Tokens are fundamental processing units of generative AI (GenAI) and large language models (LLMs), and token communication (TC) is essential for enabling remote AI-generate content (AIGC) and wireless LLM applications. Unlike traditional bits, each of which is independently treated, the semantics of each token depends on its surrounding context tokens. This inter-token dependency makes TC vulnerable to outage channels, where the loss of a single token can significantly distort the original message semantics. Motivated by this, this paper focuses on optimizing token packetization to maximize the average token similarity (ATS) between the original and received token messages under outage channels. Due to inter-token dependency, this token grouping problem is combinatorial, with complexity growing exponentially with message length. To address this, we propose a novel framework of semantic packet aggregation with lookahead search (SemPA-Look), built on two core ideas. First, it introduces the residual semantic score (RSS) as a token-level surrogate for the message-level ATS, allowing robust semantic preservation even when a certain token packet is lost. Second, instead of full search, SemPA-Look applies a lookahead search-inspired algorithm that samples intra-packet token candidates without replacement (fixed depth), conditioned on inter-packet token candidates sampled with replacement (fixed width), thereby achieving linear complexity. Experiments on a remote AIGC task with the MS-COCO dataset (text captioned images) demonstrate that SemPA-Look achieves high ATS and LPIPS scores comparable to exhaustive search, while reducing computational complexity by up to 40$\times$. Compared to other linear-complexity algorithms such as the genetic algorithm (GA), SemPA-Look achieves 10$\times$ lower complexity, demonstrating its practicality for remote AIGC and other TC applications.

This paper investigates near-field channel effects in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)–assisted uplink system with a multi-antenna base station. The considered system is based on generalized non-orthogonal multiple access (NOMA) with imperfect successive interference cancellation (SIC), encompassing space division multiple access (SDMA) and NOMA with perfect SIC as special cases. For this general setting, we develop sum-rate maximization algorithms with and without quality-of-service (QoS) constraints using an alternating optimization (AO) framework and a penalty-based nonlinear optimization (NLO) method, providing flexible performance–complexity trade-offs. We also derive an upper bound on the achievable sum rate to characterize multiplexing and beamforming gains under both near- and far-field channel conditions. The results show that AO initialized with the proposed NLO solution achieves the best performance with moderate computational complexity, aligning well with the upper-bound behavior. Furthermore, analysis and simulations demonstrate that near-field channels increase the available degrees of freedom and provide enhanced robustness to imperfect SIC and QoS constraints.

To support emerging language-based applications using dispersed and heterogeneous computing resources, the hybrid language model (HLM) offers a promising architecture, where an on-device small language model (SLM) generates draft tokens that are validated and corrected by a remote large language model (LLM). However, the original HLM suffers from substantial communication overhead, as the LLM requires the SLM to upload the full vocabulary distribution for each token. Moreover, both communication and computation resources are wasted when the LLM validates tokens that are highly likely to be accepted. To overcome these limitations, we proposecommunication-efficient and uncertainty-aware HLM (CU-HLM). In CU-HLM, the SLM transmits truncated vocabulary distributions only when its output uncertainty is high. We validate the feasibility of this opportunistic transmission by discovering a strong correlation between SLM’s uncertainty and LLM’s rejection probability. Furthermore, we theoretically derive optimal uncertainty thresholds and optimal vocabulary truncation strategies. Simulation results show that, compared to standard HLM, CU-HLM achieves up to 206× higher token throughput by skipping 74.8% transmissions with 97.4% vocabulary compression, while maintaining 97.4% accuracy.

Sliding network coding (SNC) has emerged as a promising solution for ultra-reliable and low-latency communication (URLLC) scenarios. The performance of SNC, particularly in terms of developing the encoding matrix and decoding strategy, is heavily influenced by the order h of the underlying Galois field,GF(2h). In this paper, we investigate high-order SNC, whereh> 1, to enhance transmission efficiency by employing a Vandermonde-based encoding matrix that ensures linear independence among coded packets. To maximize decoding efficiency, we design a structure-aware decoding strategy (SA-DS), which not only dynamically exploits the relationships between successfully decoded (SD) packets and the currently decoded (CD) packet, but also utilizes the first-packet deterministic decoding (FPDD) property of the Vandermonde matrix. Additionally, we develop a Markov chain-based performance analysis framework in terms of retransmission probability, packet error rate, and expected decoding delay. Numerical results demonstrate that in the evaluated settings, the proposed scheme outperforms several traditional schemes in the moderate-erasure region. In the low-erasure region, its advantage becomes particularly pronounced (reaching one to three orders of magnitude in both PER and retransmission probability while maintaining a comparable decoding delay), making it particularly suitable for URLLC applications.

Wi-Fi 7 introduces the restricted target wake time (RTWT) mechanism, which is vital for Industrial IoT (IIoT) applications requiring periodic, reliable, and low-latency communication. RTWT enables deterministic channel access by assigning scheduled transmission slots to stations (STAs), minimizing contention and interference. However, determining efficient RTWT slot assignments remains challenging in dense networks, where conventional interference graph-based models lack flexibility and scalability. To overcome this, we propose a scalable interference graph learning (IGL) framework that learns optimal interference graph representations for graph coloring-based RTWT scheduling. The IGL leverages an evolution strategy (ES) to train a neural network (NN) using a single network-wide reward, avoiding costly edge-wise feedback. Furthermore, a deep hashing function (DHF) groups interfering STAs, limiting training and inference to relevant subsets and greatly reducing complexity. Simulation results demonstrate that the proposed IGL improves slot efficiency by up to 25%, reduces packet losses by up to 30% in dynamic environments. Thanks to DHF, it also reduces the training and inference time of IGL by 4 and 8 times, respectively, and the online slot assignment time by 3 times in large networks.

Wireless time-sensitive networking (WTSN) is essential for Industrial Internet of Things. We address the problem of minimizing time slots needed for WTSN transmissions while ensuring reliability subject to interference constraints—an NP-hard task. Existing semidefinite programming (SDP) methods can relax and solve the problem but suffer from high polynomial complexity. We propose a sparse interference graph-aided SDP (SIG-SDP) framework that exploits the interference’s sparsity arising from attenuated signals between distant user pairs. First, the framework utilizes the sparsity to establish the upper and lower bounds of the minimum number of slots and uses binary search to locate the minimum within the bounds. Here, for each searched slot number, the framework optimizes a positive semidefinite (PSD) matrix indicating how likely user pairs share the same slot, and the constraint feasibility with the optimized PSD matrix further refines the slot search range. Second, the framework designs a matrix multiplicative weights (MMW) algorithm that accelerates the optimization, achieved by only sparsely adjusting interfering user pairs’ elements in the PSD matrix while skipping the non-interfering pairs. We also design an online architecture to deploy the framework to adjust slot assignments based on real-time interference measurements. Simulations show that the SIG-SDP framework converges in near-linear complexity and is highly scalable to large networks. The framework minimizes the number of slots with up to 10 times faster computation and up to 100 times lower packet loss rates than compared methods. The online architecture demonstrates how the algorithm complexity impacts dynamic networks’ performance.

This paper proposes an elevation-aware interference management framework for reconfigurable intelligent surface (RIS)-assisted downlink non-orthogonal multiple access (NOMA) in integrated low Earth orbit (LEO) satellite–terrestrial networks. In this setting, the strength of satellite interference varies strongly with the elevation angle due to path loss and shadowing. We model this effect via an elevation-dependent weighting factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha (\theta)$ </tex-math></inline-formula>, expressed as a sigmoid function and updated in real time by a lightweight proportional–integral (PI) controller driven by a scalar performance metric. The resulting <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha (\theta)$ </tex-math></inline-formula> enables an adaptive successive interference cancellation (SIC) strategy that prioritizes decoding of satellite or terrestrial components according to the instantaneous interference level and omits satellite decoding when its contribution is weak, thereby reducing unnecessary SIC stages. In parallel, a three-mode RIS codebook (terrestrial-priority, balanced, satellite-priority) is selected based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha (\theta)$ </tex-math></inline-formula> and refined online using low-complexity gradient-based phase updates. Simulations under 3GPP-compliant parameters show that the proposed scheme achieves up to 20% higher throughput than a conventional static RIS–NOMA baseline with non-adaptive RIS phases and a fixed channel-gain-based SIC order that does not exploit the elevation-dependent interference weight <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha (\theta)$ </tex-math></inline-formula>, while maintaining robust performance across a wide range of elevation angles. These results highlight the benefits of combining elevation-aware RIS control, adaptive SIC ordering, and low-overhead online RIS refinement in next-generation satellite–terrestrial NOMA networks.

This paper presents a unified modeling, estimation, and feedback framework for reconfigurable intelligent surface RIS-assisted optical wireless links. The key modeling element is a long-exposure pixel gain that extends the classical diffraction-limited response by statistically averaging angular jitter and mispointing; it admits an exact real-integral form and captures boresight attenuation and progressive sidelobe filling. The end-to-end system couples free-space path loss, Beer--Lambert atmospheric extinction, pixel-level diffraction, and optical efficiency with a unitary-pilot least-squares channel estimator and quantized phase feedback. Analysis closely matches Monte Carlo simulations and yields concrete design rules: with a surface of N=64 pixels, pilot length $M=2N$, and pilot SNR=20 dB, the normalized mean-squared error is0.005, implying an effective-SNR loss of about 0.5 and a capacity penalty of 0.007bits-s. Six-bit phase quantization introduces no measurable additional penalty at these operating points, setting a practical benchmark for feedback resolution. Training overhead scales strongly with pixel geometry: halving pixel width (quartering pixel area) increases the pilot length required to maintain the same NMSE by roughly fourfold. The framework reconciles physical-optics modeling with estimation-and-feedback design and provides a principled basis for scalable link budgeting in RIS-assisted optical networks.

A novel semi-orthogonal transmission method, the pilot data superimposed dynamic inflow (PDSDI) method, is proposed to tackle the challenges of massive access in grant-free random access wireless communication systems. This approach aims to balance large-scale user access while mitigating performance degradation associated with non-orthogonal pilot sequences. The traditional orthogonal transmission scheme is also analyzed as a baseline for comparison. Uplink rate expressions in closed form are obtained under two common channel estimation methods, that is, least squares and minimum mean square error. The analysis investigates the asymptotic behavior of important system parameters, including RiceanK-factor, antenna numbersM, and symbol power, revealing that the sum uplink rate converges to log2M, regardless of theK-factor. In addition, a parameter optimization model is developed to maximize system performance by adjusting critical variables, such as antenna number and power scaling factors, ensuring improved system capacity and efficiency. The simulation results confirm that the PDSDI method greatly exceeds the performance of the traditional scheme with respect to uplink rate and user access capacity. This work presents a practical framework for optimizing system parameters in future large-scale wireless networks.

Massive machine-type communication in satellite networks presents significant challenges for random access systems, particularly due to the limited number of terminals that can successfully access the network concurrently. To address this issue, this paper proposes a novel binary circular polarization modulation based non-orthogonal multiple access slotted ALOHA (BCPM-NOMA-SA) scheme that fully exploits the polarization characteristics of satellite transmission. The proposed approach first achieves orthogonal signal separation in the polarization domain through BCPM. Subsequently, an equivalent noise model is established by analyzing the Cramér-Rao lower bound (CRLB) of channel estimation errors. Finally, the upper bound of system throughput is derived, providing a theoretical foundation for the synergistic gains of polarization and power domain multiplexing. Simulation results demonstrate that the proposed scheme achieves 20-35% throughput improvement compared to conventional NOMA-SA and BCPM-SA schemes, showing significant performance advantages.

Real-world graph signals are inherently time-varying and evolve smoothly, making the characterization of such data challenging. We propose a novel approach for reconstructing missing time-varying graph data by leveraging the assumption that the latent variable responsible for generating this data evolves according to nonlinear dynamics. To learn these dynamics, we employ a Koopman autoencoder, and apply graph embedding techniques to map the graph data into a latent space. While existing approaches require known time-invariant Laplacian matrices, our proposed approach can perform reconstruction without needing these matrices, which can also be time-varying. Simulation results show that our method surpasses baseline approaches, achieving a reduction in reconstruction error by 47% to 61% compared to alternative methods, while effectively reconstructing time-varying graphs with dynamic structures.

Text-based communication is expected to be prevalent in 6G applications such as wireless AI-generated content (AIGC). Motivated by this, this paper addresses the challenges of transmitting text prompts over erasure channels for a text-to-image AIGC task by developing the semantic segmentation and repeated transmission (SMART) algorithm. SMART groups words in text prompts into packets, prioritizing the task-specific significance of semantics within these packets, and optimizes the number of repeated transmissions. Simulation results show that SMART achieves higher similarities in received texts and generated images compared to a character-level packetization baseline, while reducing computing latency by orders of magnitude compared to an exhaustive search baseline.

This paper centers around a multi-antenna unmanned aerial vehicle (UAV) which leverages stacked intelligent metasurface (SIM) as a green technology for signal processing at the electromagnetic wave domain. To assess the performance of this system, a radio resource allocation problem is accordingly formulated for jointly optimizing the motion trajectory and transmit power at the UAV, as well as the electromagnetic response at the SIM as decision variables. Since the problem is non-convex and challenging to solve, we reformulate it in Markov decision process form and train a distributed distributional deep deterministic policy gradient (D4PG) agent to optimize its decision variables. Concerning the significant mobility of the UAV and thus remarkable rearrangement of the system, we enhance the adaptability and generalization of the trained D4PG model by integrating meta-learning strategy. According to simulations, wave-domain beamforming via SIM at UAV leads to 40% and 22% reduction in average energy consumption, compared fullydigital and beamspace beamforming, respectively.

The application of machine learning (ML) to communication systems is expected to play a pivotal role in future artificial intelligence (AI)-based next-generation wireless networks. While most existing works focus on ML techniques for static wireless environments, they often face limitations when applied to highly dynamic environments, such as flying ad hoc networks (FANETs). This paper explores the use of data-driven Koopman approaches to address these challenges. Specifically, we investigate how these approaches can model UAV trajectory dynamics within FANETs, enabling more accurate predictions and improved network performance. By leveraging Koopman operator theory, we propose two possible approaches—centralized and distributed—to efficiently address the challenges posed by the constantly changing topology of FANETs. To demonstrate this, we consider a FANET performing surveillance with UAVs following predetermined trajectories and predict signal-to-interference-plus-noise ratios (SINRs) to ensure reliable communication between UAVs. Our results show that these approaches can accurately predict connectivity and isolation events that lead to modelled communication outages. This capability could help UAVs schedule their transmissions based on these predictions.

For global connectivity, low Earth orbit (LEO) satellites play a key role in integrating terrestrial and non-terrestrial networks, enabling Internet-of-Things (IoT) services in remote areas by connecting sensors directly to satellites. However, their orbital dynamics can cause fluctuations in packet flow, affecting latency. This paper tackles the issue by leveraging inter-satellite links (ISLs) for flow control, balancing LEO satellite buffer sizes to reduce latency in store-and-forward transmissions. We establish a necessary and sufficient condition for solution existence using effective bandwidth and, through simulations, show that ISL-based flow control significantly reduces outage rates and buffer overflow probabilities compared to conventional methods.

Long-range (LoRa) modulation offers reliable transmission performance for devices and sensors in Internet of Things (IoT) applications. LoRa modulation is a chirp spread spectrum (CSS) technique with orthogonal signaling, which results in low spectral efficiency but enables a low error rate even at relatively low signal-to-noise ratios (SNRs). In this paper, we propose a semi-coherent detection scheme that performs coherent detection using estimated channel state information (CSI) in multipath channel environments, which is obtained from the decision results of noncoherent detection. This scheme not only achieves performance close to that of coherent detection but also eliminates the need for pilot signal transmission, which is required in conventional coherent detection schemes at the expense of spectral efficiency. Simulation results demonstrate that the performance of the proposed semi-coherent detection scheme is close to that of coherent detection under various channel conditions.

