Coexistence design that enables the emerging candidate waveforms to interoperate with legacy orthogonal frequency division multiplexing (OFDM) is essential for sixth-generation standardization. This paper focuses on orthogonal time frequency space (OTFS)—a representative waveform for high-mobility scenarios—and proposes a novel coexistence paradigm between OTFS and OFDM, termed cross-domain division multiplexing (XDM), utilizing the different signal representations across multiple domains. XDM is formulated through sparse mapping in the delay-Doppler domain and periodicity-based partial superposition in the time-frequency domain. We prove that XDM holds several favorable invariant properties, which enable tractable interference characterization and suppression even under dynamic channels. We also show that XDM supports flexible numerologies and provide some standardization-oriented practice examples. For practical implementation, XDM transceivers are designed for near interference-free coexistence leveraging the invariant properties. We first develop a cross-domain waveform-level interference cancellation algorithm with ultra-low complexity for single OTFS user coexisting with OFDM. More generally, a domain-transform embedded factor-graph is constructed for multiple OTFS users, over which a cross-domain expectation propagation algorithm is proposed to achieve almost lossless recovery of the coexisting signals. Through a novel variance transfer technique, we prove that the detection errors of coexisted OTFS and OFDM respectively converge to their counterparts under interference-free transmission. Simulations show that XDM exhibits BER loss about 0.5 dB compared to interference-free transmission under various channel conditions and system configurations.

The non-linearity of power amplifiers (PAs) is a major challenge in satellite communications and high-frequency bands. Traditionally, discrete fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM) uses localized subcarrier mapping, which induces a high peak-to-average power ratio (PAPR) and obstructs the efficient operation of the PA. In this paper, we propose an interleaved mapping based DFT-s-OFDM with data length extension to reduce PAPR. To overcome the unrealistic constraints in interleaved mapping, where the occupied subcarriers must be a divisor of the total, we introduce a trans-formation method based on time-domain waveform invariance. Furthermore, we provide its equivalent frequency-domain matrix representation. The constructed waveform reduces PAPR and simultaneously mitigates inter-carrier interference (ICI) caused by non-linearity, effectively achieving resistance to PA non-linearity. Simulations show that our method achieves a gain of up to 1.6 dB in PAPR and 2.4 dB in block error rate (BLER) compared to localized mapping.

Design of advanced modulations needs to be revisited to cater for the diverse requirements of future 6th Generation (6G). While most existing modulation schemes operate coherently and need pronounced channel-tracking pilots to reach performance limits, noncoherent transmission offers a viable option by avoiding channel estimation. Noting that the noncoherent capacity-achieving input symbols are good sphere packing over Grassmannian field and each symbol is a subspace in Euclidean space, additional information can be embedded by further packing over the subspaces. In this paper, we propose a hybrid high-dimensional modulation scheme that bears both noncoherent and coherent components via multi-branch mapping. The packing problem is formulated into optimizing rotational angles of noncoherent codewords whose closed-form expressions are derived in this paper. A two-stage binary labeling as well as integration method with waveform are proposed to extend performance limits. Correspondingly, a channel-adjustable receiver is proposed to recover the noncoherent and coherent components sequentially. Simulations demonstrate that the proposed method yields Grassmannian most 2.4dB gain compared with pilot-based scheme under suitable coding pair of two branches.

Near-field multiple-input-multiple-output (NFMIMO) technology has emerged as a promising candidate for spectral efficiency enhancement in 6G. However, conventional centralized antenna arrays confines NF effects and performance gains to very short ranges. To overcome this challenge, this work focuses on a practical antenna deployment strategy termed widely spaced multi-panel. The spatial degrees-of-freedom bounds of the effective channel are presented. A novel codebook is proposed for channel state information acquisition, featuring high performance, low overhead, and implicit near-field exploitation. Simulation results at mid-band frequencies demonstrate considerable spectral efficiency gains. Remarkably, the beneficial range is up to 80 meters under line-of-sight conditions and nearly 99% of users in designated hotspot areas attain full-rank transmission. Additionally, the proposed codebook achieves performance comparable to standardized high-resolution codebooks, while requiring only one-tenth the feedback overhead, highlighting its efficiency and practicality.

Integrated sensing and communication (ISAC) is an important new research topic for 5th-generation communicationAdvanced (5G-A) and 6th-generation (6G) mobile communication systems, where waveform design should be studied. Orthogonal frequency division multiplexing (OFDM) is anticipated to be more favorable in the future 5G-A and 6G ISAC systems due to its good 5G New Radio (NR) backward compatibility. Different resource multiplexing methods, including time-division multiplexing (TDM), frequency-division multiplexing (FDM), and codedivision multiplexing (CDM), for sensing and communication can be assumed in the OFDM-based ISAC system. Due to the low peak-to-average power ratio (PAPR) and good ambiguity function properties, chirp sequence can be used for sensing in the OFDM-based ISAC system. This paper proposes a unified chirp sequence generation method for OFDM-based ISAC system. By parameter configuration, different resource multiplexing methods of sensing and communication, including TDM, FDM, and CDM, can be realized based on the proposed method. Evaluation results demonstrate that the proposed chirp sensing sequence achieves better PAPR performance for both TDM and FDM methods and lower sidelobe level of the ambiguity function for TDM compared to OFDM with Zadoff-Chu (ZC) sequence, which indicates that the proposed chirp sequence with TDM and FDM can be considered as a suitable sensing sequence for scenarios with coverage limitations.

Digital twin technology, envisioned as a cornerstone of sixth-generation (6G) networks, demands robust capabilities for dynamic and real-time environmental reconstruction. Wireless sensing plays a pivotal role in enabling digital twin systems, yet significant challenges persist. High-resolution wireless imaging typically requires large array apertures, which are often accompanied by prohibitive deployment costs and implementation complexity. This paper addresses these limitations by proposing a user equipment (UE)-assisted synthetic aperture radar (SAR) imaging scheme. The proposed approach exploits UE mobility to emulate a synthetic aperture, enabling high-resolution imaging while reusing existing wireless communication infrastructure. Advanced image reconstruction algorithms, including back projection (BP) and Fourier transform (FT)-based methods, are employed to process echo signals and accurately reconstruct target scenes. To validate the feasibility of the scheme under realistic channel conditions, typical configurations aligned with current network deployments are adopted, and ray-tracing-based channel modeling is applied to representative local areas within urban environments such as Munich and Paris. Preliminary simulation results show that, with a carrier frequency of 3.5 GHz and a bandwidth of 100 MHz, the proposed scheme achieves meter-level resolution in reconstructing building envelopes and capturing distinct structural details. These findings demonstrate the feasibility, scalability, and cost-effectiveness of the UE-assisted SAR imaging framework, paving the way for practical wireless sensing and digital twin applications in future 6 G urban environments.

Integrated sensing and communication (ISAC) is one of the most important research topics towards 5th generation (5G)-Advanced and 6th generation (6G) mobile communications. Diverse use cases could be supported from the analysis of measurement results obtained by sensing. Reference signal (RS) is necessary for the measurements, which should be designed to provide good sensing performance at low overhead. Positioning RS (PRS) defined for user equipment (UE) positioning in 5G new radio (NR) is a good starting point for sensing, whose performance on estimation accuracy and maximum unambiguous performance are validated to be good. However, the overhead of NR PRS is too high, limiting its usage for sensing. In this paper, inspired by the multiple pulse repetition frequency (PRF) method in radar systems, we propose a new sensing RS pattern composed by multiple comb RSs with co-prime comb sizes. On the one hand, the comb RS pattern has good backward compatibility with communication systems. On the other hand, the co-prime comb sizes can effectively eliminate the distance ambiguity caused by the comb RS pattern. The evaluation results show that proposed sensing RS pattern could achieve same maximum unambiguous distance with NR PRS but reduce the overhead from 7.14% to 2.21%.

