Cell-free multiple-input-multiple-output (MIMO) is poised to enable scalable next-generation cellular networks. To this end, it is crucial to optimize the cell-free MIMO link configuration, including user associations, data stream allocation, and beamforming (BF). However, the scalability of link configuration optimization is significantly challenged as signaling and computational costs increase with the number of base stations (BSs) and user equipments (UEs). To address this scalability issue, this paper proposes a distributed multi-agent deep reinforcement learning (MADRL)-based cell-free MIMO link configuration framework that leverages interference approximation to minimize signaling overhead required for channel state information (CSI) exchange. Our proposed framework reduces the solution search space suitable for distributed MADRL, by decomposing the original sum rate maximization problem into BS-specific tasks. Simulation results show that our proposed method achieves scalability, as the sum rate increases with the number of BSs and UEs.

Integrated localization and communication (ILC) will be a key enabler for providing accurate location information and high data rate in next generation networks. This paper proposes a transmission frame structure and a soft information (SI)-based localization algorithm for position-assisted communications. The proposed ILC achieves improved localization accuracy and enhanced communication rate simultaneously by accounting for the statistical characteristics of the wireless environment. Results in 3rd Generation Partnership Project (3GPP) industrial scenarios show that the SI-based localization algorithm can achieve decimeter-level accuracy. Moreover, the position-assisted communication enhances the achievable rate, especially in scenarios with high mobility.

In the rapidly evolving domain of forthcoming 6th generation (6G) networks, achieving precise dynamic positioning down to the centimeter becomes critical, particularly in complex urban scenarios as those envisioned for cooperative intelligent transport systems (C-ITSs). To face the challenges introduced by severe path loss and blockages in new 6G frequency bands, machine learning (ML) provides innovative strategies to extract locational intelligence from wide-band space-time radio signals. This paper proposes the integration of Bayesian neural networks (BNNs) into cellular multi-base station (BS) tracking systems, where uncertainties of BNNs account for finite training sets and measurement errors. Our approach utilizes a deep learning (DL)-based autoencoder (AE) structure that exploits the full channel impulse response (CIR) to infer location-centric attributes in both line-of-sight (LoS) and non-LoS (NLoS) conditions. Validations in a 3rd Generation Partnership Project (3GPP) compliant urban micro (UMi) setting, simulated with ray-tracing and traffic simulations, demonstrate the superior performances of BNN-based tracking with respect to both traditional geometric-based tracking methods and state-of-the-art DL models.

Integrated terrestrial-satellite networks (ITSNs) play an essential role in providing global and ubiquitous connectivity for next generation networks. Spectral efficiency of ITSNs depends on their integrated architecture and the operational strategies, including interference management and resource allocation. This paper proposes an efficient integrated access and backhaul (IAB) architecture for terrestrial-satellite networks considering both uplink (UL) and downlink (DL) communications. We aim to integrate terrestrial access and satellite backhaul networks by developing a novel optimization framework for their joint operation. In particular, in-band access-backhaul transmission is considered for high spectral efficiency, where a reverse time division duplexing is used to prevent both self-interference and interference between access links and backhaul links. In addition, cooperation among gNodeB is taken into account to overcome harsh propagation conditions such as blockage effects and severe pathloss. A framework for joint optimization of cooperative beamforming and resource allocation is developed to maximize the UL-DL rate region of the in-band IAB. The proposed architecture is verified using the 3rd Generation Partnership Project (3GPP) channel models. Numerical results show that the proposed architecture significantly outperforms the classical out-of-band backhauling while approaching an outer bound of the UL-DL rate region.

In the evolving landscape of 5G new radio and related 6G evolution, achieving centimeter-level dynamic positioning is pivotal, especially in cooperative intelligent transportation system frameworks. With the challenges posed by higher path loss and blockages in the new frequency bands (i.e., millimeter waves), machine learning (ML) offers new approaches to draw location information from space-time wide-bandwidth radio signals and enable enhanced location-based services. This paper presents an approach to real-time 6G location tracking in urban settings with frequent signal blockages. We introduce a novel teacher-student Bayesian neural network (BNN) method, called Bayesian bright knowledge (BBK), that predicts both the location estimate and the associated uncertainty in real-time. Moreover, we propose a seamless integration of BNNs into a cellular multi-base station tracking system, where more complex channel measurements are taken into account. Our method employs a deep learning (DL)-based autoencoder structure that leverages the complete channel impulse response to deduce location-specific attributes in both line-of-sight and non-line-of-sight environments. Testing in 3GPP specification-compliant urban micro (UMi) scenario with ray-tracing and traffic simulations confirms the BBK’s superiority in estimating uncertainties and handling out-of-distribution testing positions. In dynamic conditions, our BNN-based tracking system surpasses geometric-based tracking techniques and state-of-the-art DL models, localizing a moving target with a median error of 46 cm.