This paper compares distributed massive Multiple-Input Multiple-Output (mMIMO) and centralized MIMO in the context of over-the-air federated learning, taking into account a detailed wireless channel model that includes spatially uncorrelated Rician fading to represent the Line of Sight (LoS) component. Our simulation results provide valuable insight into how a distributed architecture can maintain a lower Mean Squared Error (MSE) in scenarios that require extensive coverage. Furthermore, our research highlights the importance of considering the impact of the LoS component and power limitations when analyzing over-the-air aggregation, as these characteristics can substantially influence aggregation performance. Adopting a distributed architecture allows devices to conserve energy while simultaneously achieving a lower MSE in the aggregation process compared to the centralized architecture. Specifically, when considering four Access Points (APs) and 200 devices, the average transmit power is reduced by approximately 31% while achieving 16% lower aggregation MSE compared to a centralized approach.

Downlink transmission of a nonorthogonal visible light communication (VLC) system, empowered by autonomous aerial vehicles (AAVs), is studied for coexisting enhanced mobile broadband (eMBB), ultrareliable low-latency communication (URLLC), and massive machine-type communication (mMTC) services. A joint resource allocation problem involving user association, transmit power, and flight trajectory of AAVs is formulated, with the goal of characterizing a multiobjective tradeoff as a weighted sum of the power consumption of each AAV and the perceived Quality of Experience (QoE) of its associated eMBB users, while ensuring the service-specific requirements for eMBB, mMTC, and URLLC are met. Assuming the imperfection of channel state information (CSI), we invoke a generalized Benders decomposition (GBD) methodology, leveraging tools from convex optimization and multiagent deep reinforcement learning to address this problem. We further analytically derive the upper and lower bounds on the reward function for each AAV as a learning agent. Extensive simulations confirm that our proposed method outperforms the single-agent counterpart in the literature, with up to a 22% reduction in power consumption and a 13% gain in perceived QoE. Additionally, compared to the globally optimal brute-force method for AAV-user association, our proposed method experiences only a trivial performance loss in a small-scale scenario.

Semantic encoders and decoders for digital semantic communication (SC) often struggle to adapt to variations in unpredictable channel environments and diverse system designs. To address these challenges, this paper proposes a novel framework for training semantic encoders and decoders to enable channel-adaptive digital SC. The core idea is to use binary symmetric channel (BSC) as a universal representation of generic digital communications, eliminating the need to specify channel environments or system designs. Based on this idea, our framework employs parallel BSCs to equivalently model the relationship between the encoder’s output and the decoder’s input. The bit-flip probabilities of these BSCs are treated as trainable parameters during end-to-end training, with varying levels of regularization applied to address diverse requirements in practical systems. The advantage of our framework is justified by developing a training-aware communication strategy for the inference stage. This strategy makes communication bit errors align with the pre-trained bit-flip probabilities by adaptively selecting power and modulation levels based on practical requirements and channel conditions. Simulation results demonstrate that the proposed framework outperforms existing training approaches in terms of both task performance and power consumption.

Cell-free technology is considered a pivotal advancement for next-generation mobile communications, which can effectively enhance the quality of service for user equipments (UEs) located at the cell edge. For cell-free systems, in this paper, we propose a hybrid downlink transmission method that combines non-orthogonal multiple access (NOMA) and multiuser diversity (MUD). To evaluate the communication performance of the system, we derive closed-form expressions for both instantaneous and average sum rates of UEs using the NOMA and MUD transmission methods. Furthermore, we comprehensively investigate the spectrum efficiency of the NOMA and MUD transmission methods to provide a basis for selecting the hybrid transmission strategy. On the basis of the proposed hybrid transmission strategy, we can derive an optimal hybrid transmission strategy for the scenarios with two access points (APs) and two UEs. Particularly, we extend the aforementioned strategy to the scenarios with multiple UEs, and formulate an optimization problem to maximize the system spectrum efficiency subject to the transmission strategy and power allocation. Furthermore, we propose a low-complexity user selection strategy and power allocation algorithm to solve the problem. Numerical results demonstrate that the hybrid transmission method and power allocation strategy can achieve higher system spectrum efficiency. Our results reveal the influence of key parameters on the downlink spectrum efficiency, analytically and numerically.

Thanks to the Internet of Things (IoT), there has been explosive growth in edge devices, which generate a tremendous amount of data that holds invaluable potential. However, conventional data mining and machine learning (ML) paradigms require transmitting raw data to data centers for further use, which puts a heavy burden on communication networks and is exposed to high privacy risks. Federated learning allows for the training of ML models using distributed datasets, which can be applied to protect data privacy and alleviate transmission burdens. Meanwhile, the technique of over-the-air (OTA) computation can be utilized to exploit the superposition property of wireless communication channels. Motivated by this, in this paper, we propose a co-phase OTA approach for communication-efficient uploading in multi-server federated learning, which does not require expansion of the uplink channel bandwidth when the numbers of users and models increase. Besides, the digital OTA with randomized transmission is proposed to overcome the disadvantages of analog OTA, where the performance analyses of analog OTA and digital OTA are deduced, respectively. Simulation results show that a lower cost function can be obtained by digital OTA while requiring fewer iterations for convergence than that in analog OTA as more users can upload.

In the Internet of Things (IoT), numerous devices and sensors are deployed to collect data sets. Although some IoT devices can process data locally, most devices may have limited power and computational capability. Since mobile edge computing (MEC) is a new paradigm to provide strong computing capability at the edge of networks close to users, these devices can offload their tasks to MEC servers. Therefore, designing an efficient computation offloading strategy to decide whether the tasks to be offloaded to MEC servers becomes crucial. In this paper, we study the computation offloading for IoT devices based on a non-cooperative game with one-shot random access, where users’ offloading decisions can be made independently to realize distributed offloading. In particular, we discuss the offloading game with and without sharing information among devices and find the Nash equilibrium (NE). Besides, we analyze the effective bandwidth as a performance metric from a device perspective, which considering the Quality of Service (QoS) of network layer while analyzing users’ offloading strategies. Simulation results show the effectiveness of proposed strategies and the impact of offloading tasks to users’ strategies in time-varying channel based on effective bandwidth.

In the Internet of Things (IoT) networks, monitoring information collection is critical for intelligent decision-making, which is a significant challenge for the sensors deployed at remote locations. Relay can effectively improve the transmission quality and transmission range of sensors by means of multi-hop transmission. It is an effective method for remote data collection in IoT networks. However, the lifetime of the relay may be dramatically reduced due to the heavy resource overhead for frequent short packet delivery. In this paper, we present an efficient relay transmission scheme for IoT networks, in which the frame aggregation technology is employed at the relay to reduce the resource overhead by sharing a common frame header and tail. Meanwhile, for the delay caused by frame aggregation, we analyze the freshness of the sensing data in terms of age of information (AoI) and take it as a constraint for the frame aggregation system. Besides, the optimal frame aggregation period is determined based on the closed-form expressions derived for the average AoI and transmission efficiency. Simulation results show that the theoretical analysis closely matches the simulations, and the proposed method significantly improves transmission efficiency compared to the traditional decode-and-forward method.

In multiple-input multiple-output (MIMO) communication systems, signal detection plays a crucial role in achieving reliable and high-performance wireless communication. However, the complexity of optimal detection methods, such as maximum likelihood (ML) detection, grows exponentially with the number of transmit antennas when exhaustive search is used, hindering practical implementation. To address this challenge, suboptimal algorithms such as successive interference cancellation (SIC)-based detection have been developed, but they suffer from error propagation. To mitigate error propagation in SIC detectors, a soft-decision based partial maximum a posteriori (MAP) method has been derived to enhance performance. Since the partial MAP method allows MIMO detection to be divided into multiple stages, detection of each layer can be approached as a regression problem, and can be carried out by deep learning (DL)-based method to reduce computational overhead. Therefore, in this paper, we propose PMAP-Net, which integrates deep neural networks (DNNs) into partial MAP method for MIMO systems. We derive the soft log-likelihood ratios (LLRs) for single and multiple signals and design the input sets of DNNs. To further reduce the number of inputs in DNNs, we decrease input dimensionality by deriving extended input sets, which alleviates computational burden to be linear with respect to the number of antennas. Simulation results demonstrate that our proposed DL-based detection algorithm can provide near-optimal performance with relatively low complexity and outperforms other DL-based detectors in various MIMO scenarios.

To keep up with the continuously growing coverage demands and attain true global coverage, the deployment of multi-layered low Earth orbit satellite constellations is necessary. Next-generation mega satellite constellations are expected to rely on inter-satellite links to relay information, which will enable fast and reliable communications between the different satellite nodes in free-space and facilitate the utilization of coverage diversity modes that can further enhance the quality-of-service provided in the network. However, materializing these high performing systems is challenging due to the complexity of the network architecture which may require long and complex simulation processes during design. In this article, we develop theoretical modeling for the probability of coverage for various diversity modes in mega satellite constellations by leveraging tools from stochastic geometry. We first develop analytical models for conventional single-shell networks and then extend these models to incorporate multi-shell networks. The analysis is validated using Monte-Carlo simulations which show a close fit to the analytical models derived. Moreover, the analytical models provide a performance baseline that is comparable to practical networks that rely on regular network architectures such as SpaceX’s Starlink. This allows network operators to devise expansion strategies to cater for expanding demands and gain insights into the performance of the network as more shells are introduced into the network.

The spectral efficiency in cell-free massive MIMO with grant-free random access under Ricean fading, utilizing a linear maximal-ratio combining detector, is the focus of this paper. Firstly, explicit expressions for the effective signal-to-interference-plus-noise ratio (SINR) are introduced, using the least squares (LS) and minimum mean squared error (MMSE) estimation methods, valid for varying Ricean K-factor and for varying numbers of access point antennas M. Furthermore, we drive the asymptotic SINR expressions under extreme conditions of infinite Ricean K-factor, infinite M, and the appropriate scaling of the users’ power. The corresponding analysis shows that as M approaches infinity, the sum uplink rate converges to $\log _{2} M$, regardless of the Ricean K-factor. Particularly, in the case of LS estimation, reducing each user’s transmit power by $1 / \sqrt{M}$ maintains the rate, converging to a definitive value irrespective of the Ricean K-factor. In the case of MMSE estimation, power is able to be scaled down by $1 / \sqrt{M}$ to achieve it when Ricean K-factor is zero, whereas the power of nonzero Ricean K-factor necessitates a reduction by $1 / M$. Finally, simulations confirm all theoretical outcomes.

By integrating recent advances in large language models (LLMs) and generative models into the emerging semantic communication (SC) paradigm, in this article we put forward to a novel framework of language-oriented semantic communication (LSC). In LSC, machines communicate using human language messages that can be interpreted and manipulated via natural language processing (NLP) techniques for SC efficiency. To demonstrate LSC’s potential, we introduce three innovative algorithms: 1) semantic source coding (SSC) which compresses a text prompt into its key head words capturing the prompt’s syntactic essence while maintaining their appearance order to keep the prompt’s context; 2) semantic channel coding (SCC) that improves robustness against errors by substituting head words with their lenghthier synonyms; and 3) semantic knowledge distillation (SKD) that produces listener-customized prompts via in-context learning the listener’s language style. In a communication task for progressive text-to-image generation, the proposed methods achieve higher perceptual similarities with fewer transmissions while enhancing robustness in noisy communication channels.

In this paper, we address a radio resource allocation challenge for multiple unmanned aerial vehicles (UAVs) connected to low Earth orbit (LEO) satellites, supporting ground users through store-and-forward transmission. Unlike existing integrated systems assuming seamless fusion of LEOs and UAVs, our practical approach treats UAVs and LEOs as nodes of distinct systems. UAVs act as relay nodes, receiving and forwarding data packets to LEO with possibly decapsulation and encapsulation. Our proposed resource allocation minimizes maximum transmission delays or buffer lengths of UAVs, formulated as a convex optimization problem. Given unknown arrival rates and channel conditions, we explore reinforcement learning for learning these critical parameters in resource allocation.

Semantic communication, emerging as a promising paradigm for data transmission, offers an innovative departure from the constraints of Shannon theory, heralding significant advancements in future communication technologies. Despite the proliferation of proposed approaches, there are still numerous challenges. In this paper, we review current semantic communication methodologies and shed light on pivotal issues and addressing certain discrepancies that exist within the field. By elucidating both dos and don'ts, we aim to provide valuable insights into the emerging landscape of semantic communication.

Low Probability of Detection (LPD) communication aims to obscure the very presence of radio frequency (RF) signals, going beyond just hiding the content of the communication. However, the use of Unmanned Aerial Vehicles (UAVs) introduces a challenge, as UAVs can detect RF signals from the ground by hovering over specific areas of interest. With the growing utilization of UAVs in modern surveillance, there is a crucial need for a thorough understanding of their unknown nonlinear dynamic trajectories to effectively implement LPD communication. Unfortunately, this critical information is often not readily available, posing a significant hurdle in LPD communication. To address this issue, we consider a case-study for enabling terrestrial LPD communication in the presence of multiple UAVs that are engaged in surveillance. We introduce a novel framework that combines graph neural networks (GNN) with Koopman theory to predict the trajectories of multiple fixed-wing UAVs over an extended prediction horizon. Using the predicted UAV locations, we enable LPD communication in a terrestrial ad-hoc network by controlling nodes' transmit powers to keep the received power at UAVs' predicted locations minimized. Our extensive simulations validate the efficacy of the proposed framework in accurately predicting the trajectories of multiple UAVs, thereby effectively establishing LPD communication.

Cell-free (CF) massive multiple-input multiple-output (MIMO), as a promising network architecture for beyond the fifth generation (5G), has a great potential to support grant-free (GF) transmission for machine-type communication (MTC). To shed light on this subject, this work aims to model and evaluate the performance of GF transmission in CF massive MIMO under a realistic network deployment scenario, where the spatial locations of both access points (APs) and devices are assumed to be random in nature. In particular, by capitalizing on the distinctive CF network architecture and features, we design a new two-disk based geometric model for GF transmission, which facilitates analysis and understanding in CF massive MIMO. Based on the proposed two-disk model, we derive an approximated closed-form expression for the access success probability by leveraging on techniques from stochastic geometry, and investigate the impact of different key system parameters on the network performance, which have not been presented previously. To highlight the performance superiority of CF massive MIMO, we further provide a comparative analysis by using an analogous single-disk model in an equivalent co-located massive MIMO network. Simulation results verify our analysis and demonstrate that CF massive MIMO is able to significantly outperform its co-located counterpart in terms of access success probability and provide robust performance against increased access density, which well suits to crowd scenarios.

Improving reliability and response time in decision-making is crucial for efficient demand response (DR) programs in smart grid (SG) environments. By precisely predicting the DR in near real time, consumer premises can be more prepared to optimize energy utilization. We propose and develop an ultrareliable low-latency communication (URLLC)-based machine learning paradigm for orchestrating DR with guaranteed reliability and timeliness. To understand the context in depth and develop new insights, we use a random forest (RF) algorithm to predict the DR program. After that, we employ MuZero reinforcement learning (MuZero RL) on top of RF-based learning with URLLC, which leverages an efficient learned model under such SG DR dynamics. It considerably improves decision-making delays with better generalization to unforeseen situations. In sharp contrast to the state-of-the-art approaches, we observe that MuZero RL enables continuous learning by self-play, achieves higher sample efficiency, and adapts well to the underlying dynamic environments. Such features of an intelligent agent are precious in the context of the considered DR program. We develop theoretical derivations and analyses to study and utilize the framework’s capabilities and demonstrate that URLLC can substantially reduce energy costs.

