Rydberg atomic (RA) receivers represent a revolutionary quantum technology for wireless communications, offering unprecedented sensitivity beyond conventional radio frequency (RF) antennas. However, these receivers detect only signal amplitude, losing critical phase information. While reference signals generated by a local oscillator (LO) can assist in phase recovery, existing modulation schemes designed for conventional systems perform poorly with this quantum detection mechanism. This paper introduces a breakthrough LO-aware adaptive modulation (LOAM) scheme specifically developed for RA receivers that dynamically adapts to complex fading channel coefficients. LOAM maximizes the minimum amplitude difference between constellation points, ensuring optimal detection performance. The innovation employs an adaptive co-linear constellation architecture aligned with the combined phase of reference signal and channel coefficient. For strong reference signals, LOAM generates symmetric constellation points centered at origin; for weak signals, it adopts non-symmetric distributions. The paper mathematically derives the threshold governing these operational regimes. Simulation results reveal the transformative impact of LOAM, demonstrating performance gains exceeding 45 dB over conventional modulation schemes, including quadrature amplitude modulation (QAM), phase-shift keying (PSK), and pulse-amplitude modulation (PAM).

Deep learning-based implicit channel state information (CSI) feedback has been introduced to enhance spectral efficiency in massive MIMO systems. Existing methods often show performance degradation in ultra-low-rate scenarios and inadaptability across diverse environments. In this paper, we propose Dual-ImRUNet, an efficient uplink-assisted deep implicit CSI feedback framework incorporating two novel plug-in preprocessing modules to achieve ultra-low feedback rates while maintaining high environmental robustness. First, a novel bi-directional correlation enhancement module is proposed to strengthen the correlation between uplink and downlink CSI eigenvector matrices. This module projects highly correlated uplink and downlink channel matrices into their respective eigenspaces, effectively reducing redundancy for ultra-low-rate feedback. Second, an innovative input format alignment module is designed to maintain consistent data distributions at both encoder and decoder sides without extra transmission overhead, thereby enhancing robustness against environmental variations. Finally, we develop an efficient transformer-based implicit CSI feedback network to exploit angular-delay domain sparsity and bi-directional correlation for ultra-low-rate CSI compression. Simulation results demonstrate successful reduction of the feedback overhead by 85% compared with the state-of-the-art method and robustness against unseen environments.

In this paper, a novel environmental mapping method is proposed to outline the indoor layout utilizing the line-of-sight (LoS) state information of extremely large aperture array (ELAA) channels. It leverages the spatial resolution provided by ELAA and the mobile terminal (MT)'s mobility to infer the presence and location of obstacles in the environment. The LoS state estimation is formulated as a binary hypothesis testing problem, and the optimal decision rule is derived based on the likelihood ratio test. Subsequently, the theoretical error probability of LoS estimation is derived, showing close alignment with simulation results. Then, an environmental mapping method is proposed, which progressively outlines the layout by combining LoS state information from multiple MT locations. It is demonstrated that the proposed method can accurately outline the environment layout, with the mapping accuracy improving as the number of service-antennas and MT locations increases. This paper also investigates the impact of channel estimation error and non-LoS (NLoS) components on the quality of environmental mapping. The proposed method exhibits particularly promising performance in LoS dominated wireless environments characterized by high Rician$K$-factor. Specifically, it achieves an average intersection over union (IoU) exceeding 80% when utilizing 256 service antennas and 18 MT locations.

This paper proposes a multi-stage position and velocity estimation algorithm in the context of near-field (NF) integrated sensing and communications (ISAC). We consider a colocated multiple-input multiple-output (MIMO) orthogonal frequencydivision multiplexing (OFDM) system. Both the wavefront curvature in NF and the wideband feature of OFDM are utilized to jointly estimate the range and the angle of the targets. The proposed algorithm relies on spectral estimation methods and maximum likelihood (ML) refinement steps for target sensing. Also, the estimation performance is compared with the CramérRao lower bound (CRLB). Given the initial estimations of the targets' position and mobility parameters through our proposed algorithm, we further develop a precoding (i.e., beamfocusing) design. The proposed design exploits the finite beam depth and width in NF with minimal beam cross-correlation of different targets across both range and angular domains, allowing better overall resolution. The precoding design also accounts for a tradeoff behavior between communication and sensing performance.

This paper presents a novel framework for importance-aware adaptive data transmission, designed specifically for real-time computer vision (CV) applications where taskspecific fidelity is critical. An importance-weighted mean square error (IMSE) metric is introduced, assigning data importance based on bit positions within pixels and semantic relevance within visual segments, thus providing a task-oriented measure of reconstruction quality. To minimize IMSE under the total power constraint, a data-importance-aware waterfilling approach is proposed to optimally allocate transmission power according to data importance and channel conditions. Simulation results demonstrate that the proposed approach significantly outperforms margin-adaptive waterfilling and equal power allocation strategies, achieving more than 7 dB and 10 dB gains in normalized IMSE at high SNRs ($>10 ~\text{dB}$), respectively. These results highlight the potential of the proposed framework to enhance data efficiency and robustness in real-time CV applications, especially in bandwidth-limited and resource-constrained environments.

Rydberg atomic receivers offer a quantum-native alternative to conventional RF front-ends by directly detecting electromagnetic fields via highly excited atomic states. While their quantum-limited sensitivity and hardware simplicity make them promising for future wireless systems, extending their use to scalable multi-antenna and multi-carrier configurations, termed Scalable Atomic-MIMO (SA-MIMO), remains largely unexplored. This paper introduces a novel RF transmitter-atomic receiver architecture that addresses this gap. The core idea lies in a novel modulation technique called Phase-Rotated Symbol Spreading (PRSS), which transforms the nonlinear phase retrieval problem inherent to atomic detection into a tractable linear demultiplexing task. PRSS enables efficient signal processing and supports scalable MUX/DeMUX operations in atomic MIMO systems. Simulation results show that the proposed system achieves up to 2.5 dB gain under optimal maximum-likelihood detection and over 10 dB under suboptimal detection in MIMO settings. These results establish PRSS-assisted SA-MIMO as a promising architecture for realizing high-sensitivity, interference-resilient atomic wireless communication.

The variety and volume of real-time video streaming services make network resources like power, frequency, and time critical to the user experience. Semantic communication, by transmitting only meaningful data, significantly reduces the load on the network, leading to faster transmission times and reduced latency-both crucial for enhancing user experience. In this paper, we develop a context-aware resource allocation framework for a semantic-aware regenerative AI-based Unmanned Aerial Vehicle (UAV) setup that accommodates both conventional and semantic communication users. Specifically, we define a semantic feature pooling mechanism, upon which a novel Quality of Experience (QoE) model is proposed. For dynamic network environments, we formulate a long-term resource allocation problem by maximizing the expected rewards. Each UAV is modeled as a learning agent, with each resource allocation solution corresponding to an action taken by the UAVs. We then develop a Multi-Agent Reinforcement Learning (MARL) framework in which each agent discovers its optimal strategy based on local observations. More specifically, we propose an agent-independent method where all agents execute a decision algorithm independently while sharing a common structure based on Q-learning. The proposed framework also facilitates intelligent traffic steering by dynamically adjusting resource distribution based on real-time context. Finally, simulation results demonstrate the effectiveness and superiority of the proposed method in terms of overall QoE.

In this paper, we address a physical layer security (PLS) framework for the integrated sensing and semantic communication (ISASC) system, where a multi-antenna dual-function semantic base station serves multiple single-antenna semantic communication users (SCUs) and monitors a malicious sensing target (MST), in the presence of a single-antenna eavesdropper (EVE), with both the MST and EVE aiming to wiretap information from the SCUs' signals. To enhance PLS, we employ joint artificial noise (AN) and dedicated sensing signal (DSS) in addition to wiretap coding. To evaluate the sensing accuracy, we derive the Cramer-Rao bound (CRB) as a function of the communication, sensing, and AN beamforming (BF) vectors. Subsequently, to assess the PLS level of the ISASC system, we determine a closed-form expression for the semantic secrecy rate (SSR). To achieve an optimal trade-off region between these two competing objectives, we formulate a multi-objective optimization problem for the joint design of the BF vectors. We apply semi-definite programming, Gaussian randomization method, and golden-section search techniques to address this problem. Simulation results demonstrate that the proposed scheme outperforms baseline schemes, achieving a superior trade-off between SSR and CRB.

Deep learning (DL)-based channel state information (CSI) feedback has shown great potential in improving spectrum efficiency in massive MIMO systems. However, DL models optimized for specific environments often experience performance degradation in others due to model mismatch. To overcome this barrier in the practical deployment, we propose UniversalNet, an ID-photo-inspired universal CSI feedback framework that enhances model generalizability by standardizing the input format across diverse data distributions. Specifically, Uni-versalNet employs a standardized input format to mitigate the influence of environmental variability, coupled with a lightweight sparsity-aligning operation in the transformed sparse domain and marginal control bits for original format recovery. This enables seamless integration with existing CSI feedback models, requiring minimal modifications in preprocessing and postprocessing without updating neural network weights. Furthermore, we propose an efficient eigenvector joint optimization method to enhance the sparsity of the eigenvector matrix by projecting the spectrum channel correlation into the eigenspace, thus improving the implicit CSI compression efficiency. Test results demonstrate that UniversalNet effectively improves generalization performance and ensures precise CSI feed-back, even in scenarios with limited training diversity and previously unseen CSI environments.

