Knowledge Of Channel Correlation Research Articles

Channel Correlation Based Zero-Forcing Beamforming in Two-User MISO Broadcast Channel

In this paper, a novel zero-forcing beamforming (ZFBF) is presented for the two-user multiple input single output broadcast channel. Based on the knowledge of channel correlations at the transmitter side, a nonlinear optimization problem is formulated to determine the beamformers so that the weighted ergodic sum-rate is maximized subject to avoiding multi-user interference and meeting a total power constraint. By factorizing each beamformer, the original complicated problem can be transformed into a simpler one. Numerical results show that the performance gap between the proposed ZFBF and the traditional instantaneous channel state information based ZFBF is within 1.5 bits per transmission.

Structure-Based Low Complexity MMSE Channel Estimator for OFDM Wireless Systems

Wireless communication systems utilizing orthogonal frequency division multiplexing (OFDM) transmissions are capable of delivering high data rates over multipath frequency selective channels. This paper deals with joint estimation/interpolation of wireless channel using pilot symbols transmitted concurrently with the data. We propose a low complexity, spectrally efficient minimum mean square error channel estimator which exploits the correlation structure of channel frequency response for reducing the complexity. Specifically, it is shown that if pilots are inserted appropriately across OFDM subcarriers, the proposed algorithm requires no matrix inversion, thereby significantly relieving the computational burden without deteriorating the performance. Moreover, the knowledge of channel correlation is also not required for the proposed estimator. Simulation results validate that the proposed technique outperforms existing low-complexity variants in terms of mean square error and computational complexity.

Multiple-Symbol Differential Sphere Detection and Decision-Feedback Differential Detection Conceived for Differential QAM

Multiple-symbol differential sphere detection (MSDSD) relies on the knowledge of channel correlation. More explicitly, for differential phase-shift keying (DPSK), the transmitted symbols' phases form a unitary matrix, which can be separated from the channel's correlation matrix by the classic multiple-symbol differential detection (MSDD), so that a lower triangular matrix extracted from the inverted channel correlation matrix is utilized for the MSDSD's sphere decoding. However, for differential quadrature amplitude modulation (DQAM), the transmitted symbols' amplitudes cannot form a unitary matrix, which implies that the MSDD's channel correlation matrix becomes amplitude dependent and remains unknown unless all the data-carrying symbol amplitudes are detected. To tackle this open problem, in this paper, we propose to determine the MSDD's nonconstant amplitude-dependent channel correlation matrix with the aid of a sphere decoder (SD) so that the classic MSDSD algorithms that were originally conceived for DPSK may also be invoked for DQAM detection. As a result, our simulation results demonstrate that the MSDSD-aided DQAM schemes substantially outperform their DPSK counterparts. However, the price paid is that the detection complexity of MSDSD is also significantly increased. To mitigate this, we then propose a reduced-complexity MSDSD search strategy specifically conceived for DQAM constellations, which separately map bits to their ring-amplitude index and phase index. Furthermore, the classic decision-feedback differential detection (DFDD) conceived for DQAM relies on a constant channel correlation matrix, which implies that these DFDD solutions are suboptimal and that they are not equivalent to the optimum MSDD operating in decision-feedback mode. With the advent for solving the open problem of MSDSD-aided DQAM, we further propose to improve the conventional DFDD-aided DQAM solutions in this paper.

Joint Power Control and Receiver Optimization of CDMA Transceivers Using Successive Interference Cancellation

In a broadband uplink code-division multiple-access transmission, the interference among users, depending on the correlation of specific channels and codes, may be significant. Hence, when successive interference cancellation (SIC) is performed at the base station, the minimization of power consumption at the mobile terminals is achieved if a joint optimization of power control and ordering (JOPCO) of user detection is carried out. In this paper, we first investigate a closed-form solution to JOPCO, based on the assumption of perfect interference cancellation and complete knowledge of channel correlation among users. By describing the JOPCO problem as an optimization on a suitable graph, it is seen that the optimum solution is NP-hard, and we approximate it by a greedy algorithm. For a practical implementation, we then model JOPCO as a discrete stochastic optimization problem, and we derive a discrete stochastic approximation (DSA) algorithm that iteratively adapts the transmit powers and the SIC detection order. The DSA solution removes the hypothesis on perfect cancellation and explicit knowledge of the users' correlation. Moreover, by a suitable description of the problem, we obtain a simplified DSA algorithm having reduced requirements in both computational complexity and memory. Performance of the various algorithms is assessed by simulations in a universal mobile telecommunications system scenario

Minimum symbol error rate precoder design for spatial multiplexing over correlated channels

The minimum symbol error rate (SER) precoder design for spatial multiplexing (SM) system with zero forcing detector over correlated channel is investigated. With the knowledge of channel correlation at the transmitter, a precoder scheme that combines eigen-beamforming with power allocation (PA) is proposed. A closed-form expression for optimal PA on the eigenbeams is presented to minimise the overall average SER through an approximate SER analysis. When compared with the minimum mean-square error (MMSE) waterfilling precoder, the pre-processing is very simple in that it avoids loop or iteration operation in waterfilling algorithm. Both the analytical and simulation results have confirmed the effectiveness of the precoder. Moreover, when the number of receive antennas is larger than that of the transmit antennas, the performance of this proposal can approximate or even exceed that of MMSE waterfilling precoder at higher signal-to-noise ratio.

On the optimum design of space-time linear-dispersion codes

In this paper, we propose to design linear-dispersion (LD) codes by minimizing the union bound based on the exact pairwise error probability (PEP). We find that the original programming is not convex, and we present a convex relaxation to the optimization problem. We employ the gradient descent methods to numerically search the optimum dispersion matrices. Simulation results show that codes optimized by the new criterion generally outperform the codes designed based on algebraic number theory. When the knowledge of spatial-fading correlation is available in advance for the design, significant gain relative to the codes designed without knowledge of channel correlation can be achieved by taking into account the correlation structure while performing the optimization. We demonstrate how to exploit knowledge of transmit and receive correlation in designing the LD codes. Numerical and simulation examples show that knowledge of transmit correlation has a large impact on the optimum performance of the LD codes. Furthermore, additional gain can be achieved from the knowledge of receive correlation.