Online adaptation to the dynamic channel conditions requires intelligent receivers with online learning ability as well as efficient training samples. In this letter, we propose a new type of reference signal termedlearning reference signal (LRS), which serves as the online training samples for the fast adaptation of the deep neural network (DNN)-aided intelligent receiver. Specifically, we propose a model-agnostic meta-learning (MAML)-based LRS design framework, where the LRS sequence is regarded as the meta parameter and is meta-learned during the offline training. The maximum loss reduction criteria for LRS design is proposed such that the online meta-update based on LRS can maximize the reduction of the symbol error rate (SER). Furthermore, a Matthew effect in gradient-based training of LRS, which causes imbalanced update on different LRS symbols, is identified and then tackled by a novel symbol bundling and multi-stage updating method to ensure convergence. From experiments, we observe that the learned LRS contains both constellation points and non-constellation points, and achieves more than 4dB SER gain compared to using arbitrary constellation points as training samples.

Reconfigurable intelligent surfaces (RISs) for coverage enhancement in millimeter-wave bands are investigated through system-level simulations. A large-scale RIS model is developed to approximate the forwarding beam pattern in both far-field and near-field conditions, and a pragmatic low-complexity RIS assignment procedure is considered. The impacts of RIS in regular cellular scenarios and those with blind-spot areas are compared. This study identifies that a substantial portion of users benefit from RIS deployment; RIS-induced interference is generally negligible, except in rare cases; and strategically deploying small- to medium-sized RISs effectively addresses blind-spot coverage.

Integrated sensing and communication (ISAC) is an important new research topic for sixth-generation (6 G), where the waveform design should be studied. The chirp-based waveform is widely used in radar systems due to its low peak-to-average power ratio (PAPR) and good autocorrelation properties and is a candidate waveform for ISAC systems. To realize the chirpbased waveform in ISAC systems without additional hardware cost, an orthogonal frequency division multiplexing (OFDM) compatible sensing waveform is proposed in this paper. Firstly, a frequency domain processing (FDP) module is proposed to realize chirp signal under the OFDM transmitter structure. Then, to satisfy different communication requirements, the sensing symbol pattern is designed on top of the FDP module to realize multiple orthogonal chirps with flexible patterns. Evaluation results demonstrate that the proposed chirp-based waveform based on the unified waveform structure can achieve 4 dB and 13 dB signal-to-noise ratio (SNR) gain over traditional OFDM waveform with Zadoff-Chu (ZC) sequence or random payloads under the same range and velocity estimation accuracy.

Sub-Terahertz (sub-THz) communication is a potential technology to enable 6 G wireless communication with extreme experience due to its rich spectrum resources. Working on sub-THz frequency, radio frequency (RF) impairments including phase noise (PN) and non-linear power amplifier (PA) become more severe and will heavily degrade communication performance. Considering the new characteristics in such high frequency, various waveform schemes have been proposed to achieve better performance under RF impairments, among which the enhanced waveforms based on discrete Fourier transform spreading orthogonal frequency diversion (DFT-s-OFDM) are highly regarded for the low peak to average power ratio (PAPR) characteristic. This paper focuses on 6 G candidate waveforms of orthogonal frequency division multiplexing with cyclic prefix (CP-OFDM), DFT-s-OFDM and enhanced DFT-s-OFDM including unified non-orthogonal waveform (uNOW), DFT-s-OFDM with frequency-domain spectral shaping (DFT-sOFDM with FDSS), and DFT-s-OFDM with FDSS and spectral extension (DFT-s-OFDM with FDSS and SE). In this paper, 6G candidate waveforms are evaluated for different sub-carrier spacing (SCS) considering PN and non-linear PA in sub-THz. Specially, waveforms are compared and analyzed from various aspects including PN robustness without compensation, PN compensation performance based on phase tracking reference signal (PT-RS), and output power back-off (OBO). In addition, under non-linear PA, this work has in-depth discussions on the changing trend of each RF requirement as OBO increases for different 6 G candidate waveforms. The results indicate that uNOW outperforms other waveforms for BLER performance considering both PN compensation and OBO, making it the promising 6 G candidate waveform.

To meet the stringent spectrum efficiency requirements of International Mobile Telecommunications (IMT)-2030 (6 G), it is essential to explore the application potential of higher frequency bands and extremely large antenna arrays (ELAA). This shift introduces near-field effects, where electromagnetic waves deviate from the far-field plane wavefront assumption and must be modeled as spherical waves under certain distance conditions. Consequently, introducing beam focusing codebooks for the near-field is crucial. The typical fifth-generation New Radio (5G NR) high-precision Type II codebook, constructed from a two-dimensional discrete Fourier transform (DFT)-based grid of beams, enables the channel state information (CSI) feedback of a set of orthogonal beam selections along with corresponding amplitude and phase information. However, it is not optimized for near-field characteristics. To satisfy the 3rd Generation Partnership Project (3GPP) Releases’ requirement for backward compatibility, we introduce the plane wave expansion (PWE) method of focused beams. This method allows for the generation of focused beams through the weighted superposition of multiple DFT beams. Based on this approach, we define a set of additional feedback contents and formats within the NR Type II codebook framework, enabling a more accurate representation of the near-field line-of-sight (LoS) CSI for feedback. Numerical simulation results substantiate the performance of the proposed NR Type II codebook enhancement scheme, achieving a performance within 2dB of the optimal beam focusing scheme with appropriate configuration.

The validity of the far-field assumption is being challenged by emerging 6 G technical trends characterized by higher carrier frequencies, larger antenna apertures, and denser networking. This paper investigates near-field line-ofsight multiple-input-multiple-output (MIMO) with a focus on the practically desirable implementation using distributed antennas. Firstly, the feasibility conditions are addressed taking into account the impact of spatial aliasing. The aliasing-free and the partially aliased cases are distinguished, where the achievable spatial degrees of freedom are shown to be subject to array apertures and antenna spacings, respectively. Secondly, a codebook-based precoding for channel feedback is proposed exploiting the inherent structure of the channel. Finally, numerical simulations show that the proposed codebook achieves a performance comparable to the standardized high resolution codebook in 5G NR, but with only one-tenth of the feedback overhead.

The sixth-generation wireless system (6G) is poised to incorporate sensing capability with communication, serving as a foundational infrastructure to facilitate emerging usage scenarios. Meanwhile, reconfigurable intelligent surface (RIS), as an innovative network deployment device, has emerged as a promising technology within the 6G radio network topology, offering a large array size at low cost and power consumption. This paper introduces RIS to further enhance high-resolution wireless imaging alongside typical communication functionalities, implemented by a ring-type codebook (RTC) suitable for near-field beam focusing and a back projection (BP) algorithm for real-aperture imaging. The near-field beam generated using RTC can offer additional range-dimension resolution capability and enable the system to dynamically adjust the granularity of beam scanning based on the different use case requirements. Numerical simulation results are provided to substantiate the viability of the proposed RIS-aided wireless imaging scheme, showcasing its ability to facilitate centimeter-level two-dimensional (2D) wireless imaging and provide additional range-dimension resolution capability for a narrowband system.

To provide sensing capability in communication systems, integrated sensing and communication (ISAC) is an important research topic towards 5GA and 6G. Many sensing services and results are related to the estimated angles of targets. Virtual aperture (VA) technique in conventional radar systems can improve sensing performances of angle by special designs of transmit and receive antennas, which is different from those in communication systems. To exploit VA in ISAC systems without additional hardware cost, a unified antenna structure and a new VA scheme under the structure are proposed. Firstly, the antennas with half-wavelength spacing in communication systems are divided into multiple transmit antenna groups and one receive antenna group for sensing. Then, time-domain multiplexing (TDM)-based orthogonal signals are considered for multiple transmit antenna groups to realize VA. Beamforming is exploited in each transmit antenna group for beamforming gain. The estimation algorithm is also proposed to obtain expected larger VA. Thanks to the unified antenna structure, the total antennas can be flexibly exploited for either beamforming gain during transmission period or larger virtual aperture during estimation period. Results show that proposed VA scheme under unified antenna structure achieves 5 dB signal-to-noise-ratio (SNR) gain at$0.1^{\circ}$angle root-mean-square-error (RMSE) and improves angle RMSE from$0.1^{\circ}$to$0.03^{\circ}$at 0 dB SNR than conventional VA.

