-complexity tradeoffs are found out. Eigenspace tracking performs well in some scenarios but the complexity and memory requirements are higher than those of the unstructured model or the adaptive algorithm. A good performance is achieved with the adaptive algorithm in all interference scenarios and the power consumption of the receiver can be reduced by adapting the interference mitigation.