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Cell-free massive multiple-input multiple-output (MIMO) systems, leveraging tight cooperation among wireless access points, exhibit remarkable signal enhancement and interference suppression capabilities, demonstrating significant performance advantages over traditional cellular networks. This paper investigates the performance and deployment optimization of a user-centric scalable cell-free massive MIMO system with imperfect channel information over correlated Rayleigh fading channels. Based on the large-dimensional random matrix theory, this paper presents the deterministic equivalent of the ergodic sum rate for this system when applying the local partial minimum mean square error (LP-MMSE) precoding method, along with its derivative with respect to the channel correlation matrix. Furthermore, utilizing the derivative of the ergodic sum rate, this paper designs a Barzilai-Borwein based gradient descent method to improve system deployment. Simulation experiments demonstrate that under various parameter settings and large-scale antenna configurations, the deterministic equivalent of the ergodic sum rate accurately approximates the Monte Carlo ergodic sum rate of the system. Furthermore, the deployment optimization algorithm effectively enhances the ergodic sum rate of this system by optimizing the positions of access points., Comment: 14 pages, 8 figures

Fixed-dimensional speaker embeddings have become the dominant approach in speaker modeling, typically spanning hundreds to thousands of dimensions. These dimensions are hyperparameters that are not specifically picked, nor are they hierarchically ordered in terms of importance. In large-scale speaker representation databases, reducing the dimensionality of embeddings can significantly lower storage and computational costs. However, directly training low-dimensional representations often yields suboptimal performance. In this paper, we introduce the Matryoshka speaker embedding, a method that allows dynamic extraction of sub-dimensions from the embedding while maintaining performance. Our approach is validated on the VoxCeleb dataset, demonstrating that it can achieve extremely low-dimensional embeddings, such as 8 dimensions, while preserving high speaker verification performance., Comment: ICSR 2024, Shenzhen

In accented voice conversion or accent conversion, we seek to convert the accent in speech from one another while preserving speaker identity and semantic content. In this study, we formulate a novel method for creating multi-accented speech samples, thus pairs of accented speech samples by the same speaker, through text transliteration for training accent conversion systems. We begin by generating transliterated text with Large Language Models (LLMs), which is then fed into multilingual TTS models to synthesize accented English speech. As a reference system, we built a sequence-to-sequence model on the synthetic parallel corpus for accent conversion. We validated the proposed method for both native and non-native English speakers. Subjective and objective evaluations further validate our dataset's effectiveness in accent conversion studies., Comment: Project page with Speech Demo: https://github.com/shinshoji01/MacST-project-page

This paper introduces Easy One-Step Text-to-Speech (E1 TTS), an efficient non-autoregressive zero-shot text-to-speech system based on denoising diffusion pretraining and distribution matching distillation. The training of E1 TTS is straightforward; it does not require explicit monotonic alignment between the text and audio pairs. The inference of E1 TTS is efficient, requiring only one neural network evaluation for each utterance. Despite its sampling efficiency, E1 TTS achieves naturalness and speaker similarity comparable to various strong baseline models. Audio samples are available at http://e1tts.github.io/ .

One of the primary challenges in short packet ultra-reliable and low-latency communications (URLLC) is to achieve reliable channel estimation and data detection while minimizing the impact on latency performance. Given the small packet size in mini-slot-assisted URLLC, relying solely on pilot-based coherent detection is almost impossible to meet the seemingly contradictory requirements of high channel estimation accuracy, high reliability, low training overhead, and low latency. In this paper, we explore differential modulation both in the frequency domain and in the time domain, and propose adopting an adaptive approach that integrates both differential and coherent detection to achieve mini-slot-assisted short packet URLLC, striking a balance among training overhead, system performance, and computational complexity. Specifically, differential (especially in the frequency domain) and coherent detection schemes can be dynamically activated based on application scenarios, channel statistics, information payloads, mini-slot deployment options, and service requirements. Furthermore, we derive the block error rate (BLER) for pilot-based, frequency domain, and time domain differential OFDM using non-asymptotic information-theoretic bounds. Simulation results validate the feasibility and effectiveness of adaptive differential and coherent detection., Comment: 14 pages, 8 figures, journal