The reconfigurable intelligent surface (RIS), as an innovative device for network deployment, has emerged as a promising technique within the sixth generation (6G) radio network topology. In pursuit of achieving higher beamforming gain within the near-field region due to the high operation frequency and large array size, near-field beamforming schemes such as the ring-type codebook (RTC) have been proposed. Nevertheless, the user equipment (UE)-specific narrow beams generated by the existing schemes are not suitable for RIS to effectively forward control channels, which may require wider beams to cover a group of UEs. In this paper, the wide beam generation scheme based on the RTC codeword selection mechanism is proposed. While combined with the proposed beam shaping methods, RIS can dynamically switch between wide and narrow beams using RTC, providing flexibility in adapting to the forwarding of both data and control channels.

Deep learning has shown promise in enhancing channel state information (CSI) feedback. However, many studies indicate that better feedback performance often accompanies higher computational complexity. Pursuing better performance-complexity tradeoffs is crucial to facilitate practical deployment, especially on computation-limited devices, which may have to use lightweight autoencoder with unfavorable performance. To achieve this goal, this paper introduces knowledge distillation (KD) to achieve better tradeoffs, where knowledge from a complicated teacher autoencoder is transferred to a lightweight student autoencoder for performance improvement. Specifically, two methods are proposed for implementation. Firstly, an autoencoder KD-based method is introduced by training a student autoencoder to mimic the reconstructed CSI of a pretrained teacher autoencoder. Secondly, an encoder KD-based method is proposed to reduce training overhead by performing KD only on the student encoder. Additionally, a variant of encoder KD is introduced to protect user equipment and base station vendor intellectual property. Numerical simulations demonstrate that the proposed methods can significantly improve the student autoencoder’s performance, while reducing the number of floating point operations and inference time to 3.05%–5.28% and 13.80%–14.76% of the teacher network, respectively. Furthermore, the variant encoder KD method effectively enhances the student autoencoder’s generalization capability across different scenarios, environments, and bandwidths.

Deep learning has been introduced to implicit channel state information (CSI) feedback and considerably outperforms codebook-based feedback methods adopted by existing systems. This work proposes a time correlation-aided deep learning-based implicit CSI feedback framework named Tbi-ImCsiNet. The long short-term memory network is introduced into the implicit CSI compression side and reconstruction side to extract and utilize the time correlation property among CSI matrices and improve the framework performance. Simulation results show that the proposed Tbi-ImCsiNet reduces approximately 58.3% of the feedback overhead compared with the method without time correlation utilization.

To provide extreme coverage towards 5G Evolution and 6G, high altitude platform station (HAPS) is an important component in non-terrestrial network (NTN) due to its deployment flexibility. Multi-beam transmission should be used in HAPS for high throughput, where each beam covers one cell with multiple users. However, intra-beam and inter-beam interference will be severe with traditional multi-user multiplexing schemes, which may affect the HAPS performance. In this paper, an enhanced non-orthogonal multiple access (eNOMA) scheme is firstly proposed to overcome intra- and inter-beam interference. Then a unified multi-user multiplexing scheme is further proposed to dynamically adapt between spatial-division multiple access (SDMA), non-orthogonal multiple access (NOMA) and eNOMA for various interference scenarios. The evaluation results show that the proposed scheme achieves maximum 48% spectrum efficiency (SE) gain over existing schemes under three user configuration.

The sixth generation (6G) wireless communication system puts forward higher requirements for positioning performance to achieve centimeter-level (cm-level) high-precision positioning performance and integration of sensing and communication (ISAC). The received signal strength (RSS) fingerprinting-based positioning technique is a widely used scheme to locate user equipment (UE) in cellular networks. However, traditional RSS fingerprinting-based scheme do not consider the ISAC performance, and the positioning accuracy is limited since the RSS values of neighboring locations may be similar. In this paper, we propose a reconfigurable intelligent surface (RIS)-aided positioning scheme that exploits the diversity provided by RIS to improve the accuracy of the RSS fingerprinting-based indoor positioning, which requires the RSS from only one access point (AP). Furthermore, by using the proposed near-field multi-focal focusing method, it can ensure the communication performance of the target UE and support the high-precision positioning of other UEs, simultaneously. Simulation results confirm the effectiveness of our proposed scheme, achieving cm-level positioning performance at a cost of reducing the average throughput by only about 10%.

The sixth generation (6G) wireless communication system requires extreme capacity and coverage, which can be achieved through the introduction of higher frequency and ultra-large-scale reconfigurable intelligent surface (RIS) array. This also enables the expansion of the near-field range to tens or even hundreds of meters. However, near-field beamforming presents different challenges than far-field beamforming, as it involves two parameters (angle and distance) rather than just angle. This requires a larger number of codewords for near-field beams and frequent beam switching due to the narrow beamwidth. To address these challenges, we propose an enhancement to the ring-type codebook (RTC) using non-uniform quantization of focal length and beamwidth control methods. Our proposed methods effectively reduce the beam management overhead and SNR fluctuation by 34% and 4dB respectively, while also improving the robustness of the beams. Overall, our proposed enhancement to the RTC can significantly improve the performance of near-field beamforming in the 6G wireless communication systems, enabling them to satisfy the extreme capacity and coverage requirements of the future.

Reconfigurable intelligent surface (RIS) has emerged as a competitive solution to the blocking problem in millimeter wave (mmWave) communications. However, due to the passive nature of the RIS, obtaining channel state information (CSI) for RIS-assisted mmWave communication systems is rather difficult. Considering that the currently available RIS hardware cannot arbitrarily switch between the active (reflection with configurable phase response) and deactivate (absorption) modes, we suggest a new beam training method for RIS-assisted mmWave communication systems in this study. First, a predefined hierarchical codebook is created using the pattern synthesis method. Then, we provide a novel hierarchical beam training method using two multi-mainlobe codewords in each layer of the hierarchical codebook for beam sweeping. Combining the results of the beam identification in all the layers will yield the ultimate ideal beam direction. Theoretical analyses demonstrate that the suggested approach can effectively reduce training overhead while ensuring successful beam alignment. Simulation results show that the practical codebook can be created successfully, and the suggested method can achieve accurate beam alignment with reduced training overhead.

The 6th generation mobile networks require large bandwidth on sub-Terahertz bands to provide more than 100 Gbps throughput. To provide enough beamforming gain to combat the path loss on high-frequency bands, large-scale antenna arrays should be used, which generate narrow and directive beams. It may require hundreds or thousands of beams to cover a cell and introduce challenges for the beam alignment during the initial access stage. As a result, both the overhead and latency of beam sweeping may become unacceptable. We propose the Hash-based beam alignment method to address this problem, including a Hash-based beam transmission scheme for the synchronization signal block (SSB) transmissions and a corresponding UE beam pair selection method. It utilizes multiple transceivers to transmit multiple beams simultaneously, where the beam order follows a pattern generated by a Hash function. Considering UE may only receive discrete SSBs randomly, we design a weighted voting algorithm for UE to fast select the optimal beam pair for the following data transmissions. Simulations demonstrate that the proposed methods can reduce about 50% of the beam alignment latency or find a better beam pair with about 3 dB higher beamforming gain given a limited searching time.

5G New Radio (NR) takes multi-carrier waveform orthogonal frequency division multiplexing with cyclic prefix (CP-OFDM) with high spectral efficiency (SE) as the core waveform, and only supports single-carrier waveform discrete Fourier transform spreading OFDM (DFT-s-OFDM) with high power efficiency (PE) when uplink coverage is limited. 5G Evolution (5GE) and 6G have diversified scenarios, including high-frequency bands and integrated space-terrestrial systems, in which the non-linearity of power amplifiers (PA) restricts the system design. Therefore, high PE or low peak to average power ratio (PAPR) becomes one of the most significant key performance indicators (KPI) for 5GE and 6G waveform design, in addition to the high SE requirement. In this paper, we propose a unified non-orthogonal waveform (uNOW) based on DFT-s-OFDM enhancement with time-domain compression and expansion (TD-CE) as well as frequency-domain spectral shaping (FDSS) to improve PAPR and SE performance, simultaneously. Firstly, we describe the uNOW transmitter structure and two receiver structures, i.e., the linear minimum mean squared error (LMMSE) time-domain equalization (TDE) for time-domain compression and enhanced LMMSE frequency-domain equalization (FDE) for time-domain expansion. Then, the basic principle of uNOW is analyzed, i.e., how TD-CE and FDSS affect the time-domain pulse-shapes to reduce PAPR. Simulation results show that when only TD-CE or FDSS is considered, both PAPR gain and throughput gain are achieved compared with DFT-s-OFDM. When considering both TD-CE and FDSS, the PAPR and throughput gain achieved by uNOW can be significantly improved. Therefore, uNOW can be considered as a promising waveform candidate for 5GE and 6G.