Integrated satellite-terrestrial networks (ISTNs) are essential for providing ubiquitous mobile ultra-broadband service in beyond 5G networks. The spectral efficiency and reliability of ISTN depend on the integrated architecture and its operational strategies including interference management and resource allocation. Our view is to integrate terrestrial access and satellite backhaul networks and to develop an optimization technique for their joint operation. This paper proposes an efficient integrated access and backhaul (IAB) architecture for satellite-terrestrial networks (STNs) based on reverse time division duplexing (TDD) considering both uplink (UL) and downlink (DL). In particular, in-band backhauling and gNodeB (gNB) cooperation are considered for high spectral efficiency and reliability. A framework for joint optimization of cooperative beamforming and resource allocation is developed to maximize the UL-DL rate region of the in-band IAB. The proposed scheme is verified using the 3rd Generation Partnership Project (3GPP) standard channel models. Results show that the proposed scheme significantly outperforms the conventional wireless backhauling, while approaching to an outer bound of the UL-DL rate region.

Integrated localization and communication (ILC) at millimeter wave (mmWave MMWAVEinit) frequencies will be a key enabler for providing accurate location information and high data rate communication in beyond fifth generation (B5G) networks. This paper proposes a transmission frame structure and a soft information (SI)-based localization algorithm for position-assisted communications. In accordance with B5G specifications, we consider multiple-input multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) networks. Theoretical limits are also derived to serve both as performance benchmark and as input for algorithm design. The proposed method enables cooperative ILC with improved localization accuracy and enhanced communication rate simultaneously. In particular, position-assisted communication at mmWave MMWAVEinit frequencies is explored accounting for the statistical characteristics of the wireless environment. Localization accuracy and communication rate are quantified in 3rd Generation Partnership Project (3GPP) network scenarios. Results show that the SI-based localization algorithm achieves decimeter-level accuracy, approaching the theoretical limit. Moreover, the position-assisted communication can provide higher communication rate with reduced overhead compared to existing techniques, especially in scenarios with high mobility.

Multi-numerology systems are considered to be promising communication frameworks in 5G new radio (NR) due to their flexibility. However, inter-numerology interference (inter-NI) degrades the quality of communication systems. Hence, techniques for mitigating inter-NI are needed to support multiple numerologies. In particular, the effect of the inter-NI should be considered in channel estimation process. We propose a pilot design which requires relatively short training overhead and mitigates the inter-NI. We stress that inter-NI does not exist in most cases when the proposed pilot signal is used. With taking account of the special case where inter-NI exists, we develop the channel estimation algorithm based on inter-NI cancellation. In addition, we analyze the effect of inter-NI cancellation algorithm which not only eliminates interference but also reduces the variance of noise. The simulation results verify that the performance of channel estimation with the proposed pilot signal is better than the quality of estimation with the case where user equipments transmit their pilot signals in a time division manner.

Wireless communication in millimeter wave frequency provides a high data rate using a wide bandwidth. However, the large array antenna is necessary to overcome the high path loss, which may result in the high feedback overhead and hardware complexity. Moreover, the characteristics of frequency selective channel should be considered due to the wide bandwidth. To deal with these problems, we propose the orthogonal frequency division modulation (OFDM)-based hybrid beamforming (HBF) scheme with limited feedback. While the conventional OFDM-based HBF schemes are based on minimizing the sum of the Frobenius norm of the differences between digital BF (DBF) and HBF as simple extension of the narrowband systems, our approach is based on the achievable sum rate maximization of all the subcarriers. In particualr, we derive a design criterion to jointly optimize the HBF matrices of the transmitter and receiver. Specifically, we propose a HBF method which designs subchannel gain product based orthogonal matching pursuit. Through this, an analog BF algorithm considering both the transitter and receiver is proposed, while the baseband BF is based on the conventional digital precoding scheme. Simulation results show that the proposed HBF scheme outperforms the conventional ones. Notably, the results show that the proposed HBF scheme is robust to channel estimation error.

A major bottleneck in millimeter wave hybrid beamforming (BF) system is the limited number of radio frequency (RF) chains at the base station (BS). For example, when the number of users is large, the joint user scheduling and BF design is needed under the hardware constraint. Fortunately, if multiple RF chains are available at both BS and users, the system performance can significantly be improved by multi-mode transmission, which assigns different numbers of streams to the users. However, the optimal multi-mode scheme may require high feedback-feedforward overhead and complexity. Furthermore, since the mmWave BF system operates in a large bandwidth, the wideband channel should be considered. To tackle this problem, we propose the single-carrier (SC) hybrid BF scheme based on the limited feedback information. We maximize the weighted sum rate by adopting the multi-mode transmission based on the RF beam alignment. The proposed SC hybrid BF scheme outperforms the existing orthogonal frequency division multiplexing (OFDM)-based hybrid BF schemes. From the analysis, we find that full spatial multiplexing gain is guaranteed if we scale the number of RF chains at the BS as smaller than the square root of the number of antennas in a large number of users regime.