Federated learning (FL) offers a promising framework for obtaining a global model by aggregating trained parameters from participating clients without transmitting their local private data. To further enhance privacy, differential privacy (DP)-based FL can be considered, wherein certain amounts of noise are added to the transmitting parameters, inevitably leading to a deterioration in communication efficiency. In this paper, we propose a novel Privacy-Preserving and Communication Efficient Federated Learning (P2CEFL) algorithm to reduce communication overhead under DP guarantee, utilizing sparse gradient and dithering quantization. Through gradient sparsification, the upload overhead for clients decreases considerably. Additionally, a subtractive dithering approach is employed to quantize sparse gradient, further reducing the bits for communication. We conduct theoretical analysis on privacy protection and convergence to verify the effectiveness of the proposed algorithm. Extensive numerical simulations show that the P2CEFL algorithm can achieve a similar level of model accuracy and significantly reduce communication costs compared to existing conventional DP-based FL methods.

Broadcast plays a vital role in wireless ad hoc networks for information dissemination, while one of the key system parameters for maximizing broadcast performance is the transmission rate. However, due to the entangled impact of various factors on the broadcast performance such as node distribution, interference, transmission rate, and multi-hop links, theoretically modeling of the impact of different parameters on the broadcast performance is intractable, and hence the optimal transmission rate is still unknown. Inspired by the similarity between the process of packet broadcasting and the spreading of contagious diseases, we resort to epidemic theory to propose a tractable approach for modeling the dynamics of message broadcast process in wireless ad hoc networks. To characterize the impact of transmission rate on the broadcast performance, a novel utility function is proposed by jointly considering the packet delivery delay and packet size. Then, we derive the dynamics of packet broadcasting analytically and obtain the approximated expression for the proposed utility function in closed-form. Furthermore, the optimization of the transmission rate is carried out based on our proposed analytical model. Simulation results show that our analytical model can accurately characterize the process of broadcasting under a wide range of system parameters and that optimizing the transmission rate can improve broadcast performance significantly.

In this paper, we propose a wireless powered communication network (WPCN) employing active intelligent reflection surface (IRS) and nonorthogonal multiple access (NOMA) to support multiple battery-free devices. We aim to maximize the sum rate by incorporating practical issues such as amplifying power constraints of active IRS and a nonlinear energy harvesting (EH) model. Since jointly optimizing IRS beamforming in wireless power transfer (WPT), IRS beamforming in wireless information transfer (WIT), and time allocation (TA) is computationally challenging, we resort to an alternating optimization (AO) algorithm in a computationally efficient manner. Additionally, we propose a one-shot (OS) optimization approach for shared amplitude control that provides closed-form expressions for IRS beamforming in WPT and WIT to be computed at a low latency. This OS solution can also serve as the initial point of the proposed AO algorithm and is used for throughput analysis in line-of-sight channels. It is shown that the proposed AO algorithm reduces the complexity of the benchmark and our proposed OS approach, despite its low computational complexity, provides a performance comparable to that of the AO solution. It is also shown that the amplifying power limit of active IRS elements affects the performance severely while the effect of nonlinear EH is not so prominent. Furthermore, the results from analysis and simulation elucidate favorable system configurations for the active IRS-aided WPCN with NOMA when compared with its passive IRS counterpart.

To enhance the massive connectivity and spectral efficiency of Internet-of-Things applications supporting batteryless devices, we integrate active intelligent reflecting surface (IRS) into multiple input single output (MISO) wireless powered communication networks (WPCNs). The networks are designed with either nonorthogonal multiple access (NOMA) or time division multiple access (TDMA) towards the maximum throughput. For this purpose, we develop alternative optimization (AO) algorithms optimizing energy beamforming (BF) and active IRS BF in wireless power transfer, active IRS BF and receive BF in wireless information transfer, and time allocation iteratively, for both NOMA and TDMA networks. The proposed algorithm for the NOMA network solves each subproblem using either a convex optimization solver or a closed-form expression with the help of with the help of fractional programming. For TDMA networks, we develop a more computationally efficient algorithm than the conventional algorithm for the TDMA network. The results show that active IRS is more effective than passive IRS with a moderate number of IRS elements for MISO WPCNs. In addition, NOMA significantly outperforms TDMA for active IRS aided MISO WPCN although a slight gain of TDMA over NOMA is observed for the single-antenna counterpart.

In this paper, we propose an over-the-air (OTA)-based approach for distributed matrix-vector multiplications in the context of distributed machine learning (DML). Thanks to OTA computation, the column-wise partitioning of a large matrix enables efficient workload distribution among workers (i.e., local computing nodes) based on their computing capabilities. In addition, without requiring additional bandwidth, it allows the system to remain scalable even as the number of workers increases to mitigate the impact of slow workers, known as stragglers. However, despite the improvements, there are still instances where some workers experience deep fading and become stragglers, preventing them from transmitting their results. By analyzing the mean squared error (MSE), we demonstrate that incorporating more workers in the OTA-based approach leads to MSE reduction without the need for additional radio resources. Furthermore, we introduce an analog coding scheme to further enhance the performance and compare it with conventional coded multiplication (CM) schemes. Through simulations, it is shown that the OTA-based approach achieves comparable performance to CM schemes while potentially requiring fewer radio resources.

In physical layer for non-orthogonal multiple access (NOMA), most existing studies focus on the non-delay-sensitive metrics such as the spectral efficiency. In order to improve user’s quality of service (QoS) in delay-sensitive communications, however, effective capacity can be adopted to the NOMA system to consider the QoS metric while analyzing capacity. In this paper, we deduce the closed form expression for effective capacity in downlink NOMA and propose three optimization problems with delay and effective capacity constraints. Firstly, a joint rate and power allocation scheme is proposed to maximize the total effective capacity. Secondly, we deduce the optimal solution of the problem for maximizing the minimum delay QoS exponent. Thirdly, a minimum total transmit power allocation scheme is proposed with the effective capacity constraint. Since the problems of maximizing effective capacity and minimizing total transmit power are non-convex, the particle swarm optimization (PSO) algorithm is used to find global optimization solutions. Simulation results show our proposed power and rate allocation scheme maximizes the effective capacity, which is better than orthogonal multiple access (OMA). Meanwhile, the optimal minimum delay QoS exponent and minimum total transmit power with effective capacity constraint have been achieved.

In this paper, we propose a physical-layer authentication (PLA) scheme based on wireless channel responses of multiple frequency bands. Channel-based PLA has been regarded as a desirable scheme as it simplifies the authentication process by comparing received and previously acquired channel state information (CSI). However, it may fail authentication due to potential disagreements in CSI among legitimate users. Moreover, in an indoor environment, spatial correlation occasionally arises between legitimate and illegitimate channels, which can degrade the authentication reliability. To mitigate undesired CSI disagreements and spatial correlations, we propose a joint utilization of channel responses in multiple frequency bands. As spatial correlation is unlikely to arise simultaneously in different frequency bands, any correlation with illegitimate channels in one band can be compensated by the other band, providing reliable authentication even in indoor environments. Similarly, any disagreement among legitimate channels in one band can be compensated by the other band that has sufficient channel coherence time. To demonstrate the performance of our proposed scheme, we constructed a test-bed based on universal software radio peripherals. The experimental analysis shows that the proposed PLA scheme can significantly improve the authentication performance as much as orders of error reduction in failures by doubling the frequency bands in channel-based PLA.

This paper presents a wireless data collection frame-work that employs an unmanned aerial vehicle (UAV) to efficiently gather data from distributed IoT sensors deployed in a large area. Our approach takes into account the non-zero communication ranges of the sensors to optimize the flight path of the UAV, resulting in a variation of the Traveling Salesman Problem (TSP). We prove mathematically that the optimal waypoints for this TSP-variant problem are restricted to the boundaries of the sensor communication ranges, greatly reducing the solution space. Building on this finding, we develop a low-complexity UAV-assisted sensor data collection algorithm, and demonstrate its effectiveness in a selected use case where we minimize the total energy consumption of the UAV and sensors by jointly optimizing the UAV's travel distance and the sensors' communication ranges.

Vector Quantized Variational Autoencoder (VQ-VAE) has shown promise in representing diverse and complex data distributions in deep learning, making it a potential solution for various applications including wireless communications. In this paper, we propose a joint source-channel coding scheme based on VQ-VAE for point-to-point wireless communication. Our approach leverages the dependence of the encoder and decoder on a given dataset and channel conditions to develop efficient encoding and decoding schemes, leading to improved reliability and efficiency even in the presence of noisy wireless channels. We demonstrate the effectiveness of our proposed approach through extensive simulations in handling realistic wireless communication scenarios. In addition, we discuss potential connections to semantic communication and highlight the secure and energy-efficient nature of our approach.

This paper proposes new framework of communication system leveraging promising generation capabilities of multimodal generative models. Regarding nowadays’ smart applications, successful communication can be made by conveying the perceptual meaning, which we set as text prompt. Text serves as a suitable semantic representation of image data as it has evolved to instruct an image or generate image through mutli-modal techniques, by being interpreted in a manner similar to human cogitation. Utilizing text can also reduce the overload compared to transmitting the intact data itself. The transmitter converts objective image to text through multi-model generation process and the receiver reconstructs the image using reverse process. Each word in the text sentence has each syntactic role, responsible for particular piece of information the text contains. For further efficiency in communication load, the transmitter sequentially sends words in priority of carrying the most information until reaches successful communication. Therefore, our primary focus is on the promising design of a communication system based on image-to-text transformation and the proposed schemes for sequentially transmitting word tokens. Our work is expected to pave a new road of utilizing state-of-the-art generative models to real communication systems.

Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems, posing unprecedented challenges in terms of its multi-modal data transmissions with stringent latency and reliability requirements. Towards enabling this wireless metaverse, in this article we propose a novel semantic communication (SC) framework by decomposing the metaverse into human/machine agent-specific semantic multiverses (SMs). An SM stored at each agent comprises a semantic encoder and a generator, leveraging recent advances in generative artificial intelligence (AI). To improve communication efficiency, the encoder learns the semantic representations (SRs) of multi-modal data, while the generator learns how to manipulate them for locally rendering scenes and interactions in the metaverse. Since these learned SMs are biased towards local environments, their success hinges on synchronizing heterogeneous SMs in the background while communicating SRs in the foreground, turning the wireless metaverse problem into the problem of semantic multiverse communication (SMC). Based on this SMC architecture, we propose several promising algorithmic and analytic tools for modeling and designing SMC, ranging from distributed learning and multi-agent reinforcement learning (MARL) to signaling games and symbolic AI.

In this paper, we suggest a method to transform a communication protocol based on deep neural network (NN) into a semantic communication protocol. We need such transformation to alleviate the issues posed by NN's lack of interpretability and redundant parameters due to overparametrization. However, transformation process is challenging because it is difficult to disambiguate the semantics while reducing the protocol's complexity. We solve the challenge by employing NN's activation patterns and probabilistic logic. Lastly, we validate our method by transforming an NN trained for a medium access control (MAC) protocol and verifying its contention performance compared to ALOHA based protocols.

While federated learning is widely studied as a distributed machine learning technique that effectively uses distributed datasets for training, there are still a number of challenges. In particular, for datasets that reside on mobile devices (or mobile phone users), the limited bandwidth becomes a bottleneck as the number of devices increases, because the local gradient vectors are be transmitted over the scarce wireless channel. Thus, the notion of over-the-air (OTA) computation has been considered to avoid bandwidth expansion due to the increase in devices by exploiting the superposition property of radio channels. In this paper, we consider a generalization of OTA to the case of multiple servers, where each server has own model. Using co-phase OTA aggregation, it is shown that a shared channel can be used for all different servers/models. We also propose a digital OTA approach for multi-server federated learning with randomized transmissions.

In Shannon’s theory, semantic aspects of communication were identified but considered irrelevant to the technical communication problems. However, semantic communication techniques have recently attracted renewed research interests in 6G because they have the capability to support an efficient interpretation of the meaning intended by the sender (or accomplishment of the goal) when dealing with multi-dimensional data such as videos, images, audio, sentences, documents etc. We propose a new hashing-based semantic communication, where our learning objective is the "semantic extraction" to produce optimal binary signatures (hash codes) by supervised learning. We evaluate the proposed framework over large image data sets and demonstrate its effectiveness in bulk transmission.

Efficient data communication and machine learning aspects are crucial for orchestrating the distributed resources of smart grid (SG) networks. 5G telecom technologies have enabled ultrareliable low-latency communication (URLLC) to provide low-latency data communication and accelerate distributed machine learning [such as federated learning (FL)] with high reliability. For critical SG operations, such as islanding detection and instability of frequency regulation, the adoption of URLLC and FL appears paramount to enable near real-time communication and collaborative decision making for resource management. However, SG with URLLC and/or FL has been poorly studied in the literature. We develop a novel framework and demonstrate our findings on the importance of URLLC and FL for efficient energy trading between distributed energy sources and to minimize energy loss by enhancing the resilience of critical SG operations. Extensive experiments using real-time data sets validate our design assumptions and ideas.

Classical medium access control (MAC) protocols are interpretable, yet their task-agnostic control signaling messages (CMs) are ill-suited for emerging mission-critical applications. By contrast, neural network (NN) based protocol models (NPMs) learn to generate task-specific CMs, but their rationale and impact lack interpretability. To fill this void, in this article we propose, for the first time, a semantic protocol model (SPM) constructed by transforming an NPM into an interpretable symbolic graph written in the probabilistic logic programming language (ProbLog). This transformation is viable by extracting and merging common CMs and their connections, while treating the NPM as a CM generator. By extensive simulations, we corroborate that the SPM tightly approximates its original NPM while occupying only 0.02% memory. By leveraging its interpretability and memory-efficiency, we demonstrate several SPM-enabled applications such as SPM reconfiguration for collision-avoidance, as well as comparing different SPMs via semantic entropy calculation and storing multiple SPMs to cope with non-stationary environments.

The bottleneck of distributed edge learning (DEL) over wireless has shifted from computing to communication, primarily the aggregation-averaging (Agg-Avg) process of DEL. The existing transmission control protocol (TCP)-based data networking schemes for DEL are application-agnostic and fail to deliver adjustments according to application layer requirements. As a result, they introduce massive excess time and undesired issues such as unfairness and stragglers. Other prior mitigation solutions have significant limitations as they balance data flow rate from workers across paths but often incur imbalanced backlogs when the paths exhibit variance, causing stragglers. To facilitate a more productive DEL, we develop a hybrid multipath TCP (MPTCP) by combining model-based and deep reinforcement learning (DRL) based MPTCP for DEL that strives to realize quicker iteration of DEL and better fairness (by ameliorating stragglers). Hybrid MPTCP essentially integrates two radical TCP developments: i) successful existing model-based MPTCP control strategies and ii) advanced emerging DRL-based techniques, and introduce a novel hybrid MPTCP data transport for easing the communication of Agg-Avg process. Extensive emulation results demonstrate that the proposed hybrid MPTCP can overcome excess time consumption and ameliorate the application layer unfairness of DEL effectively without injecting additional inconstancy and stragglers.