Reconfigurable intelligent surface (RIS) is a research hotspot for the fifth generation evolution (5GE) and the sixth generation (6G) wireless communication systems to extend high-frequency coverage. Due to the increase of the carrier frequency of the wireless communication system and the large scale of the RIS array, the near-field region is significantly expanded. Using the plane wave assumption-based discrete Fourier transform (DFT) codebook for the RIS near-field beamforming may result in a large signal-to-noise ratio (SNR) loss. Furthermore, for practical RIS with a large number of elements, it is more cost-effective to implement only discrete phase shifts with a small number of control bits for each element, for example, 1-bit for two-level (0 or$\pi$) phase shifts, which generates the mirror grating lobe with the same power as the main lobe and results in power leakage. In this paper, a ring-type codebook is proposed based on the Fresnel principle, which can achieve SNR enhancement as well as mirror grating lobe suppression for 1-bit beamforming in near-field region. Compared with DFT codebook, the proposed codebook improves the average throughput by 14% and achieves a main grating lobe suppression ratio of about 17.9dB.

Beamformer design for large-dimension reconfigurable intelligent surface (RIS) is investigated. The challenge arises from the expanded near-field of RIS where its beam-forming gain is, in general, distance-dependent due to near-field diffraction. To gain more insights into this effect, we first analyze the performance of conventional collimated beamforming using Fresnel integrals and compare it with that of focused beamforming. Then, a two-step beamforming scheme is proposed. The principle of the proposed scheme is to divide the RIS into multiple subarrays followed by steering the sub-beams of the individual subarrays to focus. Providing proper subarray size, the proposed scheme retains the low complexity of collimated beamforming and achieves a near-optimal performance, as shown both analytically and numerically.

Non-terrestrial network (NTN) is an important technique to provide extreme coverage towards 5G advanced and 6G. High-altitude-platform-station (HAPS), as one key factor in NTN, is attracting a lot of attentions. To achieve better resource utilization and performance, a unified design for integrated HAPS-Terrestrial networks is necessary, where the interference among these two systems may be important. Current methods usually consider fixed resource allocation among the two systems without considering the distribution of traffic load and hence have low resource utilization. In practice, the traffic load may change dynamically or semi-statically due to the wide coverage in HAPS, which should be considered in the design of integrated HAPS-terrestrial systems. In this paper, we proposed an interference coordination method based on the distribution of traffic load as well as the deployment of HAPS and terrestrial networks. The evaluation results show that the proposed method achieves higher throughput than existing fixed resource allocation method.

Non-terrestrial network (NTN) is an important technique to provide extreme coverage. Current fifth-generation (5G) new radio (NR) techniques mainly focus on the terrestrial networks. How to support NTN by 5G NR techniques is studied in 3rd generation partnership project (3GPP) since Release 15. After extensive studies, the first release of NR NTN is finished in Release 17. Uplink time synchronization is an important topic for both terrestrial networks and NTN, without which the interference among multiple users as well as the uplink and downlink misalignment will happen. Different from 5G NR, open-loop timing advance (TA) including common TA and UE-specific TA is firstly introduced in NR NTN. This paper introduces the latest progress in Release 17 and gives the solutions, procedures, and evaluation results for uplink synchronization in NR NTN.

Medical practitioners generally rely on multimodal brain images, for example based on the information from the axial, coronal, and sagittal views, to inform brain tumor diagnosis. Hence, to further utilize the 3D information embedded in such datasets, this paper proposes a multi-view dynamic fusion framework (hereafter, referred to as MVFusFra) to improve the performance of brain tumor segmentation. The proposed framework consists of three key building blocks. First, a multi-view deep neural network architecture, which represents multi learning networks for segmenting the brain tumor from different views and each deep neural network corresponds to multi-modal brain images from one single view. Second, the dynamic decision fusion method, which is mainly used to fuse segmentation results from multi-views into an integrated method. Then, two different fusion methods (i.e., voting and weighted averaging) are used to evaluate the fusing process. Third, the multi-view fusion loss (comprising segmentation loss, transition loss, and decision loss) is proposed to facilitate the training process of multi-view learning networks, so as to ensure consistency in appearance and space, for both fusing segmentation results and the training of the learning network. We evaluate the performance of MVFusFra on the BRATS 2015 and BRATS 2018 datasets. Findings from the evaluations suggest that fusion results from multi-views achieve better performance than segmentation results from the single view, and also implying effectiveness of the proposed multi-view fusion loss. A comparative summary also shows that MVFusFra achieves better segmentation performance, in terms of efficiency, in comparison to other competing approaches.

Non-linearity of power amplifiers (PA) is the restriction of waveform design for 5G Evolution (5GE) and 6G in high frequency bands with large bandwidth. The low peak to average power ratio (PAPR) single carrier waveform discrete fourier transform spreading orthogonal frequency division multiplexing (DFT-s-OFDM) can be used as a candidate waveform for 5GE and 6G. To satisfy the extremely high data rate requirement in 6G, how to keep the low PAPR of single carrier waveform and improve its spectral efficiency (SE) will be the challenge. In our previous study, a novel non-orthogonal waveform (NOW) design was proposed by combining DFT-s-OFDM with FTN to improve SE with low PAPR. However, due to the spectrum extension caused by FTN, large out of band emission (OOBE) and signal-to-noise ratio (SNR) loss were observed. In this paper, we propose an enhanced non-orthogonal waveform (eNOW) to improve the SE of DFT-s-OFDM by designing the frequency domain power allocation to efficiently suppress OOBE and reduce SNR loss. Simulation results demonstrate that the OOBE and SNR loss of eNOW can be significantly reduced than that of NOW by design the frequency domain power allocation. With eNOW scheme, OOBE, PAPR and throughput gain can be achieved, simultaneously.

High frequency bands with large bandwidth (e.g., millimeter-wave and terahertz frequencies) is promising for 5G Evolution and 6G, but the system design is restricted by the non-linearity of power amplifier (PA). Multi-carrier waveform of orthogonal frequency division multiplexing with cyclic prefix (CP-OFDM) may not be suitable for 5G Evolution and 6G due to its high peak to average power ratio (PAPR). Single carrier waveform represented by discrete fourier transform spreading OFDM (DFT-s-OFDM) with low PAPR could be a promising candidate waveform for 5G Evolution and 6G. Considering the extreme high data rate requirement of 5G Evolution and 6G, how to keep the low PAPR of DFT-s-OFDM and improve its spectral efficiency (SE) will be the challenge especially when high-order modulation is not applicable. In this paper, we propose a non-orthogonal waveform (NOW) to improve the SE of DFT-s-OFDM and derive the optimal time domain compression factor in terms of SNR based lossless compression. Furthermore, we investigate the impact of different compression factors to the system performance of throughput and PAPR. Simulation results demonstrate that the proposed scheme can achieve both throughput gain and PAPR gain over the traditional orthogonal waveforms DFT-s-OFDM and CP-OFDM, by flexibly adjusting the compression factor. When the compression factor is smaller than the optimal value, SNR loss to different extent may be observed.

The cellular vehicle-to-everything (C-V2X) networks can support direct vehicle-to-vehicle (V2V) communications via sidelink/PC5 interface without cellular infrastructure support. The sensing-based semipersistent scheduling (SPS) scheme is to allow vehicles to autonomously reserve and select radio resources. However, the common sensing nature of the distributed SPS algorithm gives rise to that C-V2X distributed communications can be challenged by the resource selection collisions, especially in aperiodic traffic, which results in low reliability. In this article, we propose a short-term sensing-based resource selection (STS-RS) scheme to reduce packet collisions due to resource contention, where a short-term sensing duration is configured at the beginning of the resource unit right before resource selection, and whether the packet is ultimately transmitted on the selected resource depends on the sensing result. Furthermore, analysis models of the performance for the proposed STS-RS scheme and SPS scheme defined in C-V2X mode 4 are investigated. Finally, simulations and numerical results show that the STS-RS scheme significantly reduces the packet collisions and increases the C-V2X direct communication performance compared to the SPS scheme.

