Dynamic metasurface antennas (DMAs) have emerged as a promising solution for alleviating the high power consumption, hardware cost, and physical size of multiple-input multiple-output (MIMO) architectures. This paper investigates a DMA-enabled multicast system with finite-alphabet inputs, considering the availability of either instantaneous or statistical channel state information (CSI). For the instantaneous CSI scenario, a multicast rate (MR) maximization problem is formulated, and an efficient two-stage equivalent precoder approximation (EPA)–based algorithm is proposed to jointly optimize the digital precoder and the DMA weight matrix. For the statistical CSI scenario, the ergodic MR (EMR) maximization problem is considered. To gain fundamental insights into the system design, asymptotic analyses of the EMR are conducted in the high signal-to-noise ratio (SNR) regime. The results reveal that the EMR converges to a finite constant as the SNR approaches infinity, with the convergence rate (CR) determined by the diversity order and the array gain. Motivated by these analytical insights, an array gain maximization problem is formulated and an EPA-based algorithm is proposed. Numerical results demonstrate the effectiveness of the proposed algorithms, validate the correctness of the derived high-SNR EMR analysis, and confirm the effectiveness of using DMA to enhance multicast transmission.

Six-dimensional movable antenna (6DMA) technology has been proposed to enhance the performance of integrated sensing and communication (ISAC) systems, but its increased implementation complexity poses significant challenges due to the extensive modifications required to existing base station infrastructures. In recent years, rotatable antenna has emerged as a promising technology, offering great potential for improving both wireless communication and sensing performance by flexibly adjusting the directional antenna’s pointing. However, current research on rotatable antenna is still in its infancy, with the focus primarily on wireless communication systems, and its application has not yet been extended to ISAC systems. As a representative application of rotatable antenna, the rotatable array (RA) is more practically meaningful and implementable in engineering practice. Additionally, the sub-connected structure in hybrid beamforming, where each radio-frequency chain is connected to only a subset of antenna elements, offers higher energy efficiency and is easier to realize in practice. Given this, this paper focuses on a channel model that accounts for the efficiency of the antenna radiation pattern and studies the sub-connected hybrid beamforming design for multi-user RA-aided ISAC system, aiming to reduce hardware cost and complexity while improving communication rate and sensing accuracy. Aiming at the non-convex nature with coupled variables in this problem, this paper transforms the complex fractional objective function using the fractional programming (FP) method, and then proposes an algorithm based on the alternating optimization (AO) framework, which achieves optimization by alternately solving five subproblems. Extensive simulation results demonstrate the effectiveness of the proposed RA-aided hybrid beamforming design method. It not only significantly improves the overall system performance while reducing hardware costs, but also achieves performance comparable to that of the fully-digital beamforming design with fixed-position antennas (FPA) under specific parameter configurations.

Assessing speech quality, particularly the Mean Opinion Score (MOS) directly related to human perception, is crucial for communication service providers. Neural networks can effectively address the time and financial costs associated with traditional methods for obtaining MOS. However, neural network methods still face challenges, due to the noise introduced by subjective evaluations and the difficulty of establishing a relationship between speech and MOS. Observing the differences in spectrograms and waveforms among samples with varying MOS values, a novel non-intrusive SQA method is proposed, based on an Extended Siamese Neural Network and termed SW-SQA, ensuring that the distance between encoder-generated features for two samples is positively correlated with their MOS differences, effectively capturing the differences in input data among samples with varying MOS values. Furthermore, a weighted loss function is designed to direct the network's attention to samples with lower subjective noise, thereby mitigating the impact of subjective noise. Experimental results show that SW-SQA reduces RMSE by 12.4 % compared to the state-of-the-art and improves PLCC and SRCC by 2.9 % and 2.5 %, respectively.

Accurate positioning is a prerequisite for Harmonized Communication and Sensing. Most existing fingerprint positioning methods require a large amount of data for training, which is a labor-intensive and time-consuming task. Consequently, the issue of few samples in positioning tasks is inevitable. To accurately position with a limited number of samples, a new few-shot positioning framework is proposed, named CK-GAT. Specifically, the framework initially extracts features from the limited labeled data through a meticulously designed neural network to construct high-dimensional Channel Knowledge (CK), which characterizes the features of the current environment. Subsequently, the Channel State Information (CSI) that need to be predicted are processed through the same neural network to extract features, which are then embedded into the CK. These features are enhanced by leveraging the environmental characteristics learned by Graph Attention Networks (GAT) to achieve precise positioning. Experimental results demonstrate that under the condition of limited labeled data, the proposed CK-GAT framework outperforms the current state-of-the-art and their semi-supervised variants, achieving an improvement in positioning accuracy by 0.85 m in outdoor scenarios and 0.19 m in indoor scenarios.

The aim of this article is to investigate the performance of near-field integrated sensing and communication (ISAC) systems using rotatable movable antennas (RMAs). In the proposed RMA-enabled system, the positions and rotations of antennas at the base station (BS) are dynamically adjusted to enhance both communication and sensing capabilities. Two designs are explored: 1) a sensing-centric design that minimizes the Cramér–Rao bound (CRB) with signal-to-interference-plus-noise (SINR) ratio constraints and 2) a communication-centric design that maximizes the sum-rate with a CRB constraint. To solve the formulated optimization problems, two alternating optimization (AO)-based algorithms are proposed capitalizing on the semidefinite relaxation (SDR) method and the particle swarm optimization (PSO) method. Numerical results demonstrate that: 1) the proposed rotatable MA (RMA)-enabled system outperforms the conventional fixed-position antenna and nonrotatable movable antenna (MA) systems in both sensing-centric and communication-centric designs and RMAs’ rotations show a higher performance gain in communication-centric design and 2) the proposed optimization methods achieve the Pareto boundary in both sensing-centric and communication-centric designs.