In this article, we consider ultra-reliable low-latency communication (URLLC) for efficient energy trading over a smart grid (SG) network using home-based smart meters (SM). We develop a cost-friendly privacy preservation framework based on existing demand-side energy management by employing random bidirectional energy trading among customers. Customers in our design can be either producers or consumers and mostly both (‘prosumers’). Our aim is to develop a decentralized optimization framework that not only reduces energy costs, but also improves privacy preservation and energy trading ability directly from the customer’s end. One of the vital costs for energy consumers is the supply charge. Our method can minimize it by orchestrating energy trading among customers in a decentralized adaptive fashion. To predict the energy demand by optimizing between privacy and cost, we employ an extension of the follow the regularized leader (FTRL) algorithm. We perform a theoretical analysis to demonstrate the convergence of the FTRL, the benefits of URLLC for the SG network, and the cost-effective privacy preservation ability of the proposed model. In addition to enabling energy trading efficiently, our extensive simulation results demonstrate that our proposed framework outperforms the state-of-the-art methods in terms of the cost-friendly privacy of SMs.

In this paper, we propose a method to analyze the performance of ultra-reliable low-latency communication (URLLC) with packet-level coding when no feedback from a receiver is available. Unlike conventional methods of analyzing URLLC performance using average error rate, we focus on the events of burst or clustered (reception) errors1as they can be fatal for certain real-time wireless control systems. In the proposed method, to see the impact of clustered errors on the performance, a virtual queue is considered, where the reception error sequence is regarded as the arrival process and the departure process is characterized by the target error rate. This virtual queue allows to find how often the events of clustered errors of certain sizes occur using large deviations theory. For packet-level channels modeled by an independent and identically distributed process and a two-state Markov chain, the quality-of-service exponents are derived, and the asymptotic probability of buffer overflow is obtained, which agrees with simulation results. This demonstrates that the proposed method using virtual queue is useful to find the probability of system failure due to clustered errors and allows to determine the values of key parameters of URLLC for a certain probability of system failure under given conditions.

Due to increasing demands of seamless connection and massive information exchange across the world, the integrated satellite-terrestrial communication systems develop rapidly. To shed lights on the design of this system, we consider an uplink communication model consisting of a single satellite, a single terrestrial station and multiple ground users. The terrestrial station uses decode-and-forward (DF) to facilitate the communication between ground users and the satellite. The channel between the satellite and the terrestrial station is assumed to be a quasi-static shadowed Rician fading channel, while the channels between the terrestrial station and ground users are assumed to experience independent quasi-static Rayleigh fading. We consider two cases of channel state information (CSI) availability. When perfect CSI is available, we derive the instantaneous achievable sum rate of all ground users and formulate an optimization problem to maximize the sum rate. When only channel distribution information (CDI) is available, we derive a closed-form expression for the outage probability and formulate another optimization problem to minimize the outage probability. Both optimization problems correspond to scheduling algorithms for ground users. For both cases, we propose low-complexity user scheduling algorithms and demonstrate the efficiency of our scheduling algorithms via numerical simulations.

In this paper, we study communication-efficient distributed stochastic gradient descent (SGD) with data sets of users distributed over a certain area and communicating through wireless channels. Since the time for one iteration in the proposed approach is independent of the number of users, it is well-suited to distributed SGD with a large number of users or devices. Furthermore, since the proposed approach is based on random access in machine-type communication (MTC), it can be easily employed for training models with a large number of devices in various Internet-of-Things (IoT) applications where MTC is used for their connectivity. For fading channel, we show that noncoherent combining can be used. As a result, no channel state information (CSI) estimation is required.

We consider a non-orthogonal multiple access aided ALOHA (NOMA-ALOHA) that can enhance the spectral efficiency by sending uplink packets of different power levels in wireless multiuser systems. In particular, we develop an asymmetric game theory model for NOMA-ALOHA that involves two different groups of users and analyze the mean rewards and payoffs of actions made by the users. While taking into account not only collisions, but also fading, the derived theoretical results are utilized to formulate a general-sum game with two groups of users and find mixed strategy Nash equilibrium (NE). Interestingly, the analytical and numerical results clearly show that, when the NOMA-ALOHA runs at an NE, a far-user can exploit an improved channel gain of a near-user in the other group in terms of the throughput.

In this paper, we discuss two different designs of network coding (NC) for ultra-reliable low latency communication (URLLC) over fading channels. It is shown that a conventional design has the error floor as the error rate does not go to 0 although the erasure probability approaches 0. The other design does not have this problem. By deriving an asymptotic expression for a low erasure probability, we show that how the key factors of NC designs play a role in lowering the error rate in URLLC.

Semantic communication (SC) goes beyond technical communication in which a given sequence of bits or symbols, often referred to as information, is be transmitted reliably over a noisy channel, regardless of its meaning. In SC, conveying the meaning of information becomes important, which requires some sort of agreement between a sender and a receiver through their knowledge bases. In this sense, SC is closely related to a signaling game where a sender takes an action to send a signal that conveys information to a receiver, while the receiver can interpret the signal and choose a response accordingly. Based on the signaling game, we can build a SC model and characterize the performance in terms of mutual information in this paper. In addition, we show that the conditional mutual information between the instances of the knowledge bases of communicating parties plays a crucial role in improving the performance of SC.

Smart energy management based on the Internet of Things (IoT) aims to achieve optimal energy utilization through real-time energy monitoring and analyses of power consumption patterns in IoT networks (e.g., residential homes and offices) supported by wireless technologies - this is of great significance for the sustainable development of energy. Energy disaggregation is an important technology to realize smart energy management, as it can determine the power consumption of each appliance from the total load (e.g., aggregated data). Also, it gives us clear insights into users’ daily power-consumption-related behaviours, which can enhance their awareness of power-saving and lead them to a more sustainable lifestyle. This paper reviews the state-of-the-art algorithms for energy/power disaggregation and public datasets of power consumption. Also, potential use cases for smart energy management based on IoT networks are presented along with a discussion of open issues for future study.

In pursuit of advanced wireless communications, demands for higher capacity and maximum possible efficiency have been a continuous desire since the beginning of the first generation of mobile communications. In 1948, Claude Shannon found that by controlling the wireless channels, the signal strength can be improved at the receiver side and a higher capacity can be obtained. However, controlling the signal propagation over noisy and random wireless channels had not been a convenient task so far. Nevertheless, nowadays, a revolution has been beginning in the smart world where advanced intelligent meta-surfaces (IMSs) are to support a pioneering degree of freedom in engineering wave-matter interactions across a wide range of operating frequencies, from microwave to Terahertz bands. This article presents an overview of the “Advanced Intelligent Meta-surfaces” to Various Scenarios in a comprehensive style. At a high level, we investigate the state-of-the-art of IMS for 6G where a new notion of prefabricated IMS-empowered wall assists the outdoor-to-indoor communication in high frequencies.

Nowadays, most farming technologies are gradually being transformed into real-time monitoring, control and actuation systems with the advent of the Internet of Things (IoT). These deployments of the IoT over farms have been accelerating due to advancements in sensor technology and communication protocols. Low orbit satellites, for example, has the capability to enable real-time monitoring of the crop over remote places and farms. However, there are numerous challenges to realising seamless farming with LEOs because of low power usage and long-distance transmission requirements from the LoRaIoT sensors over the farms. The main objective of this paper is to study state of the art and present a high-level link budget analysis of the ground sensors and gateways mounted over low earth orbit (LEO) satellites. While the link budget provides ideas on how LEO satellites are prepared for data networking, it also supports improving communication with optimal signal strength. We find that there is a possibility of determining an optimal set of parameters for the ground sensors and the LEO satellite to deliver the desired performance for technical readiness. We observe that different LoRa field parameters such as link budget, receiver power, receiver sensitivity, the path loss can be used at 923.3 MHz frequency. Based on the link budget analysis, we suggest the maximum feasible distance between ground sensors and LEOs. Most importantly, we performed a preliminary analysis of a beamforming approach to improve communication efficiency of the smart farming1.

Recently, the Internet of Vehicles (IoV) has been employed as an enabling technology for smart transportation, which can be further enhanced by integrating space–air–ground-integrated networks (SAGIN). Since the data packets of vehicular user equipments (VUEs) are generally short, random access is usually considered for VUEs to connect to the network. However, collisions caused by the multiple VUEs initiating random access simultaneously are inevitable. To relieve the performance degradation by collisions, data aggregation can be carried out in IoV, where aggregated packets can be relayed to a base station. In this article, we first propose an aggregators-aided random access scheme for IoV, where unmanned aerial vehicles (UAVs), as one of the key components in SAGIN, are deployed as data aggregators to help transmissions of VUEs. Then, a semi-Markov chain is used to analyze the average number of aggregated packets, and the metric of the average data to overhead ratio (ADOR) is presented to evaluate the efficiency of aggregation. Finally, the altitude of UAVs and the duration of data aggregation are optimized to maximize ADOR. By numerical simulations, the accuracy of the analysis as well as the effectiveness of the proposed scheme are validated.

Enabling ultrareliable low-latency communication (URLLC) with stringent requirements for transmitting data packets (e.g., 99.999% reliability and 1-ms latency) presents considerable uplink transmission challenges. For each packet transmission over dynamically allocated network radio resources, the conventional random access protocols are based on a request-grant scheme. This induces excessive latency and necessitates reliable control signaling, resulting in overhead. To address these problems, grant-free (GF) solutions are proposed in the fifth-generation (5G) new radio (NR). In this article, an overview and vision of the state of the art in enabling GF URLLC are presented. In particular, we first provide a comprehensive review of NR specifications and techniques for URLLC, discuss underlying principles, and highlight impeding issues of enabling GF URLLC. Furthermore, we briefly explain two key phenomena of massive multiple-input–multiple-output (mMIMO) (i.e., channel hardening and favorable propagation) and build several deep insights into how celebrated mMIMO features can be exploited to address the issues and enhance the performance of GF URLLC. Moving further ahead, we examine the potential of cell-free (CF) mMIMO and analyze its distinctive features and benefits over mMIMO to resolve GF URLLC issues. Finally, we identify future research directions and challenges in enabling GF URLLC with CF mMIMO.

Real-time remote control over wireless is an important yet challenging application in fifth-generation and beyond due to its mission-critical nature under limited communication resources. Current solutions hinge on not only utilizing ultrareliable and low-latency communication (URLLC) links but also predicting future states, which may consume enormous communication resources and struggle with a short prediction time horizon. To fill this void, in this article we propose a novel two-way Koopman autoencoder (AE) approach wherein: 1) a sensing Koopman AE learns to understand the temporal state dynamics and predicts missing packets from a sensor to its remote controller and 2) a controlling Koopman AE learns to understand the temporal action dynamics and predicts missing packets from the controller to an actuator co-located with the sensor. Specifically, each Koopman AE aims to learn the Koopman operator in the hidden layers while the encoder of the AE aims to project the nonlinear dynamics onto a lifted subspace, which is reverted into the original nonlinear dynamics by the decoder of the AE. The Koopman operator describes the linearized temporal dynamics, enabling long-term future prediction and coping with missing packets and closed-form optimal control in the lifted subspace. Simulation results corroborate that the proposed approach achieves a$38\times $lower mean squared control error at 0-dBm signal-to-noise ratio (SNR) than the nonpredictive baseline.

This article has proposed detection and physical-layer security provision for printed sensory tag systems for Internet of Things (IoT) applications. The printed sensory tags can be a very cost-effective way to speed up the proliferation of the intelligent world of IoT. The printed radio-frequency identification (RFID) of a sensory tag is chipless with the fully printable feature, Nonline-of-Sight (NLoS) reading, low cost, and robustness to the environment. The detection and adoption of security features for such tags in a robust environment are still challenging. This article initially presents a robust technology for detecting tags using both the amplitude and phase information of the frequency signature. After successfully identifying tag IDs, the article presents novel physical-layer security using a deep learning model to prevent the cloning of tags. Our experiment shows that the proposed system can detect and identify the unique physical attributes of the tag and isolate the clone tag from the genuine tag. It is believed that such real-time and precise detection and security features bring this technology closer to commercialization for IoT applications.

We consider future cellular networks and study the coexistence of delay-sensitive (DS) and delay-tolerant (DT) devices in machine-type communication (MTC). DS devices require to minimize access delay for their low delay requirements; in contrast, DT devices have flexible delay constraints. For reducing access delay, we extend fast retrial idea in the data transmissions when a group of preambles is divided into two subsets to support the time-varying nature of the traffic from DS devices. We focus on the stability dynamics by design—we derive convergence conditions by using a control-theoretic approach in terms of variation in the 1) arrival rates; 2) buffer sizing at the devices; 3) number of preambles; and 4) number of DS devices. More importantly, we discover a novel adaptive algorithm that dynamically allocates the number of preambles for DS, thus guaranteeing stability by design. We validate our findings with extensive simulations. Besides, we develop a novel framework that describes how the control theory idea can be applied to address the issue of tracking the buffers and handling coexistence in such a diverse network environment under realistic application constraints. Our extension to the control-theoretic idea predicts whether the overall system is stable, i.e., whether data flows are desynchronized. Given the data flows are desynchronized, small buffers are always sufficient. Our approach shows that a smaller DS buffer often promotes desynchronization—a virtuous cycle.

The next generation multiple access (NGMA) schemes are considered to support massive access for a large number of devices, which motivates us to develop a low-complexity approach for next generation systems. Since the generalized spatial modulation (SM) can be adopted to the system, a number of compressive sensing (CS) reconstruction algorithms are deployed for the detection of sparse signals, while the complexity of CS-based approaches is proportional to the number of antennas. In order to decrease the complexity, we propose a two-stage approach to detect sparse signals, where the received signals are divided into groups. Then, the activity variables of aggregated signals are decided and the sparse signal detection is carried out at the signals belonging to active groups. During the activity variable detection, the variational inference algorithm is applied to determine the activity variables. Moreover, in order to analyze the performance of activity variable detection, the$J$-divergence is proposed to measure the distance between the distributions, while the approximate expression of$J$-divergence is derived. Simulation results show that the proposed approach is able to provide good detection performance with low complexity. In addition, the$J$-divergence is confirmed to be useful as an evaluation metric to measure the detection performance.

With the rapid development of electronic and information technologies, the Internet of Everything (IoE) has become one of the trendiest topics in both academia and industry. Therein, many types of space/air/ground platforms need to be connected to networks for breaking down the isolation of information islands and providing various services. Space/air/ground platforms, such as satellites, unmanned aerial vehicles (UAVs), airships, balloons, terrestrial vehicles, and high-speed trains (HSTs) have emerged for accomplishing various complex tasks. Wireless communication is one of the most important technologies to support the real-time delivery of control commands and mission-related data. On the other hand, the space-air-ground integrated network has become a promising paradigm for the six-generation (6G) mobile communication network, where the aerospace and terrestrial vehicles may need to connect to existing mobile cellular networks or act as base stations (BSs) or relays to assist terrestrial wireless communications. To meet the ever-increasing demands of high capacity, wide coverage, low latency, and strong robustness for communications, it is promising to adopt large-scale antenna arrays at the transceivers to obtain considerable array gains and improve the channel quality. Antenna array-enabled beamforming technologies can facilitate spectrum reuse, interference mitigation, coverage enhancement, and physical-layer security. Antenna arrays can also be used to promote the sensing capability of space/air/ground networks, where the sensing information may be carefully processed to assist communications. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive, and tricky challenges in antenna array design, physical layer, multiple access control layer, and network layer. As a result, numerous new research issues require to be addressed, which cover a wide range of disciplines including communication theory, network theory, antenna theory, signal processing, protocol design, resource allocation, optimization, hardware implementation, and experimentation.

