In-loop filters have been comprehensively explored during the development of video coding standards due to their remarkable noise-reduction capabilities.In the early stage of video coding,in-loop filters,such as the deblocking filter,sample adaptive offset,and adap-tive loop filter,were performed separately for each component.Recently,cross-component filters have been studied to improve chroma fidel-ity by exploiting correlations between the luma and chroma channels.This paper introduces the cross-component filters used in the state-of-the-art video coding standards,including the cross-component adaptive loop filter and cross-component sample adaptive offset.Cross-component filters aim to reduce compression artifacts based on the correlation between different components and provide more accurate pixel reconstruction values.We present their origin,development,and status in the current video coding standards.Finally,we conduct discussions on the further evolution of cross-component filters.
The advent of 6G wireless networks promises unprecedented connectivity,supporting ultra-high data rates,low latency,and massive device connectivity.However,these ambitious goals introduce significant challenges,particularly in channel estimation due to complex and dynamic propagation environments.This paper explores the concept of channel knowledge maps(CKMs) as a solution to these challenges.CKMs enable environment-aware communications by providing location-specific channel information,reducing reliance on real-time pilot measurements.We categorize CKM construction techniques into measurement-based,model-based,and hybrid methods,and examine their key applications in integrated sensing and communication(ISAC) systems,beamforming,trajectory optimization of unmanned aerial vehicles(UAVs),base station(BS) placement,and resource allocation.Furthermore,we discuss open challenges and propose future research directions to enhance the robustness,accuracy,and scalability of CKM-based systems in the evolving 6G landscape.
Along with the proliferating research interest in semantic communication(SemCom),joint source channel coding(JSCC)has domi-nated the attention due to the widely assumed existence in efficiently delivering information semantics.Nevertheless,this paper challenges the conventional JSCC paradigm and advocates for adopting separate source channel coding(SSCC)to enjoy a more underlying degree of free-dom for optimization.We demonstrate that SSCC,after leveraging the strengths of the Large Language Model(LLM)for source coding and Er-ror Correction Code Transformer(ECCT)complemented for channel coding,offers superior performance over JSCC.Our proposed framework also effectively highlights the compatibility challenges between SemCom approaches and digital communication systems,particularly concern-ing the resource costs associated with the transmission of high-precision floating point numbers.Through comprehensive evaluations,we es-tablish that assisted by LLM-based compression and ECCT-enhanced error correction,SSCC remains a viable and effective solution for mod-ern communication systems.In other words,separate source channel coding is still what we need.
Wireless local area network (WLAN) witnesses a very fast growth in the past 20 years by taking the maximum throughput as the key technical objective. However, the quality of experience (QoE) is the most important concern of wireless network users. In this article, we point out that poor QoE is the most challenging problem of the current WLAN, and further analyze the key technical problems that cause the poor QoE of WLAN, including fully distributed networking architecture, chaotic random access, awkward ``high capability'', coarse-grained QoS architecture, ubiquitous and complicated interference, ``no place'' for artificial intelligence (AI), and heavy burden of standard evolving. To the best of our knowledge, this is the first work to point out that poor QoE is the most challenging problem of the current WLAN, and the first work to systematically analyze the technical problems that cause the poor QoE of WLAN. We highly suggest that achieving high experiences (HEX) be the key objective of the next generation WLAN.
A cooperative passive sensing framework for millimeter wave(mmWave) communication systems is proposed and demonstrated in a scenario with one mobile signal blocker. Specifically, in the uplink communication with at least two transmitters, a cooperative detection method is proposed for the receiver to track the blocker's trajectory, localize the transmitters and detect the potential link blockage jointly. To facilitate detection, the receiver collects the signal of each transmitter along a line-of-sight(LoS) path and a non-line-of-sight(NLoS) path separately via two narrow-beam phased arrays. The NLoS path involves scattering at the mobile blocker, allowing its identification through the Doppler fre-quency. By comparing the received signals of both paths, the Doppler frequency and angle-of-arrival(AoA) of the NLoS path can be estimated. To resolve the blocker's trajectory and the transmitters' locations, the receiver should continuously track the mobile blocker to accumulate suffi-cient numbers of the Doppler frequency and AoA versus time observations. Finally, a gradient-descent-based algorithm is proposed for joint de-tection. With the reconstructed trajectory, the potential link blockage can be predicted. It is demonstrated that the system can achieve decimeter-level localization and trajectory estimation, and predict the blockage time with an error of less than 0.1 s.