Dynamic metasurface antennas (DMAs) offer the potential to achieve large-scale antenna arrays with low power consumption and reduced hardware costs, making them a promising technology for future communication systems. This paper investigates the spectral efficiency (SE) of DMA-enabled multiuser multiple-input single-output (MISO) systems in both uplink and downlink transmissions, using only statistical channel state information (CSI) to maximize the ergodic sum rate of multiple users. For the uplink system, we consider two decoding rules: minimum mean square error (MMSE) with and without successive interference cancellation (SIC). For both decoders, we derive closed-form surrogates to substitute the original expressions of ergodic sum rate and formulate tractable optimization problems for designing DMA weights. Then, a weighted MMSE (WMMSE)-based algorithm is proposed to maximize the ergodic sum rate. For the downlink system, we derive an approximate expression for the ergodic sum rate and formulate a hybrid analog/digital beamforming optimization problem that jointly optimizes the digital precoder and DMA weights. A penalty dual decomposition (PDD)-based algorithm is proposed by leveraging the fractional programming framework. Numerical results validate the accuracy of the derived surrogates and highlight the superiority of the proposed algorithms over baseline schemes. It is shown that these algorithms are effective across various DMA settings and are particularly well-suited for system design in fast time-varying channels.

The method of training language models based on domain datasets has obtained significant achievements in the task of generating scientific paper abstracts. However, such models face problems of generalization and expensive training costs. The use of large language models (LLMs) to solve the task of generating paper abstracts saves the cost of model training. However, due to the hallucination problem of LLM, it is often necessary to improve the reliability of the results through multi-round query prompt approach such as Graph of Thoughts (GoT), which also brings additional reasoning costs. In this paper, we propose a Dynamic Graph of Thought (DGoT). It not only inherits the advantages of the existing GoT prompt approach, but also dynamically adjust the graph structure according to data characteristics while reducing model reasoning cost. Experimental results show that our method’s cost-effectiveness in abstract generation tasks is only 43.7% to 56.4% of other multi-round query prompt approaches. Our code is available at https://github.com/JayceNing/DGoT.

Online conferencing is increasingly popular in everyday communication and office work. However, various noise and transmission distortions directly impact the quality of communication and reduce the quality of experience (QoE). Communication service providers are seeking an accurate and non-intrusive speech quality assessment (SQA) method to promptly identify and address speech issues within communication systems, thereby enhancing their competitiveness in the market. Designing a reliable SQA method faces several challenges: mathematical models inadequately represent human perception, clean reference signals are hard to obtain and the mean opinion score (MOS) rating is time-consuming and labor-intensive. To address these issues, we propose a novel deep learning-based non-intrusive SQA method to predict MOS, termed SAT-SQA. It employs skip-connection convolution architecture as the backbone and transformer blocks as the neck architecture to better capture local and global features. Score prediction is conducted using an attention-based approach, which assigns weights to each speech frame to obtain a weighted global feature, and finally, a fully connected layer is used to produce the final score. We conduct experiments on three publicly available standard datasets from the Conferencing Speech 2022 Challenge: the NISQA corpus, Tencent corpus and PSTN corpus, aiming to predict MOS to closely approximate human scoring. The experimental results indicate that, compared to the current state-of-the-art methods, there is an average reduction of 17.7% in RMSE and an average increase of 4.2% in both PLCC and SRCC.

Facing the new era of the innovations and integration between virtual reality (VR) and the Internet of Things (IoT), the characteristics and model of VR traffic need to be studied to provide better quality of service guarantee. In this work, we simulate a testbed where the wearable VR glasses is connected into the wireless network provided by the server. And the VR traffic is studied from the two aspects of deterministic and stochastic component respectively. At the same time, the arrival curve in network calculus is used to build the traffic model and a novel method for VR traffic modeling is proposed. The theoretical curves in our proposed model are compared with existing theoretical methods and measured statistical data to verify the feasibility of our proposed model. Simulations indicate that our proposed method outperforms the existing method.

Non-terrestrial network (NTN) is the next generation access technology between terminals and satellites, based on new air interface technology developed by the 3rd Generation Partnership Project(3GPP) in the Release 17. While 3GPP integrates 5G NR's Physical Random Access Channel (PRACH) into NTN scenarios, it faces an issue of insufficient preambles. Currently, numerous studies have combined M sequences or Golden sequences with Zadoff-Chu (ZC) sequences to design PRACH formats to address this challenge. This paper introduces a novel PRACH format based on Golden sequences and ZC sequences. Golden sequences and ZC sequences are individually detected, resulting in the introduction of two detection methods: the Golden priority detection algorithm and the ZC priority detection algorithm. This innovative design resolves the problem of massive detection calculations found in current common detection algorithms and also increases the quantity of preambles. Finally, the simulation results show that the new scheme can obtain the accurate results with less calculation.

Non-orthogonal multiple access (NOMA) has drawn much attention due to its capability in massive connections. It enables various joint designs with advanced technologies, e.g., cooperative transmission and aerial base station (ABS)-assisted networks. In this paper, we consider an efficient NOMA enabled cross-tier joint transmission design. This design jointly applies zero-forcing beamforming and NOMA to realize joint transmission and regular transmission for the users in different tiers. We evaluate the performance of the design under a large-scale clustered ABS-assisted network scenario. Theoretical expressions for the outage probability and area outage spectral efficiency are derived. Numerical results show that, although splitting power may lead to slight degradation for the macro-cell users, the system can still expect a decreased overall outage probability because joint transmission improves the received signal quality for the ABS-tier users. The macro-cell users may not always suffer outage performance degradation since they can keep receiving signals instead of staying idle until the cross-tier joint transmission is suspended as in the orthogonal scheme. Besides, with properly selected NOMA power allocation coefficient, a more reliable communication link can be guaranteed for the macro-cell user, if compared with the conventional orthogonal scheme.

This paper analyzes the ergodic mutual information (EMI) of an intelligent reflecting surface (IRS)-aided multiple-input single-output system with finite-alphabet inputs relying on two- timescale channel state information. For the sake of unveiling important system design insights, asymptotic analyses are performed on the EMI in the regime of high signal-to-noise ratio (SNR). It is found that the EMI converges to some constant in the high-SNR regime and the rate of convergence is determined by the diversity order and the array gain. On this basis, two efficient algorithms are proposed to design the phase shifts of the IRS in order to improve the array gain as well as the EMI. All the analytical results are verified through computer simulations.

