The large PAPR of orthogonal frequency division multiplexing (OFDM) transmission is one of the serious problems for mobile communications that require severe power saving. Iterative clipping and filtering is an effective method for the PAPR reduction of OFDM signals. This paper evaluates PAPR reduction effect with a graded band-limiting filter in the iterative clipping and filtering method. The evaluation result by computer simulation shows that the excellent peak reduction effect can be obtained in the fewer iteration numbers by using a roll-off filter instead of the conventional rectangular filter, and the iteration number with the roll-off filter achieving the same PAPR is fewer by twice. The result confirms that the clipping and filtering method by using a graded band-limiting filter can achieve low peak OFDM transmission with less computational complexity.

Technology for sixth-generation (6G) mobile communication system is now being widely studied. A sub-Terahertz band is expected to play a great role in 6G to enable extremely high data-rate transmission. This paper has two goals. (1) Introduction of 6G concept and propagation characteristics of sub-Terahertz-band radio waves. (2) Performance evaluation of intelligent reflecting surfaces (IRSs) based on beamforming in a sub-Terahertz band for smart radio environments (SREs). We briefly review research on SREs with reconfigurable intelligent surfaces (RISs), and describe requirements and key features of 6G with a sub-Terahertz band. After that, we explain propagation characteristics of sub-Terahertz band radio waves. Important feature is that the number of multipath components is small in a sub-Terahertz band in indoor office environments. This leads to an IRS control method based on beamforming because the number of radio waves out of the optimum beam is very small and power that is not used for transmission from the IRS to user equipment (UE) is little in the environments. We use beams generated by a Butler matrix or a DFT matrix. In simulations, we compare the received power at a UE with that of the upper bound value. Simulation results show that the proposed method reveals good performance in the sense that the received power is not so lower than the upper bound value.

Direction of arrival (DOA) estimation is an antenna array signal processing technique used in, for instance, radar and sonar systems, source localization, and channel state information retrieval. As new applications and use cases appear with the development of next generation mobile communications systems, DOA estimation performance must be continually increased in order to support the nonstop growing demand for wireless technologies. In previous works, we verified that a deep neural network (DNN) trained offline is a strong candidate tool with the promise of achieving great on-grid DOA estimation performance, even compared to traditional algorithms. In this paper, we propose new techniques for further DOA estimation accuracy enhancement incorporating signal-to-noise ratio (SNR) prediction and an end-to-end DOA estimation system, which consists of three components: source number estimator, DOA angular spectrum grid estimator, and DOA detector. Here, we expand the performance of the DOA detector and angular spectrum estimator, and present a new solution for source number estimation based on DNN with very simple design. The proposed DNN system applied with said enhancement techniques has shown great estimation performance regarding the success rate metric for the case of two radio wave sources although not fully satisfactory results are obtained for the case of three sources.

This paper proposes a novel scheme for sequential orthogonal frequency division multiplexing channel estimation on the receiver side.The scheme comprises two methods: one improves estimation accuracy and the other reduces computational complexity. Based on a state-space model, the first method appropriately considers frequency correlation in an approach that derives a narrow-band channel gain for multiple pilot subcarriers; such consideration of frequency correlation leads to an averaging effect in the frequency domain. The second method is based on the first one and forces the observation matrix into a sparse bidiagonal matrix in order to decrease the number of mathematical processes. The proposed scheme is verified by numerical analysis.

This study investigates a real-time joint channel and hyperparameter estimation method for orthogonal frequency division multiplexing mobile communications. The channel frequency response of the pilot subcarrier and its fixed hyperparameters (such as channel statistics) are estimated using a Liu and West filter (LWF), which is based on the state-space model and sequential Monte Carlo method. For the first time, to our knowledge, we demonstrate that the conventional LWF biases the hyperparameter due to a poor estimate of the likelihood caused by overfitting in noisy environments. Moreover, this problem cannot be solved by conventional smoothing techniques. For this, we modify the conventional LWF and regularize the likelihood using a Kalman smoother. The effectiveness of the proposed method is confirmed via numerical analysis. When both of the Doppler frequency and delay spread hyperparameters are unknown, the conventional LWF significantly degrades the performance, sometimes below that of least squares estimation. By avoiding the hyperparameter estimation failure, our method outperforms the conventional approach and achieves good performance near the lower bound. The coding gain in our proposed method is at most 10 dB higher than that in the conventional LWF. Thus, the proposed method improves the channel and hyperparameter estimation accuracy. Derived from mathematical principles, our proposal is applicable not only to wireless technology but also to a broad range of related areas such as machine learning and econometrics.

In Long-Term Evolution-Advanced (LTE-A), which is currently in the process of standardization in the 3rd generation partnership project (3GPP), carrier aggregation (CA) was introduced as a main feature for bandwidth extension while maintaining backward compatibility with LTE Release 8 (Rel. 8). In the CA mode of operation, since two or more component carriers (CCs), each of which is compatible with LTE Rel. 8, are aggregated, mobility management is needed for CCs such as inter/intra-frequency handover, CC addition, and CC removal to provide sufficient coverage and better overall signal quality. Therefore, the signaling overhead for Radio Resource Control (RRC) reconfiguration for the mobility management of CCs in LTE-A is expected to be larger than that in LTE Rel. 8. In addition, CA allows aggregation of cells with different types of coverage. Therefore, the signaling overhead may be dependent on the coverage of each CC assumed in a CA deployment scenario. Furthermore, especially in a picocell-overlaid scenario, the amount of signaling overhead may be different according to whether the aggregation of CCs between a macrocell and a picocell, i.e., transmission and reception from multiple sites, is allowed or not. Therefore, this paper investigates the CC control overhead with several CC management policies in some CA deployment scenarios, including a scenario with overlaid picocells. Simulation results show that the control overhead is almost the same irrespective of the different management policies, when almost the same coverage is provided for the CCs. In addition, it is shown that the increase in the control overhead is not significant even in a CA deployment scenario with overlaid picocells. We also show that the amount of signaling overhead in a picocell-overlaid scenario with the CA between a macrocell and a picocell is almost twice as that without the CA between a macrocell and a picocell.

Sparse modeling is one of the most active research areas in engineering and science. The technique provides solutions from far fewer samples exploiting sparsity, that is, the majority of the data are zero. This paper reviews sparse modeling in radio techniques. The first half of this paper introduces direction-of-arrival (DOA) estimation from signals received by multiple antennas. The estimation is carried out using compressed sensing, an effective tool for the sparse modeling, which produces solutions to an underdetermined linear system with a sparse regularization term. The DOA estimation performance is compared among three compressed sensing algorithms. The second half reviews channel state information (CSI) acquisitions in multiple-input multiple-output (MIMO) systems. In time-varying environments, CSI estimated with pilot symbols may be outdated at the actual transmission time. We describe CSI prediction based on sparse DOA estimation, and show excellent precoding performance when using the CSI prediction. The other topic in the second half is sparse Bayesian learning (SBL)-based channel estimation. A base station (BS) has many antennas in a massive MIMO system. A major obstacle for using the massive MIMO system in frequency-division duplex mode is an overhead for downlink CSI acquisition because we need to send many pilot symbols from the BS and to get the feedback from user equipment. An SBL-based channel estimation method can mitigate this issue. In this paper, we describe the outline of the method, and show that the technique can reduce the downlink pilot symbols.