Byte-addressable non-volatile memory (NVM), as a new participant in the storage hierarchy, gives extremely high performance in storage, which forces changes to be made on current filesystem designs. Page cache, once a significant mechanism filling the perfor-mance gap between Dynamic Random Access Memory (DRAM) and block devices, is now a liability that heavily hinders the writing perfor-mance of NVM filesystems. Therefore state-of-the-art NVM filesystems leverage the direct access (DAX) technology to bypass the page cache entirely. However, the DRAM still provides higher bandwidth than NVM, which prevents skewed read workloads from benefiting from a higher bandwidth of the DRAM and leads to sub-optimal performance for the system. In this paper, we propose RCache, a read-intensive workload-aware page cache for NVM filesystems. Different from traditional caching mechanisms where all reads go through DRAM, RCache uses a tiered page cache design, including assigning DRAM and NVM to hot and cold data separately, and reading data from both sides. To avoid copying data to DRAM in a critical path, RCache migrates data from NVM to DRAM in a background thread. Ad-ditionally, RCache manages data in DRAM in a lock-free manner for better latency and scalability. Evaluations on Intel Optane Data Cen-ter (DC) Persistent Memory Modules show that, compared with NOVA, RCache achieves 3 times higher bandwidth for read-intensive work-loads and introduces little performance loss for write operations.
At present,the parameters of radar detection rely heavily on manual adjustment and empirical knowledge,resulting in low automa-tion.Traditional manual adjustment methods cannot meet the requirements of modern radars for high efficiency,high precision,and high auto-mation.Therefore,it is necessary to explore a new intelligent radar control learning framework and technology to improve the capability and automation of radar detection.Reinforcement learning is popular in decision task learning,but the shortage of samples in radar control tasks makes it difficult to meet the requirements of reinforcement learning.To address the above issues,we propose a practical radar operation rein-forcement learning framework,and integrate offline reinforcement learning and meta-reinforcement learning methods to alleviate the sample requirements of reinforcement learning.Experimental results show that our method can automatically perform as humans in radar detection with real-world settings,thereby promoting the practical application of reinforcement learning in radar operation.
The transformer is the key circuit component of the common-mode noise current when an isolated converter is working.The high-frequency characteristics of the transformer have an important influence on the common-mode noise of the converter.Traditionally,the mea-surement method is used for transformer modeling,and a single lumped device is used to establish the transformer model,which cannot be predicted in the transformer design stage.Based on the transformer common-mode noise transmission mechanism,this paper derives the transformer common-mode equivalent capacitance under ideal conditions.According to the principle of experimental measurement of the net-work analyzer,the electromagnetic field finite element simulation software three-dimensional(3D)modeling and simulation method is used to obtain the two-port parameters of the transformer,extract the high-frequency parameters of the transformer,and establish its electromagnetic compatibility equivalent circuit model.Finally,an experimental prototype is used to verify the correctness of the model by comparing the ex-perimental measurement results with the simulation prediction results.
A novel differential quasi-Yagi antenna is first presented and compared with a normal single-ended counterpart.The simulated and measured results show that the differential quasi-Yagi antenna outperforms the conventional single-ended one.The differential quasi-Yagi antenna is then used as an element for linear arrays.A study of the coupling mechanism between the two differential and the two single-ended quasi-Yagi antennas is conducted,which reveals that the TE0 mode is the dominant mode,and the driver is the decisive part to account for the mutual coupling.Next,the effects of four decoupling structures are respectively evaluated between the two differential quasi-Yagi an-tennas.Finally,the arrays with simple but effective decoupling structures are fabricated and measured.The measured results demonstrate that the simple slit or air-hole decoupling structure can reduce the coupling level from-18 dB to-25 dB and meanwhile maintain the imped-ance matching and radiation patterns of the array over the broad bandwidth.The differential quasi-Yagi antenna should be a promising an-tenna candidate for many applications.