Antenna arrays have a long history of more than 100 years and have evolved closely with the development of electronic and information technologies, playing an indispensable role in wireless communications and radar. With the rapid development of electronic and information technologies, the demand for all-time, all-domain, and full-space network services has exploded, and new communication requirements have been put forward on various space/air/ground platforms. To meet the ever increasing requirements of the future sixth generation (6G) wireless communications, such as high capacity, wide coverage, low latency, and strong robustness, it is promising to employ different types of antenna arrays (e.g., phased arrays, digital arrays, and reconfigurable intelligent surfaces, etc.) with various beamforming technologies (e.g., analog beamforming, digital beamforming, hybrid beamforming, and passive beamforming, etc.) in space/air/ground communication networks, bringing in advantages such as considerable antenna gains, multiplexing gains, and diversity gains. However, enabling antenna array for space/air/ground communication networks poses specific, distinctive and tricky challenges, which has aroused extensive research attention. This paper aims to overview the field of antenna array enabled space/air/ground communications and networking. The technical potentials and challenges of antenna array enabled space/air/ground communications and networking are presented first. Subsequently, the antenna array structures and designs are discussed. We then discuss various emerging technologies facilitated by antenna arrays to meet the new communication requirements of space/air/ground communication systems. Enabled by these emerging technologies, the distinct characteristics, challenges, and solutions for space communications, airborne communications, and ground communications are reviewed. Finally, we present promising directions for future research in antenna array enabled space/air/ground communications and networking.

Owing to their intrinsic advantages of seamless coverage and of high data rate, space-air-ground communications networks (SAGCN) are recognized as one of the emerging technologies for the future wireless communications systems. However, the broadcasting nature of wireless communications inevitably imposes security issues on SAGCN. In this paper, we consider the uplink of the full-duplex unmanned aerial vehicle (UAV)-aided three-layer SAGCN, comprising of ground Internet of Remote Things (IoRT) terminals, an unmanned aerial vehicle, and a low-earth orbit (LEO) satellite, where eavesdroppers are intercepting the information transmitted. In order to ensure a secure uplink transmission, a joint UAV deployment and power allocation scheme is conceived for maximizing the secrecy rate of the SAGCN, subject to the following constraints: i) UAV’s power, ii) the UAV deployment area, and iii) the secrecy rate, which are imposed on the different layers. More explicitly, once we formulate a joint optimization problem to maximize the secrecy rate, we decouple the variables and decompose the original problem into multiple subproblems in a tractable manner. Then, we simplify the subproblems with the aid of slack variables and solve them relying on the successive convex approximation method. Following this, initialization schemes are designed to exploit the one-direction greedy method for diverse environment settings, for speeding up the convergence of the iterative algorithm proposed. Finally, simulation results reveal that the convergence can be achieved within a small number of iterations by the proposed initialization scheme, while the algorithm conceived is capable of attaining a substantial improvement of the secrecy rate for the SAGCN.

We consider a non-orthogonal multiple access in a random-access ALOHA system, in which each user randomly accesses one out of different time slots and send uplink packets based on power differences. In the context of an asymmetric game, we propose a non-orthogonal multiple access ALOHA system based on multi-agent reinforcement learning tools that can help each user to find its best strategies of improving the rates of successful action choices. While taking into account not only collisions, but also fading, we analyze the mean rewards of actions under general settings and focus on the case that involves two different groups of users. To characterize the behaviors of accessing strategies, we apply multi-agent action value methods that consider either greedy or non-greedy actions, combined with an acceleration gradient descent. Our results show that in the proposed system, users employing the greedy action-based methods can be randomly divided into two groups of users and increase the rates of successful action choices. Interestingly, in relatively limited channels, such greedy methods turn many of users to be with a state of barring-access. In this case, the proposed acceleration, non-greedy action methods are shown to reduce such unfairness, at a loss of successful action rates.

The intelligent transportation has been extensively investigated as an enabling technology for ubiquitous data processing and content sharing among vehicles and terrestrial infrastructures. In intelligent transportation systems, numerous vehicles and infrastructures are connected for information and data sharing to enable different operations. Since there are some urban areas that face the traffic congestion or cannot be well served, space-air-ground integrated networks (SAGIN) can be carried out to provide continuous network connectivity for vehicles. In particular, unmanned aerial vehicles (UAVs) are deployed as data collectors to receive data packets from vehicles due to the advantages of high mobility and low operating cost. It is noteworthy that the information freshness is critical to enable services for timely decision, e.g., autonomous driving and accident prevention. In this paper, we develop UAV-aided intelligent transportation systems to enhance the usage of vehicular networks and support low latency vehicular services, where the concept of age-of-information (AoI) is adopted to measure the freshness of data packets of vehicles. Then, the performance of UAV-aided intelligent transportation systems is analyzed in terms of the average AoI. In addition, the deployment of multiple UAVs is optimized to minimize the average peak AoI according to the traffic intensity of vehicles under seamless coverage, finite queue, and coverage probability constraints. To this end, the deployment optimization problem is formulated as a multi-constrained non-convex optimization problem and solved by considering each soft constraint separately. Simulation results show that our proposed system can provide timely data transmission.

In this paper, we propose a network coding (NC) based approach to ultra-reliable low-latency communication (URLLC) over erasure channels. In transmitting multiple data packets, we demonstrate that the use of random NC can improve the reliability in terms of decoding error probability, while it incurs a longer decoding delay than a well-known$K$-repetition. To avoid a long decoding delay, we consider a sliding NC (SNC) design that allows a highly reliable transmission of each data packet with a guaranteed decoding delay. A few design examples are derived and their decoding error rates are analyzed. Through the analysis, we can show that the decoding error rate of SNC is much lower than that of$K$-repetition at the same spectral efficiency, which means a more reliable transmission can be achieved using SNC than$K$-repetition.

Massive multiple input multiple output (mMIMO) assisted grant-free random access (RA) (mGFRA) has been considered a promising RA scheme for future machine-type communication (MTC). In mGFRA, the nature that RA user equipments (UEs) blindly interplay each other in the presence of preamble collision degrades the performance of RA UEs. To address the issue, a mMIMO assisted semi-grant-free RA (mSGFRA) scheme is considered in this paper, where a downlink feedback based on the preamble detection after the preamble phase is introduced. With such a feedback, RA UEs experiencing preamble collision are enforced to keep silent in data-transmission phase, which in turn enhances the performance of RA UEs without experiencing preamble collision. To understand the performance behaviours of mSGFRA, we first analyse the preamble detection performance in mSGFRA and reveal that accurate collided-preamble detection can be achieved with the assistance of mMIMO. Then, we analyse and compare the performance of mSGFRA and mGFRA in terms of success probability. Simulation results validate theoretical analysis and confirm the potential superiority of mSGFRA to mGFRA.

Remote state monitoring over wireless is envisaged to play a pivotal role in enabling beyond 5G applications ranging from remote drone control to remote surgery. One key challenge is to identify the system dynamics that is non-linear with a large dimensional state. To obviate this issue, in this article we propose to train an autoencoder whose encoder and decoder are split and stored at a state sensor and its remote observer, respectively. This autoencoder not only decreases the remote monitoring payload size by reducing the state representation dimension but also learns the system dynamics by lifting it via a Koopman operator, thereby allowing the observer to locally predict future states after training convergence. Numerical results under a non-linear cart-pole environment demonstrate that the proposed split learning of a Koopman autoencoder can locally predict future states, and the prediction accuracy increases with the representation dimension and transmission power.

In machine-type communication (MTC), random access has been employed for a number of devices and sensors to access uplink channels using a pool of preambles. To support different priorities, random access can be generalized with multiple resource blocks (RBs), which however results in a poor spectral efficiency. In this paper, in order to achieve a high spectral efficiency, random access with layered preambles (RALP) is proposed so that devices of two different priorities, namely type-1 and type-2 devices, can co-exist within one RB based on the notion of power-domain non-orthogonal multiple access (NOMA).

In the Internet-of-Things (IoT), different types of devices can co-exist within a network. For example, there can be cheap but inflexible devices as well as flexible devices in terms of radio frequency (RF) capabilities. Thus, in order to support different types of devices in different ways and improve throughput, we propose a multichannel random access scheme based on power-domain non-orthogonal multiple access (NOMA), where each flexible or dynamic device (DD) can dynamically choose one of multiple channels when it has a packet to send. Since a DD can choose either an under-utilized channel or a channel of high gain depending on channel selection criteria, we also study a selection criterion that can take into account not only the system, but also user preference indicators in order to have a balanced performance.

In machine-type communication (MTC), a group of devices or sensors may need to send their data packets with certain access delay limits for delay-sensitive applications or real-time Internet-of-Things (IoT) applications. In this case, 2-step random access approaches would be preferable to 4-step random access approaches that are employed for most MTC standards in cellular systems. While 2-step approaches are efficient in terms of access delay, their access delay is still dependent on retransmission strategies. Thus, for a low access delay, fast retrial that allows immediate retransmissions can be employed as a retransmission strategy. In this article, we study 2-step random access approaches with fast retrial as a buffered multichannel ALOHA with a fast retrial and derive an analytical way to obtain the Quality-of-Service (QoS) exponent for the distribution of queue length so that key parameters can be decided to meet QoS requirements in terms of access delay. Simulation results confirm that the derived analytical approach can provide a good approximation of QoS exponent.

In this article, for efficient data collection with limited bandwidth, data-aided sensing (DAS) is applied to the Gaussian process regression (GPR) that is used to learn data sets collected from sensors in the Internet-of-Things systems. We focus on the interpolation of sensors' measurements from a small number of measurements uploaded by a fraction of sensors using GPR with DAS. Thanks to active sensor selection, it is shown that GPR with DAS can provide a good estimate of a complete data set compared to that with random selection. With multichannel ALOHA, DAS is generalized for distributed selective uploading when sensors can have feedback of predictions of their measurements so that each sensor can decide whether or not it uploads by comparing its measurement with the predicted one. Numerical results show that modified multichannel ALOHA with predictions can help improve the performance of GPR with DAS compared to the conventional multichannel ALOHA with equal uploading probability.

Preamble resources are scarce and precious in random access (RA), which need to be efficiently utilized to support critical machine-type communication (MTC) with stringent access requirements. In this article, we study a grant-free RA scenario for critical MTC in the context of massive multiple-input–multiple-output (MIMO). To enhance the access reliability of delay-sensitive devices within a predefined latency budget, two dynamic preamble-resource partitioning (DPP) schemes are proposed. Particularly, by leveraging massive MIMO, we first analytically investigate the feasibility of DPP in the considered RA scenario and demonstrate its performance superiority to the conventional scheme. Based on the analytical results, we then propose a greedy DPP scheme that performs locally optimal preamble-resource partitioning in each RA slot. To find the globally optimal DPP solution, we further propose a reinforcement learning (RL)-based scheme by modeling the considered RA scenario as a Markov decision process. Simulation results show the practicality and effectiveness of the proposed DPP schemes. In particular, to achieve a target access failure rate of$1\times 10^{-2}$, the proposed RL-based scheme is able to enhance the RA traffic load by 42% and improve the preamble resource utilization by over 25% compared to the conventional baseline scheme.

In the Internet of Things (IoT), numerous IoT devices are deployed for environment sensing, information collecting, and data transmitting to enable different operations, including patrol monitor, industrial automation, and system control. Considering the limited power and computation capability of IoT devices, mobile-edge computing (MEC) is applied to enhance the usage of IoT. Since unmanned aerial vehicles (UAVs) can be used as MEC servers, they become an efficient means to collect data packets and assist computation. In this article, we develop UAV-aided IoT systems, where the performance of data collection is analyzed in terms of packet loss rate and data quantity using a Markov chain. Then, in order to meet the diverse service requirements, the computation frequency of UAV is designed according to the preference coefficients of the cost on energy and time consumption. Finally, the system Age of Information (AoI) is considered to define the freshness of data packets, where the models of single-IoT device and multi-IoT devices with first-come–first-served (FCFS) principle and M/M/1 queuing are analyzed. The simulation results show that the proposed system is able to provide robust data collection and efficient computation for IoT devices.

In 5G and beyond wireless scenarios, it is expected to support tremendous amount of communicating machines. With an exponential increase of machine-to-machine (M2M) system deployments, efficient approaches to massive access by a large number of devices are to be studied. In this paper, we propose a massive multiple-input multiple-output (MIMO) based grant-free random access (RA) with resolution of preamble collision for massive access. Based on the channel hardening and favorable propagation characteristics of massive MIMO, collided signals processed at the base station (BS) can be viewed as a variation of superposition modulation, and are to be recovered by successive interference cancellation (SIC) techniques. Besides, taking into consideration the effect of pass loss and fractional power control (FPC), analytic expressions of success probability of the proposed collision resolution with conjugate beamforming (CB) and zero-forcing beamforming (ZFB) are derived. With simulation results, we verify the analyses and show that the proposed protocol can resolve most preamble collisions.

In 5G massive machine-type communication (mMTC), power-limited or battery-free parasite devices such as radio frequency identification (RFID) tags, can use the transmitted signals from host devices as ambient signals for backscatter communications to send information to a base station (BS). Unmanned aerial vehicles (UAVs) can be employed as host devices to help transmissions of parasite devices due to the advantages of high mobility and low operating cost. In this paper, we propose a signal detection approach based on the central limit theorem to detect the presence of parasite devices and separate parasite signals from host signals. Then, closed-form expressions for the probability of error detection and the bit error rate (BER) are derived. Moreover, the trajectory planning of multiple UAVs is optimized with the consideration of minimizing the energy consumption of UAV swarms to serve parasite devices. Theoretical and simulation results show that our proposed method provides good detection performance for parasite devices. It also shows that the trajectory planning of multiple UAVs is optimized.

There is a growing interest in the transition from 4-step random access to 2-step random access in machine-type communication (MTC), since 2-step random access is well-suited to short message delivery in various Internet of Things (IoT) applications. In this paper, we study a 2-step random access approach that uses code division multiple access (CDMA) to form multiple channels for data packet transmissions with a spreading factor less than the number of channels. As a result, the length of data transmission phase in 2-step random access can be shorter at the cost of multiuser interference. To see how the decrease of the length of data transmission phase can improve the spectral efficiency, we derive the throughput as well as the spectral efficiency. From the results of analysis, we can show that the 2-step CDMA-based random access approach can have a higher spectral efficiency than conventional 2-step approach with orthogonal channel allocations, which means that the performance of MTC can be improved by successfully transmitting more data packets per unit time using CDMA. This is also confirmed by simulation results.

In this article, a Co-existing Preamble and Data (CoPD) random access approach for machine-type communications (MTC) is proposed and studied in massive multiple input multiple output (MIMO) systems. Unlike conventional grant-free random access in which preambles are time-multiplexed with data, the proposed approach enables CoPD transmissions by different groups of devices, which leverages favourable propagation of massive MIMO and successive interference cancellation (SIC) technique. To fully understand the performance behavior of the proposed approach, the error characteristics of channel estimation (CE) due to SIC error propagation are investigated. To avoid the CE error variance growing arbitrarily with time that leads to data transmission failure, key conditions, with respect to preamble length$L$, traffic intensity$\lambda $, and average number of data packets per active device$\bar b$, are derived. Under the conditions, we demonstrate that the throughput of the proposed CoPD approach cannot be arbitrarily high by increasing$\bar b$and$\lambda $even though the antenna array size, denoted by$M$, grows arbitrarily in massive MIMO. Accordingly, its maximum throughput and success probability are theoretically obtained. Simulation results verify our analysis results and confirm that the maximum throughput of the proposed CoPD approach can be at least 4 times than that of the conventional one. Furthermore, its success probability approaches 1 when$M \to \infty $as long as the derived conditions are met.

Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA). In this paper, by leveraging distributed massive multiple input multiple output (mMIMO) together with machine learning, a novel machine learning based framework solution is proposed to address the preamble collision problem in GFRA. The key idea is to identify and employ the neighboring access points (APs) of a collided RA UE for its data decoding rather than all the APs, so that the mutual interference among collided RA UEs can be effectively mitigated. To this end, we first design a tailored deep neural network (DNN) to enable the preamble multiplicity estimation in GFRA, where an energy detection (ED) method is also proposed for performance comparison. With the estimated preamble multiplicity, we then propose a K-means AP clustering algorithm to cluster the neighboring APs of collided RA UEs and organize each AP cluster to decode the received data individually. Simulation results show that a decent performance of preamble multiplicity estimation in terms of accuracy and reliability can be achieved by the proposed DNN, and confirm that the proposed schemes are effective in preamble collision resolution in GFRA, which are able to achieve a near-optimal performance in terms of uplink achievable rate per collided RA UE, and offer significant performance improvement over traditional schemes.

In this paper, by considering the average mean squared error (AMSE) of channel estimation, we primarily obtain the closed-from expressions of the probability density function (PDF) and cumulative distribution function of AMSE for the least squares (LS)/minimum mean squared error (MMSE) estimation method as the line-of-sight (LOS) component is known, where the asymptotic analysis is executed in Rayleigh fading and strong LOS conditions. Secondly, the closed-form expressions for the expectation of AMSE ( Expamse) and variance of AMSE ( Varamse) are acquired, where Varamseis inversely proportional to the number of antennas ( M). As M becomes infinite, the PDF of AMSE at Expamsehas an order of root M. When the pilot power decreases with M in a power law, the LS case keeps deteriorating while the MMSE case converges to a constant which basically depends on the Rician K-factor. Next, the spectral efficiency is investigated by considering AMSE. When Expamseaccelerates, the spectral efficiency of the LS method keeps dropping and that of the MMSE method firstly is degraded and then is improved to a constant except Rayleigh fading. Finally, all results are validated via simulations.

With the increasing adoption of millimeter-waves (mmWave) over cellular networks, outdoor-to-indoor (O2I) communication has been one of the challenging research problems due to high penetration loss of buildings. To address this, we investigate the practicability of utilizing reconfigurable intelligent surfaces (RISs) for assisting such O2I communication. We propose a new notion of prefabricated RIS-empowered wall consisting of a large number of chipless radio frequency identification (RFID) sensors. Each sensor maintains its own bank of delay lines. These sensors which are built within the building walls can potentially be controlled by a main integrated circuit (IC) to regulate the phase of impinging signals. To evaluate our idea, we develop a thorough performance analysis of the RIS-based O2I communication in the mmWave network using stochastic-geometry tools for blockage models. Our analysis facilitates two closed-form approximations of the downlink signal-to-noise ratio (SNR) coverage probability for RIS-based O2I communication. We perform extensive simulations to evaluate the accuracy of the derived expressions, thus providing new observations and findings.

Massive machine-type Internet of Things (IoT) communication (mMTIC) has the potential for high impact in the anticipated future industry 4.0 sensor networking applications. However, the energy limitation and battery life of the IoT nodes have always been one of the long-standing problems. Clustering routing protocol (CRP) being the most efficient existing approach often suffers when nodes closer to the sink depletes their energy, thereby producing an unwanted energy hole, where packets in flight toward the sink often get interrupted. Considering mMTIC covering a large geographical area, such as monitoring bush fires, the multihop communication among the nodes often causes such an energy hole problem. In this article, we develop an artificial-intelligence-based CRP framework for incorporating a small periphery of a fixed shaped area to ameliorate such energy holes. Our proposed framework is not only energy-optimized but also acts as a robust approach for massive communication and informed data collection.

In machine-type communication (MTC), random access has been employed for a number of devices and sensors to access uplink channels using a pool of preambles. To support different priorities due to various quality-of-service (QoS) requirements, random access can be generalized with multiple pools, which may result in low spectral efficiency. In this paper, for high spectral efficiency, random access with layered preambles (RALP) is proposed to support devices with two different priorities based on the notion of power-domain non-orthogonal multiple access (NOMA). In RALP, two groups of devices, namely type-1 and type-2 devices, are supported with different priorities, where type-1 devices have higher priority than type-2 devices. Closed-form expressions are derived for the detection performance of preambles transmitted by type-1 devices, which can be used for a certain performance guarantee of type-1 devices of high priority. Low-complexity preamble detection methods are also discussed.

In this paper, we study grant-free random access with massive multiple input multiple output (MIMO) systems. We first show that the performance of massive MIMO based grant-free random access is mainly decided by the probability of preamble collision. The implication of this is that although the number of antennas can be arbitrarily large, the (average) number of successful packet transmissions can be limited by the number of preambles. Then, we propose an approach to preamble collision reduction without increasing the number of preambles using the notion of superpositioned preambles (S-preambles). Since the channel estimation for conjugate beamforming can be carried out when unused S-preambles are known, a simple approach is derived to detect unused S-preambles and its performance is analyzed for a special case (superpositions with 2 preambles). Based on analysis and simulation results, it is confirmed that the proposed approach with S-preambles can increase the success probability with the same spectral efficiency as the conventional approach.

In this paper, we consider exploration for multi-channel ALOHA by transmitting preambles before transmitting data packets and show that the maximum throughput can be improved by a factor of 2-e-1≈ 1.632, which can be seen as the gain of exploration. In the proposed approach, a base station (BS) needs to send the feedback information to active users to inform the numbers of transmitted preambles in multiple channels, which can be reliably estimated as in compressive random access. Simulation results also confirm the results from analysis.

In this paper, we study ultra-reliable and low-latency communication (URLLC) under fading using repetition diversity through multiple orthogonal radio resource blocks (e.g., multiple frequency or time bins). We investigate an approach to find an upper-bound on the packet error rate when a finite-length code is used. From simulation results, we find that the bound is reasonably tight for wide ranges of signal-to-noise ratio (SNR) and the number of multiple bins. Thus, the derived bound can be used to determine key parameters to guarantee the performance of URLLC in terms of the packet error rate.

Ambient backscatter communication (AmBC) has been introduced to address communication and power efficiency issues for short-range and low-power Internet-of-Things (IoT) applications. On the other hand, reconfigurable intelligent surface (RIS) has been recently proposed as a promising approach that can control the propagation environment especially in indoor communication environments. In this paper, we propose a new AmBC model over ambient orthogonal-frequency-division-multiplexing (OFDM) subcarriers in the frequency domain in conjunction with RIS for short-range communication scenarios. A tag transmits one bit per each OFDM subcarrier broadcasted from a WiFi access point. Then, RIS augments the signal quality at a reader by compensating the phase distortion effect of multipath channel on the incident signal. We also exploit the special spectrum structure of OFDM to transmit more data over its squeezed orthogonal subcarriers in the frequency domain. Consequently, the proposed method improves the bit-error-rate (BER) performance and provides a higher data rate compared to existing AmBC methods. Analytical and numerical evaluations show the superior performance of the proposed approach in terms of BER and data rate.

Security becomes increasingly important due to various attacks from adversaries in wireless sensor networks. This work considers a resilient distributed estimation of an unknown parameter with a cluster‐based approach when some agents are adversarial. A two‐phase algorithm is adopted to perform parameter estimation and detect attacks. First, a cluster scheme is proposed to make sure that each cluster is connected. Then, the attack is detected and estimation is achieved with a consensus+innovation estimator in each cluster. Finally, the cluster heads combine the consensus estimates in each cluster and exchange with other cluster heads to achieve unknown parameter estimation. In addition, the detection sensitivity under different cluster schemes is also compared. Numerical examples illustrate that the proposed cluster‐based approach can improve the convergence rate and detection sensitivity.

Over the last decade, the Internet of Things (IoT) has been employed as an enabling technology for the smart ocean. As one of the key technologies in the IoT, machine-type communication (MTC) has been considered to support devices' connectivity. In the MTC, random access is introduced for devices to share a common access channel during the packet transmission with low signaling overhead. However, the collision caused by the presence of multiple devices is inevitable. Since maritime sensors have limited energy sources, in this article, we propose a relay-aided random access (RARA) scheme for the smart ocean, where retransmissions are carried out by maritime buoys with the relay function, to deal with collisions. In the RARA scheme, a base station (BS) is able to recover multiple collided signal packets simultaneously by using multiuser detection with multiple copies of collided signals forwarded by buoy nodes. As a result, our proposed scheme becomes energy efficient and reliable to be suitable for the smart ocean. Theoretical and simulation results show that a high throughput and a low outage probability can be achieved with a large number of buoy nodes.

In this article, we study data-aided sensing (DAS) for a system consisting of a base station (BS) and a number of nodes, where the BS becomes a receiver that collects measurements or data sets from the nodes that are distributed over a cell. DAS is an iterative data collection scheme that allows the BS to efficiently estimate a target signal (i.e., all nodes' measurements) with a small number of measurements (compared to random polling). In DAS, a set of nodes are selected in each round based on the data sets that are already available at the BS from previous rounds for efficient data collection. We consider DAS for measurements that are correlated with Gaussian in this article. The resulting DAS is referred to as Gaussian DAS. Using the mean-squared error (MSE) criterion, in each round, the BS is able to choose a node that has a data set to minimize the MSE of the next round. Furthermore, we generalize Gaussian DAS in two different ways: 1) with multiple parallel channels to upload measurements from nodes using random access and 2) with a model selection, where a multiarmed bandit problem formulation is used to combine the model selection with DAS.

In this article, we study the compressive random access (CRA) with two stages for machine-type communication (MTC) in cellular Internet of Things (IoT). In particular, we consider the case that each user (IoT device or sensor) has only one short message (of the same length) when it is activated to send data in IoT applications. Two different CRA-based random access schemes are discussed (one is conventional and the other is new based on a simplified handshaking process). Based on the throughput analysis, we show that the CRA-based random access scheme with simplified handshaking process can outperform as its length of the payload is adaptively decided depending on the number of active users. The simulation results confirm that the derived throughput expressions agree with them and can be used to design a random access system for MTC with each active device or sensor that has one short message.

As unmanned aerial vehicles (UAVs) are becoming more popular and the demand for wireless links for UAVs is increasing, it is crucial to develop air-to-ground (A2G) wireless links for high-speed UAVs. Suffering from the high mobility and limitation of transmission power of UAVs, A2G wireless links become unstable to provide high quality communication services. In this paper, we design robust A2G wireless links for high speed UAVs, where conjunct power control is developed together with switched beamforming to maximize the power efficiency and minimize the fluctuation of A2G wireless links of millimeter wave (mmWave) signal transmission. We first present channel models for A2G wireless links of high-speed UAVs, which can be virtually seen as multiple-input multiple-output (MIMO) channels. To maximize the power efficiency, a conjunct power control problem is formulated to allocate powers for wireless links between antenna arrays on UAVs and access points (APs). For switched beamforming, beamformers are designed to provide a certain time-invariant signal-to-interference-plus-noise ratio (SINR) to minimize the SINR fluctuation of A2G wireless links. From theoretical analysis and numerical results, it is shown that the proposed architecture is able to provide robust and high quality A2G wireless links for high-speed UAV communication systems.

Massive multiple-input multiple-output (mMIMO) based grant-free random access (RA) (mGFRA) has been studied for massive machine-type communication (mMTC). In the existing works of mGFRA, it is implicitly assumed that each RA user equipment (UE) in mMTC has the same data-packet size. However, this may not be the case in practice as RA UEs in different applications can have different numbers of bits to send, which may result in data packets of different sizes. In addition to that, preamble collision is a key issue that degrades the performance of mGFRA when orthogonal preamble is used. In this article, we aim to address the preamble collision issue under a practical mGFRA scenario that consists of two different types of RA UEs (with short and large data packets). Specifically, by exploiting different sizes of data packets, we propose a triangular non-orthogonal RA (T-NORA) scheme, where the preamble transmission of RA UEs with small-sized data packet is embedded into the data transmission of RA UEs with large-sized data packet so that the preamble collision between two different types of RA UEs can be avoided. As a result, an improved performance over conventional mGFRA can be achieved by T-NORA with the assistance of mMIMO. Through analytical and simulation results, we can confirm that the proposed T-NORA scheme outperforms the conventional mGFRA scheme.

Massive MIMO based grant-free random access (mGFRA) is a promising RA technique for massive access with low signaling overhead. However, due to a limited orthogonal preamble size, preamble collision is a key factor that constrains the success probability of mGFRA. In this paper, we examine the potential of applying non-orthogonal preambles to mitigate the issue, where two typical non-orthogonal sequences, i.e., Gaussian distribution sequences and Zadoff-Chu (ZC) sequences, are taken into account. Asymptotic behaviors of the success probabilities with the non-orthogonal preamble sequences are analyzed and compared using derived closed-forms. Through the analysis we reveal that ZC sequences outperform Gaussian ones. However, though non-orthogonal sequences are able to reduce or even alleviate the preamble collision, they do not necessarily provide better performance than the orthogonal counterpart. In addition, regarding orthogonal sequences as a single-root case of ZC sequences, we show that there exists an optimal number of ZC roots that maximizes the success probability for mGFRA, which is low-bounded by orthogonal baseline. Simulation results evaluate the performance of non-orthogonal sequences in mGFRA under various practical system parameters, and validate our analysis.

We propose an autonomous blockchain-based federated learning (BFL) design for privacy-aware and efficient vehicular communication networking, where local on-vehicle machine learning (oVML) model updates are exchanged and verified in a distributed fashion. BFL enables oVML without any centralized training data or coordination by utilizing the consensus mechanism of the blockchain. Relying on a renewal reward approach, we develop a mathematical framework that features the controllable network and BFL parameters (e.g., the retransmission limit, block size, block arrival rate, and the frame sizes) so as to capture their impact on the system-level performance. More importantly, our rigorous analysis of oVML system dynamics quantifies the end-to-end delay with BFL, which provides important insights into deriving optimal block arrival rate by considering communication and consensus delays. We present a variety of numerical and simulation results highlighting various non-trivial findings and insights for adaptive BFL design. In particular, based on analytical results, we minimize the system delay by exploiting the channel dynamics and demonstrate that the proposed idea of tuning the block arrival rate is provably online and capable of driving the system dynamics to the desired operating point. It also identifies the improved dependency on other blockchain parameters for a given set of channel conditions, retransmission limits, and frame sizes.1However, a number of challenges (gaps in knowledge) need to be resolved in order to realise these changes. In particular, we identify key bottleneck challenges requiring further investigations, and provide potential future research directions.1An early version of this work has been accepted for presentation in IEEE WCNC Wksps 2020 [1].

In this paper, we study the application of opportunistic non-orthogonal multiple access (NOMA) mode to short-message transmissions with user's power control under a finite power budget. It is shown that opportunistic NOMA mode, which can transmit multiple packets per slot, can dramatically lower the session error probability when $W$ packets are to be transmitted within a session consisting of $W_{\mathrm{ S}}$ slots, where $W_{\mathrm{ S}} \ge W$ and the slot length is equivalent to the packet length, compared to orthogonal multiple access (OMA) where at most one packet can be transmitted in each slot. From this, opportunistic NOMA mode can be seen as an attractive approach for uplink transmissions. We derive an upper-bound on the session error probability as a closed-form expression and also obtain a closed-form for the NOMA factor that shows the minimum possible ratio of the session error probability of opportunistic NOMA to that of OMA. Simulation results also confirm that opportunistic NOMA mode has a much lower session error probability than OMA.