Antenna selection can handle the cost and complexity issues in massive multiple-input multiple-output (MIMO) channels. In this article, the sum-rate capacity of a multiuser massive MIMO uplink channel is analyzed. A mathematically tractable sum-rate capacity upper bound is derived for the considered system. Furthermore, for a sufficiently large base station (BS) antenna number, a deterministic equivalent (DE) of the sum-rate capacity bound is derived. Based on this DE, the sum-rate capacity is shown to grow double logarithmically with the number of BS antennas. Numerical experiments confirm the validity of the analytical results.

Ultra-dense network is the key technology of 5G. It provides mobile users with high transmission rates and efficient radio resource management. However, due to the dense deployment of base stations and the small coverage of a single base station in the ultra-dense network, user equipment may have to perform handovers more frequently. The current handover mechanism is more like a “passive handover” triggered by objective conditions. So frequent handover may degrade the user experience and affect the overall performance of the network. Therefore, it is necessary to predict the trajectory of the mobile user, and then take the prediction results into account to perform a more positive and intelligent handover. In response to this problem, in this paper, we propose an LSTM model that can predict a mobile user’s next location based on his historical trajectory and which cell he should connect to at the next moment. We use real mobile user trajectory data for training and testing. Reassuringly, it is best to achieve a prediction accuracy of 92.155% in the scenario we assumed.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technique to enhance the system spectral efficiency. This paper investigates the ergodic channel capacity (ECC) of an RIS-aided multiple-input multiple-output channel under the assumption that the transmitter-RIS, RIS-receiver, and transmitter-receiver channels contain deterministic line-of-sight paths. Novel expressions are derived to characterize the upper and lower bounds of the ECC. To unveil more system insights, asymptotic analyses are performed to the system ECC in the limit of large signal-to-noise ratio (SNR) and number of reflecting elements (REs). Theoretical analyses suggest that the RIS’s deployment can shape the ECC curve by influencing its high-SNR power offset and the ECC can get improved by increasing the number of REs.

In this paper, we propose a two-layers computing task offload architecture for cloud game scene, including edge servers and cloud server. In order to better simulate the real environment, we modeled the uplink and downlink communication channels for the first time. In order to reduce the energy consumption and time delay of the system, this paper adopts Multi-Agent Reinforcement Learning Algorithm - Bidirectionally-Coordinated Nets(BiCNet) algorithm to realize it. Each edge server is regarded as an agent. Compared with the single agent reinforcement learning algorithm, the decision made by BiCNet saves 300% of the time delay and 200% of the system energy consumption. The experimental results show that the offloading decision made by BiCNet algorithm can most effectively balance the consumption of energy cost and time delay, and can achieve the effect of energy saving for the system while ensuring the user experience.

We investigate multiuser detection under uplink asynchronous scenario, where the triangular successive interference cancellation (T-SIC) detection scheme is exploited. As the existing analysis was conducted under some ideal assumptions for simplicity, the asynchronous scenario is analyzed under rigorous assumptions in this paper so that important insights are revealed for practical systems. Specifically, the average symbol error rate (SER) formulas are derived for a two-user system with arbitrary$M$-ary quadrature amplitude modulation ($M$-QAM) over Rayleigh fading channels. These solutions are shown to have higher accuracies compared to the existing solutions. Furthermore, a novel iterative detection algorithm is proposed, i.e., the parallel T-SIC method, which is more efficient and effective compared to the iterative T-SIC method. Insightful discussion is conducted in terms of the potentials of the concerned iterative detection method. Simulation results show that the most significant improvement of the iterative detection reflects on the SER for the strongest user, and the reduction is always within 50% of the SER compared to the primary detection. The gains obtained by the iterative detection is negligible for higher order$M$-QAM cases.

The low density superposition modulation (LDSM) scheme is one of the NOMA schemes which can support ubiquitous Internet of Things (IoT) devices. This paper focuses on designing the sparse signature matrix with a large girth for LDSM under imperfect channel state information (CSI) for 6G Integrating Sensing and Communications networks. Based on the orthogonal pilot and linear minimum mean square error (LMMSE) estimation, the LDSM optimized by bare-bone particle swarm optimization (BBPSO) algorithm has a larger girth and can gather more accurate information in the process of iterative decoding convergence. An extrinsic information transfer (EXIT) chart analysis is designed for the LDSM-OFDM system as a theoretical analysis tool. The simulation results show that the optimized LDSM outperforms the reference LDSM system, bringing about a 0.5 dB performance gain.

Mobile streaming media on subways or trains has become a typical and popular scenario in daily life. However, because of the unstable wireless network channel and fluctuations caused by movement, the constant bit rate streaming media brings low-quality service. The adaptive streaming media seems to solve the problems, but the premise is that clients can accurately estimate the network bandwidth. When the client moves at high speed, the irregular fluctuations of network channel make accurate estimation difficult, leading to the low efficiency of the adaptive streaming media. When clients move on subways or trains, the location, speed and the cell load can be known by the wireless network side. With this information, the available network bandwidth can be accurately calculated in advance. Thus, we propose an architecture called Cell-Cluster Network-Assisted Adaptive Media Streaming (CCNA) to optimize the vehicular streaming media over wireless network. We build a cell cluster to introduce the assistance of the wireless network side and estimate the network bandwidth more accurately. The architecture can help clients select the video segment with the highest QoE under the constraint of limited network bandwidth and make the best use of the network bandwidth resources. The experimental results show that compared with traditional adaptive streaming media transmission modes, our architecture improves the average playback bit rate by 32% and the utilization of network bandwidth by 34% at the same time with the least video stalls.

