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This paper documents a simple parametric polynomial line-of-sight channel model for 100–450 GHz band. The band comprises two popular beyond fifth generation (B5G) frequency bands, namely, the D band (110–170 GHz) and the low-THz band (around 275–325 GHz). The main focus herein is to derive a simple, compact, and accurate molecular absorption loss model for the 100–450 GHz band. The derived model relies on simple absorption line shape functions that are fitted to the actual response given by complex but exact database approach. The model is also reducible for particular sub-bands within the full range of 100–450 GHz, further simplifying the absorption loss estimate. The proposed model is shown to be very accurate by benchmarking it against the exact response and the similar models given by International Telecommunication Union Radio Communication Sector. The loss is shown to be within ±2 dBs from the exact response for one kilometer link in highly humid environment. Therefore, its accuracy is even much better in the case of usually considered shorter range future B5G wireless systems.

Reconfigurable intelligent surface (RIS) is a programmable structure that can be used to control the propagation of electromagnetic waves by changing the electric and magnetic properties of the surface. By placing these surfaces in an environment, the properties of radio channels can be controlled. This opens up new opportunities to improve the performance of wireless systems. In this paper, the basic operation of antenna array and metasurface based RIS is described. While the current long term (6G) research on RIS often prioritizes very high frequencies from tens to hundreds of GHz, this paper puts emphasis rather on operating frequencies below 10 GHz which promise a much faster to market track for RIS applications. For this purpose, review of the literature on the use of RIS in wireless communication applications operating below 10 GHz frequency band is provided.

In the in-band full-duplex(FD) systems, the self-interference (SI) power can be more than 100 dB higher than the power of the received data signal. In order to enable the FD transmission, several SI cancelation stages are needed in a FD transceiver. By combining the cancelation at the radio frequency (RF) with a specially designed antenna and cancelation circuitry and SI cancelation at the digital baseband, the required level of SI cancelation can be achieved even with a non-linear power amplifier. In this paper, a FD transceiver architecture is modeled with simulation tools that allow to use realistic antenna and analog transceiver models and at the same time enable algorithm studies. The analog SI cancelation at the RF is controlled by the baseband digital processing unit, and the tuning of the RF canceler is performed with an automatic gain control enhanced iterative algorithm. The combined cancelation performance of the antenna and RF canceler varies between 62 and 82 dB depending on the studied cases. The digital baseband SI cancelation is based on the Hammerstein model in order to take the power amplifier non-linearity into account. The coefficients of the Hammerstein model are estimated with a self-orthogonalizing adaptive algorithm. When realistic phase noise and IQ imbalance values are taken into account, the SI after all the cancelation stages can decrease the signal-to-interference-and-noise-ratio (SINR) by few decibels (dB). In order to further enhance the SI cancelation, the Hammerstein based SI canceler is extended to cancel also the effect of the receiver IQ imbalance. With the extended baseband canceler, the cancelation performance is mainly limited by the phase noise

Piecewise linearization techniques require dividing the signal into multiple pieces each linearized individually. Machine learning (ML) is one of the useful tools to perform the automatic division of these pieces. Complexity reduction in the classification of piecewise digital predistortion is possible through carefully constructing features from both the signal statistics and the power amplifier (PA) characteristics. Our paper introduces two low-complex classical ML-based methods that facilitate the classification of baseband input data into distinct segments. These methods effectively linearize PA behavior by employing tailored Volterra models corresponding to each segment. Moreover, we perform an in-depth analysis of the proposed schemes to further optimize their classification and regression complexities. The two proposed low-complexity approaches are validated by laboratory experiments and show up to 4 dB error vector magnitude (EVM) improvement over the conventional approach for a class A PA at 28 GHz. Similarly, the EVM improvement is up to 2 dB over the vector-switched general memory polynomial scheme. With only one indirect learning architecture iteration, the two proposed schemes obey the 5G new radio standard up to 6.5 dB and 7 dB output backoff, respectively.