Due to the availability of wide bandwidth, high frequency band is promising for 5G evolution and 6G. With the increase of antenna ports and bandwidth, existing subband-level precoding consumes high feedback overhead and restricts the precoding granularity. However, channel sparsity can be observed in transformed angular-delay domain in mmWave systems with massive antennas. In this letter, to utilize the sparsity, we propose a transform domain precoding (TDP) to design precoder and feedback. Realistic factors including hybrid beamforming, frequency domain windowing and power allocation are analyzed and evaluated by link-level simulations. Results show that 60%~89% overhead reduction and 2.38%~21% spectrum efficiency (SE) enhancement can be achieved by TDP.

Existing wireless mobile networks configure the uplink (UL) and downlink (DL) resources based on the orthogonal duplex principle, which has significantly evolved from the static frequency/time division duplex (FDD/TDD) to the semi-dynamic TDD in 4G and the fully-dynamic TDD in 5G. Fueled by the successful realizations of full duplex (FD) transmission, non-orthogonal duplex (NOD) emerges as an attractive candidate technique for future networks to improve the bidirectional throughput. In this paper, we investigate the performance limit of the NOD scheme, which imports the FD mode on the basis of fully-dynamic TDD. We first focus on a single-cell scenario by assuming no cooperation among cells, and strive to characterize the bidirectional capacity region by finding the capacity-optimal transmission mode scheduling policy. We obtain the optimal policies for the systems with and without modulation and coding scheme (MCS), respectively, where the policies are based on the instantaneous channel gains and the distribution of channels. We proceed to develop a scheduling policy that only relies on the instantaneous channel and queue information, and extend it to the multicell multiuser scenario with coordinated scheduling. Numerical and simulation results demonstrate the great potential of the NOD scheme in increasing bidirectional capacity and reducing the queuing delay.

Non-orthogonal multiple access (NOMA) will provide massive connectivity for future Internet of Things. However, the intrinsic non-orthogonality in NOMA makes it non-trivial to approach the performance limit with only conventional communication-theoretic tools. In this paper, we resort to deep multi-task learning for end-to-end optimization of NOMA, by regarding the overlapped transmissions as multiple distinctive but correlated learning tasks. First of all, we establish a unified multi-task deep neural network (DNN) framework for NOMA, namely DeepNOMA, which consists of a channel module, a multiple access signature mapping module, namely DeepMAS, and a multi-user detection module, namely DeepMUD. DeepMAS and DeepMUD are automatically trained in a data-driven fashion, and a multi-task balancing technique is then proposed to guarantee fairness among tasks as well as to avoid local optima. To further exploit the benefits of communication-domain expertise, we introduce constellation shape prior and inter-task interference cancellation structure into DeepMAS and DeepMUD, respectively. These sophisticated designs help to reduce the implementation complexity without sacrificing DNN's universal function approximation property, which makes DeepNOMA a universal transceiver optimization approach. Detailed experiments and link-level simulations show that higher transmission accuracy and lower computational complexity can be simultaneously achieved by DeepNOMA under various channel models, compared with state-of-the-art.

The vehicle to everything communication in long term evolution (LTE V2X) supports vehicle-to-vehicle (V2V) communication directly via PC5 interface. In LTE V2X mode 4, vehicles autonomously select radio resources based on a distributed scheduling scheme. However, some researches show that this sensing based semi-persistent scheduling (SPS) scheme incurs low transmission reliability due to resource contention especially when the transmissions are aperiodic. Therefore, a cluster-based resource selection scheme is proposed to reduce the resource collision, where resources are divided into different resource sets and each cluster head selects its available resource set based on measurement. Simulation results show that, compared with LTE V2X mode 4, the proposed scheme has better performance for both periodic and aperiodic traffic.

Tactile Internet of Things (IoT) requires ultraresponsive and ultrareliable connections for massive IoT devices. As a promising enabler of tactile IoT, grant-free nonorthogonal multiple access (NOMA) exploits the joint benefit of grant-free access and nonorthogonal transmissions to achieve low latency massive access. However, it suffers from the reduced reliability caused by random interference. Hence, we formulate a variational optimization problem to improve the reliability of grant-free NOMA. Due to the intractability of this problem, we resort to deep learning by parameterizing the intractable variational function with a specially designed deep neural network, which incorporates random user activation and symbol spreading. The network is trained according to a novel multiloss function where a confidence penalty based on the user activation probability is considered. The spreading signatures are automatically generated while training, which matches the highly automatic applications in tactile IoT. The significant reliability gain of our scheme is validated by simulations.

In the 5th generation (5G) mobile networks, an important enhancement aspect for massive MIMO is Channel State Information (CSI) feedback. For massive MIMO, quantities of antennas are utilized to provide better coverage, which brings challenge for implementation as well as opportunity for improving multi-user (MU)-MIMO performance. To provide better performance for MU-MIMO, high resolution CSI feedback was introduced in Rel.14. Base stations can acquire more accurate channel information from high resolution CSI to facilitate scheduling for multiple users with reduced inter-user interference. However, high resolution CSI feedback specified in current 3GPP still has large room to be improved due to big gap between current CSI feedback and ideal CSI feedback. In this paper, we investigate possible enhancement directions towards high resolution CSI, e.g., to improve the quantization resolution by combining more beams, to increase the supported rank, etc. High resolution codebook construction and higher rank multi-user scheduling schemes are provided. System evaluation is performed to verify the effectiveness of those schemes. Simulation results show that improving quantization resolution can bring decent performance gain, and supporting higher ranks can bring larger performance gain. Both schemes will introduce large overhead. To balance performance and overhead, supporting higher rank high resolution is preferred. The challenges to specify these schemes are also analyzed.

With the increasing demand for vehicular data transmission, limited dedicated cellular spectrum becomes a bottleneck to satisfy the requirements of cellular vehicle-to-everything (V2X) users. To address this issue, we study the coexistence problem between cellular V2X users and vehicular ad-hoc network (VANET) users over the unlicensed spectrum. To facilitate the coexistence, we design an energy sensing based spectrum sharing scheme, where cellular V2X users are able to access the unlicensed channels fairly, thereby reducing the collisions of data transmission. In order to maximize the number of active cellular V2X users, we formulate the scheduling and resource allocation problem as a two-sided many-to-many matching with peer effects. A dynamic vehicle-resource matching algorithm (DV-RMA) is then proposed to solve this problem. Simulation results show that the proposed scheme outperforms the existing approaches in supporting the massive connectivity.

Massive MIMO is one of the key technologies of the 5th generation mobile networks (5G), which enables base stations to serve higher order of users with spatial division multiplexing. Non-linear precoding schemes for multi-user (MU) transmissions, especially Tomlinson-Harashima precoding (THP), is one of the candidate techniques for future enhancements of 5G massive MIMO. As system-level performance investigations usually provide key results for the decision on introducing a technique into the specifications of mobile networks, we propose a physical layer signal model of THP for system-level evaluations. It models the major differences on signal processing between THP and conventional linear precoding schemes. With the introduced model, the spectrum efficiency of THP is investigated in two typical 5G scenarios where high order MU-MIMO will be used. The system-level evaluations demonstrate the potential gain of THP in practical multi-cell networks, and it verifies the advantages of THP on multi-user MIMO downlink transmissions. The observations from the investigations with different user scheduling and parameter settings also provide us hints on the further performance optimization of THP based downlink transmissions.

