The extreme multiple-input multiple-output (X-MIMO) framework is vital for 6G due to its capacity for extensive spatial multiplexing using numerous antenna ports. As antenna ports increase, the need for channel state information reference signals (CSI-RS) increases, leading to high overhead and energy usage, which limits data resources and performance gains. Although engaging all antennas ports for data transmission and reception reaps the high performance of X-MIMO, channel measurement from the reduced number of ports increases the efficiency. This study introduces a deep learning-based method for CSI reconstruction that reduces overhead in X-MIMO systems, called Transformer-based CSI feedback with subsampled CSI-RS (TS-CSI-RS). The TS-CSI-RS reconstructs the full-port channel information from undersampled measurement via Transformer. Simulations in a realistic channel environment show that TS-CSI-RS significantly reduces CSI-RS overhead (up to 75%) compared to conventional 5G systems, allowing spatial adaptation without activating all antenna ports for CSI measurement.

In modern wireless systems, the process of acquiring channel state information (CSI) has become increasingly important and challenging due to the conflicting needs of expanding antenna arrays and achieving high spectrum efficiency. Recent solutions leverage joint source and channel coding (JSCC) realized by an auto-encoder structure to enable a hybrid optimization of CSI compression and transmission, which shows great potential to improve the effectiveness of CSI feedback. However, the gap between research and realization is still big. This article proposes a comprehensive JSCC method for CSI feedback, including a technique solution for eliminating the impacts of residential fading and radio frequency ($\mathbf{R F}$) imperfections, and a protocol design to support the realization of the method in practical cellular systems. Furthermore, a hardware proof-ofconcept (PoC) platform was developed to validate the JSCC-based CSI feedback, and the results demonstrate the superiority of the method in realistic environments.

Modern base stations exploit distributed architectures defined by the O-RAN standard for efficient operation. In ORAN, the base station functionality is split between a radio unit (RU) and a distributed unit (DU). Further, based on 3GPP split option 7.2, O-RAN specifies two categories of RU: Cat-A (or 7.2A) wherein precoding is performed at the DU, and Cat-B (or 7.2B) wherein precoding operation is offloaded to the RU. Hence, in 7.2B, the precoding information is transferred from DU to RU, but, after application of beamspace compression (BSC). This involves conversion of the precoders to beamspace, which is followed by quantization of its real and imaginary coefficients. However, the BSC approach is suboptimal as it does not fully exploit the underlying correlation in the precoding vectors. To address this limitation, we propose a deep learning (DL) based beamspace precoder compression approach for O-RAN. Using 3GPP compliant simulations, we show that the proposed approach decreases the precoder transfer overhead by up to 85%, while also achieving a spectral efficiency gain of up to 25%. Furthermore, we show that by compressing the precoders in the beamspace, the operational complexity can be drastically reduced, specifically due to reduction in auto-encoder (AE) input dimensionality.

We consider a deep learning (DL) based approach to optimize channel state information (CSI) reporting in massive multiple-input and multiple-output (MIMO) systems. For CSI compression, existing methods use frequency-domain (FD) and spatial-domain (SD) correlation, whereas correlation also exists in the time-domain (TD) under fading conditions. Hence, we propose a DL-based three-dimensional compression (DL-3DC) approach to improve CSI reporting accuracy by using correlation in FD, SD and TD. In addition, to reduce the feedback overhead, we propose two DL-based CSI prediction methods: eigenvector based and MIMO channel based. We then integrate CSI prediction with DL-3DC at the UE and propose a joint CSI compression and prediction (JCCP) scheme to improve the CSI accuracy and reporting overhead trade-off. Although, UE-side JCCP is efficient when the future CSI application time instance is known in advance. To support prediction when this is unknown, we propose BS-side JCCP to predict the CSI at the BS after it is received. Through simulations, we show that DL-3DC improves the CSI reporting accuracy by up to 13.8% compared to the latest CSI reporting mechanism in New Radio (NR). Further, we show that JCCP significantly reduces the reporting overhead by up to 87.5% compared to NR CSI reporting.