The reconstruction of sparse signal is an active area of research. Different from a typical i.i.d. assumption, this paper considers a non-independent prior of group structure. For this more practical setup, we propose EM-aided HyGEC, a new algorithm to address the stability issue and the hyper-parameter issue facing the other algorithms. The instability problem results from the ill condition of the transform matrix, while the unavailability of the hyper-parameters is a ground truth that their values are not known beforehand. The proposed algorithm is built on the paradigm of HyGAMP (proposed by Rangan et al.) but we replace its inner engine, the GAMP, by a matrix-insensitive alternative, the GEC, so that the first issue is solved. For the second issue, we take expectation-maximization as an outer loop, and together with the inner engine HyGEC, we learn the value of the hyper-parameters. Effectiveness of the proposed algorithm is also verified by means of numerical simulations.

This paper investigates the linear precoding design for multiple-input multiple-output (MIMO) multiple access channel (MAC) with finite-alphabet inputs. Since the mutual information (MI) and its gradient-minimum mean-square error (MMSE) matrix lack closed-form solutions, traditional MI/MMSE based precoding scheme suffers from a prohibitive burden of computation. To address this problem, we first derive an asymptotically optimal lower bound of MI, which is more computationally efficient. Based on the lower bound, we derive a suboptimal precoding scheme that maximizes the weighted sum-rate (WSR) of MIMO MAC with finite-alphabet inputs. Furthermore, a low-complexity precoding algorithm is proposed to obtain the numerical results of precoding matrices. Numerical results confirm a higher efficiency of the proposed precoding scheme compared with traditional MI/MMSE based precoding strategies.

This letter considers the problem of a joint estimation for channel fading and user activity in an uplink grant-free massive MIMO system equipped with low-precision analog-to-digital converters (ADCs). Different from existing works, the joint estimation is formalized as a non-overlapping group problem, where the components of compound channel involving user activity indicator and channel fading are independent with condition distribution rather than independent Bernoulli-Gaussian. Based on this new formulation, a new algorithm leveraging hybrid generalized approximate passing (HyGAMP) is then developed including GAMP part (channel estimation) and loopy belief propagation (LBP) part (user activity detection), where the strong correlation among elements in each row of the channel matrix can be decoupled in LBP part. By exchanging the information between the GAMP part and the LBP part, the proposed algorithm improves the performance of channel estimation and user activity detection as compared to earlier results. In addition, the simulation results verify that the proposed algorithm develops the performance of conventional methods dramatically.

This letter considers a joint user activity, channel and data estimation problem in an uplink grant-free massive MIMO systems with low-precision analog-to-digital converters (ADCs). This joint estimation is firstly formalized as a non-overlapping group sparse problem, in which the components of compound channel follow independent conditional distribution. To address this problem, a new algorithm consists of the celebrated bilinear generalized approximate message passing (BiG-AMP) and loop belief propagation (LBP) is then proposed, where the strong connection of compound channel can be decoupled in LBP part. By exchanging the information between BiG-AMP part and LBP part, the proposed algorithm improves the performance of channel estimation compared with HyGAMP based method, in which the estimated payload data of proposed algorithm are utilized to aid channel estimation and it leads to relatively few pilot symbols to achieve equivalent channel and data estimation performances. The simulation results confirm that our proposed joint estimation algorithm improves the performance of the existing works in terms of user activity, channel and data estimation.

Transmit antenna selection (TAS) is an emerging technology in physical layer security. To provide new insights into the achievable secrecy performance of TAS in practical communication systems, this paper investigates the average secrecy rate (ASR) and secrecy outage probability (SOP) under practical modulation schemes in TAS aided multiple-input multiple-output multiple-antenna eavesdropper (MIMOME) wiretap channels over Rayleigh fading. Particularly, this research concentrates more on the square M-ary quadrature amplitude modulation (M-QAM). Furthermore, in the considered MIMOME channel, a single antenna is selected to transmit the secret message, and selection combining (SC) or maximal-ratio combining (MRC) is utilized at the legitimate receiver and the eavesdropper. Based on this system model, novel expressions for the ASR and SOP are formulated to characterize the secrecy performance of the finite-alphabet driven MIMOME channel. Besides exact analysis, an asymptotic analysis is performed using the considered performance metrics in high signal-to-noise ratio (SNR) regime. Theoretical analyses suggest that the asymptotic ASR and SOP converge to finite constants in high SNR regime due to the discrete constellation constraint, and we find that the asymptotic behaviour of discrete inputs differs from that of Gaussian inputs. Furthermore, we derive concise expressions to characterize the rate of convergence (ROC) of the ASR and SOP, respectively. To unveil more system design insights, we discuss the relationship between the ROC and several important system parameters such as the antenna number and the modulation order.

To analyze the achievable performance of antenna selection (AS) in practical multi-antenna systems, this paper studies the receive antenna selection (RAS) in single-input multiple-output (AS-SIMO) systems under discrete inputs. We first propose an approximate expression to evaluate the instantaneous mutual information (MI) of M-ary quadrature amplitude modulation (M-QAM) signaling over additive white Gaussian noise (AWGN) channels. Then, by exploiting this approximate formula, we develop a closed-form formula for the ergodic MI in AS-SIMO systems with M-QAM signaling. Additionally, we also analyze the asymptotic MI for a large number of receive antennas Nr. This asymptotic analysis suggests that the scaling rate of the MI with Nr becomes zero rate in contrast to the double logarithmic rate under Gaussian inputs. Besides, our result is also extended to discuss the mutual information of multiple-input multiple-output (MIMO) systems having discrete inputs with receive antenna selection, and an upper bound for the MI is derived. Finally, the derived result is applied to analyze several performance measures of the discrete inputs driven ASSIMO systems. Specifically, it is first used to discuss the relationship between the ergodic MI and the number of active antennas. Our investigation shows that this relationship follows Pareto principle, i.e., 80% of the MI of full-antenna selection can be achieved via 20% of the total antennas. Then, our proposed approximation is employed to analytically study the effective MI which takes channel estimation (CE) into consideration, indicating that CE is a main limit of large-scale systems. Moreover, the energy efficiency (EE) is explored on the basis of our results, and we find there exists an optimal number of active antennas to maximize the energy efficiency. In addition to theoretical derivations, all the analytical results are validated by numerical simulations.

