Shicheng Hu

Selected mapping (SLM) is an efficient peak-to-average-power-ratio (PAPR) reduction algorithm for orthogonal frequency division multiplexing (OFDM) systems. Conventional SLM requires extra resources for side information transmission. If the side information is transmitted implicitly, significantly high computation overhead is imposed to the receiver at the user equipment (UE) side. In the orthogonal frequency division multiple access (OFDMA) system, the SLM can not be directly applied since the UE does not have access to the signal of other UEs. In this paper, we propose a novel SLM algorithm for the OFDMA system that requires no explicit side information transmission. In the OFDMA system, we assume that the data transmitted to different UEs are assigned in groups of resource units (RUs). Pilots are available in each RU for channel estimation. We design phase rotation sequences in segments of RUs. The side information is embedded in the pilot transmission. With carefully chosen phase rotations, the proposed algorithm can achieve near the theoretical PAPR performance. More importantly, with the proposed SLM algorithm, each UE can detect its own data without the knowledge of other UEs. Besides, detection of the implicit side information with the proposed algorithm is more robust than that with existing SLM algorithms. Numerical results validate the performance of the proposed algorithm.

Phase noise is one of the major impairments that degrade the performance of orthogonal frequency division multiplexing (OFDM) systems. When an OFDM system works at high-order modulation or high carrier frequency, phase noise's impairments cannot be ignored. Existing phase noise estimation algorithms either require a large number of pilots or have high computation complexity. In this paper, we propose a low complexity blind data detector based on the maximum a posteriori (MAP) criterion. With prior knowledge of slow time-varying property of phase noise, a simplified iterative phase noise estimation and data detection algorithm is developed. Compared to existing algorithms, the proposed algorithm does not require pilot data transmission, and achieves satisfactory performance with low computation complexity. Simulation results validate the effectiveness of proposed algorithm.

Hybrid analog/digital beamforming architecture compromises cost and power consumption of the massive multiple-input multiple-output (massive MIMO) system. On the other hand, the mismatches among different radio frequency (RF) chains, particularly the mismatches of analog phase shifters, can destroy the beamforming accuracy and significantly degrade the system performance. In this paper, we study the system performance of hybrid beamforming systems in the presence of analog mismatches. An upper bound of the system performance is derived. Simulation results show that in typical hybrid beamforming settings, the performance loss can be as large as 64% with analog mismatches. The results also suggest that the advantage of increasing the number of transmit antennas or increasing the signal-to-noise ratio (SNR) is diminishing even with limited analog mismatches. To deal with the mismatch, we propose a new two-phase beamforming algorithm that compensates for the analog mismatches with digital precoder simulation shows, the proposed beamforming algorithm provides satisfactory performance.

In orthogonal frequency division multiplexing (OFDM) systems, peak-to-average power ratio (PAPR) is one of the major factors that affect the power efficiency of transmitter. There exist many PAPR reduction methods for the conventional fully digital multiple-input multiple-output (MIMO) systems but there is little discussion on the PAPR reduction for the hybrid beamforming systems. When some output signals of the digital to analogue converter (DAC) are combined by the analogue beamforming network before power amplifier (PA), the one to one mapping between the digital signal and the signal passing through PAs is destroyed. In such hybrid beamforming architectures, the existing methods can only reduce the PAPR of DAC input signal but not PAPR of PA input signal. In this paper, we investigate whether and how we can extend existing PAPR reduction methods to the hybrid beamforming systems. We propose a novel PAPR reduction method, which extends the iteratively clipping and filtering (ICF) to the hybrid beamforming systems. To the best of our knowledge, our proposed algorithm is the first PAPR reduction algorithm dedicated for the hybrid beamforming systems. From the simulation, our proposed algorithm can achieve a smaller PAPR than the improved selective mapping (SLM) algorithm at the cost of minor signal distortion.