Streaming voice conversion has become increasingly popular for its potential in real-time applications. The recently proposed DualVC 2 has achieved robust and high-quality streaming voice conversion with a latency of about 180ms. Nonetheless, the recognition-synthesis framework hinders end-to-end optimization, and the instability of automatic speech recognition (ASR) model with short chunks makes it challenging to further reduce latency. To address these issues, we propose an end-to-end model, DualVC 3. With speaker-independent semantic tokens to guide the training of the content encoder, the dependency on ASR is removed and the model can operate under extremely small chunks, with cascading errors eliminated. A language model is trained on the content encoder output to produce pseudo context by iteratively predicting future frames, providing more contextual information for the decoder to improve conversion quality. Experimental results demonstrate that DualVC 3 achieves comparable performance to DualVC 2 in subjective and objective metrics, with a latency of only 50 ms., Comment: Accepted by Interspeech 2024

This letter introduces a novel framework for dense Visual Simultaneous Localization and Mapping (VSLAM) based on Gaussian Splatting. Recently, SLAM based on Gaussian Splatting has shown promising results. However, in monocular scenarios, the Gaussian maps reconstructed lack geometric accuracy and exhibit weaker tracking capability. To address these limitations, we jointly optimize sparse visual odometry tracking and 3D Gaussian Splatting scene representation for the first time. We obtain depth maps on visual odometry keyframe windows using a fast Multi-View Stereo (MVS) network for the geometric supervision of Gaussian maps. Furthermore, we propose a depth smooth loss and Sparse-Dense Adjustment Ring (SDAR) to reduce the negative effect of estimated depth maps and preserve the consistency in scale between the visual odometry and Gaussian maps. We have evaluated our system across various synthetic and real-world datasets. The accuracy of our pose estimation surpasses existing methods and achieves state-of-the-art. Additionally, it outperforms previous monocular methods in terms of novel view synthesis and geometric reconstruction fidelities., Comment: Accepted by IEEE Robotics and Automation Letters

The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols. Consequently, error propagation can occur in traditional turbo receivers, leading to performance degradation. Moreover, the processing delay induced by information exchange between different modules may also be undesirable for URLLC. To address the issues, we advocate to perform joint channel estimation, detection, and decoding (JCDD) for MIMO URLLC systems encoded by short low-density parity-check (LDPC) codes. Specifically, we develop two novel JCDD problem formulations based on the maximum a posteriori (MAP) criterion for Gaussian MIMO channels and sparse mmWave MIMO channels, respectively, which integrate the pilots, the bit-to-symbol mapping, the LDPC code constraints, as well as the channel statistical information. Both the challenging large-scale non-convex problems are then solved based on the alternating direction method of multipliers (ADMM) algorithms, where closed-form solutions are achieved in each ADMM iteration. Furthermore, two JCDD neural networks, called JCDDNet-G and JCDDNet-S, are built by unfolding the derived ADMM algorithms and introducing trainable parameters. It is interesting to find via simulations that the proposed trainable JCDD receivers can outperform the turbo receivers with affordable computational complexities., Comment: 17 pages, 12 figures, accepted by IEEE Transactions on Wireless Communications

Accent transfer aims to transfer an accent from a source speaker to synthetic speech in the target speaker's voice. The main challenge is how to effectively disentangle speaker timbre and accent which are entangled in speech. This paper presents a VITS-based end-to-end accent transfer model named Accent-VITS.Based on the main structure of VITS, Accent-VITS makes substantial improvements to enable effective and stable accent transfer.We leverage a hierarchical CVAE structure to model accent pronunciation information and acoustic features, respectively, using bottleneck features and mel spectrums as constraints.Moreover, the text-to-wave mapping in VITS is decomposed into text-to-accent and accent-to-wave mappings in Accent-VITS. In this way, the disentanglement of accent and speaker timbre becomes be more stable and effective.Experiments on multi-accent and Mandarin datasets show that Accent-VITS achieves higher speaker similarity, accent similarity and speech naturalness as compared with a strong baseline., Comment: Accepted by NCMMSC2023