This paper studies the receive antenna selection in massive multiple-input multiple-output (MIMO) systems. The receiver, equipped with a large-scale antenna array whose size is much larger than that of the transmitter, selects a subset of antennas to receive messages. An asymptotic approximated upper capacity bound is derived in the limit of massive MIMO systems over independent and identical distributed Rayleigh flat fading channel, assuming that the channel state information (CSI) is only available at the receiver. Compared with the conventional derivations, this approximate result requires much less computing with the guarantee of considerably high precision. Furthermore, the asymptotic theory is separately applied to two scenarios which is based on whether the total amount of the selected antennas exceeds that of the transmit antennas. Besides analytical derivations, simulation results are provided to demonstrate the approximation precision of the asymptotic results and the tightness of the capacity bound.

Unmanned aerial vehicle (UAV) communication system equipped with multiple input and multiple output (MIMO) antennas has been recognized as an efficient technique for obtaining high spectral efficiency. To improve the system capacity in UAV communication scenario, this paper investigates the user clustering and downlink user scheduling strategy. We first develop a channel correlation based K-means algorithm for user clustering. Furthermore, to diminish intergroup interference, a greedy user scheduling algorithm driven by the K-means clustering strategy, is devised to appropriately select the users with low-correlated channels to schedule at the same time slot. Simulation results are provided to demonstrate the superior performance of the proposed scheduling algorithm over conventional method. It is observed that the presented K-means aided algorithm can achieve near maximum average sum rate with much lower computational complexity.

This paper studies the secrecy performance of multiple-input multiple-output (MIMO) wiretap channels, also termed as multiple-input multiple-output multiple-eavesdropper (MIMOME) channels, under transmit antenna selection (TAS) and BPSK/QPSK modulations. In the main channel between the transmitter and the legitimate receiver, a single transmit antenna is selected to maximize the instantaneous Signal to Noise Ratio (SNR) at the receiver. At the receiver and the eavesdropper, selection combining (SC) is utilized. Additionally, suppose that the transmitted message is modulated by BPSK/QPSK modes. We first derive the closed-form approximated expression for the ergodic secrecy rate under Rayleigh fading, assuming that the channel state information of the eavesdropper (CSIE) is available at the transmitter. Next, analytical formulas for the approximated and asymptotic secrecy outage probability are also developed when CSIE is unavailable (NCSIE). Simulation results are provided to demonstrate the approximation precision of the derived results above. Furthermore, the asymptotic results reveal that the secrecy diversity order degrades into 0 due to the finite-alphabet inputs, which is totally different from that driven by the Gaussian inputs.

The deployment of Internet of Things (IoT) technologies in unlicensed spectrum is a candidate feature for 5G to support massive connections of IoT devices. Current IoT unlicensed band technologies, such as Sigfox and LoRa, are all at an early stage of deployment without a significant market share. In this context, the MulteFire (MF) Alliance has envisioned to adapt the cellular NB-IoT design to operate within the Sub-1 GHz unlicensed spectrum. However, the diverse regulatory requirements within this unlicensed band put a hurdle to the worldwide deployment of unlicensed band IoT technologies. To settle this challenge, MF has designed a specific framework for narrow-band (NB)-IoT systems operating on unlicensed spectrum (NB-IoT-U), which can be utilized under both the Federal Communications Commission (FCC) and European Telecommunication Standards Institute (ETSI) regulatory requirements. In this paper, enhanced synchronization and physical broadcasting signals are proposed based upon the framework designed by MF with the aim to allow a more robust detection, and to fulfil the coverage targets set for this technology. Furthermore, in order to allow the system to operate as a frequency hopping spread spectrum (FHSS) system, a novel frequency hopping pattern generator compliant with the regulatory requirements is designed, and its performance is evaluated.

Mutual information (MI) between channel inputs and outputs plays a critical role in communication systems. However, with finite-alphabet inputs, no closed-form solution exists to the MI of multiple-input multiple-output (MIMO) channel. Although numerical integration methods can be used to estimate MI, it can be quite slow, especially for larger input-output dimensions. This paper presents a new way to approximately evaluate the MI of MIMO channel with equiprobable M-ary inputs. With the aid of entropy power Gaussian approximation, a MIMO channel can be approximately decomposed into parallel independent single-input multiple-output (SIMO) channels which are, in terms of MI, equivalent to single-input single-output (SISO) channels. Thus the MI of MIMO channel can be approximately evaluated with that of multiple SISO channels. With this result, the computational complexity can be reduced to linear complexity. Furthermore, we proof that the proposed approximation gives the true MI with massive receiver antennas. Simulation results verify the accuracy and effectiveness of the proposed method.

Cloud Radio Access Network (C-RAN) is widely considered a significant part of fifth generation (5G) networks. This paper offers flexible Software Defined Radio (SDR) testbed for C-RAN researchers and developers to verify network function and evaluate system performance. In 5G C-RAN architecture, a Baseband Unit (BBU) of C-RAN is split into a Centralized Unit (CU) and a Distributed Unit (DU). As a consequence, a SDR testbed based on OpenAirInterface is built and extended to support the CU/DU split. In order to support communications between the CU and DU units, a succinct and effective interface protocol is designed. Specifically, the CU/DU control plane split at the RRC/RLC point is primarily addressed, so that User Equipment (UE) can complete attach UE and detach UE procedures through the decoupled CU and DU units. Besides, the CU/DU user plane split at the PDCP/RLC point is accomplished. Therefore, two independent units, CU and DU, can substitute for BBU. Finally, this paper evaluates the testbed using a real network setup, a commercial Evolved Packet Core (EPC), a remote radio head (RRU) and several commercial UEs. The results show that the testbed can successfully attach UE, detach UE and transmit data to commercial UEs over the air.