As the scale of software systems expands,maintaining their stable operation has become an extraordinary challenge.System logs are semi-structured text generated by the recording function in the source code and have important research significance in software service anomaly detection.Existing log anomaly detection methods mainly focus on the statistical characteristics of logs,making it difficult to distinguish the se-mantic differences between normal and abnormal logs,and performing poorly on real-world industrial log data.In this paper,we propose an unsu-pervised framework for log anomaly detection based on generative pre-training-2(GPT-2).We apply our approach to two industrial systems.The experimental results on two datasets show that our approach outperforms state-of-the-art approaches for log anomaly detection.
Hybrid beamforming(HBF)has become an attractive and important technology in massive multiple-input multiple-output(MIMO)millimeter-wave(mmWave)systems.There are different hybrid architectures in HBF depending on different connection strategies of the phase shifter network between antennas and radio frequency chains.This paper investigates HBF optimization with different hybrid architec-tures in broadband point-to-point mmWave MIMO systems.The joint hybrid architecture and beamforming optimization problem is divided into two sub-problems.First,we transform the spectral efficiency maximization problem into an equivalent weighted mean squared error mini-mization problem,and propose an algorithm based on the manifold optimization method for the hybrid beamformer with a fixed hybrid archi-tecture.The overlapped subarray architecture which balances well between hardware costs and system performance is investigated.We fur-ther propose an algorithm to dynamically partition antenna subarrays and combine it with the HBF optimization algorithm.Simulation results are presented to demonstrate the performance improvement of our proposed algorithms.
The railway mobile communication system is undergoing a smooth transition from the Global System for Mobile Communications-Railway(GSM-R)to the Railway 5G.In this paper,an empirical path loss model based on a large amount of measured data is established to predict the path loss in the Railway 5G marshalling yard scenario.According to the different characteristics of base station directional anten-nas,the antenna gain is verified.Then we propose the position of the breakpoint in the antenna propagation area,and based on the breakpoint segmentation,a large-scale statistical model for marshalling yards is established.
With the development of wireless communication,the 6G mobile communication technology has received wide attention.As one of the key technologies of 6G,terahertz(THz)communication technology has the characteristics of ultra-high bandwidth,high security and low environmental noise.In this paper,a THz duplexer with a half-wavelength coupling structure and a sub-harmonic mixer operating at 216 GHz and 204 GHz are designed and measured.Based on these key devices,a 220 GHz frequency-division multiplexing communication system is proposed,with a real-time data rate of 10.4 Gbit/s for one channel and a transmission distance of 15 m.The measured constellation diagram of two receivers is clearly visible,the signal-to-noise ratio(SNR)is higher than 22 dB,and the bit error ratio(BER)is less than 10-8.Further-more,the high definition(HD)4K video can also be transmitted in real time without stutter.
Unsourced random access（URA） is a new perspective of massive access which aims at supporting numerous machine-type users. With the appearance of carrier frequency offset（CFO）, joint activity detection and channel estimation, which is vital for multiple-input and multiple-output URA, is a challenging task. To handle the phase corruption of channel measurements under CFO, a novel compressed sensing algorithm is proposed, leveraging the parametric bilinear generalized approximate message passing framework with a Markov chain support model that captures the block sparsity structure of the considered angular domain channel. An uncoupled transmission scheme is proposed to reduce system complexity, where slot-emitted messages are reorganized relying on clustering unique user channels. Simulation results reveal that the proposed transmission design for URA under CFO outperforms other potential methods.