This paper investigates how to achieve integrated sensing and communication (ISAC) based on a cell-free radio access network (CF-RAN) architecture with a minimum footprint of communication resources. We propose a new passive sensing scheme. The scheme is based on the radio frequency (RF) fingerprint learning of the RF radio unit (RRU) to build an RF fingerprint library of RRUs. The source RRU is identified by comparing the RF fingerprints carried by the signal at the receiver side. The receiver extracts the channel parameters from the signal and estimates the channel environment, thus locating the reflectors in the environment. The proposed scheme can effectively solve the problem of interference between signals in the same time-frequency domain but in different spatial domains when multiple RRUs jointly serve users in CF-RAN architecture. Simulation results show that the proposed passive ISAC scheme can effectively detect reflector location information in the environment without degrading the communication performance., Comment: 11 pages, 6 figures, submitted on 28-Feb-2023, China Communication, Accepted on 14-Sep-2023

In recent years, quantum computing has entered the Noisy Intermediate-Scale Quantum (NISQ). However, NISQ devices have inherent limitations in terms of connectivity and hardware noise, necessitating the transformation of quantum logic circuits for correct execution on NISQ chips. The synthesis of CNOT circuits considering physical constraints can transform quantum algorithms into low-level quantum circuits, which can be directly executed on physical chips. In the current trend, quantum chip architectures without Hamiltonian paths are gradually replacing architectures with Hamiltonian paths due to their scalability and low-noise characteristics. To this end, this paper addresses the nearest neighbor synthesis of CNOT circuits in the architecture with and without Hamiltonian paths, aiming to enhance the fidelity of the circuits after execution. Firstly, a key-qubit priority mapping model for the general architecture with and without Hamiltonian paths is proposed. Secondly, the initial mapping is further improved by using tabu search to reduce the number of CNOT gates after circuit synthesis and enhance its fidelity. Finally, the noise-aware CNOT circuit nearest neighbor synthesis algorithm for the general architecture is proposed based on the key-qubit priority mapping model. Experimental results show that the proposed method can enhance the fidelity of the CNOT circuit by about 64.7% on a real quantum computing device, achieving a significant optimization effect. Furthermore, the method can be extended to other circuits, thereby improving the overall performance of quantum computing on NISQ devices.

Voice conversion is becoming increasingly popular, and a growing number of application scenarios require models with streaming inference capabilities. The recently proposed DualVC attempts to achieve this objective through streaming model architecture design and intra-model knowledge distillation along with hybrid predictive coding to compensate for the lack of future information. However, DualVC encounters several problems that limit its performance. First, the autoregressive decoder has error accumulation in its nature and limits the inference speed as well. Second, the causal convolution enables streaming capability but cannot sufficiently use future information within chunks. Third, the model is unable to effectively address the noise in the unvoiced segments, lowering the sound quality. In this paper, we propose DualVC 2 to address these issues. Specifically, the model backbone is migrated to a Conformer-based architecture, empowering parallel inference. Causal convolution is replaced by non-causal convolution with dynamic chunk mask to make better use of within-chunk future information. Also, quiet attention is introduced to enhance the model's noise robustness. Experiments show that DualVC 2 outperforms DualVC and other baseline systems in both subjective and objective metrics, with only 186.4 ms latency. Our audio samples are made publicly available., Comment: Accepted by ICASSP2024