This paper studies the transmit antenna selection in massive multiple-input multiple-output (MIMO) wiretap channels, also termed as multiple-input multiple-output multiple-eavesdropper (MIMOME) channels. The transmitter, equipped with a large-scale antenna array whose size is much larger than that of the legitimate receiver and eavesdropper, selects a subset of antennas to transmit messages. A branch-and-bound (BAB) search based algorithm for antenna selection in independent and identical distributed Rayleigh flat fading channel is proposed to maximize the secrecy capacity between the transmitter and the legitimate receiver when the transmit power is equally allocated into the selected antennas. Furthermore, the proposed algorithm is separately applied to two scenarios which is based on whether the channel side information of the eavesdropper (CSIE) is available at the transmitter. Simulation results show that the proposed algorithm has the same performance as the exhaustive search under both scenarios but with much lower complexity.

To realize the high data rates with low complexity in MIMO systems, linear detections are commonly used where the received signal vector is first transformed to a new vector and then detected on per symbol basis. However, the typical linear detectors such as zero forcing detector and minimum mean-square error detector are not optimal in terms of maximizing the achievable data rate of MIMO system under linear detection constraint. In this letter, we investigate the optimal linear transformer that can maximize such rate with finite constellation inputs. By capitalizing on the relationship between mutual information and the MMSE between channel inputs and outputs, the optimal linear detector (OLD) can be expressed as a fixed-point equation and be solved iteratively. Analytical results prove the consistence between the proposed OLD and the joint maximum likelihood detector (JMLD) in the low- and high-SNR region. The further simulation results verify the consistence, and moreover, at moderate SNR, OLD can significantly improve the achievable data rate and is close to that of JMLD.

Fluctuations in wireless channel conditions have a negative impact on naked-eye three-dimensional (3D) video streaming. While offering free stereo perception, it poses a challenge to wireless networks for the large volumes of video traffic. To obtain better viewing experiences, a trade-off between stereo perception improvement and freeze avoidance is necessary. Though adaptive streaming seems to work well, traditional bandwidth estimation methods are at risk of overestimation which may lead to stall or underestimation which may cause resource waste. To improve the quality of experience (QoE), a novel scheme named QoE-driven and Network-assisted Naked-eye 3D Adaptive Video Streaming over Wireless Network (QNANA) is proposed to transmit naked-eye 3D video streaming over wireless networks adaptively with network assistance. In view of the constraint of bandwidth capacity, the scheme can select the video of the highest QoE under the current bandwidth conditions, so as to achieve the highest QoE overall. Besides, the clients can follow the fluctuating channel conditions timely and accurately. Results show that the scheme can realize the lowest prediction error and the best resource utility with up to 54% reduction in total stall durations compared to traditional bandwidth estimation methods.

Since the performance of Turbo and Low Density Parity Check Code (LDPC) codes are very close to Shannon limit, a great deal of attention has been placed on the capacity of additive white Gaussian noise (AWGN) and fading channels with arbitrary channel inputs. However, no closed-form solution has been developed due to the complicated Gaussian integrations. In this paper, we investigate the capacities of AWGN and Rayleigh fading channels with M-ary inputs, e.g. phase shift keying (PSK) and quadrature amplitude modulation (QAM). By firstly adopting successive interference cancellation (SIC) and the entropy power Gaussian approximation, the capacity of an AWGN channel with M-QAM and M-PSK modulations can be approximated as the superposition of the capacity of an AWGN channel with BPSK modulation. Furthermore, the ergodic capacity of a Rayleigh fading channel can be derived directly by averaging the instantaneous capacity of an AWGN channel with respect to the probability density function (PDF) of the fading channel gain. Simulation results verify the effectiveness of the proposed method for estimating the capacity of AWGN and Rayleigh fading channels of all SNR ranges.

Virtual reality (VR) and augmented reality (AR) applications with interactions are usually delay-sensitive. Fast delivery of three-dimensional (3-D) models in VR/AR over wireless networks has been a crucial task due to limited bandwidth and packet error. In this paper we propose an application layer content-aware joint source-channel coding scheme based on forward error correction (FEC). A 3D model is progressively encoded to a base mesh and several refinement layers consisting of connectivity and geometric data. A new design of LT code with a special coding graph has been proposed to provide unequal error protection (UEP) to these two kinds of data. Then a statistical measure is used to optimize the rate allocation for each refinement layer, which is scalable to both network bandwidth and packet loss rate (PLR). Experimental results show that the proposed streaming scheme can achieve the objectives of low transmission delay and small distortion under serious loss.

Deployment of Long Term Evolution (LTE) in unlicensed spectrum has been a candidate feature to meet the explosive growth of traffic demand since 3GPP release 13. To further explore the advantage of unlicensed bands, in this context the operation of both uplink and downlink has been supported and studied in the subsequent releases. However, it has been identified that scheduled uplink transmission performance in unlicensed spectrum is significantly degraded due to the double listen-before-talk (LBT) requirements at both eNB when sending the uplink grant, and at the scheduled UEs before transmission. In this paper, in order to overcome this issue, a novel uplink transmission scheme, which does not require any grant, is proposed, and the details regarding the system design are provided. By modeling the dynamics in time of the LBT for both a system that employs a conventional uplink scheme, as well as the proposed scheme, it is verified through analytical evaluation that the double LBT scheme for uplink transmission greatly reduces the channel access probability for the UE, and leads consequently to performance loss, while the proposed scheme is able to alleviate this issue. System level simulation results, compliant with the LTE standard, show that the proposed scheme can achieve a significant performance gain in terms of throughput with negligible performance loss for the downlink, and other technologies operating in the same spectrum.

In recent years, data traffic over cellular networks has experienced an exponential rise, primarily due to the explosion of smartphones, tablets and laptops. This increase in data traffic on cellular networks has caused an immediate need for offloading traffic for optimum performance of both voice and data services. The paper focuses on the coverage improvements of high floor users by offloading these users to picos. Four antenna patters with different vertical half power beam width are applied in both outband and inband pico scenarios. The simulation results indicate that both outband and inband picos can offload the same amount of high floor users but the outband picos can provide a much better coverage than macros while inband pico cannot significantly improve the coverage performance.