Generative foundation models can revolutionize the design of semantic communication (SemCom) systems by enabling high fidelity exchange of semantic information at ultra-low rates. In this work, a generative SemCom framework utilizing pre-trained foundation models is proposed, where both uncoded forward-with-error and coded discard-with-error schemes are developed for the semantic decoder. Using the rate-distortion-perception theory, the relationship between regenerated signal quality and transmission reliability is characterized, which is proven to be non-decreasing. Based on this, semantic values are defined to quantify the semantic similarity between multimodal semantic features and the original source. We also investigate semantic-aware power allocation problems that minimize power consumption for ultra-low rate and high fidelity SemComs. Two semantic-aware power allocation methods are proposed by leveraging the non-decreasing property of the perception-error relationship. Based on the Kodak dataset, perception-error functions and semantic values are obtained for image tasks. Simulation results show that the proposed semantic-aware method significantly outperforms conventional approaches, particularly in the channel-coded case (up to 90% power saving).

Life-transformative applications such as immersive extended reality are revolutionizing wireless communications and computer vision (CV). This paper presents a novel framework for importance-aware adaptive data transmissions, designed specifically for real-time CV applications where task-specific fidelity is critical. A novel importance-weighted mean square error (IMSE) metric is introduced as a task-oriented measure of reconstruction quality, considering sub-pixel-level importance (SP-I) and semantic segment-level importance (SS-I) models. To minimize IMSE under total power constraints, data-importance-aware waterfilling approaches are proposed to optimally allocate transmission power according to data importance and channel conditions, prioritizing sub-streams with high importance. Simulation results demonstrate that the proposed approaches significantly outperform margin-adaptive waterfilling and equal power allocation strategies. The data partitioning that combines both SP-I and SS-I models is shown to achieve the most significant improvements, with normalized IMSE gains exceeding 7 dB and 10 dB over the baselines at high SNRs (> 10 dB). These substantial gains highlight the potential of the proposed framework to enhance data efficiency and robustness in real-time CV applications, especially in bandwidth-limited and resource-constrained environments.

As a representative application paradigm for supporting volumetric video, point cloud is able to capture three-dimensional spatial information of objects, offering dynamic and immersive experiences to end users. Compared to the conventional bit-to-bit communication principle, the design of semantic communication ingeniously utilizes joint source and channel coding to transmit semantic features instead. By taking into account the common content delivery requirements of low bandwidth consumptions and low latency, while maintaining high resolutions, we present in this paper a novel framework called Spatial-Temporal Semantic Point Cloud Transmission (ST-SPCT). Compared to the existing point cloud compression and semantic communication methods that extract and reconstruct features only in the spatial dimension, the ST-SPCT simultaneously extracts and reconstructs spatial-temporal semantic features more deeply, thus significantly reducing computational time and data volume, while ensuring negligible compromises in peak signal-to-noise ratio (PSNR), chamfer distance (CD) metrics, and frame continuity according to our systematic experiment results.

Extremely large aperture array (ELAA) is a promising multiple-input multiple-output (MIMO) technique for next generation mobile networks. In this paper, we propose two novel approaches to accelerate the convergence of current iterative MIMO detectors in ELAA channels. Our approaches exploit the static components of the ELAA channel, which include line of sight (LoS) paths and deterministic non-LoS (NLoS) components due to channel hardening effects. This paper proposes novel convergence acceleration techniques for fast iterative ELAA-MIMO detection by leveraging the static channel component, including the LoS paths and deterministic NLoS components that arise due to channel hardening. Specifically, these static channel components are utilized in two ways: as preconditioning matrices for general iterative algorithms, and as initialization for quasi-Newton (QN) methods. Simulation results show that the proposed approaches converge significantly faster compared to current iterative MIMO detectors, especially under strong LoS conditions with high Rician K-factor. Furthermore, QN methods with the proposed initialization matrix consistently achieve the best convergence performance while maintaining low complexity.

In this paper, we propose the Turbo principle (of using parallel concatenated channel encoders separated by an interleaver and iterative soft-input and soft-output decoding between the constituent decoders) onto Polar and LDPC codes resulting in Turbo-Polar, Turbo-LDPC-Polar and Turbo-LDPC schemes with the aim of enhancing the BLER performance while also reducing the decoding complexity. All the proposed turbo-coded schemes are decoded using the traditional Belief Propagation (BP) algorithm based on low-density parity-check iterative decoding through a factor graph. Monte Carlo simulation results confirm the superiority of Turbo-LDPC and Turbo-LDPC-Polar schemes in BLER performance over state-of-the-art cyclic redundancy check-aided successive cancellation List decoding (CA-SCL) of Polar Codes with a large list size of 32 for large block lengths (larger than 3072 bits) while having reduced computational decoding complexity in comparison to CA-SCL decoding in an additive white Gaussian noise (A WGN) channel. Furthermore, Turbo-LDPC (based on 5G New Radio specifications for LDPC code) outperforms the standalone 5G-NR LDPC code and achieves about 1 dB gain at a BLER of 10–4over correlated slow fading Rayleigh channel; however, being turbo-iterative in nature, it has higher complexity (about six-fold) than the standalone 5GNR LDPC code.

Guessing random additive noise decoding (GRAND) is a universal decoding that estimates the transmitted codewords by guessing the noise bit sequences and reversing their impact until legitimate codewords are found. This paper investigates one of its variants, namely Soft-GRAND (or SGRAND) of Polar codes over an interference channel where power domain non-orthogonal multiple access (NOMA) channel access protocol is in operation. Monte Carlo simulation results confirm the robustness of the SGRAND decoding over these channels. More specifically, the joint CRC-aided SGRAND decoding outperforms state-of-the-art CRC-aided list decoding (of Polar codes) with a list size of 32 by approximately 0.5 dB and 1.5 dB at block error rate (BLER) of 10−4over additive white Gaussian noise (AWGN) and Rayleigh fading channels, respectively, and also offers lower complexity at mid-to-high signal to noise ratios (beyond 5 dB).

Computation task offloading plays a crucial role in facilitating computation-intensive applications and edge intelligence, particularly in response to the explosive growth of massive data generation. Various enabling techniques, wireless technologies and mechanisms have already been proposed for task offloading, primarily aimed at improving the quality of services (QoS) for users. While there exists an extensive body of literature on this topic, exploring computation offloading from the standpoint of task types has been relatively underrepresented. This motivates our survey, which seeks to classify the state-of-the-art (SoTA) from the task type point-of-view. To achieve this, a thorough literature review is conducted to reveal the SoTA from various aspects, including architecture, objective, offloading strategy, and task types, with the consideration of task generation. It has been observed that task types are associated with data and have an impact on the offloading process, including elements like resource allocation and task assignment. Building upon this insight, computation offloading is categorized into two groups based on task types: static task-based offloading and dynamic task-based offloading. Finally, a prospective view of the challenges and opportunities in the field of future computation offloading is presented.

In this paper, a novel spatially non-stationary fading channel model is proposed for multiple-input multiple-output (MIMO) system with extremely-large aperture service-array (ELAA). The proposed model incorporates three key factors which cause the channel spatial non-stationarity:1) link-wise path-loss;2) shadowing effect;3) line-of-sight (LoS)/non-LoS state. With appropriate parameter configurations, the proposed model can be used to generate computer-simulated channel data that matches the published measurement data from practical ELAA-MIMO channels. Given such appealing results, the proposed fading channel model is employed to study the cumulative distribution function (CDF) of ELAA-MIMO channel capacity. For all of our studied scenarios, it is unveiled that the ELAA-MIMO channel capacity obeys the skew normal distribution. Moreover, the channel capacity is also found close to the Gaussian or Weibull distribution, depending on users’ geo-location and distribution. More specifically, for single-user equivalent scenarios or multiuser scenarios with short user-to-ELAA distances (e.g., 1 m), the channel capacity is close to the Gaussian distribution; and for others, it is close to the Weibull distribution. Finally, the proposed channel model is also employed to study the impact of channel spatial non-stationarity on linear MIMO receivers through computer simulations. The proposed fading channel model is available at https://github.com/ELAA-MIMO/non-stationary-fading-channel-model.

Current iterative multiple-input multiple-output (MIMO) detectors suffer from slow convergence when the wireless channel is ill-conditioned. The ill-conditioning is mainly caused by spatial correlation between channel columns corresponding to the same user equipment, known as intra-user interference. In addition, in the emerging MIMO systems using an extremely large aperture array (ELAA), spatial non-stationarity can make the channel even more ill-conditioned. In this paper, user-wise singular value decomposition (UW-SVD) is proposed to accelerate the convergence of iterative MIMO detectors. Its basic principle is to perform SVD on each user’s sub-channel matrix to eliminate intra-user interference. Then, the MIMO signal model is effectively transformed into an equivalent signal (e-signal) model, comprising an e-channel matrix and an e-signal vector. Existing iterative algorithms can be used to recover the e-signal vector, which undergoes post-processing to obtain the signal vector. It is proven that the e-channel matrix is better conditioned than the original MIMO channel for spatially correlated (ELAA-)MIMO channels. This implies that UW-SVD can accelerate current iterative algorithms, which is confirmed by our simulation results. Specifically, it can speed up convergence by up to 10 times in both uncoded and coded systems.