One key feature of ultra-reliable low-latency communications (URLLC) in 5G is to support short packet transmission (SPT). However, the pilot overhead in SPT for channel estimation is relatively high, especially in high Doppler environments. In this paper, we advocate the adoption of differential modulation to support ultra-low latency services, which can ease the channel estimation burden and reduce the power and bandwidth overhead incurred in traditional coherent modulation schemes. Specifically, we consider a multi-connectivity (MC) scheme employing differential modulation to enable URLLC services. The popular selection combining and maximal ratio combining schemes are respectively applied to explore the diversity gain in the MC scheme. A first-order autoregressive model is further utilized to characterize the time-varying nature of the channel. Theoretically, the maximum achievable rate and minimum achievable block error rate under ergodic fading channels with PSK inputs and perfect CSI are first derived by using the non-asymptotic information-theoretic bounds. The performance of SPT with differential modulation and MC schemes is then analysed by characterizing the effect of differential modulation and time-varying channels as a reduction in the effective SNR. Simulation results show that differential modulation does offer a significant advantage over the pilot-assisted coherent scheme for SPT, especially in high Doppler environments., Comment: 15 pages, 9 figures

Despite imperfect score-matching causing drift in training and sampling distributions of diffusion models, recent advances in diffusion-based acoustic models have revolutionized data-sufficient single-speaker Text-to-Speech (TTS) approaches, with Grad-TTS being a prime example. However, the sampling drift problem leads to these approaches struggling in multi-speaker scenarios in practice due to more complex target data distribution compared to single-speaker scenarios. In this paper, we present Multi-GradSpeech, a multi-speaker diffusion-based acoustic models which introduces the Consistent Diffusion Model (CDM) as a generative modeling approach. We enforce the consistency property of CDM during the training process to alleviate the sampling drift problem in the inference stage, resulting in significant improvements in multi-speaker TTS performance. Our experimental results corroborate that our proposed approach can improve the performance of different speakers involved in multi-speaker TTS compared to Grad-TTS, even outperforming the fine-tuning approach. Audio samples are available at https://welkinyang.github.io/multi-gradspeech/

The explosive growth of data results in more scarce spectrum resources. It is important to optimize the system performance under limited resources. In this paper, we investigate how to achieve weighted throughput (WTP) maximization for cell-free (CF) multiuser MIMO (MU-MIMO) multicarrier (MC) systems through resource allocation (RA), in the cases of finite blocklength (FBL) and infinite blocklength (INFBL) regimes. To ensure the quality of service (QoS) of each user, particularly for the block error rate (BLER) and latency in the FBL regime, the WTP gets maximized under the constraints of total power consumption and required QoS metrics. Since the channels vary in different subcarriers (SCs) and inter-user interference strengths, the WTP can be maximized by scheduling the best users in each time-frequency (TF) resource and advanced beamforming design, while the resources can be fully utilized. With this motivation, we propose a joint user scheduling (US) and beamforming design algorithm based on the successive convex approximation (SCA) and gene-aided (GA) algorithms, to address a mixed integer nonlinear programming (MINLP) problem. Numerical results demonstrate that the proposed RA outperforms the comparison schemes. And the CF system in our scenario is capable of achieving higher spectral efficiency than the centralized antenna systems (CAS).

A future millimeter-wave (mmWave) massive multiple-input and multiple-output (MIMO) system may serve hundreds or thousands of users at the same time; thus, research on multiple access technology is particularly important.Moreover, due to the short-wavelength nature of a mmWave, large-scale arrays are easier to implement than microwaves, while their directivity and sparseness make the physical beamforming effect of precoding more prominent.In consideration of the mmWave angle division multiple access (ADMA) system based on precoding, this paper investigates the influence of the angle distribution on system performance, which is denoted as the angular multiplexing gain.Furthermore, inspired by the above research, we transform the ADMA user grouping problem to maximize the system sum-rate into the inter-user angular spacing equalization problem.Then, the form of the optimal solution for the approximate problem is derived, and the corresponding grouping algorithm is proposed.The simulation results demonstrate that the proposed algorithm performs better than the comparison methods.Finally, a complexity analysis also shows that the proposed algorithm has extremely low complexity., Comment: 12 pages,16 figures