This paper discusses a new 5G-Advanced duplex scheme called sub-band full-duplex (SBFD) and the details of implementing it for 5G TDD spectrum. The SBFD requires an advanced self interference cancellation (SIC) which is designed to handle extremely high level of cross link interference. Novel SIC techniques were designed for base stations (BSs) operating on 3.4 GHz with massive MIMO and on 26 GHz with multi-beam capability. By utilizing electromagnetic walls in conjunction with electromagnetic bandgap, an extremely high level of isolation between transmit and receive antennas is achieved. Residual interference is further removed using fractional nonlinear interference estimation in baseband. Two Proofs-of-Concepts (PoCs) have been implemented for feasibility verification. The first PoC was for a massive MIMO BS operating with 32 cross polarized antenna ports on 3.4 GHz and a bandwidth of 100 MHz. The second PoC was for a multi-beam BS with 256 antenna elements operating on 26 GHz and a bandwidth of 400 MHz. It was observed that the self-interference in SBFD can be suppressed in real time to a level of less than 0.7 dB over the RX noise floor for both 3.4 GHz and 26 GHz in an outdoor environment.

In this paper, an advanced duplex scheme, referred to as cross division duplex (XDD), is proposed to use flexible downlink (DL) and uplink (UL) allocation in time division duplex (TDD) carrier. By implementing self-interference cancellation (SIC) functions at the base station, the proposed XDD enables to simultaneously operate UL and DL on different frequency subband in the same channel. To do so, both TDD's ability to efficiently handle asymmetric UL and DL traffic and frequency division duplexing's coverage and latency advantage can be achieved together. To verify the feasibility of XDD, a Proof-of-Concept is developed and self-interference can be removed almost perfectly with proper implementation of SIC without guardband between DL and UL subband. From evaluation results, UL coverage, as well as latency, was improved using a longer UL duty period in XDD. Based on XDD, in 5G-Advanced, a significantly higher resource utilization than the existing 5G will be possible.

Major goal of ultra reliable and low latency communication (URLLC) is to reduce the latency down to a millisecond (ms) level while ensuring reliability of the transmission. Since the current uplink transmission scheme requires a complicated handshaking procedure to initiate the transmission, to meet this stringent latency requirement is a challenge in wireless system design. In particular, in the time division duplexing (TDD) systems, supporting the URLLC is difficult since the mobile device has to wait until the transmit direction is switched to the uplink. In this paper, we propose a new approach to support a low latency access in TDD systems, called channel aware sparse transmission (CAST). Key idea of the proposed scheme is to encode a grant signal in a form of sparse vector. This together with the fact that the sensing mechanism preserves the energy of the sparse vector allows us to use the compressed sensing (CS) technique in CAST decoding. From the performance analysis and numerical evaluations, we demonstrate that the proposed CAST scheme achieves a significant reduction in access latency over the 4G LTE-TDD and 5G NR-TDD systems.

Future mobile communication systems need to support wide variety of new services and applications. In order to support these, ITU-R introduced new types of use cases. One such use case, called ultra reliable and low latency communication (URLLC), concerns the reduction of latency down to a millisecond level while ensuring the reliability of the transmission. In case of uplink transmission, supporting the stringent latency requirement of URLLC is quite challenging due to a time-consuming and complicated handshaking process. In the time division duplexing (TDD) systems, satisfying the latency requirement is far more difficult since the mobile device cannot transmit the data when the subframe is directed to the downlink. In this paper, we propose a new grant signaling scheme, referred to as channel-aware sparse signaling (CASS), to achieve a low latency access in the TDD-based URLLC systems. Key idea of CASS is to map the grant information into a small number of subcarriers and then decode it using a small number of early received samples. From the numerical evaluations, we demonstrate that the proposed CASS scheme achieves significant reduction in access latency over the conventional LTE-TDD systems.