Dynamic time division duplex (TDD) is one promising way to improve the spectrum efficiency of the wireless communication networks since flexible traffic adaptation can be achieved by dynamically changing uplink (UL) or downlink (DL) transmission direction. However, it also brings some new challenges because of the introduction of cross-link interference, including DL-to-UL interference (e.g., gNB-to-gNB interference) and UL-to-DL interference (UE-to-UE interference). Besides, fully dynamic TDD is promising for 5G, which makes the interference management more intractable. In this paper, we investigate the interference management schemes for 5G dynamic TDD and propose a new interference suppression schemes using advanced receivers, e.g., minimum mean square error interference rejection and cancellation (MMSE-IRC) receiver and enhanced MMSE-IRC (eMMSE-IRC) receiver. The analytic expressions of MMSE-IRC and eMMSE-IRC receiver for dynamic TDD interference suppression are given by theoretical analysis. Also, one interference measurement scheme is proposed to support the proposed interference suppression schemes. The simulation results confirm the performance gain of the proposed interference suppression schemes compared with the baseline minimum mean square error (MMSE) receiver and show promising performance gain of dynamic TDD.

Uplink non-orthogonal multiple access (NOMA) is a promising technique to meet the requirements of the fifth generation (5G) and beyond systems. Various NOMA schemes have been proposed in both academia and industry. However, most existing schemes assume equal average received power, which limits the performance. We propose three enhancements of uplink NOMA to achieve the requirements of massive connectivity and high reliability in 5G, where unequal average received power is exploited as part of the multiple access signature. First, the optimal sequences targeting to generalized Welch-bound equality (GWBE) are obtained for unequal average received power. Then user grouping with multi-level received powers is proposed for better successive interference cancellation (SIC) at the receiver. Finally, sequence grouping based on the cross-correlation properties of sequences is proposed to reduce inter- and intra-group interference. Simulation results show that by incorporating multi-level received powers and sequence grouping into existing NOMA schemes, for an NOMA system with 400% overloading and fixed signature allocation, 3 dB and 10 dB signal-to-noise ratio (SNR) gains at 0.1 block error rate (BLER) target can be achieved compared with existing NOMA schemes and orthogonal multiple access (OMA), respectively. Besides, 0.01 BLER target can be achieved while an error floor exists in existing NOMA schemes. Under random sequence selection, collision probability is reduced by multi-level powers. In addition, GWBE sequences achieve lower BLER than existing sequences and the gain is large especially for low BLER requirements. This shows that the proposed scheme can support larger connectivity and higher reliability.

Grant‐free transmission, which simplifies the signaling procedure via uplink instant transmission, has been recognized as a promising multiple access protocol to address the massive connectivity and low latency requirements for future machine type communications. The major drawback of grant‐free transmission is that the contaminations among uncoordinated transmissions can reduce the data throughput and deteriorate the outage performance. In this paper, we propose a rate‐adaptive multiple access (RAMA) scheme to tackle the collision problems caused by the grant‐free transmission. Different from the conventional grant‐free (conv‐GF) scheme which transmits a single signal layer, RAMA transmits the signals with a multilayered structure, where different layers exhibit unequal protection property. At the receiver, the intra‐ and interuser successive interference cancellation (SIC) receiving algorithm is employed to detect multiple data streams. In RAMA, the users can achieve rate adaptation without the prior knowledge of the channel conditions, since the layers with high protection property can be successfully recovered when the interference is severe, while other layers can take advantage of the channel when the interference is less significant. Besides, RAMA also facilitates the SIC receiving since the multiple layers in the transmission signals can provide more opportunities for interference cancellation. To evaluate the system performance, we analyze the exact expressions of the throughout and the outage probability of both conv‐GF and RAMA. Finally, theoretical analysis and simulation results validate that the proposed RAMA scheme can simultaneously achieve higher average throughput and lower outage performance than conv‐GF. Meanwhile, RAMA shows its robustness with large user activation probability, where the collisions among users are severe.

Time division duplex (TDD) plays an important role in 5G systems because it employs an unpaired spectrum and offers channel reciprocity. However, employing TDD will lead to a long transmission latency due to the uplink/downlink transmission constraints. In this paper, we study the frame structure design for low latency communications in 5G TDD systems. We propose a frame structure design method that implements a shorter transmission time interval (TTI) and more switching points to allow lower latency in TDD systems. Analysis and evaluation results show that a shorter TTI and more switching points are not only beneficial in reducing user plane latency but also yield better performance in terms of user perceived throughput and user packet delay compared with current frame structure in LTE.

Massive multiple-input multiple-output (MIMO) has been considered as one of the key technologies in 5G communication, owing to its promising potential to increase the spectrum efficiency significantly. The capacity grows rapidly with the increasing number of antennas. However, in real-time environment, the more antennas are employed, the more computing resources are required, and this increase is even in a nonlinear progression. In this work, radio computing architecture (RCA) with multiple parallel general purpose processors (GPPs) and automatic distribution system (ADS) are utilized to overcome the strict real-time constraints and establish a highly flexible prototype platform for massive MIMO research. The feasibility and performance of the GPP-based prototype has been investigated through field trial. A collocated 64-RF-channel base station (BS) with each RF channel driving 3 antennas can simultaneously serve twelve 8-antenna user equipments (UEs). The maximum overall bandwidth is 200 MHz, and the maximum flow number is pre-defined as 24. In this field trial, a peak total user throughput of 11.29 Gbps and a cell spectrum efficiency of 79.82 bps/Hz are achieved. As far as we know, this is the world's highest throughput ever achieved in the sub 6 GHz band. Furthermore, apart from multi-user MIMO, single-user MIMO is also tested in this field trial.

The 5th generation of mobile communications system (5G) is nowadays expected to support very diverse applications, devices, and services such as enhanced mobile broadband (eMBB) and the Internet of things (IoTs). In order to meet the future requirements, new 5G candidate radio access technologies have been proposed and studied. For example, Massive multiple-input multiple-output (MIMO) is an attractive technology to further improve the spectrum efficiency and to boost system capacity. This paper presents a large scale field experimental trial of downlink time division duplex (TDD) Massive MIMO using the developed 5G test bed at the 4.5 GHz band. Specifically, the system performance of multi-user (MU) Massive MIMO with different numbers of sets of user equipment (UEs) and spatial layers is investigated, including the impact of different inter-UE separations and UE deployments, which have not been well investigated compared to the previous studies. The experimental results show that the throughput performance for a wide UE separation linearly increases as the number of layers and UEs increases, while the performance improvement is diminished when inter-UE separation narrows. In addition, the maximum total user throughput of 11.29 Gbps, which corresponds to a spectrum efficiency of 79.82 bps/Hz/cell, was achieved with 24 layers. The results indicate that TDD Massive MIMO using the developed 5G test bed greatly improves the spectrum efficiency for large scale MU-MIMO.

Hybrid beamforming architecture that combines analog beamforming and digital beamforming has been widely accepted in the emerging millimeter-wave (mmWave) systems with large antenna arrays. However, the design of codebooks and beam training procedures at the analog stage is problematic in mmWave system. In this study, we propose a feasible two-stage 3D codebook design consisting of a primary codebook and an auxiliary codebook. The primary codebook generates a basic directional beam, and the number of phase shifts is quite limited in order to keep low hardware complexity. The small-size auxiliary codebook creates the finer beams that are centered on each primary beam. Furthermore, a beam search scheme designed for the two- stage codebook is proposed. We use a binary tree search method with antenna selection for the primary beam search, and a directed beam scan for the auxiliary beam search. Simulation results verify the performance advantages of our proposed codebook in beamforming gain and spectral efficiency for multi-user multiple-input multiple-output (MU-MIMO) systems. Moreover, training complexity comparisons indicate that our specially designed two-stage beam search scheme significantly outperforms other existing solutions.

Recently, NTT DOCOMO and Huawei conducted a large-scale experimental trial of key technologies for the 5th generation (5G) mobile systems. Downlink multi-user (MU) transmission with massive MIMO in the time-division duplex (TDD) mode is one of the technologies evaluated in this trial. With linear and non-linear downlink precoding schemes, we investigated the practical performance of MU massive MIMO systems under different numbers of users, user distributions, and different radio frequency channel calibration settings with up to 24 users deployed. When linear precoding is used, the cell spectrum efficiency reaches 39 bit/s/Hz, and it reaches 43 bit/s/Hz when non-linear precoding is used. In term of the cell throughput, up to 1.35 Gbps cell throughput was observed with a linear precoder and 100 MHz bandwidth; and 343 Mbps cell throughput was observed with a non-linear precoder and 20 MHz bandwidth. Based on these investigations, we verified the feasibility and performance of a TDD massive MIMO system for 5G mobile systems, and studied the impact of key factors on the system performance.