This paper investigates the resource allocation problem combined with fronthaul precoding and access link sparse precoding design in cloud radio access network (C-RAN) wireless fronthaul systems.Multiple remote antenna units (RAUs) in C-RAN systems can collaborate in a cluster through centralized signal processing to realize distributed massive multiple-input and multiple-output (MIMO) systems and obtain performance gains such as spectrum efficiency and coverage.Wireless fronthaul is a flexible, low-cost way to implement C-RAN systems, however, compared with the fiber fronthaul network, the capacity of wireless fronthaul is extremely limited.Based on this problem, this paper first design the fronthaul and access link precoding to make the fronthaul capacity of RAUs match the access link demand.Then, combined with the precoding design problem, the allocation optimization of orthogonal resources is studied to further optimize the resource allocation between fronthaul link and access link to improve the performance of the system.Numerical results verify the effectiveness of the proposed precoding design and resource allocation optimization algorithm., Comment: 5 pages, 2 figures

Ultra-reliable and low-latency communications (URLLC) is firstly proposed in 5G networks, and expected to support applications with the most stringent quality-of-service (QoS). However, since the wireless channels vary dynamically, the transmit power for ensuring the QoS requirements of URLLC may be very high, which conflicts with the power limitation of a real system. To fulfill the successful URLLC transmission with finite transmit power, we propose an energy-efficient packet delivery mechanism incorparated with frequency-hopping and proactive dropping in this paper. To reduce uplink outage probability, frequency-hopping provides more chances for transmission so that the failure hardly occurs. To avoid downlink outage from queue clearing, proactive dropping controls overall reliability by introducing an extra error component. With the proposed packet delivery mechanism, we jointly optimize bandwidth allocation and power control of uplink and downlink, antenna configuration, and subchannel assignment to minimize the average total power under the constraint of URLLC transmission requirements. Via theoretical analysis (e.g., the convexity with respect to bandwidth, the independence of bandwidth allocation, the convexity of antenna configuration with inactive constraints), the simplication of finding the global optimal solution for resource allocation is addressed. A three-step method is then proposed to find the optimal solution for resource allocation. Simulation results validate the analysis and show the performance gain by optimizing resource allocation with the proposed packet delivery mechanism., Comment: 16 pages, 11 figures

Voice conversion is an increasingly popular technology, and the growing number of real-time applications requires models with streaming conversion capabilities. Unlike typical (non-streaming) voice conversion, which can leverage the entire utterance as full context, streaming voice conversion faces significant challenges due to the missing future information, resulting in degraded intelligibility, speaker similarity, and sound quality. To address this challenge, we propose DualVC, a dual-mode neural voice conversion approach that supports both streaming and non-streaming modes using jointly trained separate network parameters. Furthermore, we propose intra-model knowledge distillation and hybrid predictive coding (HPC) to enhance the performance of streaming conversion. Additionally, we incorporate data augmentation to train a noise-robust autoregressive decoder, improving the model's performance on long-form speech conversion. Experimental results demonstrate that the proposed model outperforms the baseline models in the context of streaming voice conversion, while maintaining comparable performance to the non-streaming topline system that leverages the complete context, albeit with a latency of only 252.8 ms.

This paper proposes a multi-level cooperative architecture to balance the spectral efficiency and scalability of cell-free massive multiple-input multiple-output (MIMO) systems. In the proposed architecture, spatial expansion units (SEUs) are introduced to avoid a large amount of computation at the access points (APs) and increase the degree of cooperation among APs. We first derive the closed-form expressions of the uplink user achievable rates under multi-level cooperative architecture with maximal ratio combination (MRC) and zero-forcing (ZF) receivers. The accuracy of the closed-form expressions is verified. Moreover, numerical results have demonstrated that the proposed multi-level cooperative architecture achieves a better trade-off between spectral efficiency and scalability than other forms of cell-free massive MIMO architectures., Comment: 5 pages, 3 figures