The cumulative distribution function (CDF) of a non-central$\chi ^{2}$-distributed random variable (RV) is often used when measuring the outage probability of communication systems. For ultra-reliable low-latency communication (URLLC), it is important but mathematically challenging to determine the outage threshold for an extremely small outage target. This motivates us to investigate lower bounds of the outage threshold, and it is found that the one derived from the Chernoff inequality (named Cher-LB) is the most effective lower bound. This finding is associated with three rigorously established properties of the Cher-LB with respect to the mean, variance, reliability requirement, and degrees of freedom of the non-central$\chi ^{2}$-distributed RV. The Cher-LB is then employed to predict the beamforming gain in URLLC for both conventional multi-antenna systems (i.e., MIMO) under first-order Markov time-varying channel and reconfigurable intellgent surface (RIS) systems. It is exhibited that, with the proposed Cher-LB, the pessimistic prediction of the beamforming gain is made sufficiently accurate for guaranteed reliability as well as the transmit-energy efficiency.

In this paper, a correlation matching pursuit (CMP) procedure is proposed to handle the vector perturbation (VP) problem for nonlinear precoding (NLP) in downlink multiuser multi-antenna (i.e., MU-MIMO) systems. Basically, CMP consists of two sub-procedures, i.e., correlation matching and correlation pursuit. The former takes the charge of direct calculation of the perturbation integers through a set of established convex optimization problems; the latter is responsible for selecting the perturbation vector for updating the precoded vector. Iterative execution of both procedures renders CMP being modelled as a searching tree which consists of multiple nodes and paths. The sequence correlation between the precoded vector and perturbation vectors is revealed crucial to the performance optimality and thus used as metric of the path search. Given that the single-path search is suboptimal, we propose multi-path schemes to enable exploitation on the path diversity and thus further improve the performance. Complexity analysis and computer simulations demonstrate that CMP-based NLP algorithms serve as low-cost VP solutions with significantly lowered processing latency and meanwhile, comparable performance compared to prior arts.

Signal detection in large multiple-input multiple-output (large-MIMO) systems presents greater challenges compared to conventional massive-MIMO for two primary reasons. First, large-MIMO systems lack favorable propagation conditions as they do not require a substantially greater number of service antennas relative to user antennas. Second, the wireless channel may exhibit spatial non-stationarity when an extremely large aperture array (ELAA) is deployed in a large-MIMO system. In this paper, we propose a scalable iterative large-MIMO detector named ANPID, which simultaneously delivers 1) close to maximum-likelihood detection performance, 2) low computational-complexity (i.e., square-order of transmit antennas), 3) fast convergence, and 4) robustness to the spatial non-stationarity in ELAA channels. ANPID incorporates a damping demodulation step into stationary iterative (SI) methods and alternates between two distinct demodulated SI methods. Simulation results demonstrate that ANPID fulfills all the four features concurrently and outperforms existing low-complexity MIMO detectors, especially in highly-loaded large-MIMO systems.

This paper examines the uplink transmission of a single-antenna handsheld user to a cluster of satellites, with a focus on utilizing the inter-satellite links to enable cooperative signal detection. Two cases are studied: one with full CSI and the other with partial CSI between satellites. The two cases are compared in terms of capacity, overhead, and bit error rate. Additionally, the impact of channel estimation error is analyzed in both designs, and robust detection techniques are proposed to handle channel uncertainty up to a certain level. The performance of each case is demonstrated, and a comparison is made with conventional satellite communication schemes where only one satellite can connect to a user. The results of our study reveal that the proposed constellation with a total of 3168 satellites in orbit can enable a capacity of 800 Mbits/sec through cooperation of 12 satellites with and occupied bandwidth of 500 MHz. In contrast, conventional satellite communication approaches with the same system parameters yield a significantly lower capacity of less than 150 Mbits/sec for the nearest satellite.

The cumulative distribution function (CDF) of a non-central$\chi^{2}$-distributed random variable (RV) is often used when measuring the outage probability of communication systems. For adaptive transmitters, it is important but mathematically challenging to determine the outage threshold for an extreme target outage probability (e.g., 10−5or less). This motivates us to investigate lower bounds of the outage threshold, and it is found that the one derived from the Chernoff inequality (named Cher-LB) is the most effective lower bound. The Cher-LB is then employed to predict the multi-antenna transmitter beamforming-gain in ultra-reliable and low-latency communication, concerning the first-order Markov time-varying channel. It is exhibited that, with the proposed Cher-LB, pessimistic prediction of the beamforming gain is made sufficiently accurate for guaranteed reliability as well as the transmit-energy efficiency.

The design of iterative linear precoding is recently challenged by extremely large aperture array (ELAA) systems, where conventional preconditioning techniques could hardly improve the channel condition. In this paper, it is proposed to regularize the extreme singular values to improve the channel condition by deducting a rank-one matrix from the Wishart matrix of the channel. Our analysis proves the feasibility to reduce the largest singular value or to increase multiple small singular values with a rank-one matrix when the singular value decomposition of the channel is available. Knowing the feasibility, we propose a low-complexity approach where an approximation of the regularization matrix can be obtained based on the statistical property of the channel. It is demonstrated, through simulation results, that the proposed low-complexity approach significantly outperforms current preconditioning techniques in terms of reduced iteration number for more than 10% in both ELAA systems as well as symmetric multi-antenna (i.e., MIMO) systems when the channel is i.i.d. Rayleigh fading.

Consider robot swarm wireless networks where mobile robots offload their computing tasks to a computing server located at the mobile edge. Our aim is to maximize the swarm lifetime through efficient exploitation of the correlation between distributed data sources. The optimization problem is handled by selecting appropriate robot subsets to send their sensed data to the server. In this work, the data correlation between distributed robot subsets is modelled as an undirected graph. A least-degree iterative partitioning (LDIP) algorithm is proposed to partition the graph into a set of subgraphs. Each subgraph has at least one vertex (i.e., subset), termed representative vertex (R-Vertex), which shares edges with and only with all other vertices within the subgraph; only R-Vertices are selected for data transmissions. When the number of subgraphs is maximized, the proposed subset selection approach is shown to be optimum in the AWGN channel. For independent fading channels, the max-min principle can be incorporated into the proposed approach to achieve the best performance.

We focus on the signal detection for large quasi-symmetric (LQS) multiple-input multiple-output (MIMO) systems, where the numbers of both service (M) and user (N) antennas are large and N/M → 1. It is challenging to achieve maximum-likelihood detection (MLD) performance with square-order complexity due to the ill-conditioned channel matrix. In the emerging MIMO paradigm termed with an extremely large aperture array, the channel matrix can be more ill-conditioned due to spatial non-stationarity. In this paper, projected-Jacobi (PJ) is proposed for signal detection in (non-) stationary LQS-MIMO systems. It is theoretically and empirically demonstrated that PJ can achieve MLD performance, even when N/M = 1. Moreover, PJ has square-order complexity of N and supports parallel computation. The main idea of PJ is to add a projection step and to set a (quasi-) orthogonal initialization for the classical Jacobi iteration. Moreover, the symbol error rate (SER) of PJ is mathematically derived and it is tight to the simulation results.

With the huge number of broadband users, automated network management becomes of huge interest to service providers. A major challenge is automated monitoring of user Quality of Experience (QoE), where Artificial Intelligence (AI) and Machine Learning (ML) models provide powerful tools to predict user QoE from basic protocol indicators such as Round Trip Time (RTT), retransmission rate, etc. In this paper, we introduce an effective feature selection method along with the corresponding classification algorithms to address this challenge. The simulation results show a prediction accuracy of 78% on the benchmark ITU ML5G-PS-012 dataset, improving 11% over the state-of-the-art result whilst reducing the model complexity at the same time. Moreover, we show that the local area network round trip time (LAN RTT) value during daytime and midweek plays the most prominent factor affecting the user QoE.

In this paper, we investigate the uplink transmission of a single-antenna handheld user to a cluster of satellites. Taking advantage of the inter-satellite links, the satellites can cooperate which each other to jointly detect the received signal. We examine a scenario in which the satellite cluster lacks access to the instantaneous channel state information (CSI). Thus, using only statistical CSI, we design the joint detection by minimizing the mean square error (MSE). We calculate the ergodic capacity using the properties of the Wishart matrix, and then for low-SNR scenarios we provide a closed-form approximation for it. Our numerical results demonstrate the effectiveness of the detection scheme, along with the proximity of the approximation to the actual ergodic capacity. Considering a mega constellation with 3168 satellites in low earth orbit (LEO), we show that a capacity of more than 10 MB/sec can be achieved even if only the statistical CSI is known to the receiver, and a capacity of up to 38 MB/sec can be achieved with perfect instantaneous CSI.

