Discrete Prolate Spheroidal Basis Expansion Research Articles

Digital Self-Interference Cancellation for Full-Duplex UAV Communication System over Time-Varying Channels

Full-duplex unmanned aerial vehicle (UAV) communication systems are characterized by mobility, so the self-interference (SI) channel characteristics change over time constantly. In full-duplex UAV communication systems, the difficulty is to eliminate SI in time-varying channels. In this paper, we propose a pilot-aid digital self-interference cancellation (SIC) method. First, the pilot is inserted into the data sequence uniformly, and the time-varying SI is modeled as a linear non-causal function. Then, the time-varying SI channel is estimated by the discrete prolate spheroidal basis expansion model (BEM). The error of block edge channel estimation is reduced by cross-block interpolation. The result of channel estimation is convolved with the transmitted data to obtain the reconstructed SI, which is subtracted from the received signal to achieve SIC. The simulation results show that the SIC performance of the proposed method outperforms the dichotomous coordinate descent recursive least square (DCD-RLS) and normalized least mean square (NLMS) algorithms. When the interference to noise ratio (INR) is 25 dB, the performance index normalized least mean square (NMSE) is reduced by 5.5 dB and 4 dB compared with DCD-RLS and NLMS algorithms, which can eliminate SI to the noise floor, and the advantage becomes more obvious as the INR increases.

Iterative SAGE-Based Joint MCFOs and Channel Estimation for Full-Duplex Two-Way Multi-Relay Systems in Highly Mobile Environment

Two-way relay network based on full-duplex technique has the potential to enhance the spectral efficiency significantly, and increase capacity in future 5G mobile communication systems. However, the self-interference of full-duplex communication severely limits the performance of a two-way relay network. The cancellation of self-interference for multi-relay full-duplex two-way relay systems in highly mobile environment is very challenging due to time-frequency doubly selective channel on the one hand and multiple carrier frequency offsets on the other hand. In this paper, we propose a novel semi-blind estimator to jointly estimate multiple carrier frequency offsets and doubly selective self-interference channels in highly mobile two-way relay systems with orthogonal frequency-division multiplexing modulation in the presence of residual self-interference. We use discrete prolate spheroidal basis expansion model to capture rapid time variations of the channel. The proposed iterative space-alternating generalized expectation maximization-based semi-blind algorithm uses received data symbols along with received pilot symbols to obtain improved frequency offsets and channel estimate with significantly less number of pilot overhead. The proposed estimator converges in almost two iterations and achieves significant improvement over the pilot-based method. The Cramer–Rao lower bounds of the semi-blind joint estimation are also derived.

Basis expansion model based spectral efficient channel recovery scheme for spatial‐temporal correlated massive MIMO systems

In this study, a spectral efficient channel recovery scheme is proposed for spatial-temporal correlated sparse massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing systems. By exploiting the spatial-temporal correlations of massive MIMO channels, superimposed time-frequency training sequences (TSs) are jointly utilised for accurate channel state information (CSI) acquisition. Moreover, a novel time-frequency TS pattern design scheme is proposed to improve the recovery performance based on structured compressive sensing theory. Furthermore, the discrete prolate spheroidal basis expansion model of massive MIMO channels in the space-domain is utilised to reduce the frequency-domain pilots overhead and improve the spectral efficiency. Simulation results show that the proposed scheme could achieve accurate CSI with great improvement in spectral efficiency and computational complexity.

Two‐step interference cancellation scheme in doubly selective channel estimation using superimposed training

Channel estimation for single-input–multiple-output systems over doubly selective channel using superimposed training and discrete prolate spheroidal basis expansion model is considered. To remove the interference from unknown data, a two-step interference cancellation scheme is proposed, where in the first step, a first-order statistics-based estimator with cyclic mean removal before transmission is proposed. In this step, only interference components corresponding to cyclic mean of data sequence are removed, which avoids the conflict between total interference elimination and symbol recovery. In the second step, a low complexity iterative interference cancellation scheme is proposed to further improve estimation performance with detected symbols at the receiver's end. Simulation results show that the proposed iterative scheme has the symbol error rate (SER) performance slightly inferior to that of the partially-data-dependent approach but with less computational complexity and outperforms the data-dependent scheme in terms of SER as well as mean-square error over doubly selective channels.

Multiuser/MIMO Doubly Selective Fading Channel Estimation Using Superimposed Training and Slepian Sequences

We consider doubly selective multiuser/multiple-input-multiple-output (MIMO) channel estimation and data detection using superimposed training. The time- and frequency-selective fading channel is assumed to be well described by a discrete prolate spheroidal basis expansion model (DPS-BEM) using Slepian sequences as basis functions. A user-specific periodic (nonrandom) training sequence is arithmetically added (superimposed) at low power to each user's information sequence at the transmitter before modulation and transmission. A two-step approach is adopted, where, in the first step, we estimate the channel using only the first-order statistics of the observations. In this step, however, the unknown information sequence acts as interference, resulting in a poor signal-to-noise ratio (SNR). We then iteratively reduce the interference in the second step by employing an iterative channel-estimation and data-detection approach, where, by utilizing the detected symbols from the previous iteration, we sequentially improve the multiuser/MIMO channel estimation and symbol detection. Simulation examples demonstrate that, without incurring any transmission data rate loss, the proposed approach is superior to the conventional time-multiplexed (TM) training for uncoordinated users, where the multiuser interference in channel estimation cannot be eliminated and is competitive with the TM training for coordinated users, where the TM training design allows for multiuser-interference-free channel estimation.

On Doubly Selective Channel Estimation Using Superimposed Training and Discrete Prolate Spheroidal Sequences

Channel estimation and data detection for frequency-selective time-varying channels are considered using superimposed training. We employ a discrete prolate spheroidal basis expansion model (DPS-BEM) to describe the time-varying channel. A periodic (nonrandom) training sequence is arithmetically added (superimposed) at low power to the information sequence at the transmitter before modulation and transmission; therefore, there is no loss in data transmission rate compared to time-multiplexed (TM) training. We first estimate the channel using DPS-BEM and only the first-order statistics of the observations. In this estimator the unknown information sequence acts as interference resulting in a poor signal-to-noise-and-interference ratio (SNIR) for channel estimation. We then apply a data-dependent superimposed training sequence, to either totally or partially cancel out the effects of the unknown information sequence at the receiver on channel estimation. In total cancellation, at certain frequencies, the information-bearing components are nulled. To compensate for this information loss, we investigate a partially-data-dependent (PDD) superimposed training scheme where a tradeoff is made between interference cancellation and frequency integrity. Design of certain parameters for PDD superimposed training is also investigated. Finally, a deterministic maximum likelihood (DML) approach is used iteratively to enhance channel estimation and data detection. Computer simulation examples show that the proposed approaches are competitive with the conventional TM training without incurring data-rate loss.