In this paper, we investigate network-assisted full-duplex (NAFD) cell-free millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems with digital-to-analog converter (DAC) quantization and fronthaul compression. We propose to maximize the weighted uplink and downlink sum rate by jointly optimizing the power allocation of both the transmitting remote antenna units (T-RAUs) and uplink users and the variances of the downlink and uplink fronthaul compression noises. To deal with this challenging problem, we further apply a successive convex approximation (SCA) method to handle the non-convex bidirectional limited-capacity fronthaul constraints. The simulation results verify the convergence of the proposed SCA-based algorithm and analyze the impact of fronthaul capacity and DAC quantization on the spectral efficiency of the NAFD cell-free mmWave massive MIMO systems. Moreover, some insightful conclusions are obtained through the comparisons of spectral efficiency, which shows that NAFD achieves better performance gains than co-time co-frequency full-duplex cloud radio access network (CCFD C-RAN) in the cases of practical limited-resolution DACs. Specifically, their performance gaps with 8-bit DAC quantization are larger than that with 1-bit DAC quantization, which attains a 5.5-fold improvement., Comment: accepted by China Communications

Smart Internet of Vehicles (IoV) as a promising application in Internet of Things (IoT) emerges with the development of the fifth generation mobile communication (5G). Nevertheless, the heterogeneous requirements of sufficient battery capacity, powerful computing ability and energy efficiency for electric vehicles face great challenges due to the explosive data growth in 5G and the sixth generation of mobile communication (6G) networks. In order to alleviate the deficiencies mentioned above, this paper proposes a mobile edge computing (MEC) enabled IoV system, in which electric vehicle nodes (eVNs) upload and download data through an anchor node (AN) which is integrated with a MEC server. Meanwhile, the anchor node transmitters radio signal to electric vehicles with simultaneous wireless information and power transfer (SWIPT) technology so as to compensate the battery limitation of eletric vehicles. Moreover, the spectrum efficiency is further improved by multi-input and multi-output (MIMO) and full-duplex (FD) technologies which is equipped at the anchor node. In consideration of the issues above, we maximize the average energy efficiency of electric vehicles by jointly optimize the CPU frequency, vehicle transmitting power, computing tasks and uplink rate. Since the problem is nonconvex, we propose a novel alternate interior-point iterative scheme (AIIS) under the constraints of computing tasks, energy consumption and time latency. Results and discussion section verifies the effectiveness of the proposed AIIS scheme comparing with the benchmark schemes., Comment: 17 pages, 9 figures, EURASIP J. Adv. Signal Process

Network-assisted full-duplex (NAFD) distributed massive multiple input multiple output (M-MIMO) enables the in-band full-duplex with existing half-duplex devices at the network level, which exceptionally improves spectral efficiency. This paper analyzes the impact of low-resolution analog-to-digital converters (ADCs) on NAFD distributed M-MIMO and designs an efficient bit allocation algorithm for low-resolution ADCs. The beamforming training mechanism relieves the heavy pilot overhead for channel estimation, which remarkably enhances system performance by guiding the interference cancellation and coherence detection. Furthermore, closed-form expressions for spectral and energy efficiency with low-resolution ADCs are derived. The multi-objective optimization problem (MOOP) for spectral and energy efficiency is solved by the deep Q network and the non-dominated sorting genetic algorithm II. The simulation results corroborate the theoretical derivation and verify the effectiveness of introducing low-resolution ADCs in NAFD distributed M-MIMO systems. Meanwhile, a set of Pareto-optimal solutions for ADC accuracy flexibly provide guidelines for deploying in a practical NAFD distributed M-MIMO system.

Voice conversion for highly expressive speech is challenging. Current approaches struggle with the balancing between speaker similarity, intelligibility and expressiveness. To address this problem, we propose Expressive-VC, a novel end-to-end voice conversion framework that leverages advantages from both neural bottleneck feature (BNF) approach and information perturbation approach. Specifically, we use a BNF encoder and a Perturbed-Wav encoder to form a content extractor to learn linguistic and para-linguistic features respectively, where BNFs come from a robust pre-trained ASR model and the perturbed wave becomes speaker-irrelevant after signal perturbation. We further fuse the linguistic and para-linguistic features through an attention mechanism, where speaker-dependent prosody features are adopted as the attention query, which result from a prosody encoder with target speaker embedding and normalized pitch and energy of source speech as input. Finally the decoder consumes the integrated features and the speaker-dependent prosody feature to generate the converted speech. Experiments demonstrate that Expressive-VC is superior to several state-of-the-art systems, achieving both high expressiveness captured from the source speech and high speaker similarity with the target speaker; meanwhile intelligibility is well maintained.