In this paper, a compressed sensing based channel acquisition is proposed for beyond 5G systems where the single carrier waveform is used over the millimeter wave spectrum. The key idea of the proposed technique, 2-dimensional compressed channel recovery (2D-CCR), is to estimate channel impulse response based on randomly selected time-domain pilot samples transmitted in a localized portion of the bandwidth. As a result of 2D-CCR, channel information of full bandwidth can be acquired from compressed sensing without having to take the measurement on the entire bandwidth. From the numerical evaluations, it is observed that the proposed scheme outperforms conventional channel estimation schemes in terms of estimation accuracy.

This paper studies a hybrid automatic repeat and request (HARQ) mechanism for multicast/broadcast services in 5G New Radio (NR) systems. HARQ is widely used in unicast transmission to improve reliability and resource efficiency under the fading channel. However, for multicast/broadcast, it is difficult to apply conventional HARQ technology because the probability of retransmission becomes high as the number of receiver increases. In this paper, we propose a novel and efficient retransmission scheme, called outer code- based HARQ. After the initial transmission of a given data packet for multicast/broadcast, each receiver sends feedback information on how many code blocks (CBs) are broken instead of which CBs are broken. Then, for retransmission of the data, the transmitter generates parity CBs by using outer code such as Reed- Solomon (RS) or Raptor code and transmits the parity CBs after applying inner code such as low density parity check (LDPC) code. Numerical results show that the proposed scheme outperforms the conventional HARQ schemes, reducing more than 50% of retransmissions.

Massive machine type communications (mMTC) is a service category in 5G to support Internet of Things (IoT). Typically, mMTC-based services require small volume of information. Since the current data transmission principle requires long codeblock to maximize the coding gain and hence is not adequate for short packet transmission, multiplexing mechanism to support short packet transmission in mMTC is required. In this paper, we propose a new type of uplink data transmission scheme suitable for the mMTC, called sparse vector coding (SVC). Key idea behind the proposed technique is to transmit the information after the sparse transformation. By mapping the information into the sparse vector and then transmitting it after the random non-orthogonal spreading, we cast the symbol detection problem into the sparse signal recovery problem in compressed sensing. We show from the simulations in the LTE uplink scenario and massive access scenario in 5G that the proposed SVC scheme outperforms conventional approaches and is very effective in short packet transmissions.

Ultra reliable and low latency communication (URLLC) is a newly introduced service category in 5G to support delay-sensitive applications. In order to support this new service category, 3rd Generation Partnership Project (3GPP) sets an aggressive requirement that a packet should be delivered with 10^-5 block error rate within 1 ms transmission period. Since the current wireless standard designed to maximize the coding gain by transmitting capacity achieving long code-block is not relevant for this purpose, entirely new transmission strategy is required. In this paper, we propose a new approach to transmit short packet information, called sparse vector coding (SVC). Key idea behind the proposed method is to transmit the control channel information after the sparse vector transformation. By mapping the transmit information into the position of nonzero elements and then transmitting it after the random spreading, we obtain underdetermined sparse system for which the principle of compressed sensing can be applied. From the numerical evaluations on realistic channel setting and decoder performance analysis, we demonstrate that the proposed SVC technique is very effective in URLLC transmission and outperforms the 4G LTE and LTE-Advanced physical downlink control channel (PDCCH) scheme.

Ultra reliable and low latency communications (URLLC) is a new use case in 5G wireless systems to accommodate emerging mission-critical services and applications. These include driverless vehicles and drone-based deliveries, smart cities and factories, remote medical diagnosis and surgery, and artificial intelligence-based personalized assistants. In order to support URLLC, there should be changes in the air interface (a.k.a. physical-layer). In this article, we provide physical layer challenges and solutions in 5G URLLC downlink. We discuss requirements of URLLC and then present the physical layer issues and new solutions including data structure, multiplexing schemes, and reliability assurance technologies, which have been adopted in the 5G new radio (NR).