With the rapid increase of traffic in cellular networks, low power nodes overlaid within a macro network is considered to be a cost-effective way to significantly enhance the capacity of cellular networks. In this paper, the impact of the pico cell antenna pattern and tilt on the performance of TD-LTE networks is analyzed in 3D Scenario. The optimal values of antenna tilts and the choice of antenna pattern are given under certain restriction. Besides, the simulation results show that the pico antenna pattern and tilt can significantly affect the key performance indices of the network, namely, the SINR, the capacity, the pico connected user ratio and others. In addition, the simulation results show that picos equipped with uptilt antennas can provide good coverage for high floor users.

Information secrecy is achievable in a broadcast wireless communication system when the adversary channel witness a greater disruption as against the main channel. The secrecy level need to be enhanced as the adversary terminal can use advance method to eaves drop the hidden message being exchanged between the two legitimate communicating partners. The use of relay- assisted node in information-theoretic approach at the physical layer security has been widely researched into but they are mostly operate in a half-duplex mode (HD). With the advancement in electronic technology, the use of a single antenna in full duplex mode (FD) is possible. We explore this property in enhancing the security of a broadcast wireless system at the physical layer. In this work, the transmitter generates artificial noise (AN) while the legitimate receiver(LR) also transmits another jamming noise (JN) from its antenna to unintended receiver (UR) node. The effect of these multiple noises are examined on the secrecy capacity and probability outage capacity on the UR channel. Mathematical analysis and the numerical simulation results show improvement in channel secrecy capacity over the conventional approaches.

The latest video coding standard, High Efficiency Video Coding (HEVC), has a large amount of complexity in its inter coding. To reduce the complexity of the inter coding for HEVC, an adaptive searching range algorithm is proposed in this paper. The proposed method separates inter coding into a learning stage and predicting stage. In the learning stage, the relationship between the prediction units (PU) difference, the length of the motion vector prediction (MVP), and the best searching range is learned. In the predicting stage, the k-nearest neighbor algorithm is adopted to find the best searching range based on the learned data. Experiments showed that 91% of the complexity of the motion estimation is reduced compared with the full search method.

The latest standard of High Efficiency Video Coding (HEVC) has a better coding efficiency compared to H.264/AVC. It is reported that the bitrate of the video applied HEVC is the half of video applied H.264/AVC at the same encoding quality. However, the cost of improvement is the increasing computational complexity which is mainly brought by the quadtree based coding tree unit (CTU). In this paper, a fast coding units (CU) splitting and pruning method is proposed to speed up the process of searching the best partition for CTU. Experiment has shown that our method can save 46% computational complexity on average at the cost of increasing Bjontegaard delta rate (BD-rate) by 0.82% when the method is applied to sequences in Class A.

Outdoor Micro cell has been considered as a cost efficient way to increase in-building coverage and capacity. Micro cell configured with up-tilting antenna potentially improve the high floor coverage in high rise buildings. In this paper, the network performance of micro site is studied using a realistic TD-LTE network located in city center of Beijing with many high rise buildings. Micro User Equipment (UE)'s location and offloading ratio impacted by antenna up-tilting is evaluated through system level simulation. In the simulation, we assume micro site is configured with three directional antennas, and deployed using dedicated carrier. To model accurately the user distribution and radio propagation, the three-dimensional (3D) city map is employed. A 3D ray-tracing tool is applied to estimate the propagation of the studied area. A semi-empirical building penetration extension is proposed to derive indoor predictions for each floor based on outdoor predictions at ground-level, combining with a height gain model. Simulation results indicate that micro antenna up- tilting will diminish the coverage area of the micro sites, but give a significant improvement on the offloading capability for high floor UEs.

For multi-node networks, a distributed compressed sensing scheme based on one-bit quantization is proposed, where signals at each node consist of common component and innovation component. To reduce the transmission cost, each node derives the measurements as the sign information of the compressed samples by using one-bit quantization. Based on the received sign information from different nodes, two joint recovery algorithms are designed to recover the signal of each node. Simulation results show that the proposed scheme outperforms both the independent reconstruction scheme and the scheme based on multi-bit quantization.

This paper proposes an iterative decoding algorithm for network coding HARQ (NC-HARQ) retransmission scheme in LDPC system. Including two erroneous codewords and one retransmitted network coded word, a larger Tanner graph is applied in the decoding algorithm. We modify the process of iterative decoding and separate the external information into two parts which come from channel coding and network coding respectively. Different iteration numbers are set in these two parts and a weighting factor is multiplied to the latter one. Simulation results show that, compared with traditional HARQ in which the repeat codewords are the erroneous codewords themselves, the proposed scheme can not only reduce the retransmission number but also improve the throughput significantly.

The performance of Perceptual Evaluation of Speech Quality (PESQ), a most prevailing voice service Quality of Experience (QoE) assessment method, is evaluated in Chinese environment. A normative Chinese speech database has been developed, especially considered the distortions from the real transmission situation. Through the huge subjective result, a distinct conclusion can be drawn that it is not convinced to use PESQ assess Chinese voice service directly. A non-linear compensation factor is proposed in this paper to make the universal PESQ be suitable for the Chinese voice service accurately. After compensation, the score of PESQ matches the Mean Opinion Score (MOS) much more, and the residual error distribution has also been improved greatly compared with the result given in ITU P.862.1.