To support machine-type communication (MTC), massive multiple-input multiple-output (MIMO) has been considered for grant-free random access. In general, the performance of grant-free random access with massive MIMO is limited by the number of preambles and the number of active devices. In particular, when there are a number of active devices transmitting data packets simultaneously, the signal-to-interference-plus-noise ratio (SINR) cannot be high enough for successful decoding. In this paper, in order to improve performance, we consider immediate re-transmissions for an active device that has a low SINR although it does not experience preamble collision to exploit re-transmission diversity (RTD) gain. To see the performance of the proposed approach, we perform throughput analysis with certain approximations and assumption. Since the proposed approach can be unstable due to immediate re-transmissions, conditions for stable systems are also studied. Simulations are carried out and it is shown that analysis results reasonably match simulation results.

In this paper, for low-latency transmissions in machine-type communications, we study multichannel ALOHA with fast retrial that can shorten the access delay by immediate retransmissions of collided packets. To analyze the access delay of multichannel ALOHA with fast retrial, we employ an erasure channel model, which is equivalent to the widely used collision model, and study the effective capacity and the quality of service (QoS) exponent. Through the asymptotic analysis, we show that the QoS exponent grows logarithmically with the system dimension (i.e., the number of subchannels), which implies that multichannel ALOHA with fast retrial can effectively shorten the average access delay by increasing the system dimension. However, the growth rate of the effective capacity is shown to be slower than linear when the system dimension increases, which becomes the cost of improved access delay. In particular, the effective capacity is proportional to (N/ lnN), where N is the number of subchannels. From the QoS exponent, the tail probability of queue is obtained and compared with simulation results, which confirms that the theoretical results from the derived QoS agree with the simulation results for large systems.

In this paper, data-aided sensing as a cross-layer approach in Internet-of-Things (IoT) applications is studied, where multiple IoT nodes collect measurements and transmit them to an access point (AP). It is assumed that measurements have a sparse representation (due to spatial correlation) and the notion of compressive sensing (CS) can be exploited for efficient data collection. For data-aided sensing, a node selection criterion is proposed to efficiently reconstruct a target signal through iterations with a small number of measurements from selected nodes. Together with compressive random access (CRA) to collect measurements from nodes, compressive transmission request is proposed to efficiently send a request signal to a group of selected nodes. Error analysis on compressive transmission request is carried out and the impact of errors on the performance of data-aided sensing is studied. Simulation results show that data-aided sensing allows to reconstruct the target information with a small number of active nodes and is robust to nodes' decision errors on compressive transmission request.

In this article, we study backscatter communication (BC) for power-limited devices that are connected to a network for the Internet of Things (IoT), where joint estimation and detection is carried out at a receiver to detect signals from a backscatter device (BD) with ambient orthogonal frequency-division multiplexing (OFDM) carrier. In conventional BC, in order to avoid the difficulty of the carrier estimation, the energy detector is usually considered at a receiver at the cost of poor performance. To improve the performance, in this article, we consider a novel approach that allows the carrier estimation at the receiver via joint estimation and detection. In particular, in the proposed approach, the matched-filtering at the BD (for the transmitter filter) is employed to impose a certain property that allows efficient and reliable carrier estimation via joint estimation and detection. Through the performance analysis and simulation results, we show that the matched-filtering at the BD in the proposed approach can improve the performance.

Random access has been considered for machine-type communication (MTC). In particular, a random access scheme with a pool of preambles is widely studied, which is similar to multichannel ALOHA, to support a number of MTC devices. In this paper, we study optimal approaches for user activity detection in random access with preambles over fading channels. Since the computational complexity grows exponentially with the number of preambles, a low-complexity detector is derived using Markov chain Monte Carlo (MCMC) approaches that can approximately solve an optimal maximum a posteriori detection problem. The resulting MCMC detector can enjoy a trade-off between performance and complexity, while its complexity to obtain a sample is linearly proportional to the number of preambles. A performance analysis for optimal detection is also studied to see the optimal performance. Simulation results confirm that the MCMC detector performs better than compressive sensing-based approaches and can provide a near optimal performance under certain conditions with a reasonable computational complexity.

In this paper, we study the effective capacity and quality of service (QoS) exponent for low-latency communications through multiple channels under fading. We consider both the cases where the instantaneous channel state information (CSI) and statistical CSI are available at a transmitter that may have sporadic traffics. In particular, when a fixed transmission rate is chosen by the transmitter with statistical CSI, we derive an upper-bound on the (packet) error probability, which can be used not only to guarantee a certain reliability but also to derive a lower-bound on the QoS exponent for short-packet transmissions under fading. Simulation results with short-length codes are obtained and compared with the derived bounds on the error probability and QoS exponent, which show that the derived bounds are reasonably tight to be used to guarantee reliability (in terms of the error probability) as well as latency (in terms of the QoS exponent).

In this paper, we consider a simple coding scheme for spatial modulation, where the same set of active transmit antennas is repeatedly used over consecutive multiple transmissions. Based on a Gaussian approximation, an approximate maximum likelihood (ML) detection problem is formulated to detect the indices of active transmit antennas. We show that the solution to the approximate ML detection problem can achieve a full coding gain. Furthermore, we develop a low-complexity iterative algorithm to solve the problem with low complexity based on a well-known machine learning approach, i.e., variational inference. Simulation results show that the proposed algorithm can have a near ML performance. A salient feature of the proposed algorithm is that its complexity is independent of the number of active transmit antennas, whereas an exhaustive search for the ML problem requires a complexity that grows exponentially with the number of active transmit antennas.

Compressive random access (CRA) is a random access scheme that has been considered for massive machine-type communication (MTC) with non-orthogonal spreading sequences, where the notion of compressive sensing (CS) is used for low-complexity detectors by exploiting the sparse device activity. In this paper, we study the application of power-domain non-orthogonal multiple access (NOMA) to CRA in order to improve the performance of CRA (or increase the number of devices to be supported). We consider Gaussian spreading sequences and derive design criteria through a large-system analysis to determine the power levels for power-domain NOMA. From simulation results, we can confirm that the number of incorrectly detected device activity can be reduced by applying NOMA to CRA.

In this paper, we consider hybrid uplink non-orthogonal multiple access (NOMA) that can support more users by exploiting the notion of power-domain NOMA. In hybrid uplink NOMA, we do not consider centralized power control as a base station (BS) needs instantaneous channel state information (CSI) of all users which leads to a high signaling overhead. Rather, each user is allowed to perform power control under fading in accordance with a truncated channel inversion power control policy. Due to the lack of coordination of centralized power control, users in the same resource block compete for access. To analyze users' behavior, evolutionary game can be considered so that each user can choose transmission strategies to maximize payoff in hybrid uplink NOMA with power control. Evolutionarily stable strategy (ESS) is characterized with fixed costs as well as costs that depend on channel realizations, and it is also shown that hybrid uplink NOMA can provide a higher throughput than orthogonal multiple access (OMA). To update the state in evolutionary game for hybrid uplink NOMA, the replicator dynamic equation is considered with two possible implementation methods.

The proliferation of devices with multiple wireless interfaces and (automobile) manufacturers' interest in implementing reliable connectivity for vehicles create an ideal scenario for multipath transmission control protocol (MPTCP) over vehicular networks. Next generation vehicular networks will be highly dynamic and exploit MPTCP over various wireless technologies including WiFi and 5th generation (5G) cellular networks to form the Internet of vehicles (IoV). However, the probability of packet loss can be high and/or packets arrive at the destination out-of-order due to time-varying heterogeneous wireless paths. Further, the IoV traffic is delay sensitive, which urges the need to investigate MPTCP algorithms for reliable communication over heterogeneous lossy networks, while satisfying delay constraints. We approach these challenges by jointly using load balancing and forward error correction (FEC) for performing coupled congestion control inside MPTCP. As a result, the proposed MPTCP-IoV is TCP-friendly by design, and provably stable and convergent to a unique equilibrium point. In addition, a comprehensive mathematical analysis (developed to approximate the reordering delay) is carried out and shown that the delay penalty for MPTCP-IoV over such networks becomes insignificant when considering the potential gains obtained by the convergence of multiple networks. Experiments are carried out and the results demonstrate these characteristics.

We propose a physical-layer challenge-response authentication approach in this paper based on combined shared secret key and channel state information between two legitimate nodes in an orthogonal frequency division multiplexing system. The proposed approach can be used even if the correlation of channel coefficients exists, which can be exploited to extract the shared secret key in conventional approaches. Furthermore, channel coding is employed to mitigate the difference between the two estimated channels as well as channel fading and background noise. As a result, in the proposed physical-layer authentication approach, the decoder's output can be used for authentication and provide a reliable decision under active attack.

Non-orthogonal multiple access (NOMA) is one of the potential multiuser supporting techniques in the fifth generation (5G) cellular systems due to its higher spectrum efficiency (SE) and cell-edge throughput. Vector-perturbation (VP) is widely known as one of the nonlinear precoding schemes that achieves near-capacity performance in practical wireless multi-input-multi-output (MIMO) communication systems. In this paper, we propose a hybrid transmission strategy based on VP and NOMA (VP-NOMA) by designing a beamforming matrix with the power allocation strategy to minimize total transmit power for certain quality of service (QoS) requirements. Rather than searching for the optimal beamforming matrix, we propose a more intuitive sub-optimal algorithm, called iteration beamforming for VP-NOMA systems (IBVP-NOMA), to find beamforming vectors. Further, different user clustering strategies are considered and compared to enhance the performance of the VP-NOMA systems. The simulation results demonstrate that the proposed method requires lower transmit power than the NOMA system without VP.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) can provide a good performance with a diversity technique. However, the performance improve- ment depends on the correlation of the frequency-domain channel coefficients, which is usually high if the number of paths is limited. In this paper, in order to see the impact of interleaving on the performance with a diversity technique, we derive a closed- form expression for the pairwise error probability (PEP). From simulation results, we can confirm that the theoretical results agree with them and help understand the role of interleaving in conjunction with a diversity technique.

Orthogonal frequency division multiplexing (OFDM) with index modulation (IM) (or OFDM-IM) has attracted increasing attention due to its high spectral efficiency as well as high energy efficiency for next generation wireless communication systems. Despite its advantages over OFDM, it also inherits drawbacks of OFDM, e.g., a high peak-to-average power ratio (PAPR) problem. In this paper, we propose a novel PAPR reduction method for OFDM-IM by exploiting the availability of multiple mapping rules for IM. While the proposed approach is similar to the selected mapping (SLM) scheme for conventional OFDM, the main difference is that it is based on multiple mapping rules that are naturally given for IM (thus, this method is not applicable to OFDM). Furthermore, in order to avoid the transmission of side information (of the used mapping rules), a blind search detector that combines a blind search method and a low-complexity maximum likelihood (ML) detector is proposed. From the simulation results, we can observe that the proposed PAPR reduction method for OFDM-IM has a lower computational complexity than that of the SLM method for conventional OFDM, while providing similar or slightly better performances in terms of bit error rate (BER) and PAPR reduction.

Recently, machine type communications (MTC) has attracted attention to support massive connectivity for devices within a cellular system. Most existing schemes for MTC are based on a handshaking procedure that may cause a high access delay although the size of data packets is small. Thus, we can consider a one-shot or grant-free system of low signaling overhead for MTC. In the one-shot system, data and control signals can be transmitted without handshaking, and compressive sensing based multiuser detection (CS-MUD) is used at a receiver in order to recover multiple signals that are simultaneously transmitted from (unknown) devices. In this paper, we study the throughput and delay of a one-shot system as well as a handshaking system. We derive closed-form expressions for the throughput and delay when fast retrial is used for the retransmissions of collided packets (which can effectively shorten the access delay) under a high signal-to-noise ratio (SNR) environment. From the analysis and simulation results, we can see that the one-shot system performs better than the handshaking system for devices of short packets.

Orthogonal frequency division multiplexing index modulation (OFDM-IM) has been emerging as a promising solution to increase the reliability at low complexity. However, the transmit diversity of the OFDM-IM schemes has been limited to two, due to unbalanced transmit diversity between index bits and complex data bits. In this work, we propose a new precoded OFDM-IM employing several spread matrices, named as (S-OFDM-IM). This aims to increase the transmit diversity, exploiting not only a subset of active sub-carrier indices, but also spreading the active indices over all the sub-carriers available. In this context, the transmit diversity in the detection of both index bits and data bits will be increased, properly taking into account both the multipath and index diversities. As for low-complexity detection, we propose two linear-complexity detections that exploit the minimum mean square error and the zero-forcing, along with the likelihood ratio. To analyse the performance, the bit error probability is derived and the transmit diversity analysis is provided. Through simulation results, it is clearly presented that the proposed scheme can outperform the benchmarks in terms of the transmit diversity, being higher than two. The enhanced transmit diversity is desired to the low complexity machine type applications with high reliability.

Compressive sensing (CS) encryption has attracted increasing attention as a lightweight encryption technique for the Internet of things (IoT) and wireless sensor networks (WSNs). Since devices and sensors may have limited hardware, it might be needed to consider quantization for CS encryption. In this paper, we consider a 1-bit CS encryption using artificial noise and a retransmission strategy with interleaving to mitigate degradation of detection performance at a legitimate receiver caused by the quantization errors. Through the analysis and simulation results, we demonstrate that the retransmission strategy can improve the detection performance at a legitimate receiver, while it slightly improves decision performance to estimate a measurement matrix at an eavesdropper. In addition, the proposed 1-bit CS encryption has a better performance than the existing CS encryption in terms of security, while mitigating the performance degradation caused by quantization errors of detection probability at a legitimate receiver.

In this study, the authors take into account the imperfect channel state information for beamforming (BF) scheme in a dual‐hop multiple‐input multiple‐output (MIMO) amplify‐and‐forward (AF) relay network. Since the overall performance has been decided by the signal‐to‐interference‐plus‐noise ratio (SINR) at the destination, the authors derive the optimal BF weights that maximise the SINR at the desination. To evaluate the performance of the relay network, the authors also derive closed‐form expressions for the outage probability and probability density function of the received SINR. Computer simulations are carried out, which show the superiority of the designed optimal BF scheme, the impacts of channel errors and antenna configurations on the performance of the MIMO AF relay systems, and the validity of the analytical expressions. It is shown that due to the channel errors, the performance of the relay network is bounded no matter how much power is injected into the system.

Here, the authors propose an energy‐efficient strategy using multiple antennas for secure distributed detection in wireless sensor networks (WSNs). In multiple access channel, it is possible to communicate between sensors and ally fusion centre with perfect secrecy by an encryption with channel state information (CSI). However, the sensors in an active group may need to constantly report in the encryption as long as the CSI remains unchanged, which deteriorates the energy efficiency in terms of the lifetime of WSN. The authors solve the problem by using random beamforming (e.g. antenna subset modulation). Furthermore, the authors investigate the impact of the number of active antennas on the network lifetime. Through the analysis and simulation, the authors demonstrate that the proposed strategy has a higher energy efficiency than the conventional strategy, while maintaining the same reliability and security.

In this paper, we study a compressive random access scheme that allows the active device identification with unique sparse identification sequences in machine-type communications to support a number of devices. Since the performance of active device identification can be easily degraded by the background noise as well as distortion by frequency-selective fading channels due to the sparsity of identification sequences, we propose a channel coding approach for sparse identification sequences and derive a low-complexity method for maximum likelihood decoding. Based on the proposed channel coding approach, we can significantly improve the performance of active device identification. We also consider a low-complexity detection method to detect signals transmitted with sparse identification sequences and derive a closed-form expression for the bit error rate.