The objective of this work is to address one of the recent ITU AI/machine learning challenges, specifically on the RF power amplifier (PA) behavioral modelling. This paper presents a novel model namely real-valued time-delay temporal convolutional network (RVTDTCN) to learn the long-term memory effect from PA in 5G base-station transmitters. The proposed model employs dilated convolutions to incorporate information from various time stamps and capture intricate temporal dependencies. This is achieved without the requirement of additional coefficients. In our experiment, the ITU ML5G-PS-007 datasets collected from commercial 5G base-stations are employed for both training and testing purposes. The performance of the learned PA behavior is evaluated using both normalized mean square error (NMSE) and adjacent channel error power ratio (ACEPR). The results show that the RVTDTCN model achieves competitive performance with the state-of-the-art feedforward neural network (FNN) models but reducing the computational complexity by 60%. Moreover, the proposed model outperforms the state-of-the-art convolutional neural network (CNN) model by 2-3dB in NMSE and ACEPR, with a similar complexity.

In this paper, computational task allocation schemes with correlated data are investigated to minimize the energy consumption for a symbiotic robot swarm. In such a swarm, tasks need to be computed cooperatively with data from multiple robots. Therefore, data needs to be transmitted to one selected robot. However, the correlated data among robots can increase the energy consumption of transmission and computation due to redundancy. To solve this problem, a model is proposed to investigate the data correlation versus distance among the robots. Based on this model, three task allocation strategies are further proposed. Energy consumption of the robot swarm is reduced through the selection of the robot to transmit data either based on channel gain or data correlation. MATLAB-based simulation results show that the proposed task allocation strategies can significantly reduce the energy consumption of a symbiotic robotic swarm compared to state-of-the-art.

This paper conceives a novel sparse code multiple access (SCMA) codebook design which is motivated by the strong need for providing ultra-low decoding complexity and good error performance in downlink Internet-of-things (IoT) networks, in which a massive number of low-end and low-cost IoT communication devices are served. By focusing on the typical Rician fading channels, we analyze the pair-wise error probability of superimposed SCMA codewords and then deduce the design metrics for multi-dimensional constellation construction and sparse codebook optimization. For significant reduction of the decoding complexity, we advocate the key idea of projecting the multi-dimensional constellation elements to a few overlapped complex numbers in each dimension, called low projection (LP). An emerging modulation scheme, called golden angle modulation (GAM), is considered for multi-stage LP optimization, where the resultant multi-dimensional constellation is called LP-GAM. Our analysis and simulation results show the superiority of the proposed LP codebooks (LPCBs) including one-shot decoding convergence and excellent error rate performance. In particular, the proposed LPCBs lead to decoding complexity reduction by at least 97% compared to that of the conventional codebooks, whilst owning large minimum Euclidean distance. Some examples of the proposed LPCBs are available athttps://github.com/ethanlq/SCMA-codebook.

In this paper, a novel nonlinear precoding (NLP) technique, namely constellation-oriented perturbation (COP), is proposed to tackle the scalability problem inherent in conventional NLP techniques. The basic concept of COP is to apply vector perturbation (VP) in the constellation domain instead of symbol domain; as often used in conventional techniques. By this means, the computational complexity of COP is made independent to the size of multi-antenna (i.e., MIMO) networks. Instead, it is related to the size of symbol constellation. Through widely linear transform, it is shown that COP has its complexity flexibly scalable in the constellation domain to achieve a good complexity-performance tradeoff. Our computer simulations show that COP can offer very comparable performance with the optimum VP in small MIMO systems. Moreover, it significantly outperforms current sub-optimum VP approaches (such as degree-2 VP) in large MIMO whilst maintaining much lower computational complexity.

In vehicle-to-infrastructure (V2I) networks, a cluster of multi-antenna access points (APs) can collaboratively conduct transmitter beamforming to provide data services (e.g., eMBB or URLLC). The collaboration between APs effectively forms a networked linear antenna-array with extra-large aperture (i.e., network-ELAA), where the wireless channel exhibits spatial non-stationarity. Major contribution of this work lies in the analysis of beamforming gain and radio coverage for network-ELAA non-stationary Rician channels considering the AP clustering. Assuming that: 1) the total transmit-power is fixed and evenly distributed over APs, 2) the beam is formed only based on the line-of-sight (LoS) path, it is found that the beamforming gain is concave to the cluster size. The optimum size of the AP cluster varies with respect to the user’s location, channel uncertainty as well as data services. A user located farther from the ELAA requires a larger cluster size. URLLC is more sensitive to the channel uncertainty when comparing to eMBB, thus requiring a larger cluster size to mitigate the channel fading effect and extend the coverage. Finally, it is shown that the network-ELAA can offer significant coverage extension (50% or more in most of cases) when comparing with the single-AP scenario.

Concerning ultra-reliable low-latency communication (URLLC) for the downlink operating in the frequency-division multiple-access with random channel assignment, a lightweight power allocation approach is proposed to maximize the number of URLLC users subject to transmit-power and individual user-reliability constraints. Provided perfect channel-state-information at the transmitter (CSIT), the proposed approach is proven to ensure maximized URLLC users. Assuming imperfect CSIT, the proposed approach still aims to maximize the URLLC users without compromising the individual user reliability by using a pessimistic evaluation of the channel gain. It is demonstrated, through numerical results, that the proposed approach can significantly improve the user capacity and the transmit-power efficiency in Rayleigh fading channels. With imperfect CSIT, the proposed approach can still provide remarkable user capacity at limited cost of transmit-power efficiency.

In this paper, a novel spatially non-stationary channel model is proposed for link-level computer simulations of massive multiple-input multiple-output (mMIMO) with extremely large aperture array (ELAA). The proposed channel model allows a mix of non-line-of-sight (NLoS) and LoS links between a user and service antennas. The NLoS/LoS state of each link is characterized by a binary random variable, which obeys a correlated Bernoulli distribution. The correlation is described in the form of an exponentially decaying window. In addition, the proposed model incorporates shadowing effects which are non-identical for NLoS and LoS states. It is demonstrated, through computer emulation, that the proposed model can capture almost all spatially non-stationary fading behaviors of the ELAA-mMIMO channel. Moreover, it has a low implementational complexity. With the proposed channel model, Monte-Carlo simulations are carried out to evaluate the channel capacity of ELAA-mMIMO. It is shown that the ELAA-mMIMO channel capacity has considerably different stochastic characteristics from the conventional mMIMO due to the presence of channel spatial non-stationarity.

Quantization is the characterization of analogue-to-digital converters (ADC) in massive MIMO systems. The design of quantization function or quantization thresholds is found to relate to quantization step, which is the factor that adapts with the changing of transmit power and noise variance. With the objective of utilizing low-resolution ADC is reducing the cost of massive MIMO, we propose an idea as if it is necessary to have adaptive-threshold quantization function. It is found that when maximum-likelihood (ML) is employed as the detection method, having quantization thresholds fixed for low-resolution ADCs will not cause significant performance loss. Moreover, such fixed-threshold quantization function does not require any information of signal power which can reduce the hardware cost of ADCs. Simulations have been carried out in this paper to make comparisons between fixed-threshold and adaptive-threshold quantization regarding various factors.

Task offloading to mobile edge computing (MEC) has emerged as a key technology to alleviate the computation workloads of mobile devices and decrease service latency for the computation-intensive applications. Device battery consumption is one of the limiting factors needs to be considered during task offloading. In this paper, multi-task offloading strategies have been investigated to improve device energy efficiency. Correlations among tasks in time domain as well as task domain are proposed to be employed to reduce the number of tasks to be transmitted to MEC. Furthermore, a binary decision tree based algorithm is investigated to jointly optimize the mobile device clock frequency, transmission power, structure and number of tasks to be transmitted. MATLAB based simulation is employed to demonstrate the performance of our proposed algorithm. It is observed that the proposed dynamic multi-task offloading strategies can reduce the total energy consumption at device along various transmit power versus noise power point compared with the conventional one.

In this paper, a novel end-to-end learning approach, namely JTRD-Net, is proposed for uplink multiuser single-input multiple-output (MU-SIMO) joint transmitter and non-coherent receiver design (JTRD) in fading channels. The basic idea lies in the use of artificial neural networks (ANNs) to replace traditional communication modules at both transmitter and receiver sides. More specifically, the transmitter side is modeled as a group of parallel linear layers, which are responsible for multiuser waveform design; and the non-coherent receiver is formed by a deep feed-forward neural network (DFNN) so as to provide multiuser detection (MUD) capabilities. The entire JTRD-Net can be trained from end to end to adapt to channel statistics through deep learning. After training, JTRD-Net can work efficiently in a non-coherent manner without requiring any levels of channel state information (CSI). In addition to the network architecture, a novel weight-initialization method, namely symmetrical-interval initialization, is proposed for JTRD-Net. It is shown that the symmetrical-interval initialization outperforms the conventional method (e.g. Xavier initialization) in terms of well-balanced convergence-rate among users. Simulation results show that the proposed JTRD-Net approach takes significant advantages in terms of reliability and scalability over baseline schemes on both i.i.d. complex Gaussian channels and spatially-correlated channels.