In this paper, we compare I/Q imbalance compensation schemes for massive multi-input multi-output (MIMO) system in presence of I/Q imbalance at both transmitter and receiver. Transceiver I/Q imbalance can be compensated jointly by the base station or separately by both base station and user terminals. We show that the achievable sum rate of self-compensation scheme which compensates its I/Q imbalance separately is severely deteriorated in presence of channel estimation error and the I/Q imbalance parameter estimation error. In contrast, the achievable sum rate of the joint compensation scheme with RZF precoding in the base station shows robust performance against channel estimation error and the I/Q imbalance parameter estimation error.

In this paper, we propose a CRC-aided soft-output (CASO) detection for uplink multi-user multiple-input multiple-output systems with one-bit ADCs to improve the error rate while maintaining low latency. After CRC checking, the CASO detection constructs a refined subcode of a spatial-domain code by exploiting successfully decoded messages from the previous iteration. Based on the refined subcode, more reliable soft messages can be extracted to utilize in the rest decoding process. Since multiple messages can be successfully canceled with the aid of CRC checking, the CASO detection can prevent error propagation and suppress increment of latency. Simulation results demonstrate that the CASO detection can provide improved error rate compared to the conventional detections though increment of latency is insignificant for high SNR.

As millimeter wave (mmWave) beamforming (BF) system can increase the areal capacity in ultra-dense network (UDN), a joint design of user association and hybrid BF is challenging due to the high feedback overhead and complicated interference management where many communication nodes use large antenna arrays. In addition, the wireless backhaul should be incorporated because it allows a flexible design, while the wired backhaul is difficult to be installed as the number of small base stations increases. To integrate the wireless backhaul and access, the time resource partitioning between the backhaul and access transmissions is needed, which affects the network performance combined with the user association and BF design. In this regard, this paper deals with the joint problem of the time resource allocation, user association, and BF design to maximize the weighted sum rate. Then a two-stage design approach is proposed based on the limited channel feedback to reduce the complexity and overhead. Simulation results show that the proposed limited feedback hybrid BF scheme outperforms the baseline schemes. In addition, we find that the maximum spatial multiplexing gain can be achieved if the number of antennas of each node increases with an order of larger than the square of the number of RF chains. At the same time, the multi-node diversity gain can be obtained in mmWave UDN, which depends on the path loss exponent of line-of-sight.

The digital-analog hybrid beamforming technique with a number of antennas is necessary for the millimeter wave (mmWave) communications to overcome both high complexity of large array system and high path loss of mmWave signals. Since the hybrid beamforming system has multiple radio-frequency (RF) chains, the simultaneous transmission is possible which is known as the space division multiple access (SDMA). However, we cannot apply the digital precoding schemes to the hybrid beamforming system due to the hardware constraints of RF beamforming matrix. In this paper, we propose a joint scheduling and hybrid beamforming system with partial side information. Then we provide the achievable sum rate upper bound and the scaling law of the asymptotic sum rate. It is shown that our system can take advantage of the same multi-user diversity gain with the digital system. Interestingly, the proposed hybrid system outperforms the digital system in the low SNR regime which is suitable for mmWave channel.

Millimeter wave (mmWave) experiences a high path-loss in free space. To overcome this weakness, antenna array is used to obtain a high beamforming gain. However, due to high complexity of digital beamforming systems, the hybrid structure consisting of baseband and analog precoder has been proposed. We consider the well known block diagonalization (BD) precoding for transmission technique. Because of the practical constraints such as channel estimation error, the limited number of radio frequency (RF) chains and the quantized analog phase shifters, the BD precoder cannot provide accurate interference-free effective channel. In this paper, we propose the extended BD precoding method which extends the null space of interference channels to reduce the effect of practical constraints. Simulation results show that the proposed method provides much improved sum rate and error performances than original BD precoder.

Millimeter wave (mmWave) signals experience a significant path-loss in free space. To overcome this weakness, a large number of antennas are needed to obtain a high beamforming gain. Although a large number of antennas can be implemented in small area due to the short wavelength, the digital beamforming techniques cannot be implemented easily due to the high complexity of hardwares. To solve this problem, the hybrid beamforming systems which have smaller number of radio frequency (RF) chains are proposed in the literature. Although the hybrid beamforming systems may achieve the spectral efficiencies of the digital beamforming systems closely, the spectral efficiency cannot be monotonically increase along with the number of data streams due to the limited scattering in mmWave channel. In this paper, we provide a guide for the design of mmWave hybrid beamforming systems. We find the optimal number of streams, and present the spectral efficiency achievable region in which the system guarantees the reliable communications with the lowest cost.