To meet the challenging capacity requirement of 5G, a set of radio access technologies may be needed, including technologies to improve the spectrum efficiency, to utilize more available spectrum, and network densification, etc. In this paper, we focus on how to combine massive multiple-input and multiple-output (MIMO) and dense small cell (DSC) deployment to boost the system capacity. Under the framework of adjacent channel heterogenous network (HetNet) architecture, clustered small cell deployment with two dimensional active antenna array system (2D AAS) is investigated. The suitable massive MIMO array structure is identified for small cell deployment. Then the coverage issue of clustered small cell deployment with the directional 2D AAS is examined and both packet throughput and area throughput are evaluated with the increasing antenna number as well as the increasing small cell density. Based on the system-level simulation results, the combination of massive MIMO and DSC is revealed to be a promising solution for 5G. Moreover, considering the balance between performance and cost, we further discuss the low-cost massive MIMO scheme for small cell, i.e., hybrid beamforming, and list several challenges to be addressed.

Massive MIMO is envisioned as one of the key technologies for the 5th generation (5G) wireless communication systems. Hybrid beamforming is a promising and feasible solution to massive MIMO due to the achievable balance between performance and implementation cost, especially for the high frequency band. Different from user data channels and signals, common channels and signals cannot benefit from the user-specific beamforming gain, therefore, will suffer from a coverage issue due to the large propagation loss in the high frequency band. In order to provide an enhanced coverage for common channels and signals, in this paper we propose a novel hybrid beamforming transmission scheme, in which multiple analog beams are transmitted simultaneously via analog beamforming at the radio frequency (RF) front-end and cyclic shift (CS)-based beamforming is utilized in the digital back-end. With a proper CS setting, the received channel impulse responses (CIRs) from different beams can always be combined orthogonally. Therefore, a more stable and robust coverage of common channels and signals can be guaranteed. Both theoretical analysis and numerical evaluation justify its effectiveness.

Massive MIMO is one of the promising technologies to be employed in the next generation mobile communication systems. Multi-user transmission is the nature of massive MIMO due to the high order of degree of freedoms and large beamforming gain provided by the large scale antenna arrays mounted at the base station side. Considering this nature and the characteristics of massive MIMO channels, we propose wideband and high-order multi-user MIMO transmission mode for future radio access. Under such transmission mode, the simple but efficient scheduling strategies, e.g., all-user on all-subband scheduling, can be considered for downlink scheduling to obtain the potential gain of massive MIMO. System-level performance and detailed analysis on the characteristics of channel state are provided through simulations, which show the advantages of proposed wideband and high-order MU-MIMO transmission mode for massive MIMO systems.

In order to satisfy the increasing traffic requirement, dynamic Time Division Duplexing (TDD) Downlink/Uplink (DL/UL) time configuration is introduced to provide a more flexible service and improve the system performance. In a dynamic TDD system, the DL/UL time configuration can be changed based on the traffic load. Since Hybrid Automatic Repeat Request (HARQ) timing may become misaligned in dynamic TDD system, HARQ timing and feedback signalling design need to be considered for dynamic TDD system. In this paper, HARQ timing issue in LTE system is investigated and novel HARQ feedback signalling design schemes for dynamic TDD system are proposed. Performance evaluation and numerical analysis are provided to show the effectiveness of the proposed schemes. By using the proposed schemes, the feedback signalling overhead can be reduced and spectrum efficiency can be improved.

Coordinated Multi-Point (CoMP) transmission is a key technology for LTE-Advanced (LTE-A) to increase the downlink cell-edge throughput. Generally speaking, multi-cell channel state information (CSI) is needed at multiple coordinated eNBs to achieve the performance gain promised by CoMP in downlink. Sounding reference signal (SRS) could be an efficient tool to enable multi-cell CSI acquisition for time division duplex (TDD) LTE-A (TD-LTE-A) systems because of the inherent channel reciprocity in TDD systems. In this study, we investigate multi-cell SRS coordination schemes for TD-LTE-A downlink CoMP. We introduce a CoMP-specific sounding zone that facilitates multi-cell SRS coordination and then propose and compare three coordinated SRS resource allocation schemes within the CoMP-specific sounding zone, i.e., cyclic shift (CS) coordination, comb coordination, and orthogonal cover code (OCC) for the SRS. Computer simulations show the effectiveness of multi-cell SRS coordination and its necessity is revealed by the sum-rate analysis of TD-LTE-A downlink CoMP.

In this paper, we investigate the feasibility of applying interference alignment (IA) technology to multi-cell coordinated beamforming (CB) in a future release of the Long Term Evolution (LTE) standard. Specifically, we establish a link level model for a two-cell system according to the current LTE- Advanced (LTE-A) specifications, and evaluate the performance of an IA based CB scheme. The link-level performance of the IA based CB scheme is compared to that of conventional coordination schemes to identify the potential gain. We establish different simulation configurations to identify the factors that restrict the practical deployment of the IA based CB scheme and the potential requirements for extra standardization support.

This paper investigates low complexity coordinated beamforming (CB) schemes that adopt interference alignment (IA). The majority of existing IA CB schemes rely on iterative solutions that have drawbacks such as sensitivity to initialization conditions, high computational complexity, and infeasible system requirements. Low-complexity non-iterative solutions only exist for specific scenarios. In this paper, we first propose an IA scheme for a new scenario. Secondly, we discuss low complexity diversity enhancement methods which can be applied on top of any IA method to form the final IA CB scheme. The proposed scheme can achieve high multiplexing gain via IA while improving the capacity of the conventional IA schemes at the low signal-to-noise-ratio (SNR) regime.

This paper proposes a novel frame structure for Orthogonal Frequency Division Multiplexing (OFDM) based broadband wireless access and its application. The proposed frame structure, which is robust against inter-symbol interference (ISI) caused by a very large delay spread of multi-paths, is applicable to ultra-large-cell environments where the cell radius is comparable to that for broadcast services without changing key parameters of the OFDM frame such as the guard interval duration and symbol duration, which are optimized for cellular networks. Simulation results show that the proposed method has an advantage compared to the conventional OFDM frame structure when the delay spread is extremely large, i.e., excess delay exceeds the guard interval duration, and the ISI is the dominant factor in degrading the performance.

We propose a low complexity synchronization method for the 3rd Generation Partnership Project (3GPP) long term evolution (LTE) mobile communication systems. A low complexity detection window, synchronization state machine, and two-stage detection are used to lower the computational complexity. The positions of the detection window and received samples are recorded, and the position and length of the detection window are controlled by the state machine. The positions are compared to decide if the received sample is useful for synchronization signal detection. Such techniques avoid unnecessary computations especially when implementing a receiver on a software defined radio (SDR) platform with general-purpose processors. We analyze the computational complexity of the proposed method and evaluate its performance based on simulations and using a Universal Software Radio Peripheral (USRP)-based SDR platform. The evaluation shows that the proposed method provides reliable and real-time synchronization signals on the considered SDR platform.

As the 3GPP long term evolution (LTE) system actively evolves, advanced multi-user multiple-input multiple-output (MU-MIMO) schemes have attracted increasing interest in the engineering field. We have proposed a QR decomposition (QRD) based MU-MIMO scheme for the evolving LTE time division duplex (TDD) system to achieve high spectrum efficiency, assuming ideal channel reciprocity. In order to investigate the promising performance of TDD MU-MIMO in practice, we implement the proposed QRD-based TDD MU-MIMO scheme together with an actual over-the-air (OTA) antenna calibration scheme into our FPGA-based hardware testbed for the evolving LTE TDD system to examine its performance under the constraint of practical channel reciprocity. Measurement results show that channel reciprocity indeed could be achieved with the OTA antenna calibration and the QRD-based MU-MIMO scheme can effectively make use of this channel reciprocity to achieve high spectrum efficiency data transmission for both downlink and uplink. Moreover, the influence of antenna calibration on the TDD MU-MIMO performance is further investigated and several considerations related to antenna calibration are addressed.