This paper aims to handle the joint transmitter and noncoherent receiver design for multiuser multiple-input multiple-output (MU-MIMO) systems through deep learning. Given the deep neural network (DNN) based noncoherent receiver, the novelty of this work mainly lies in the multiuser waveform design at the transmitter side. According to the signal format, the proposed deep learning solutions can be divided into two groups. One group is called pilot-aided waveform, where the information-bearing symbols are time-multiplexed with the pilot symbols. The other is called learning-based waveform, where the multiuser waveform is partially or even completely designed by deep learning algorithms. Specifically, if the information-bearing symbols are directly embedded in the waveform, it is called systematic waveform. Otherwise, it is called non-systematic waveform, where no artificial design is involved. Simulation results show that the pilot-aided waveform design outperforms the conventional zero forcing receiver with least squares (LS) channel estimation on small-size MU-MIMO systems. By exploiting the time-domain degrees of freedom (DoF), the learning-based waveform design further improves the detection performance by at least 5 dB at high signal-to-noise ratio (SNR) range. Moreover, it is found that the traditional weight initialization method might cause a training imbalance among different users in the learning-based waveform design. To tackle this issue, a novel weight initialization method is proposed which provides a balanced convergence performance with no complexity penalty.

Multi-access edge computing for mobile computing-task offloading is driving the extreme utilization of available degrees of freedom (DoF) for ultra-reliable low-latency downlink communications. The fundamental aim of this work is to find latency-constrained transmission protocols that can achieve a very-low outage probability (e.g. 0.001%). Our investigation is mainly based upon the Polyanskiy-Poor-Verdú formula on the finite-length coded channel capacity, which is extended from the quasi-static fading channel to the frequency selective channel. Moreover, the use of a suitable duplexing mode is also critical to the downlink reliability. Specifically, time-division duplexing (TDD) outperforms frequency-division duplexing (FDD) in terms of the frequency diversity-gain. On the other hand, FDD takes the advantage of having more temporal DoF in the downlink, which can be exchanged into the spatial diversity-gain through the use of space-time coding. Numerical study is carried out to compare the reliability between FDD and TDD under various latency constraints.

In this paper, a novel unsupervised deep learning approach is proposed to tackle the multiuser frequency synchronization problem inherent in orthogonal frequency-division multiple-access (OFDMA) uplink communications. The key idea lies in the use of the feed-forward deep neural network (FF-DNN) for multiuser interference (MUI) cancellation taking advantage of their strong classification capability. Basically, the proposed FF-DNN consists of two essential functional layers. One is called carrier-frequency-offsets (CFOs) classification layer that is responsible for identifying the users' CFO range, and another is called MUI-cancellation layer responsible for joint multiuser detection (MUD) and frequency synchronization. By such means, the proposed FF-DNN approach showcases remarkable MUI-cancellation performances without the need of multiuser CFO estimation. In addition, we also exhibit an interesting phenomenon occurred at the CFO-classification stage, where the CFO-classification performance get improved exponentially with the increase of the number of users. This is called multiuser diversity gain in the CFO-classification stage, which is carefully studied in this paper.

In this paper, unsupervised deep learning solutions for multiuser single-input multiple-output (MU-SIMO) coherent detection are extensively investigated. According to the ways of utilizing the channel state information at the receiver side (CSIR), deep learning solutions are divided into two groups. One group is called equalization and learning, which utilizes the CSIR for channel equalization and then employ deep learning for multiuser detection (MUD). The other is called direct learning, which directly feeds the CSIR, together with the received signal, into deep neural networks (DNN) to conduct the MUD. It is found that the direct learning solutions outperform the equalization-and-learning solutions due to their better exploitation of the sequence detection gain. On the other hand, the direct learning solutions are not scalable to the size of SIMO networks, as current DNN architectures cannot efficiently handle many co-channel interferences. Motivated by this observation, we propose a novel direct learning approach, which can combine the merits of feedforward DNN and parallel interference cancellation. It is shown that the proposed approach trades off the complexity for the learning scalability, and the complexity can be managed due to the parallel network architecture.

The aim of this paper is to handle the multi-frequency synchronization problem inherent in orthogonal frequency-division multiple access (OFDMA) uplink communications, where the carrier frequency offset (CFO) for each user may be different, and they can be hardly compensated at the receiver side. Our major contribution lies in the development of a novel OFDM receiver that is resilient to unknown random CFO thanks to the use of a CFO-compensator bank. Specifically, the whole CFO range is evenly divided into a set of sub-ranges, with each being supported by a dedicated CFO compensator. Given the optimization for CFO compensator a NP-hard problem, a machine deep-learning approach is proposed to yield a good sub-optimal solution. It is shown that the proposed receiver is able to offer inter-carrier interference free performance for OFDMA systems operating at a wide range of SNRs.

Considering a densely populated area where a mobile device, with a single RF chain, shares its message with a set of mobile devices through narrowband mmWave channel, an analogue-beam splitting approach is proposed to achieve a good capacity and coverage trade-off. The proposed approach aims at maximizing the capacity of the mmWave multicast channel through antenna-element grouping and adaptive phase shifting, which takes into account of the inter-beam interference. When receivers are randomly distributed on a circle centered at the transmitter, according to the uniform distribution, it is found that the impact of inter-beam interference on the channel capacity can be negligibly small, and thus the analogue-beam splitting approach can be largely simplified in practice. Computer simulations are carried out to elaborate our theoretical study and demonstrate considerable advantages of the proposed analogue-beam splitting approach.

In this paper, a multi-antenna assisted virtual full-duplex (FD) relaying with reliability-aware iterative decoding at destination node is proposed to improve system spectral efficiency and reliability. This scheme enables two half-duplex relay nodes, mimicked as FD relaying, to alternatively serve as transmitter and receiver to forward their received signals regardless of the decoding errors. Meanwhile, the inter-relay interference is cancelled with QR-decomposition. Then, by deploying the proposed reliability-aware iterative detection/decoding process at destination node, the inter-frame interference and error propagation effect can be simultaneously mitigated. Simulation results show that, without extra cost of time delay and signalling overhead, our proposed scheme outperforms the conventional selective decode-and-forward (S-DF) relaying schemes, such as cyclic redundancy check based S-DF relaying and threshold based S-DF relaying, by up to 8 dB in terms of bit-error-rate.

In this paper, a novel approach, namely, real-complex hybrid modulation (RCHM), is proposed to scale up multiuser multiple-input multiple-output (MU-MIMO) detection with particular concern on the use of equal or approximately equal service antennas and user terminals (UTs). By RCHM, we mean that UTs transmit their data sequences with a mix of real and complex modulation symbols interleaved in the spatial and temporal domain. It is shown that, through the system outage probability, RCHM can combine the merits of real and complex modulations to achieve the best spatial diversity-multiplexing tradeoff that minimizes the required transmit-power given a sum rate. The signal pattern of RCHM is optimized with respect to the real-to-complex symbol ratio as well as power allocation. It is also shown that RCHM equips the successive interference canceling MU-MIMO receiver with near-optimal performances and fast convergence in Rayleigh fading channels. This result is validated through our mathematical analysis of the average bit-error-rate as well as extensive computer simulations considering the case with single or multiple base stations.

In this paper, a symbol-level selective transmission for full-duplex (FD) relaying networks is proposed to mitigate error propagation effects and improve system spectral efficiency. The idea is to allow the FD relay node to predict the correctly decoded symbols of each frame, based on the generalized square deviation method, and discard the erroneously decoded symbols, resulting in fewer errors being forwarded to the destination node. Using the capability for simultaneous transmission and reception at the FD relay node, our proposed strategy can improve the transmission efficiency without extra cost of signaling overhead. In addition, targeting on the derived expression for outage probability, we compare it with half-duplex relaying case and provide the transmission power and relay location optimization strategy to further enhance the system performances. The results show that our proposed scheme outperforms the classic relaying protocols, such as cyclic redundancy check-based selective decode-and-forward (S-DF) relaying and threshold-based S-DF relaying in terms of outage probability and bit error rate. Moreover, the performances with optimal power allocation are better than those with equal power allocation, especially when the FD relay node encounters strong self-interference and/or it is close to the destination node.

Multiuser multiple-input multiple-output (MUMIMO) nonlinear precoding techniques face the problem of poor computational scalability to the size of the network. In this paper, the fundamental problem of MU-MIMO scalability is tackled through a novel signal-processing approach, which is called degree-2 vector perturbation (D2VP). Unlike the conventional VP approaches that aim at minimizing the transmit-to-receive energy ratio through searching over an N-dimensional Euclidean space, D2VP shares the same target through an iterative-optimization procedure. Each iteration performs vector perturbation over two optimally selected subspaces. By this means, the computational complexity is managed to be in the cubic order of the size of MU-MIMO, and this mainly comes from the inverse of the channel matrix. In terms of the performance, it is shown that D2VP offers comparable bit-error-rate to the sphere encoding approach for the case of small MU-MIMO. For the case of medium and large MU-MIMO when the sphere encoding does not apply due to unimplementable complexity, D2VP outperforms the lattice-reduction VP by around 5-10 dB in Eb/No and 10-50 dB in normalized computational complexity.

