No abstract

We focus on the design of vector perturbation (VP) precoding for multiuser multiple-inputsingle-output (MU-MISO) broadcast channel systems where the centralized transmitter equipped with multiple antennas and communicates simultaneously to multiple single-antenna receivers. While conventional VP requires the feedback of the channel matrix at the transmitter for precoding and the power scaling factor at the receivers for detection, VP precoding has so far been developed and analyzed under assumptions that the transmitter has perfect channel state information (CSI) or the receivers have perfect knowledge of the channel-and data-dependent power scaling factors. In practical limited feedback scenarios, wireless communication systems suffer from limited time and frequency resource for pilots to feed-forward information and only a quantized version of power scaling factors is available at the receivers; under such limitations, the performance of VP precoding will degrade significantly compared with ideal scenarios and would always encounter an error floor at mid-to-high signal-to-noise ratio (SNR) regions. Motivated by such observations, we propose a robust VP precoder design, which takes the imperfectness of CSI and power scaling factor jointly into account under the criterion of minimum mean-square error (MMSE). The closed-form expressions of the proposed precoder are then derived. As illustrated by the simulation results, the proposed VP precoder is less sensitive to CSI and power scaling factor imperfections compared with the classic VP precoder and other existing MMSE-based VP precoders, as it has a lower error floor when imperfectness is assumed to be fixed, and power scaling factor accuracy is shown to offer a non-linear performance gain compared with that of the linear gain CSI accuracy could offer.INDEX TERMS Imperfect channel state information (CSI), multiple-input multiple-output (MIMO) broadcast channel, non-linear precoding, power scaling factor, vector perturbation (VP).

Small cell networks (SCNs) have emerged as promising technologies to meet the data traffic demands for the future wireless communications. However, the benefits of SCNs are limited to their hard handovers between base stations (BSs). In addition, the interference is another challenging issue. To solve this problem, this study employs a cooperative transmission mechanism focusing on correlated Rician/Gamma fading channels with zero-forcing receivers. The analytical expressions for the achievable sum rate, symbol error rate and outage probability are derived, which are applicable to arbitrary Rician factors, correlation coefficients, the number of antennas, and remain tight across entire signal-to-noise ratios (SNRs). Asymptotic analyses at the high and low SNR regimes are carried out in order to further reveal the insights of the model parameters on the system performance. Monte-Carlo simulation results validate the correctness of their derivations. Numerical results indicate that the theoretical expressions provide sufficiently accurate approximation to simulated results.

Massive multiple-input-multiple-output (MIMO), also known as very-large MIMO systems, is an attracting technique in 5G and can provide higher rates and power efficiency than 4G. Linear-precoding schemes are able to achieve the near optimal performance, and thus are more attractive than non-linear precoding schemes. However, conventional linear precoding schemes in massive MIMO systems, such as regularized zero-forcing (RZF) precoding, have near-optimal performance but suffer from high computational complexity due to the required matrix inversion of large size. To solve this problem, we utilize the Cholesky-decomposition and Sherman-Morrison lemma and propose CSM (Cholesky and Sherman-Morrison strategy)-based precoding scheme to the matrix inversion by exploiting the asymptoti cally orthogonal channel property in massive MIMO systems. Results are evaluated numerically in terms of bit-error-rate (BER)and average sum rate. Comparing with the Neumann series approximation of inversing matrix, it is concluded that, with fewer operations, the performance of CSM-based precoding is better than conventional methods in massive MIMO configurations.

No abstract