We investigate the problem of two-cell multi-user co-channel transmission, wherein both inter-cell interference and intra-cell inter-user interference should be well managed. Interference alignment (IA) is theoretically proved to be a promising technology for managing co-channel interference. It can be used to mitigate inter-cell interference. When multiple users are served by a single cell, advanced multi- user multiple-input multiple output (MU-MIMO) transmission can be used to manage the intra-cell inter-user interference. In this paper, we present an efficient two-cell coordinated linear beamforming method where IA is combined with MU-MIMO to achieve high performance multi-cell cooperative transmission. The effectiveness of the proposed method is verified by numerical evaluations.

Multi-cell cooperative processing (MCP) is widely recognized as a key technology for next generation cellular networks due to its promising potential of increasing the spectrum utilization efficiency. The performance of novel MCP schemes in terms of e.g., capacity, throughput, are mostly evaluated numerically but not analytically. Although the latter approach offers more insights into the true nature, the high complexity of coordinated multi-cell systems usually makes analytical derivation intractable. In this paper, we make a comprehensive study of the rate region achieved by different downlink MCP schemes with per-cell power constraints. We derive in closed form the corresponding rate region boundaries based on simple but representative Wyner type model. The obtained analytical results provide a useful reference of the performance and complexity tradeoff of those schemes.

Multi-user multiple input multiple output (MU-MIMO) systems can achieve high spectral efficiency for wireless communication. The design of optimal transmit precoding for max-sum-rate (MSR) MU-MIMO downlink transmission is a non-trivial problem. Recent research approached this problem by exploiting the relationship between max-sum-rate and the minimum-mean-squared-error (MMSE) and proposed several iterative schemes. Based on these research results, we propose enhanced iterative MSR algorithms for linear MU-MIMO precoding which improve the sum rate performance and reduce the computational complexity. The effectiveness of the proposed method is verified by numerical simulations.

Multiple-input multiple-output (MIMO) spatial multiplexing has been adopted by 3GPP long term evolution (LTE) to increase the peak spectrum efficiency. However, it will lose its effectiveness for cell edge users due to the inter-cell interference (ICI). Therefore, 3GPP LTE-Advanced (LTE-A) is considering to introduce some multi-cell cooperative transmission techniques to solve this problem. In this paper, we first propose the distributed multi-Tx multi-Rx (MTMR) MIMO spatial multiplexing for LTEA downlink, which only needs partial cooperation, so the heavy backhauling overhead and the high implementation complexity encountered in the traditional full cooperation scheme can be avoided. Meanwhile, because of the efficient utilization of multicell transmit power and spatial degree of freedom, a higher spectrum efficiency can be achieved by MTMR MIMO spatial multiplexing in good channel conditions compared with the other partial cooperation schemes. Then in order to ensure the highest possible cell edge spectrum efficiency to be achieved in different channel conditions, we further integrate multiple distributed cooperation modes into the same framework and introduce a mechanism of adaptive cooperation mode selection. Both qualitative analysis and quantitative simulations show the proposal's effectiveness and indicate it a promising candidate for the cooperative transmission in LTE-A downlink.

In 3GPP long term evolution (LTE) uplink, single transmit antenna is adopted due to its simplicity and acceptable performance. However, in order to satisfy the higher requirement of LTE-Advanced (LTE-A) on uplink spectrum efficiency, multiple transmit antennas must be supported in LTE-A uplink. At the same time, the backwards compatibility to LTE should be taken into considerations. Therefore, the demodulation reference signal (DMRS) as well as channel estimation have to be re-designed for LTE-A uplink to support multiple-input multiple-output (MIMO) transmission. In this paper, we propose the DMRS design for LTE-A MIMO uplink via frequency domain code division multiplexing (FD-CDM) with the maximum distance binding (MDB), which can minimize the interference among multiple transmit antennas. More importantly, it is backwards compatible to the DMRS in LTE uplink, i.e., few modifications to the current specifications are needed. Furthermore, the improved uplink channel estimation with dynamic channel impulse response reservation (DOR2) and frequency domain windowing/dewindowing is proposed to enhance the channel estimation accuracy and uplink reliability for both LTE and LTE-A. Computer simulations demonstrate their effectiveness.

Channel estimation based on two-dimensional discrete- time Fourier transform interpolation (2D-DFTI) is suitable for orthogonal frequency division multiplexing (OFDM) systems because of its robustness to doubly-selective fading channels and high computational efficiency. However, its performance will degrade significantly for packet OFDM systems with virtual subcarriers due to the Gibbs phenomenon in both frequency- domain and time-domain. We have already proposed the frequency- domain enhanced DFT interpolation (FD-EDFTI) to effectively suppress the Gibbs phenomenon in frequency-domain, while in this paper the time-domain enhanced DFT interpolation (TD-EDFTI) is further proposed to mitigate the Gibbs phenomenon in time-domain. Therefore, doubly-selective channel estimation based on 2D-EDFTI can be easily implemented for packet OFDM systems with virtual subcarriers by concatenating FD-EDFTI with TD-EDFTI to efficiently suppress the Gibbs phenomenon in both frequency- domain and time-domain, while keeping good robustness and high computational efficiency.

Frequency-hopped OFDM (FH-OFDM) technology will provide the additional benefits of frequency diversity and inter-cell interference averaging. However, it is difficult to get the channel state information. In this paper, we propose a method of channel estimation over slow fading channels for FH-OFDM systems. In the algorithm, the channel impulse response is estimated by correlation operation, by using the cyclic shift zero- correlation of CAZAC (constant amplitude zero auto-correlation) sequence. In FH-OFDM systems, the influence of noise on channel estimation precision is restrained and inter-cell users' interference can be eliminated. At the same time, the algorithm is adapted to any hopping pattern.

This paper proposes a novel preamble structure and a synchronization method based on central-symmetric sequence for OFDM systems. The preamble structure can be easily constructed in frequency domain and is spectrum efficient which can afford time and frequency synchronization, channel estimation, SNR estimation. And a new timing estimator is also proposed according to the preamble structure, which combined the merits of delayed and symmetric correlation based methods. The proposed timing estimator can well suppress interferences and get better timing performance. Compared to the typical Park's method, proposed method is super in low SNR environment and almost the same in high SNR environment.

Traditional discrete Fourier transform (DFT) based channel estimation will suffer from significant performance degradation due to the edge effect in practical orthogonal frequency division multiplexing (OFDM) systems with virtual subcarriers. In this paper, a novel enhanced DFT-based channel estimation method is proposed, which can effectively mitigate the edge effect caused by virtual subcarriers via edge value repetition in the frequency domain and zero insertion in the time domain, thus to improve the overall channel estimation performance for the usable subcarriers. Moreover, it does not need a priori statistical characteristics of the wireless channel and the complexity of channel estimation is not increased. Therefore, it is especially suitable for hardware implementation.

This paper describes a channel estimation scheme which utilizes interlaced transmit pilots to estimate multiple input multiple output (MIMO) channel responses in orthogonal frequency division multiplexing (OFDM) systems. This least square (LS) based channel estimation scheme adopts discrete Fourier transform (DFT) to improve the estimation accuracy. The constraint condition of the estimation scheme is obtained using frequency-domain sampling theorem. Simulation shows that the proposed scheme shows good stability when the constraint condition fails, which is much better than other two commonly-used MIMO-OFDM channel estimation schemes.

Channel state information (CSI) is critical for the performance of orthogonal frequency division multiplexing (OFDM) systems. In this paper, we propose time-domain minimum mean square error (MMSE) channel estimation based on subspace tracking for OFDM systems, which can increase the channel estimation accuracy as well as lower the computational complexity. Furthermore, it requires little prior channel statistical information and can track the time-varying wireless channel effectively. With proper training sequences design on transmitter antennas, it can be easily applied to multiple inputs multiple outputs (MIMO) OFDM systems. Computer simulations demonstrate its good performance

Conventional automatic repeat request (ARQ) schemes perform poorly in wireless fading channels. In this paper, variable packet size adaptive modulation (AM) ARQ scheme is proposed. The performance of discrete packet size AM selective repeat (SR) ARQ schemes for Rayleigh fading channels is analyzed and simple analytical expressions of its throughput are presented. The optimum switching thresholds that maximize the throughput are found. Theoretic analysis and numerical results indicate that a small number of packet sizes per modulation mode can get good performance in fading channels.