This paper focuses on energy-efficient coordinated multi-point (CoMP) downlink in multi-antenna multi-cell wireless communications systems. We provide an overview of transmit beamforming designs for various energy efficiency (EE) metrics including maximizing the overall network EE, sum weighted EE, and fairness EE. Generally, an EE optimization problem is a nonconvex program for which finding the globally optimal solutions requires high computational effort. Consequently, several low-complexity suboptimal approaches have been proposed. Here, we sum up the main concepts of the recently proposed algorithms based on the state-of-the-art successive convex approximation (SCA) framework. Moreover, we discuss the application to the newly posted EE problems including new EE metrics and power consumption models. Furthermore, distributed implementation developed based on alternating direction method of multipliers (ADMM) for the provided solutions is also discussed. For the sake of completeness, we provide numerical comparison of the SCA based approaches and the conventional solutions developed based on parametric transformations (PTs). We also demonstrate the differences and roles of different EE objectives and power consumption models.

We study the coordinated reception amongst multiple base stations (BS) in the uplink cellular system, where each mobile station (MS) is served by an associated multiple BS set (MBS). Under the individual signal-to-interference-plus-noise ratio (SINR) constraint per MS, power control and receive beamforming are jointly optimized with adaptive MBS selection to minimize the total transmit power. An iterative optimization algorithm is presented accordingly. The joint optimization problem is nonconvex in general, but it can be optimally solved by the proposed algorithm as long as it is feasible. To find the optimum, the proposed algorithm requires the exhaustive search over all BSs per MS. To reduce the complexity in the large-scale cellular network, we propose simplified schemes where a subset of BSs is preselected based on the large-scale fading factors. By limiting the search in the subset, the complexity is reduced significantly. Although the obtained power vector with the simplified algorithm is not optimal, a performance close to the macronetwork full coordination can be achieved by carefully choosing the sizes of the pre-selected BS subset and MBS. The significant advantage is proven by simulations.

6G networks have the burden to provide not only higher performance compared to 5G, but also to enable new service domains as well as to open the door over a new paradigm of mobile communication. This paper presents an overview on the role and key challenges of signal processing (SP) in future 6G systems and networks from the conditioning of the signal at transmission to MIMO precoding and detection, from channel coding to channel estimation, from multicarrier and non-orthogonal multiple access (NOMA) to optical wireless communications and physical layer security (PLS). We describe also the core future research challenges on technologies including machine learning based 6G design, integrated communications and sensing (ISAC), and the internet of bio-nano-things.

The most important third generation (3G) cellular communications standard is based on wideband CDMA (WCDMA). Receivers based on TDMA style channel equalization at the chip level have been proposed for a WCDMA downlink employing long spreading sequences to ensure adequate performance even with a high number of active users. These receivers equalize the channel prior to despreading, thus restoring the orthogonality of users and resulting in multiple-access interference (MAI) suppression. In this paper, an overview of chip-level channel equalizers is delivered with special attention to adaptation methods suitable for the WCDMA downlink. Numerical examples on the equalizers′ performance are given in Rayleigh fading frequency-selective channels.

The backward and forward consecutive mean excision (CME/FCME) algorithms are diagnostic methods for outlier (signal) detection. Since they are computationally simple, they have applications for both narrowband signal detection in cognitive radios and interference suppression. In this paper, a theoretical performance analysis framework of the CME algorithms is presented. The analysis provides simple tests of the detectability of the signals based on their shape in the considered domain (e.g., spectrum). As a consequence, results can be used to quickly check whether the CME/FCME algorithms are usable for a given problem or not without the need to resort to time consuming computer simulations. The computer simulations for random and orthogonal frequency division multiplexing (OFDM) signals show that the presented analysis is able to predict the detectability of signals well.

This paper proposes practical coordinated linear transmit-receive processing schemes for the uplink (UL) of multiuser multiple-input multiple-output (MIMO) systems in the time division duplex (TDD) mode. The base station (BS) computes the transmission parameters in a centralized manner and employs downlink (DL) pilot signals to convey the information of the beam selection and beamformers to be used by the terminals. When coexisting with the DL transmit-receive zero forcing, the precoded DL demodulation pilots can be reused for UL beam allocation so that no additional pilot overhead is required. Furthermore, the locally available channel state information (CSI) of the effective MIMO channel is sufficient for the terminals to perform transmit power and rate allocation independently. In order to reduce the UL pilot overhead as well, we propose reusing the precoded UL demodulation pilots in turn for partial CSI sounding. The achievable sum rate of the system is evaluated in time-varying fading channels and with channel estimation. According to the results, the proposed UL transmission strategy provides increased rates compared to single-user MIMO transmission combined with user selection as well as to UL antenna selection transmission, without being sensitive to CSI uncertainty.