The aim of this letter is to share a novel concept termed pseudo-pilot, which offers a simple and efficient approach of nonpilot-assisted channel estimation. Our key idea is to transfer the uncertainty of several payload symbols into the uncertainty of symbol interleavers by employing a bank of interleavers at the transmitter. Those uncertainty-transferred symbols serve as pseudo-pilots for the receiver to perform channel estimation. The uncertainty of symbol interleavers is then removed in the procedure of decoding. Performance and scalability of the pseudo-pilot technique are evaluated through both theoretical analysis and computer simulations.

Fingerprint-based indoor positioning technique is one of sustainable approaches to provide highly accurate information about mobile user's location. However, it requires the constructing of a high-resolution radio map, which is time consuming and labor intensive. In this paper, a novel less-calibration Wi-Fi-based indoor positioning is proposed. The new technique takes advantage of regular mobile users' movement inside the environment to build the radio map. During these movements mobile users can measure and record received signal strength (RSS) from available wireless access points. The radio map will be adapted to environment changes using the recent collected RSSs data. The proposed algorithm does not use extra hardware (e.g., accelerometer, magnetometer) to exploit the collected RSSs. Experimental results showed that the proposed technique can be used to build the radio map with surveying only one-third of the area of interest (about two-third of the effort is reduced). The achieved localization error and location precision are competitive with the current proposed solutions.

This paper presents a joint space-frequency user scheduling approach to enhance the random beamforming (RBF) with limited feedback in multiple-input -multiple-output (MIMO) broadcast channels. Such scheme was shown to obtain the optimal scaling law of sum rate in the large number of user regime. However, for a small number of user cases, the system degrees-of-freedom cannot be well exploited, and the accuracy of predicting users' signal-to-interference-plus-noise ratios (SINRs) is also degraded. Motivated by these, two strategies are proposed to solve the problems. Specifically, the conventional spatial domain RBF is first extended to the space-frequency domain. The first strategy aims to maximize the number of active users based on users' initially predicted SINRs; the second strategy is to schedule the maximum number of users, whose preferred beams can coexist with each others, with more accurate users' SINRs prediction method. Computer simulations are carried out to examine the proposed strategies in terms of the sum rate and the number of active users. It is shown that the first strategy achieves close performance to the corresponding brute-force search with lower complexity. Moreover, the second strategy improves the performance by accurately predicting users' SINRs at the price of relatively increased complexity and feedback overhead.

This paper brings to light the reasoning and principles shaping the standardization direction in the 3rd Generation Partnership Project (3GPP) regarding provision of low complexity Machine Type Communication (MTC), via Long Term Evolution (LTE) of 3GPP. It elaborates 3GPP's approaches and suggestions towards addressing the physical layer challenges and discusses solutions 3GPP proposed specifically in relation to the low complexity operation of MTC devices and the coverage extension of current cellular networks to challenging scenarios of MTC. Results of comprehensive set of simulations are presented to assess the impact of repetition coding and power boosting on the physical downlink shared channel (PDSCH). In addition, novel ideas are presented to reduce the complexity of channel estimation, one of the most computation intensive blocks in the baseband receive chain. These results all leap towards finding the physical layer design for low cost MTC devices via 3GPP LTE.

Relay with opportunistic channel assignment (OCA) is an intermediate wireless node, which forwards messages through temporarily unused licensed channels. The spectral efficiency of OCA relay largely depends on the channel usage. This paper shows that, with a practical model of channel usage, the OCA relay outperforms the relay with fixed channel assignment by up to 0.8 bits/s/Hz/user. This interesting result is observed through our extensive investigation of the demodulation-and-forward relay adopting various resource allocation algorithms, which include the round-robin, joint channel and power allocation, best-user selection, and combinations of them. Moreover, the communication delay of all the considered OCA relaying schemes is evaluated through Monte Carlo simulations.

In this paper a multiple-input multiple-output (MIMO) cognitive system is investigated. In this setting the receivers in the primary user (PU) group null interference with the application of zero-forcing (ZF) filters and the transmit signals are adapted to meet individual rate constraints. The objective is to maximize the rate of the secondary user (SU), subject to a SU power constraint and individual PU rate constraints. Since the PU receiver cooperates with the SU by actively reserving part of its receive space for interference, the problem critically depends on the feasibility of the PU ZF operation and this in turn translates into a restriction on the number of SU transmit antennas. To investigate the behavior of the system in terms of SU rate and PU power sacrifice a SU transmit antenna selection method is proposed. It is demonstrated that PU cooperation enables SU communication in situations where transmit zero-forcing beamforming or opportunistic interference alignment (OIA) remains infeasible. In addition, simulation results highlight the advantage of PU cooperation over OIA in situations where the SU only just has enough transmit antennas to perform OIA.

Consider spatial interweave opportunistic spectrum reuse scheme, primary user with power constraints maximizes its achievable rate by water-filling its power on singular values of its channel links and leaving some eigen modes free to use. Therefore, secondary user can transmit using this free subspace in order to avoid interference at primary receiver. By employing transmit beamforming, secondary user can design its precoding matrix, where the interference to primary user can be aligned to the free subspace. As a result of non-degraded primary user's performance, perfect channel state information (CSI) is required at secondary transmitter. Unfortunately, this kind of CSI is hard to realize, it is therefore in this paper to investigate a limited CSI scheme. By secondary transmitter sensing the label of the Grassmannian quantized channel vectors, the proposed criterion can upper-bound the overall interference at primary receiver with a constant value, which is independent of secondary transmit power. We also study an alternative primary user power allocation strategy in order to balance the overall system performance.

Emerging low date-rate Machine-to-Machine (M2M) communications call for promising Hybrid Automatic Repeat-reQuest (HARQ) schemes with improved link reliability and feedback efficiency. This motivates us to develop a novel adaptive HARQ scheme by exploiting the knowledge of Channel Quality Information (CQI), which can be obtained through either channel estimation techniques or location- aided technology. The proposed adaptive HARQ scheme will determine the suitable transmission mode to guarantee a specific Frame Error Rate (FER) during the first transmission with the aid of location information. Simulation results demonstrated that our proposed scheme can achieve more attractive throughput performance, while maintaining a similar FER, compared to conventional HARQ schemes.

This paper presents a novel pilot-assisted spectrum sensing technique for orthogonal frequency-division multiplexing (OFDM) systems. The main idea is based upon the physical nature that subcarriers carrying pilots or payload data have different first-order and second-order statistical properties. These differences vanish when the spectrum of interest is unoccupied. Therefore, the decision of spectrum availability can be formed based upon these differences, which can be explored through employment of frequency-domain differential operations. Thanks to the differential operations, the proposed technique has less sensitivity of the noise power uncertainty problem caused by imperfect hardware. Performance of the proposed technique is analytically formulated in terms of probability of false alarm (PFA) and probability of detection (PD). Computer simulations are carried out to elaborate the analytical results. It is shown that the second-order statistics based proposed technique outperforms the conventional pilot-assisted technique up to 7 dB. Moreover, it is shown that the first-order statistics based proposed technique outperforms the second-order statistics based proposed technique for small normalized Doppler shifts (≤ 0.013). However, the second-order statistics based proposed technique offers better performance for larger normalized Doppler shifts.

This paper presents a novel joint rate adaptation and relay selection scheme for multi-relay networks adopting half-duplex best-relay decode-and-forward protocol. The proposed scheme aims to maximize the overall transmission rate when relays are allowed to forward messages using different rates from the source. It is shown that the proposed scheme outperforms the conventional adaptive scheme in terms of the spectral efficiency (e.g. by 10.1% improvement for SNR=10 dB). Furthermore, in order to reduce signaling overhead of the proposed scheme, a number of joint discrete-rate adaptation and relay selection approaches are proposed for both non-reciprocal and reciprocal channels. The relay selection is basically a two-stage scheme. At the rm first stage, a set of relays are selected based on mixed channel quality information (CQI), i.e., the knowledge of CQI varies for different links; at the second stage, the best relay within the set is selected based on instantaneous CQI, which is obtained through carefully designed signaling protocols. It is shown that the proposed discrete-rate adaptation schemes can offer comparable spectral efficiency to the conventional adaptive scheme with significantly reduced signaling overhead.

Recently developed adaptive modulation schemes for opportunistic decode-and-forward (DF) relaying require knowledge of instantaneous channel-state-information (CSI) of all links. This paper aims at reducing signalling overhead of modulation-adaptive opportunistic DF relaying through exploitation of mixed channel knowledge. A novel semi-deterministic approach is proposed for performing joint rate-adaptation and best-relay selection. The selection process employs two criteria using mixed channel knowledge. A new metric namely the probability of missing the best relay (PMBR) is introduced, as only when the best relay is selected, the full diversity order can be achieved. It is shown that the proposed approach can achieve a good trade-off between the spectral efficiency and signalling overhead.

One of the major challenges of Cellular network based localization techniques is lack of hearability between mobile terminals (MTs) and base stations (BSs), thus the number of available anchors is limited. In order to solve the hearability problem, previous work assume some of the MTs have their location information via Global Positioning System (GPS). These located MT can be utilized to find the location of an un-located MT without GPS receiver. However, its performance is still limited by the number of available located MTs for cooperation. This paper consider a practical scenario that hearability is only possible between a MT and its home BS. Only one located MT together with the home BS are utilized to find the location of the un-located MT. A hybrid cooperative localization approach is proposed to combine time-of-arrival and received signal strength based fingerprinting techniques. It is shown in simulations that the proposed hybrid approach outperform the stand-alone time-of-arrival techniques or received signal strength based fingerprinting techniques in the considered scenario. It is also found that the proposed approach offer better accuracy with larger distance between the located MT and the home BS.