Offline reinforcement learning(ORL)aims to learn a rational agent purely from behavior data without any online interaction.One of the major challenges encountered in ORL is the problem of distribution shift,i.e.,the mismatch between the knowledge of the learned policy and the reality of the underlying environment.Recent works usually handle this in a too pessimistic manner to avoid out-of-distribution(OOD)queries as much as possible,but this can influence the robustness of the agents at unseen states.In this paper,we propose a simple but effective method to address this issue.The key idea of our method is to enhance the robustness of the new policy learned offline by weakening its confidence in highly uncertain regions,and we propose to find those regions by simulating them with modified Generative Adversarial Nets(GAN)such that the generated data not only follow the same distribution with the old experience but are very difficult to deal with by them-selves,with regard to the behavior policy or some other reference policy.We then use this information to regularize the ORL algorithm to pe-nalize the overconfidence behavior in these regions.Extensive experiments on several publicly available offline RL benchmarks demonstrate the feasibility and effectiveness of the proposed method.
The accuracy of acquired channel state information(CSI)for beamforming design is essential for achievable performance in multiple-input multiple-output(MIMO)systems.However,in a high-speed moving scene with time-division duplex(TDD)mode,the acquired CSI depending on the channel reciprocity is inevitably outdated,leading to outdated beamforming design and then performance degradation.In this paper,a robust beamforming design under channel prediction errors is proposed for a time-varying MIMO system to combat the degra-dation further,based on the channel prediction technique.Specifically,the statistical characteristics of historical channel prediction errors are exploited and modeled.Moreover,to deal with random error terms,deterministic equivalents are adopted to further explore potential beam-forming gain through the statistical information and ultimately derive the robust design aiming at maximizing weighted sum-rate performance.Simulation results show that the proposed beamforming design can maintain outperformance during the downlink transmission time even when channels vary fast,compared with the traditional beamforming design.
Brain-computer interfaces(BCI)use neural activity as a control signal to enable direct communication between the human brain and external devices.The electrical signals generated by the brain are captured through electroencephalogram(EEG)and translated into neu-ral intentions reflecting the user's behavior.Correct decoding of the neural intentions then facilitates the control of external devices.Rein-forcement learning-based BCIs enhance decoders to complete tasks based only on feedback signals(rewards)from the environment,building a general framework for dynamic mapping from neural intentions to actions that adapt to changing environments.However,using traditional reinforcement learning methods can have challenges such as the curse of dimensionality and poor generalization.Therefore,in this paper,we use deep reinforcement learning to construct decoders for the correct decoding of EEG signals,demonstrate its feasibility through experi-ments,and demonstrate its stronger generalization on motion imaging(MI)EEG data signals with high dynamic characteristics.
Satellite communications has been regarded as an indispensable technology for future mobile networks to provide extremely high data rates,ultra-reliability,and ubiquitous coverage.However,the high dynamics caused by the fast movement of low-earth-orbit(LEO)satel-lites bring huge challenges in designing and optimizing satellite communication systems.Especially,admission control,deciding which users with diversified service requirements are allowed to access the network with limited resources,is of paramount importance to improve network resource utilization and meet the service quality requirements of users.In this paper,we propose a dynamic channel reservation strategy based on the Actor-Critic algorithm(AC-DCRS)to perform intelligent admission control in satellite networks.By carefully designing the long-term reward function and dynamically adjusting the reserved channel threshold,AC-DCRS reaches a long-run optimal access policy for both new calls and handover calls with different service priorities.Numerical results show that our proposed AC-DCRS outperforms traditional channel reservation strategies in terms of overall access failure probability,the average call success rate,and channel utilization under vari-ous dynamic traffic conditions.
This paper proposes a raptor-like low-density parity-check(RL-LDPC)code design together with the corresponding decoder hard-ware architecture aiming at next-generation mobile communication.A new kind of protograph different from the 5G new radio(NR)LDPC ba-sic matrix is presented,and a code construction algorithm is proposed to improve the error-correcting performance.A multi-core layered de-coder architecture that supports up to 100 Gbit/s throughput is designed based on the special protograph structure.