Fifth generation (5G) wireless networks are currently being developed to handle wide variety of use cases. In order to support these cases, new types of requirements other than throughput enhancement have been introduced. One such requirement is to reduce the latency down to a millisecond (ms) level in ultra reliable and low latency communications (URLLC). In case of uplink transmission, supporting this stringent latency requirement is quite challenging since the scheduling procedure is a time-consuming and complicated handshaking process. In time division duplexing (TDD) systems, satisfying the latency requirement is far more difficult since the mobile device cannot transmit the data when the subframe is assigned for the downlink. In this paper, we propose a low latency access scheme suitable for TDD-based URLLC. Key idea of the proposed scheme is to transmit the latency sensitive data immediately after the grant signaling. To support the fast uplink access, we introduce a fast grant signaling scheme based on the compressed sensing technique. Numerical results confirm that the proposed uplink access scheme is very effective in TDD-based URLLC systems.

Fifth generation (5G) wireless networks are currently being developed to handle wide variety of use cases. In order to support these cases, new types of requirements other than throughput enhancement have been introduced. One such requirement is to reduce the latency down to a millisecond (ms) level in ultra reliable and low latency communications (URLLC). In case of uplink transmission, supporting this stringent latency requirement is quite challenging and problematic since the scheduling procedure is a time-consuming and complicated handshaking process. In time division duplexing (TDD) systems, satisfying the latency requirement is far more difficult since the mobile device cannot transmit the data when the subframe is assigned for the downlink. In this paper, we propose a new type of uplink transmission scheme for TDD-based URLLC. Key idea of the proposed scheme is to transmit the latency sensitive data immediately after performing the ultra-short one-way signaling from the basestation to the mobile device. To reduce the processing time of grant signal, we present a fast signaling mechanism, referred to as channel-aware sparse signaling (CASS). Numerical results confirm that the proposed uplink transmission scheme is very effective in TDD-based URLLC systems.

Fifth generation wireless standards require much lower latency than what current wireless systems can guarantee. The main challenge in fulfilling these requirements is the development of short packet transmission, in contrast to most of the current standards, which use a long data packet structure. Since the available training resources are limited by the packet size, reliable channel and interference covariance estimation with reduced training overhead are crucial to any system using short data packets. In this paper, we propose an efficient receiver that exploits useful information available in the data transmission period to enhance the reliability of the short packet transmission. In the proposed method, the receive filter (i.e., the sample covariance matrix) is estimated using the received samples from the data transmission without using an interference training period. A channel estimation algorithm to use the most reliable data symbols as virtual pilots is employed to improve quality of the channel estimate. Simulation results verify that the proposed receiver algorithms enhance the reception quality of the short packet transmission.

Ultra reliable and low latency communication (URLLC) is a newly introduced service category in 5G to support delay-sensitive applications. In order to support this new service category, the 3rd Generation Partnership Project (3GPP) sets an aggressive requirement that a packet should be delivered with 10-5packet error rate within 1-ms transmission period. Since the current wireless transmission scheme, which is designed to maximize the coding gain by transmitting the capacity achieving long codeblock, is not relevant for this purpose, and a new transmission scheme to support URLLC is required. In this paper, we propose a new approach to support the short packet transmission, called sparse vector coding (SVC). The key idea behind the proposed SVC technique is to transmit the information after the sparse vector transformation. By mapping the information into the position of nonzero elements and then transmitting it after random spreading, we obtain an underdetermined sparse system for which the principle of compressed sensing can be applied. From the numerical evaluations and performance analysis, we demonstrate that the proposed SVC technique is very effective in URLLC transmission and outperforms the 4G LTE and LTE-Advanced scheme.

Cell-less communication is one of a promising technology of 5G wireless communications for providing a user-centric approach that goes beyond the regional cell of the conventional cellular system. In order to provide user-centric services, location information of the mobile station is required. In this paper, we propose a new localization technique to support the cell-less communication. The proposed scheme consists of two parts. First, estimating the signal parameters, angle of arrival (AOA) and time of arrival (TOA) through the maximum likelihood estimation. After the signal parameter estimation, the localization of the mobile station is performed using the estimated AOA-TOA information. Simulation results demonstrate that the proposed method achieves substantial performance in estimating the location of the mobile station.