Location-based service (LBS) provides users with personalized experience. With the latest Bluetooth low energy (BLE) technology, switched antenna array and constant tone extension (CTE) are introduced enabling angle of arrival (AoA) estimation for improved positioning accuracy. However, phase noise may significantly degrade the AoA estimation performance. Such impairment and its mitigation method are not well discussed in existing literature. In this paper, we theoretically analyze the performance degradation of AoA estimation caused by phase noise in BLE system. Based on the specific antenna switching pattern, an improved multiple signal classification (MUSIC) algorithm is proposed. We further propose an expectation maximization MUSIC (EM-MUSIC) algorithm to improve the AoA estimation accuracy by estimating the phase noise with extended Kalman filter. Simulation results show that at typical SNR level, the proposed algorithm can reduce the estimation error by more than 55% compared with existing algorithms. The proposed EM-MUSIC algorithm can greatly improve the positioning accuracy and is ideal for LBS.

Phase noise can cause significant performance degradation in multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, especially for high-order data transmission. To mitigate the effect of phase noise on data transmission, pilot-based and blind-based algorithms are widely adopted in the existing works, which suffer from spectral efficiency degradation or formidable computational cost due to the large-scale and time-dependent properties of phase noise. In this paper, we propose an efficient multi-level maximum a posteriori (MMAP)-based blind data detection algorithm to address the phase noise compensation in MIMO-ODFM systems. The proposed algorithm, exploiting the spectral low-dimensional property of phase noise and the approximate message passing (AMP) rule, achieves a near optimal detection performance. The exploitation of low-pass characteristics of phase noise spectrum significantly reduces the computational complexity, and the adoption of AMP principle ensures a linear complexity of the algorithm with respect to the number of antennas and subcarriers. Thus, a good complexity-accuracy trade-off is obtained. Besides, the proposed algorithm is applicable to the scenarios of commonly shared oscillators and independent oscillators. The numerical experiments show that the proposed pilot-free data detection algorithm can achieve superior data transmission performance for channels with strong phase noise at a low complexity.

Selected mapping (SLM) is an efficient peak-to-average-power-ratio (PAPR) reduction algorithm for orthogonal frequency division multiplexing (OFDM) systems. Conventional SLM requires extra resources for side information transmission. If the side information is transmitted implicitly, significantly high computation overhead is imposed to the receiver at the user equipment (UE) side. In the orthogonal frequency division multiple access (OFDMA) system, the SLM can not be directly applied since the UE does not have access to the signal of other UEs. In this paper, we propose a novel SLM algorithm for the OFDMA system that requires no side information transmission. With the proposed SLM algorithm, each UE can receive its own data without the knowledge of other UEs. The SLM demapping at the UE side is much simplified. Besides, detection of the implicit side information with the proposed algorithm is more robust than existing SLM algorithms. Numerical results validate the theoretical performance of the proposed algorithm.

UConsidering the Event Detection Problem (EDP) in the large-scale Wireless Sensor Network (WSN), the conventional methods generally rely on some prior information, which obstacles the actual application. In this paper, a deep learning-based algorithm, named as Alternating Direction Multiplier Method Network (ADMM-Net), is proposed for the EDP. Firstly, the low rank and sparse matrix decomposition is adopted to capture the spatial-temporal correlation of events. After that, the EDP is formulated as a constrained optimization problem and solved by the Alternating Direction Multiplier Method (ADMM). However, the optimization algorithm suffers from low convergence. Besides, the algorithm’s performance heavily relies on the careful selection of prior parameters. By adopting the conception of “unfolding” in deep learning field, a deep learning network which is named ADMM-Net, is proposed for the EDP in this paper. The ADMM-Net is obtained by unfolding the ADMM algorithm. The ADMM-Net is with fixed layers, whose parameters can be trained via supervised learning. No prior information is required. Compared to the conventional methods, the proposed ADMM-Net does not require any prior information while enjoying fast convergence. Simulation results on both synthesis and realistic datasets verify the effectiveness of the proposed ADMM-Net.

In the massive multi-input multi-output system, the peak-to-average power ratio (PAPR) is one of the factors which greatly affect the performance of the transmitter. The existing PAPR reduction methods are based on the fully-digital architecture, which can not effectively reduce the PAPR of signal at the transmitting antennas in the hybrid beamforming architecture. To address this problem, an improved selective mapping (SLM) method is proposed, which adopts independent phase rotations to the initial input signal and then calculates the PAPR at the transmitting antennas and finally transmits the sequence with minimum PAPR. Besides, the upper bound and lower bound of PAPR at the transmitting antennas are analyzed. Theoretical analysis and simulation results suggest that the proposed improved SLM can effectively reduce the PAPR at the transmitting antennas in the hybrid beamforming architectures.