Cooperative communication is an alternative way to generate spatial diversity. However due to the extra time slot required for relay transmission, the spectral efficiency (SE) is reduced. Adaptive modulation can optimize the spectral efficiency by adopting the transmission parameters to the channel conditions. This requires the perfect channel state information (CSI) feedback at the transmitter. However, in real life this is not practical. The feedback channel is noisy and fading which causes feedback errors. In this contribution, first we investigate the performance of adaptive modulation schemes for cooperative communications with perfect feedback, the adaptive modulation is applied both by the source and the relay. Then the impact of noisy feedback channels on the performance is analysed. The simulation results demonstrate the spectral efficiency improvement, and illustrates that the effect of the noisy feedback can be ignored when the feedback channel signal-to-noise ratio (SNR) is relatively high i.e. 10 dB.

This paper presents a novel differential energy detection scheme based on extremes of order statistics for sensing OFDM signals. The underlying initiative of this approach is applying the order statistics of the differential Energy Spectral Density in frequency domain. The proposed technique takes advantage of the channel selectivity which is inherited from high data-rate communications. The introduced frequency diversity allows this approach to meet FCC requirements even in low SNR environments i.e., (-25 ,-10) dB. Analytical results of sensing performance are provided in terms of both probability of false alarm and probability of detection. Furthermore, computer simulations show that the proposed technique outperforms two most commonly used source detection approaches namely conventional energy detection and cyclostationarity based detection for up to 10 dB gain in low SNR environments.

Previous work about cooperative localization in cellular networks usually consider a centralized processor (CP) is available for location estimation. This paper consider cooperative localization in a distributed base station (BS) scenario, where there is no CP, and the distributed BSs are responsible for location estimation. In this scenario, Global Positioning System (GPS) enable mobile terminals (MTs), i.e., located MTs, are employed as reference nodes.Then, several located MTs can help to find the locations of an un-located MT, by estimating the distance between the un-located MT using received signal strength techniques. Two localization approaches are proposed, the first approach requires only one BS to collect all the assistance information for localization and estimate the location. The second approach distribute the location estimation task to several BSs. The communication overhead between distributed BSs are investigated for these two approaches. Moreover, by taking into account the effect of imperfect location knowledge of the located MTs, the accuracy limits of both approaches are derived. The simulation results shows that compared with the first approach, the second approach can reduce the communication overhead between distributed BSs with the paid of accuracy.

This paper consider cooperative localization in cellular networks. In this scenario, several located mobile terminals (MTs) are employed as reference nodes to find the location of an un-located MT. The located MTs sent training sequences in the uplink, then the un-located MT perform distance estimation using received signal strength techniques. The localization accuracy of the un-located MT is characterized in terms of squared position error bound (SPEB). By taking into account the imperfect a priori location knowledge of the located MTs, the SPEB is derived in a closed-form. The closed-form indicate that the effect of the imperfect location knowledge on SPEB is equivalent to the increase of the variance of distance estimation. Moreover, based on the obtained closed-form, a MT selection scheme is proposed to decrease the number of located MTs sending training sequences, thus reduce the training overhead for localization. The simulation results show that the proposed scheme can reduce the training overhead with the paid of accuracy. And with the same training overhead, the accuracy of the proposed scheme is better than that of random selection.

In this paper, a cooperative iterative water-filling approach is investigated for two-user Gaussian interference channel. State-of-the-art approaches only maximize the individual user's own rate and always model interference as noise. Our proposed approach establishes user cooperation through sharing network side information. It iteratively maximizes the sum-rate of both users subject to distributed power constraint. Interference is optimally regarded as message or noise. Three efficient rate-sharing schemes are also investigated between two users based on priority. Numerical results are performed in frequency-selective environment. It is observed that the proposed approach offers significantly performance improvement in comparison with conventional iterative water-filling approaches.

Orthogonal relay based cooperative communication enjoys distributed spatial diversity gain at the price of spectral efficiency. This work aims at improving the spectral efficiency for orthogonal opportunistic decode-and-forward (DF) relaying through employment of novel adaptive modulation scheme. The proposed scheme allows source and relay to transmit information in different modulation formats, while the MAP receiver is employed at destination for the diversity combining. Given the individual power constraint and target bit-error-rate (BER), the proposed scheme can significantly improve the spectral efficiency in comparison with the non-adaptive DF relaying and adaptive direct transmission.

Line-of-sight (LoS) time-of-arrival (ToA) is one of key parameters for network-based localization techniques. Its estimation accuracy depends on the bandwidth of transmitted signals. This paper aims to investigate the LoS-ToA estimation for an interleaved OFDMA based network, where the signal bandwidth is not sufficiently wide to offer the acceptable estimation accuracy. In this situation, an over-sampling approach is proposed to improve the resolution. Moreover, the proposed scheme does not require modification of mobile communications systems. It is found that the proposed approach can significantly improve the ToA estimation performance, and offer adequate accuracy even for the low signal-to-noise ratio (SNR) range.

Adaptive modulation can improve the throughput of cooperative communications, which has been shown in previous research. So far the adaptive modulation is applied with respect to perfect transmitter side Channel State Information (CSI) in most current work. In this paper we have investigated the modulation-adaptive cooperation in Rayleigh fading channels providing imperfect CSI at senders. The adaptive modulation scheme with rate adaptation at the relay proposed in our previous work has been extended here first. Then a new adaptive modulation scheme with adaptation at both the source and relay is proposed. Simulation results show the proposed two schemes can provide significant improvement in throughput over the fixed DF relaying cooperation even with imperfect feedback; the throughput can be further improved with the proposed scheme allowing adaptive modulation at both the source and relay.

Spectrum sensing is one of key enabling techniques to advanced radio technologies such as cognitive radios and ALOHA. This paper presents a novel non-cooperative spectrum sensing approach that can achieve a good trade-off between latency, reliability and computational complexity. Our major idea is to exploit the first-order cyclostationarity of the primary user's signal to reduce the noise-uncertainty problem inherent in the conventional energy detection approach. It is shown that the proposed approach is suitable for detecting the primary user's activity in the interweave paradigm of cognitive spectrum sharing, while the active primary user is periodically sending training sequence. Computer simulations are carried out for the typical IEEE 802.11g system. It is observed that the proposed approach outperforms both the energy detection and the second-order cyclostationarity approach when the observation period is more than 10 frames corresponding to 0.56 ms.

Linear interpolation is often employed for chunk-based OFDMA uplink because of its low complexity and good performance. However, the linear interpolation error induced by the mismatch between the linear interpolation model and the actual channel can be detrimental to high-data-rate systems operating in high signal-to-noise-ratio (SNR) range or high mobility scenario. In this paper, enhanced linear interpolation schemes with small time-direction interpolation error are proposed, particularly for the chunk-based OFDMA uplink. Unlike the conventional linear interpolation, which is based on the assumption that the real and imaginary parts of channel coefficients are separately linearly varying, the proposed schemes are based on an interesting observation that the amplitude and phase of a communication channel are also linearly varying within a short time duration, e.g. a chunk duration. Simulation results show the proposed schemes can effectively improve the performance.

In OFDM-based systems, data symbols are usually transmitted together with pilots, which are used for channel estimation or prediction. More pilots in use can further improve the accuracy of channel estimation or prediction. However, this can also result in the efficiency loss of channel gain allocated for data transmission. In this paper, we investigate the joint pilot and data adaptation regarding pilot allocation, payload power and bits. Three different implementation approaches are proposed, the performance is evaluated and shows joint pilot and data adaptation can considerably improve system overall performance, and the efficiency gain related to the pilot-allocation adaptation can be significant.

This paper presents a method to counteract the detrimental effects of carrier frequency offsets (CFOs) in the uplink of OFDMA-based wireless networks. The CFO values of different users are first estimated from the received decomposed signal using the best linear unbiased estimator (BLUE). The CFOs effects are then compensated based on the estimated values and also taking advantage of an iterative parallel interference cancellation (PIC) scheme. A novel contribution of this paper is introducing an iterative concatenated estimation and compensation algorithm for suppressing the residual interference due to imperfect estimators. The introduced method is successful where the estimator converges after only one iteration with a significant improvement in estimation accuracy. Also very close BER performance results can be achieved to that of systems with perfect knowledge or without CFOs particularly at practical SNRs.

This paper presents a novel blind method to estimate carrier frequency offsets (CFOs) in OFDMA uplink. The major idea is employing a linear precoder to establish time correlation, which offers a second-order moments based blind CFO estimation for each user. With a careful precoder design, the interference from adjacent users can be considerably mitigated in CFO estimation. Since the majority of interference power is from adjacent users, the proposed method shows significant robustness to multiuser interference (MUI) even without the aid of virtual subcarriers. Simulation results show that the proposed approach offers significant performance improvement in comparison with state-of-the-art approaches in multiuser environments.