In this paper, we propose a novel pilot beamforming and CSI acquisition strategy for IoT systems to achieve reduction in pilot overhead and enhancement in the channel estimation quality. Key idea of the proposed technique, called time-domain sparse beamforming (TDSB), is to sparsify the time-domain channel vector using the antenna-domain beamforming. As a result of TDSB, wideband channel information can be acquired with partial pilot symbols in time and frequency. From the numerical evaluations, we show that the proposed scheme outperforms conventional channel estimation schemes, and achieves substantial reduction in the pilot overhead.

5G wireless standards require a much lower latency than what current wireless systems can guarantee. The main challenge to fulfill this requirement is the capability to support short packet transmission, in contrast to most of the current standards which use a long data packet structure. In this paper, we propose an efficient receiver technique that exploits information obtained during the data transmission period to improve the reception quality of the short packet transmission. Two key ingredients of the proposed method are 1) estimation of the receiver filter using the received samples in the data transmission period, not in the interference training period, and 2) soft decision- directed channel estimation that uses the data symbols for re-estimation of the channels. Numerical results confirm the effectiveness of the proposed receiver algorithms.

Acquiring reliable channel state information (CSI) at the basestation is crucial to fully exploit the advantages of massive multiple-input multiple-output (MIMO) systems. However, giving the basestation knowledge of the downlink channel is an important and challenging problem in frequency division duplexing (FDD) systems when the number of basestation antennas is large. In this paper, we propose an eigenvalue decomposition based feedback compression (EFC) framework that transforms the channel information for pursuing further reduction in feedback overhead. The main idea of EFC is to use the compressed channels for generating short-term CSI by exploiting the dominant eigendirections of long-term channel-statistical information. By using the proposed codebook, each user terminal sends the compressed channel information as short-term feedback and channel-statistical information as long-term feedback. Numerical results demonstrate that the proposed method achieves significant feedback overhead reduction over the conventional beamforming techniques under the same target sum rate requirement.

LTE-Advanced system has been deployed with 2 and 4 transmission antennas (Tx) while the specification supports up to 8Tx. Due to deployment space, antenna dimension and complexity, operators have not been interested in the deployment of 8Tx systems. Recently, three dimensional (3D) beamforming using 2D active antenna array has attracted significant attention in the wireless industry. By incorporating 2D active array into LTE-A systems, the system offers freedom in controlling radiation on elevation and horizontal dimension. In addition, 2D array antenna increases the number of antennas without exceeding form-factor where the conventional antennas are deployed. When the number of antennas increases in the form of 2D arrangement, spatial separation can be realized simultaneously in horizontal and elevation domain and vertical beam-steering can increase SINR of UEs in high floors. In this paper, we study the system operations and implementations for supporting 3D beamforming with 8Tx antennas. In our schemes, by reusing the conventional CSI feedback framework, the system can operate 2D active array without harming the backward compatibility. Evaluation results show that 3D beamforming provides capacity boosting over the conventional 2D beamforming systems while keeping same antenna structure.

Licensed band communication systems have recently begun trying to utilize unlicensed bands. Due to lack of coordination, however, this spectrum utilization results in strong interference to other unlicensed band equipment (UBE). This problem highlights the need for coexistence scenario that will prevent UBE performance degradation. In this paper, we propose a new access control mechanism for downlink cellular networks using the unlicensed band. First, we analyze the impact of the cellular networks on the UBE outage performance. Based on the analysis, we then obtain a decision threshold for the unlicensed band access control. In the proposed control mechanism, the decision threshold is adaptively controlled by considering the density of the UBE. The numerical results demonstrate that the proposed access control mechanism guarantees the UBE's target performance under various network circumstances.