The ever-increasing number of spent lithium-ion batteries (LIBs) has presented a serious waste-management challenge. Aluminium and copper current collectors are important components in LIBs and take up a weight percentage of more than 15%. Direct reuse of current collectors can effectively reduce LIB waste and provide an alternative renewable source of aluminium and copper. Besides, it also prevents long manufacturing processes and associated energy input and material consumption. However, there is a lack of work on the direct reuse of current collectors in the literature. Herein, aluminium and copper current collectors are reclaimed from commercial spent LIBs with different chemical treatments and successfully reused for LiNi0.6Mn0.2Co0.2O2 cathodes and graphite anodes, respectively. The reclaimed current collectors treated with different processes show different surface compositions and morphology to pristine ones, resulting in distinctive wettability, adhesion and electrical conductivity. The reused current collectors show similar electrochemical performance to the pristine one at low C rates, while extra caution should be taken at high C rates for aluminium current collectors due to relatively low contact conductivity. This work provides substantial evidence that the direct reuse of aluminium and copper current collectors is possible and highlights the importance of the surface morphology of current collectors.

A quantum circuit must be preprocessed before implementing on NISQ devices due to the connectivity constraint. Quantum circuit mapping (QCM) transforms the circuit into an equivalent one that is compliant with the NISQ device's architecture constraint by adding SWAP gates. The QCM problem asks the minimal number of auxiliary SWAP gates, and is NP-complete. The complexity of QCM with fixed parameters is studied in the paper. We give an exact algorithm for QCM, and show that the algorithm runs in polynomial time if the NISQ device's architecture is fixed. If the number of qubits of the quantum circuit is fixed, we show that the QCM problem is NL-complete by a reduction from the undirected shortest path problem. Moreover, the fixed-parameter complexity of QCM is W[1]-hard when parameterized by the number of qubits of the quantum circuit. We prove the result by a reduction from the clique problem. If taking the depth of the quantum circuits and the coupling graphs as parameters, we show that the QCM problem is still NP-complete over shallow quantum circuits, and planar, bipartite and degree bounded coupling graphs.

To improve the poor performance of distributed operation and non-scalability of centralized operation in traditional cell-free massive MIMO, we propose a cell-free distributed collaborative (CFDC) massive multiple-input multiple-output (MIMO) system based on a novel two-layer model to take advantages of the distributed cloud-edge-end collaborative architecture in beyond 5G (B5G) internet of things (IoT) environment to provide strong flexibility and scalability. We further ultilize the proposed CFDC massive MIMO system to support the low altitude three-dimensional (3-D) coverage scenario with unmanned aerial vehicles (UAVs), while accounting for 3-D Rician channel estimation, user-centric association and different scalable receiving schemes. Since coexisted UAVs and ground users (GUEs) cause greater interference, we ultilize user-centric association strategy and minimum-mean-square error (MMSE) channel state information (CSI) estimation to obtain the estimated CSI of UAVs and GUEs. Under the CFDC scenarios, scalable receiving schemes as maximum ratio combing (MRC), partial zero-forcing (P-ZF) and partial minimum-mean-square error (P-MMSE) can be performed at edge servers and the closed-form expressions for uplink spectral efficiency (SE) are derived. Based on the derived expressions, we propose an efficient power control algorithm by solving a multi-objective optimization problem (MOOP) between maximizing the average SE of UAVs and GUEs simultaneously with Deep Q-Network (DQN). Numerical results verify the accuracy of the derived closed-form expressions and the effectiveness of the coexisted UAVs and GUEs transmission scheme in CFDC massive MIMO systems. The SE analysis under various system parameters offers numerous flexibilities for system optimization., Comment: The work is further studied and the content of the paper is updated. So, temporarily withdrawn for these reasons