In this paper, we propose a rate-adaptive bit and power loading approach for the OFDM-based relaying communications. The cooperative relay operates in the half-duplex amplify-and-forward mode. The source and the relay has the separate power constraints. The maximum-ratio combining is employed at the destination for maximizing the received SNR. Assuming the perfect channel knowledge available at all nodes, the proposed approach is to maximize the throughput (the number of bits/symbol) at the given power constraint and the target link performance. Unlike the water-filling method, the proposed approach does not need the iterative loading process, and can offer the sub-optimum performance. Computer simulations are used to test the proposed approach for various scenarios with respect to the relay location or the distributed power allocation.

A transmitter with cognitive capability can sense talk between the other transmitter-receiver pairs. When this transmitter knows full or partial message of the others, it can choose an efficient strategy to access the transmission medium. This is referred to as cognitive radio channel. This work aims to investigate multi-tone transmission over two-user cognitive radio channels where cross-talk interference is weak. Cognitive transmitter (Ttimes1) is assumed to have full knowledge of message that is sent by the other transmitter (Ttimes2) to its corresponding receiver (Rtimes2). Channel capacity is carefully analyzed for frequency-selective scenarios. Efficient power-allocation strategies at Ttimes1 are investigated for various wireless environments. It is shown that Ttimes1 can find an efficient resource-accessing strategy if the channel gain of Ttimes1-Rtimes1 (the corresponding receiver) link is larger than the channel gain of Ttimes2-Rtimes2 link. In this case, the cognitive transmitter Ttimes1 can offer better performance by employing equal power allocation approach. Otherwise, it is not worthy for Ttimes1 to access transmission medium of Ttimes2-Rtimes2 link.

In this paper, we investigate the optimum pilot placement along time direction for the chunk-based OFDMA uplink. The proposed optimization is performed within a single chunk, where the channel estimation is based on the least-square linear interpolation. Unlike the state-of-the-art optimum pilot placements, the proposed scheme takes into account the impact of interpolation error in addition to the noise. It is established that in the presence of the large Doppler shift, the impact of interpolation error shows the dominative effect in the large SNR range, the optimum pilot spacing should be 1/radic3 of the chunk length regardless of the channel realization. However, in the most practical scenarios, the impact of the interpolation error is too small to affect the overall performance compared to the noise. Therefore, the optimization of pilot placement should be focused on minimizing the noise.

The wireless hybrid enhanced mobile radio estimators (WHERE) consortium researches radio positioning techniques to improve various aspects of communications systems. In order to provide the benefits of position information available to communications systems, hybrid data fusion (HDF) techniques estimate reliable position information. Within this paper, we first present the scenarios and radio technologies evaluated by the WHERE consortium for wireless positioning. We compare conventional HDF approaches with two novel approaches developed within the framework of WHERE. Yet, HDF may still provide insufficient localization accuracy and reliability. Hence, we will research and develop new cooperative positioning algorithms, which exploit the available communications links among mobile terminals of heterogeneous wireless networks, to further enhance the positioning accuracy and reliability.

Doubly differential modem turns out to be a promising technology for coping with unknown frequency offsets with the pay of signal-to-noise ratio (SNR). In this paper, we propose to compensate the SNR loss by employing the detection-forward cooperative relay. The receiver can employ two kind of combiners to attain the achievable spatial diversity-gain. Performance analysis is carefully investigated for the Rayleigh-fading channel. It is shown that the SNR-compensation is satisfied for the large-SNR range.

In this paper, we investigate the adaptive bit and power loading approach for the OFDM-based relaying communications. The relaying protocol is half-duplex outage symbol-level-selection detection-and-forward. The source and the relay has the separate power constraint. The maximum-ratio combining is employed at the destination to achieve the maximum distributed spatial diversity-gain. Assuming the perfect channel knowledge available at all nodes, we propose a sub-optimum rate-adaptive bit and power loading approach to maximize the number of bits/symbol for the target link performance. The proposed approach offers lower complexity for implementation as well as the integer number of bits/symbol. Computer simulations are used to examine the proposed approach for various scenarios with respect to the relay location and the distributed power allocation.

Cooperative communications can exploit the distributed spatial diversity-gain to improve the link performance. In this paper, we investigate the application of adaptive modulation concept to the decode-and-forward (DF) based cooperative network. With the relay nodes geographically close to the destination, we assume the perfect channel feedback is available only at the relay nodes, and propose a class of novel modulation-adaptive cooperation schemes (MACSs). The proposed schemes are first investigated in the single-relay scenario, and then extended to the multi-relay scenario. Simulation results show that the proposed schemes can offer the significant throughput-improvement in comparison with conventional DF systems.

Channel estimation for the chunk-based OFDMA uplink communications is based on the non-ideal interpolation schemes, e.g., polynomial interpolation. The channel estimation performance depends on the modeling mismatch, pilot pattern and the signal-to-noise ratio. In this paper, we first investigate the channel estimation performance for the polynomial interpolation, which takes into account the impact of modeling mismatch and thermal noise. This is then followed by a novel sub- optimum pilot-placement scheme, which can considerably improve channel estimation and overall system performance for the consecutive chunks scenario, i.e., each user has several consecutive chunks along the frequency direction.

Cooperative communications can exploit the distributed spatial diversity to improve the multiple-link performance. In this paper, the average symbol error probability (SEP) is carefully investigated for the three-node relaying structure, in which the relaying protocol is amplify-and-forward, and the modulation scheme can be N-PSK or N-QAM. We first exploit the optimistic condition to derive the lower bound of SEP. This is the generalization of the existing Anghel-Kaveh formulation. Then, the upper bound is also provided based on the exponential approximation to the Bessel function. This bound turns out to be tighter than the Anghel-Kaveh upper bound. Numerical results are provided to confirm the analytical analysis.

In this paper, we investigate the optimum pilot placement for the chunk-based OFDMA uplink. The proposed scheme is optimized for the single-chunk scenario, i.e., the channel estimation is carried out for each individual chunk, and the channel estimator is based on the least-square linear interpolation. Unlike the state-of-the-art optimum pilot placements, the proposed scheme takes into account the impact of modeling mismatch in addition to the noise. It is established that the optimum pilot placement for the large-SNR range should minimize the impact of the modeling mismatch. The optimum pilot spacing along the frequency direction is 1/radic3 of the chunk bandwidth. The pilot spacing should be increased (up to the chunk width) with decreasing the signal-to-noise ratio. Computer simulations are provided, and show a good match with the analytical result.

The statistics-based channel estimators can estimate the channel state information (CSI) in the static channel but can only obtain the averaged CSI (ACSI) in the time-varying (TV) channel. This letter investigates both first-order statistics (FOS) and second-order statistics (SOS)-based channel estimators for the pseudo random postfix (PRP) orthogonal frequency-division multiplexing system in the slowly TV channel. It is shown that the FOS-based approach outperforms the SOS-based approaches in the ACSI estimation. Using estimated ACSIs for the channel equalization, simulation results indicate that the SOS-based approaches converge to the FOS-based approach in the high signal-to-noise ratio range.

This letter proposes a novel carrier frequency offset (CFO) estimation method for generalized multicarrier code-division multiple access (CDMA) systems in unknown frequency-selective channels utilizing hidden pilots. It is established that CFO is identifiable in the frequency domain by employing cyclic statistics (CS) and linear regression (LR) algorithms. We show that the CS-based estimator is capable of mitigating the normalized CFO (NCFO) to a small error value. Then, the LR-based estimator can be employed to offer more accurate estimation by removing the residual quantization error after the CS-based estimator. Simulation results are presented together with the theoretical analysis, and a good match between them is observed.

The packet priority calculation method, also referred to as packet selection policy, is a necessary component for sorted priority based packet schedulers. A new packet priority calculation method, called smallest middle-point finish time first (SMFF), is proposed. An analysis model, called packet rate proportional server plus (PRPS+), based on the packet rate proportional server (PRPS) is developed to act as the start point for the discussion on the SMFF. It is shown that the packet schedulers based on sorted-priority can be modeled by the PRPS+ class, (such as weighted fair queuing, start-time fair queuing, self clocked fair queuing and worst-case fair weighted fair queuing); it is demonstrated that the scheduling fairness is improved if their priority calculation methods are replaced by SMFF.

The family of repetition coded (RC) multicarrier system has a few members, such as multicarrier code division multiple access and intercarrier interference self-canceling scheme etc. This paper investigates three modes of the RC-OFDM system in the presence of unknown block fading channel. Relying on the code redundance, blind channel identification and equalization algorithms are developed. In the multitransmitter system, frequency-domain optimum precedes are proposed to solve the channel ambiguity. Simulation results are provided to corroborate the theoretical analysis.

A new orthogonal multicarrier bandwidth modulation (OMBM) scheme is proposed in this paper. The distinct feature of this scheme is that the frequency domain data is used to decide the number of orthogonal subcarriers for wireless transmissions. Different bandwidth of the OMBM symbol in the time domain reflects on the different data in the frequency domain. A discrete Fourier transform (DFT) based minimum mean square error (MMSE) estimator is employed to conduct the bandwidth detection. Analysis and simulation results demonstrate that the OMBM scheme is a flexible modulation structure and not sensitive to noise interference. Some properties and possible application of the OMBM code are also introduced.