Goal-oriented proactive dialogue systems are designed to guide user conversations seamlessly towards specific objectives by planning a goal-oriented path. However, previous research has focused predominantly on optimizing these paths while neglecting the inconsistencies that may arise between generated responses and dialogue contexts, including user profiles, dialogue history, domain knowledge, and subgoals. To address this issue, we introduce a model-agnostic two-stage Consistency Reflection and Correction (CRC) framework. Specifically, in the consistency reflection stage, the model is prompted to reflect on the discrepancies between generated responses and dialogue contexts, identifying inconsistencies and suggesting possible corrections. In the consistency correction stage, the model generates responses that are more consistent with the dialogue context based on these reflection results. We conducted experiments on various model architectures with different parameter sizes, including encoder-decoder models (BART, T5) and decoder-only models (GPT-2, DialoGPT, Phi3, Mistral and LLaMA3), and the experimental results on three datasets demonstrate that our CRC framework significantly improves the consistency between generated responses and dialogue contexts.

Packet loss may affect a wide range of applications that use voice over IP (VoIP), e.g. video conferencing. In this paper, we investigate a time-domain convolutional recurrent network (CRN) for online packet loss concealment. The CRN comprises a convolutional encoder-decoder structure and long short-term memory (LSTM) layers, which have been shown to be suitable for real-time speech enhancement applications. Moreover, we propose lookahead and masked training to further improve the performance of the CRN framework. Experimental results show that the proposed system outperforms a baseline system using only LSTM layers in terms of two objective metrics – perceptual evaluation of speech quality (PESQ) and short-term objective intelligibility (STOI); it also reduces the word error rate (WER) more than the baseline when used as a frontend for speech recognition. The advantage of the proposed system is also verified in a subjective evaluation by the mean opinion score (MOS).

In this paper, the theoretical analysis showed that the length of the route and the interference of links were the main factors that influence the throughput of the network. Then, a dynamic channel allocation algorithm base on the length of routes and the interference of links was proposed to enhance the system throughput. In the proposed algorithm, ant colony algorithm was used to collect the information of network, such as the length of routes and the interference of links. Then, the priority of link access channel was decided according to the collected information. The simulation results showed that the proposed algorithm could improve the whole network throughput obviously.

In this study, the authors consider multi‐user millimetre wave (mmWave) multiple‐input multiple‐output (MIMO) downlink communications for multi‐carrier scenarios, e.g. orthogonal frequency‐division multiplexing (OFDM) a common analogue precoder and combiner for the transmitter and receiver to maximise spectral efficiency is the main challenge for multi‐carrier MIMO systems, they propose two methods to solve this problem. The first method designs the analogue precoder and combiner based on the channel average of all subcarriers, which helps reduce computational complexity. Moreover, the channels of all subcarriers can be viewed as a third‐order tensor, and the second method is based on tensor unfolding, which makes full use of the channel information of all subcarriers. When the common analogue precoder and combiner are fixed, they consider designing a digital precoder to maximise the signal‐to‐leakage‐plus‐noise ratio of each user in every subcarrier, which leads to closed‐form solutions compared with the block diagonal method. Simulation results demonstrate that the performance of the proposed methods is close to that of the fully digital precoding method, and increasing the number of RF transceiver chains helps improve the performance of the proposed methods.

In this paper, we propose a hybridly connected structure for hybrid beamforming in millimeter-wave (mmWave) massive MIMO systems, where the antenna arrays at the transmitter and receiver consist of multiple sub-arrays, each of which connects to multiple radio frequency (RF) chains, and each RF chain connects to all the antennas corresponding to the sub-array. In this structure, through successive interference cancelation, we decompose the precoding matrix optimization problem into multiple precoding sub-matrix optimization problems. Then, near-optimal hybrid digital and analog precoders are designed through factorizing the precoding sub-matrix for each sub-array. Furthermore, we compare the performance of the proposed hybridly connected structure with the existing fully and partially connected structures in terms of spectral efficiency, the required number of phase shifters, and energy efficiency. Finally, simulation results are presented to demonstrate that the spectral efficiency of the hybridly connected structure is better than that of the partially connected structure and that its spectral efficiency can approach that of the fully connected structure with the increase in the number of RF chains. Moreover, the proposed algorithm for the hybridly connected structure is capable of achieving higher energy efficiency than existing algorithms for the fully and partially connected structures.

This paper considers hybrid beamforming (HBF) for the point-to-point (P2P) millimeter wave (mmWave) massive MIMO systems. The optimal hybrid precoding and combining matrices that maximizes the system capacity can be obtained based on the singular value decomposition (SVD) of the channel matrix. Then, the optimal unconstrained hybrid digital and analog precoders (combiners) are designed according to the polar decomposition of the optimal hybrid precoding (combining) matrix. Considering the actual hardware constraints, we propose a joint transmitter and receiver HBF algorithm based upon polar decomposition. In this algorithm, the hybrid analog constrained precoding and combining matrices can be derived without having to incur an excessive computational complexity of an iterative approach. Simulation results show that the proposed algorithm can approach the performance of optimal unconstrained precoding, and is insensitive to the accuracy of the channel state information (CSI).

In the process of the standardization of 3GPP LTE system and the promotion of wireless network resource deployment, two different wireless communication systems may be deployed with adjacent frequency channels in neighboring area, resulting in adjacent channel interference (ACI). In order to ensure the two systems work properly, research on ACI is necessary so that appropriate measures can be taken to avoid the interference. Currently, study of the industry on the system coexistence interference problem is generally only for single antenna scene. However, with the application of MIMO (multi-input multi-output) system and array antenna in 4G and 5G communication, research on the coexistence requirements between two LTE systems with 3D-MIMO technology is very important. In this paper, we discuss beam forming and antenna pattern of array antenna and introduce ACI model. Furthermore, system-level coexistence simulation is conducted to evaluate the RF features of transmitter and receiver with multi-antenna on BS performance. We calculate the required ACLR values to achieve 5% throughput loss. Simulation results show that in MIMO system, ACLR requirement is more stringent, and correlation levels among output signals has impact on ACI.

This paper considers hybrid beamforming (HBF) for a downlink multiuser millimeter-wave (mmWave) multiple input multiple output (MIMO) system. For this system, we propose a signal-to-leakage-and-noise (SLNR) based HBF algorithm. In this algorithm, we first consider jointly designing the analog precoder and combiner to maximize the RF chain gain between the BS and each user. Then, the digital precoder is designed with the aim at maximizing SLNR, when the analog precoder and combiner are fixed. This optimization problem can be further equivalent to generalized eigenvalue problem, and the optimal digital precoding vector for each stream can be obtained one by one by using generalized Rayleigh-Ritz quotient. Simulation results demonstrate that the performance of the proposed algorithm outperforms the existing algorithms, and are extremely close to the hypothetical single-user (no interference) case.