With the rise of the user base of smartphones, the cellular networks are facing increasing uplink signalling traffic pressure caused mainly by the soaring background traffic of smartphone applications. Therefore, this paper studies the LTE radio resource control (RRC) state transition, which is highly related to the amount of signalling traffic, caused by the background traffic. Firstly, we propose a general method for generating the background traffic using a burst model, taking the Gtalk as an example. Secondly, a novel theoretical model is proposed to calculate the number of RRC state transition, the probability of a smartphone's being in RRC CONNECTED and the distribution of the number of smartphones in RRC CONNECTED at a given epoch, all of which are key metrics for the cellular networks. Finally, both theoretical and simulation results are compared for a typical LTE macro cell scenario containing 300 cell phones, 80% of which are smartphones. The study also shows that the Gaussian distribution, which turns out to be a very good approximation, can be used to fit the distribution of the instantaneous number of smartphones in RRC CONNECTED in a LTE cell.

In this paper, we present a bit-loading scheme for coded OFDM systems. In the proposed scheme, the subcarriers with multilevel modulation are decomposed into multiple binary equivalent channels each with an effective bit-level signal-to-noise ratios (SNR) depending only on the symbol-level SNR and the bit position. Thus, the problem of allocating bits among subcarriers has been converted to simply selecting the best binary channels. Simulation results have verified the superior performance to the classical algorithm.

In this paper, capacity and coverage performances by deploying vertical sectorization in HSPA networks are evaluated. A novel resource-scheduling scheme for vertical sectorization is proposed to improve the SINR of cell-edge users. Performances of both conventional vertical sectorization and proposed mechanism are investigated through analysis and simulations. A three dimensional propagation model is used in the system level simulator. Our results indicate in a regular HSPA network conventional vertical sectorization is able to provide up to 59.2% capacity gain at the cost of significantly increased network outage levels. On the other hand, the proposed scheme can remarkably improve network outage performance while providing maximal 21% network throughput enhancement.

This paper studies the uplink information-theoretical sum capacity of single cell, multi-user cellular systems with minimum SINR constraint. The uplink of single cell multiuser systems is interference limited. If one user increases its transmission power, it will bring more interference to other users. Therefore, power control should be used to balance all users' transmission power. To achieve the maximum system capacity, the optimal power allocation schedules users according to their link gains, which will lead to unfair problem. In order to avoid this problem, a QoS constraint in the form of minimum SINR constraint could be introduced. In previous works, some properties of the optimal allocation under this constraint have been found. However, it is still difficult to get the optimal power allocation. In this paper, we have found some new mathematical properties of the optimal power allocation, and based on these properties, the algorithm for getting the optimal power allocation is proposed. Numerical results are also presented to show the effects of minimum SINR constraint on system capacity.

This paper studies the optimal power control which can maximize the information-theoretical sum rate of the uplink of multi-cell, multi-user cellular systems equipped with successive interference cancelation (SIC). One important property for the optimal power control is presented: for the optimal power allocation, there will be at least one user transmitting at peak power in the system, and at most one user transmitting at part of the peak power in each cell. With this property, the peak throughput for multi-cell, multi-user cellular systems can be approached by simulated annealing algorithm. System level simulations show that the gain of SIC highly depends on the distribution of users. When users distribute uniformly in multi-cell cellular system, the gain of SIC is not obvious.

In [1], it is shown that the word error rate (WER) of binary codes under binary phase-shift keying (BPSK) modulation can be evaluated via the square radius distribution of decision region and a simple approximation formula of WER for turbo-like codes is presented. In this paper, we extend the method in [1] to more general cases including high-order modulations and fading channels. Moreover, an improved closed-form approximate WER formula is also proposed which can reduce the approximation error to 0.05 dB under a BPSK modulation and additive white Gaussian noise (AWGN) channel for the signal-to-noise ratio (SNR) of interesting in wireless systems.

LTE-Advanced extends the number of transmit antennas of evolved NodeB (eNB) up to 8, which will provide substantial spatial degrees of freedom. However, the user is usually equipped with a small number of antennas due to space constraints. Thus, multi-user beamforming (MU-BF) was proposed to dramatically boost the spectrum efficiency by transmitting multiple users' data on the same time-frequency resource. The co-channel interference (CCI) among users scheduled on the same resource can be eliminated by block diagonalization (BD); however, the performance of downlink MU-BF with BD relies on the accuracy of channel estimation to a great extent. Therefore, the effect of imperfect channel estimation on MU-BF for the downlink of LTE-Advanced system is studied in this paper. The reference signal targeting data demodulation (DM-RS) and uplink sounding reference signal (SRS) for deriving the downlink channel in TDD system are considered with different error models respectively. The estimation errors are modeled through link level simulations. It is shown that the imperfect SRS and DM-RS channels degrade the system performance in different degree through system level simulation.

We propose a novel adaptive interleaving scheme for orthogonal frequency division multiplexing (OFDM) systems. Based on the channel state information, the approach selects one interleaver from a pool of candidate interleavers. As the selected one is expected to have larger minimum Euclidean distance (MED) at receiver, the technique can significantly improve the decoding error rate performance. Compared with the adaptive interleaving schemes proposed in literatures, the proposed scheme has no interleaving pattern synchronization problem and requires only a few bits for the signaling.

The complex central semi-correlatedWishart matrix is the product of a zero-mean row-wise correlated (or column-wise correlated) complex Gaussian matrix and its Hermitian transposition. In this paper, we investigate the distribution of the smallest eigenvalue of the Wishart matrix, which is generated from the square Gaussian matrix. A closed-form expression for distribution of the smallest eigenvalue is derived. Compared with previous results, our expression is very concise, since the distribution has been known in terms of matrix determinants. Numerical results that confirm the expression are also presented. And a complicated equality is proved due to the fact that our distribution is mathematically equivalent to those reported.

Wishart matrix and its eigenvalues are very important to the analysis of multiple-antenna systems. In this paper, we derive a polynomial expression for the distribution of the smallest eigenvalue of Wishart matrices. Compared with earlier results in matrix determinant forms, our expression is much easier to handle. The expression obtained applies for Wishart matrices of arbitrary orders and is an extension to Edelman's and Park's results. Moreover, our work here can also be viewed as an interesting proof of one complicated equality, since the distribution is unique and our result must be equivalent to earlier researchers'. In a word, with the result of this paper, further mathematical analysis on the statistics of the smallest eigenvalue becomes possible.