In this paper, we investigate the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs). In order to predict the content popularity with high accuracy and low complexity, we propose a Gaussian process based regressor to model the content request pattern. Firstly, the relationship between content features and popularity is captured by our proposed model. Then, we utilize Bayesian learning to train the model parameters, which is robust to overfitting. However, Bayesian methods are usually unable to find a closed-form expression of the posterior distribution. To tackle this issue, we apply a stochastic variance reduced gradient Hamiltonian Monte Carlo (SVRG-HMC) method to approximate the posterior distribution. To utilize the computing resources of other fog access points (F-APs) and to reduce the communications overhead, we propose a quantized federated learning (FL) framework combining with Bayesian learning. The quantized federated Bayesian learning framework allows each F-AP to send gradients to the cloud server after quantizing and encoding. It can achieve a tradeoff between prediction accuracy and communications overhead effectively. Simulation results show that the performance of our proposed policy outperforms the existing policies., Comment: 26 pages, 10 figures

Ultra-reliable low latency communications (uRLLC) is adopted in the fifth generation (5G) mobile networks to better support mission-critical applications that demand high level of reliability and low latency. With the aid of well-established multiple-input multiple-output (MIMO) information theory, uRLLC in the future 6G is expected to provide enhanced capability towards extreme connectivity. Since the latency constraint can be represented equivalently by blocklength, channel coding theory at finite block-length plays an important role in the theoretic analysis of uRLLC. On the basis of Polyanskiy's and Yang's asymptotic results, we first derive the exact close-form expressions for the expectation and variance of channel dispersion. Then, the bound of average maximal achievable rate is given for massive MIMO systems in ideal independent and identically distributed fading channels. This is the study to reveal the underlying connections among the fundamental parameters in MIMO transmissions in a concise and complete close-form formula. Most importantly, the inversely proportional law observed therein implies that the latency can be further reduced at expense of spatial degrees of freedom.

Building a high-quality singing corpus for a person who is not good at singing is non-trivial, thus making it challenging to create a singing voice synthesizer for this person. Learn2Sing is dedicated to synthesizing the singing voice of a speaker without his or her singing data by learning from data recorded by others, i.e., the singing teacher. Inspired by the fact that pitch is the key style factor to distinguish singing from speaking voice, the proposed Learn2Sing 2.0 first generates the preliminary acoustic feature with averaged pitch value in the phone level, which allows the training of this process for different styles, i.e., speaking or singing, share same conditions except for the speaker information. Then, conditioned on the specific style, a diffusion decoder, which is accelerated by a fast sampling algorithm during the inference stage, is adopted to gradually restore the final acoustic feature. During the training, to avoid the information confusion of the speaker embedding and the style embedding, mutual information is employed to restrain the learning of speaker embedding and style embedding. Experiments show that the proposed approach is capable of synthesizing high-quality singing voice for the target speaker without singing data with 10 decoding steps., Comment: Submitted to INTERSPEECH 2022

In this paper, we consider a joint unicast and multi-group multicast cell-free distributed massive multiple-input multiple-output (MIMO) system, while accounting for co-pilot assignment strategy based channel estimation, pilot contamination and different precoding schemes. Under the co-pilot assignment strategy, we derive the minimum-mean-square error (MMSE) channel state information (CSI) estimation for unicast and multicast users. Given the acquired CSI, the closed-form expressions for downlink achievable rates with maximum ratio transmission (MRT), zero-forcing (ZF) and MMSE beamforming are derived. Based on these expressions, we propose an efficient power allocation scheme by solving a multi-objective optimization problem (MOOP) between maximizing the minimum spectral efficiency (SE) of multicast users and maximizing the average SE of unicast users with non-dominated sorting genetic algorithm II (NSGA-II). Moreover, the MOOP is converted into a deep learning (DL) problem and solved by an unsupervised learning method to further promote computational efficiency. Numerical results verify the accuracy of the derived closed-form expressions and the effectiveness of the joint unicast and multigroup multicast transmission scheme in cell-free distributed massive MIMO systems. The SE analysis under various system parameters and the trade-off regions between these two conflicting optimization objectives offers numerous flexibilities for system optimization.