In this paper, we study a random access scheme for distributed estimation to estimate a target signal through multiple sensors in wireless sensor networks (WSNs). Since the random access scheme is based on (slotted) ALOHA, it might be possible to implement a WSN for distributed estimation using machine-type communications within cellular systems. Unfortunately, in conventional ALOHA, since the signal at each node is assumed to be independent, when the signals of nodes or sensors are correlated, it may not be efficient. Thus, in order to take into account the correlation of sensors' observations, we propose a transmission rule for each sensor based on local reliability of sensor's local estimate in ALOHA, which can improve the performance of distributed estimation. A generalization with multiple subchannels is also considered and it is shown that the performance can be improved in terms of mean squared error.

In this paper, we study a layered random access scheme based on non-orthogonal multiple access to improve the throughput of multichannel ALOHA. At a receiver, successive interference cancellation is carried out across layers to remove the signals that are already decoded. A closed-form expression for the total throughput is derived under certain assumptions. It is shown that the transmission rates of layers can be optimized to maximize the total throughput and the proposed scheme can improve the throughput with multiple layers. Furthermore, it is shown that the optimal rates can be recursively found using multiple individual 1-D optimizations. We also modify the proposed layered random access scheme with contention resolution repetition diversity for reliable transmissions with a delay constraint. It is shown to be possible to have a low outage probability if the number of copies can be optimized, which is desirable for high reliability low latency communications.

In this paper, we propose a simple power and rate allocation scheme for uplink non-orthogonal multiple access based on a multicarrier system when a practical modulation and coding scheme is employed at each user. Since information-theoretic approaches with asymptotic settings are not considered, the proposed power and rate allocation scheme is able to provide a guaranteed performance with any practical block codes of finite length in terms of the probability of code word error. For the performance prediction in the power and rate allocation with given channel realizations, we employ non-asymptotic approaches for bounds on the probability of code word error. Through simulation results, we can confirm that the probability of code word error can be lower than or close to the target error rate using the proposed power and rate allocation.

In this paper, we consider multichannel ALOHA for a wireless sensor network (WSN), where sensors' signals are correlated. The impact of the number of channels on the length of information that an access point (AP) can collect is investigated. Under a certain distributed signal source model, in order for the AP to have a sufficiently long length of information, we show that the number of channels is to be larger than the square of the average number of active nodes. Thus, when the number of channels is limited, we need to use re-transmissions. To this end, we propose an adaptive transmission approach that can effectively exploit the correlation between the collected signals at the AP and nodes' signals using Slepian-Wolf coding to shorten the total transmission time, which results in a more energy efficient WSN.

Non-orthogonal multiple access (NOMA) can improve the spectral efficiency by exploiting the power domain and successive interference cancellation, and in order to improve the throughput, it can be applied to random access that plays a crucial role in the Internet of Things to support connectivity for a number of devices with sparse activity. In this paper, we formulate a game when NOMA is applied to ALOHA to decide users' access or transmission probabilities. We consider a payoff function based on an energy-efficiency metric and drive the mixed strategy Nash equilibrium (NE). Once the NOMA-ALOHA game is successfully formulated, we generalize it to take into account users' different fading and multiple orthogonal channels, which might be necessary for NOMA-ALOHA to support a number of users in a realistic environment. A distributed algorithm based on gradient play is derived to find the mixed strategy NE (or transmission probabilities) of multichannel NOMA-ALOHA with fading.

In random access for machine-type communications, it is required not only to detect active devices, but also to estimate their channel state information when they transmit preambles. In this paper, we consider an approach for simultaneous multipacket channel estimation and reception (SMuCER) with a multicarrier system under frequency-selective fading, where the channel estimation becomes challenging due to frequency-selective fading and different round-trip delay between the devices. In the proposed approach, circular-shifted preambles are used to effectively increase the number of preambles (that can lower the probability of preamble collision), and compressive sensing (CS)-based multiuser detection (MUD) is employed. In order to have a good performance of CS-based MUD for SMuCER, we consider Alltop sequences. Furthermore, we formulate an optimization problem to maximize the number of successfully recovered active devices, where a large single system is to be divided into multiple subsystems. It is shown that multiple subsystems have not only a higher throughput, but also a lower computational complexity than a large single system.

In conventional orthogonal frequency division multiplexing (OFDM) with index modulation (OFDM-IM), the active subcarriers can convey information bits by modulated symbols as well as their indices. To detect modulated symbols, coherent detection is usually considered, which requires the channel state information (CSI) estimation for all subcarriers at a receiver. In OFDM-IM, however, since only a fraction of subcarriers are active, the overhead for pilot transmissions through all subcarriers to allow the receiver to estimate the CSI could be excessive, in particular, under a fast fading environment. To avoid this difficulty, we consider noncoherent OFDM-IM, where only the indices of active subcarriers are used to convey information bits. For a better performance, a transmit diversity scheme is also studied. We derive a closed-form expression for the probability of index error when no transmit diversity is considered under frequency-selective Rayleigh fading. We also derive an upper-bound when the transmit diversity scheme is employed. From the upper-bound, the diversity order is also clearly shown.

This paper investigates the opportunity for a repetition coded multi-carrier index keying-orthogonal frequency division multiplexing (MCIK-OFDM), termed repeated MCIK-OFDM (ReMO), which can provide superior performance over existing schemes at the same spectral efficiency. Unlike the classical scheme, the proposed scheme activates a subset of subcarriers and modulates them with the same M-ary data symbol, while additional information is conveyed by the active sub-carrier indices. This approach not only provides the frequency diversity gains in the M-ary symbol detection but also improves the index detection, leading to considerable improvement in the transmit diversity. For performance analysis, we derive tight closed-form expressions for the symbol error probability and the bit error rate, under both perfect and imperfect channel state information (CSI). These expressions provide insight into the achievable performance gains, system designs, and impacts of various CSI conditions. Finally, simulation results are given to illustrate the superior performance achieved by our scheme over existing schemes under different CSI uncertainties.

In this paper, we explore non-orthogonal multiple access (NOMA) in millimeter-wave (mm-wave) communications (mm-wave-NOMA). In particular, we consider a typical problem, i.e., maximization of the sum rate of a 2-user mm-wave-NOMA system. In this problem, we need to find the beamforming vector to steer towards the two users simultaneously subject to an analog beamforming structure, while allocating appropriate power to them. As the problem is non-convex and may not be converted to a convex problem with simple manipulations, we propose a suboptimal solution to this problem. The basic idea is to decompose the original joint beamforming and power allocation problem into two sub-problems which are relatively easy to solve: one is a power and beam gain allocation problem, and the other is a beamforming problem under a constant-modulus constraint. Extension of the proposed solution from 2-user mm-wave-NOMA to more-user mm-wave-NOMA is also discussed. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results also show that the proposed sub-optimal solution achieves close-to-bound sum-rate performance, which is significantly better than that of time-division multiple access.

In this paper, we consider a compressive sensing (CS) based compression method for aggregated power signals of smart meters in advanced metering infrastructure (AMI). If detailed power signals with high sampling rates are available for energy management systems, advanced services such as demand response and power disaggregation can be provided. However, typical smart meters have limited bandwidth resources and may require aggressive data compression for supporting the intensively sampled data. In order to alleviate the overhead for the detailed power signal, a CS based compression scheme is proposed in this study to take advantage of the sparsity of power signals based on the appliance load superposition modeling. Through simulations, the CS based compression schemes demonstrate improved energy efficiency while maintaining good reconstruction performance, compared to the wavelet based compression.

We consider index modulation (IM) for single-carrier (SC) systems in this paper. Compared with conventional orthogonal frequency division multiplexing (OFDM) IM, the resulting approach, which is referred to as SCIM, has a better performance due to the path diversity gain under a multipath fading environment. For sparse IM, since compressive sensing (CS) algorithms can be used for low-complexity signal detection, we consider the orthogonal matching pursuit (OMP) algorithm to perform the signal detection in SCIM. A transmit diversity scheme is also proposed for both OFDM-IM and SCIM, which can not only improve the performance in terms of the diversity gain, but also increase the number of information bits per signal block.

Compressive random access has been considered for machine-type communications (MTC) as it has a potential to support massive connectivity by exploiting the sparsity of device activity. However, its performance limitations are not well studied yet compared with other well-known candidates for MTC, e.g., multichannel ALOHA. In this paper, we investigate the stability of compressive random access with a controlled access probability strategy and derive the maximum stable throughput to see performance limitations. From the analysis results, we can show that the maximum stable throughput of compressive random access is higher than that of multichannel ALOHA by a factor of 2. As a result, we can claim that compressive random access can support more devices than multichannel ALOHA in MTC.

In this paper, we study random access for machinetype communications (MTC) based on a multiple access scheme that exploits different channel state information (CSI) from active devices under frequency-selective fading, which is called channel division multiple access (ChDMA). In ChDMA, in order to use a low-complexity compressive sensing (CS) algorithm for multiuser detection (MUD) with a high transmission rate, we employ index modulation. Since the CSI is used as a signature for MUD, the CSI estimation becomes crucial, but suffers from the pilot collision. In order to lower the probability of pilot collisions, multiple transmissions of randomly selected pilots are considered.

We study random access for machine-type commu- nications (MTC) based on the notion of compressive sensing (CS) over a distributed input distributed output (DIDO) system consisting of a number of remote radio heads (RRHs) that are closely located to devices. In a DIDO system, each active device can have a signature that depends on its connectivity to RRHs and the signatures of active devices are estimated and used for signal detection in random access using a CS algorithm. We derive a relation between key parameters such as the average number of active devices and the number of the outputs of a DIDO system for a good performance.

In order to improve the spectral efficiency of downlink in a multiuser system, nonorthogonal multiple access (NOMA) has been extensively studied for 5th generation (5G) systems. NOMA allows a group of users to share a given channel by exploiting the power difference. Since downlink multiuser beamforming can also be used to support multiple users for a given channel, in this paper, we combine NOMA and downlink beamforming for a millimeter (mm)-wave multiuser system. Since the performances of NOMA and downlink beamforming depend on the channel conditions, we consider a problem formulation that incorporates NOMA in multiuser beamforming selectively depending on the channel conditions. As a result, we can have NOMA beamforming that can outperform conventional beamforming for mm-wave channels.

In downlink non-orthogonal multiple access (NOMA), beamforming can be employed to effectively mitigate the multiple access interference (MAI) with beamforming gain. It is well-known that user clustering can play a crucial role in simplifying beamforming in NOMA. In this paper, we find the solution to a minimum transmit power beamforming problem for given pair of users in a cluster and propose a simple, but effective user clustering approach in conjunction with the minimum transmit power beamforming.

In machine-type communications (MTC), a number of devices are present in a cell, but only a few of them attempt to access by sending preambles or pilots. For the sparse activity of devices, compressive sensing (CS) can be applied for joint detection of active devices and estimation of channel profiles. In this paper, a theoretical connection between pilots and the coherence of a CS measurement matrix is revealed for CS-based detection and estimation. Then, the Zadoff-Chu (ZC) and the power residue (PR) sequences are studied as potential candidates for pilot signals in CS-based random access. It is also shown that the CS measurement matrices from the ZC- and the PR-based pilots have theoretically bounded low coherence, which presents a theoretical guarantee of reliable recovery performance. Simulation results demonstrate that the proposed deterministic pilots outperform random binary and complex-valued pilots for CS-based random access in MTC.

To enhance the spectral efficiency of geostationary Earth orbit (GEO) satellite communication systems with scarce GEO resources, cooperative transmission is widely used in GEO satellite collocation (GEOSC) systems. Current analysis on GEOSC channels is usually based on the hypothesis that channels are line‐of‐sight (LOS) ones, while multipath components are ignored. In this study, a more realistic cooperative transmission method is studied for GEOSC systems over Rician fading channels, where multipath components are taken into consideration in conjunction with LOS components. On the basis of this model, a practical user selection strategy with opportunistic beamforming is studied to optimise the capacity of GEOSC systems. Simulation results show that the GEOSC system using the techniques developed in this study has better performance comparing with the ones using existing approaches.

In this study, the authors investigate the resource allocation (RA) problem for the downlink orthogonal frequency division multiplexing based non‐orthogonal multiple access (OFDM‐NOMA) system. The RA problem is decomposed into two subproblems of subcarrier allocation (SA) and power allocation (PA). For the SA, a user grouping based greedy algorithm is proposed under the assumption that power is uniformly distributed among all the selected users. For the PA, the authors propose the iterative water‐filling and specific user rate maximising criterion with minimum rate constraints (iterative WF + SURMC‐MRC) scheme to jointly consider the PA problem among the selected users on one subcarrier and the PA problem among subcarriers. The simulation results show that the spectral efficiency performance of the proposed iterative WF + SURMC‐MRC scheme outperforms those of the (non‐iterative) WF + SURMC‐MRC scheme and the uniform distribution (UD) + SURMC‐MRC scheme. Moreover, the iterative WF + SURMC‐MRC scheme has advantages in resisting against the user overloading compared with the (non‐iterative) WF + SURMC‐MRC scheme.

In this study, the authors investigate the power allocation (PA) problem and spectral efficiency (SE) analysis for the single antenna downlink non‐orthogonal multiple access (NOMA) system under the sum rate maximising criteria with minimum rate constraints (SRMC‐MRC). For the PA problem, they propose the duality scheme for SRMC‐MRC which is considered as the optimal solution for the PA problem on one orthogonal subband in the single antenna NOMA system. They propose the specific user rate maximising criteria with minimum rate constraints (SURMC‐MRC) scheme to decrease the computational complexity of SRMC‐MRC. The PA problem on one subband under SURMC‐MRC is proved to be equivalent to SRMC‐MRC. Numerical results show that both the SE and fairness performance for SURMC‐MRC can strictly approach to those of the duality scheme in the whole signal‐to‐noise ratio (SNR) region. They prove that NOMA under SRMC‐MRC can obtain SE performance advantage in the high SNR region but accompany with some disadvantages in the low SNR region over the orthogonal multiple access system. This conclusion is verified through numerical results under different parameter conditions.

In this paper, we consider sparse index multiple access for uplink random access in a wireless system of a number of devices when a fraction of them are active. This multiple access scheme is suitable for the case that an access point (AP) needs not only to receive data symbols, but also to identify active devices when there are a number of devices with unique identification sequences (the number of devices can be easily more than a million) with low signaling/control overhead. We propose a two-stage transmission scheme for random access and derive computationally efficient methods to estimate the channel state information (CSI) of active devices over frequency-selective fading channels in the first stage and to perform joint active device identification and data detection in the second stage using a well-known sparse signal estimation method in compressive sensing. Simulation results demonstrate that the proposed approach can successfully estimate the CSI of active devices under reasonable conditions and identify the unique identification sequences or vectors of active devices with a high probability. For example, when 6 out of 64 devices become active, the AP can identify all six devices (using estimated CSI) with a probability higher than 1 - 10-2over frequency-selective fading channels.

In nonorthogonal multiple access (NOMA), the power difference of multiple signals is exploited for multiple access and successive interference cancellation is employed at a receiver to mitigate co-channel interference. Thus, NOMA is usually employed for coordinated transmissions and mostly applied to downlink transmissions where a base station performs coordination for downlink transmissions with full channel state information. In this paper, however, we show that NOMA can also be employed for non-coordinated transmissions such as random access for uplink transmissions. We apply a NOMA scheme to multichannel ALOHA and show that the throughput can be improved. In particular, the resulting scheme is suitable for random access when the number of subchannels is limited since NOMA can effectively increase the number of subchannels without any bandwidth expansion.

Based on successive hypothesis testing, we propose an approach for sparse signal recovery and apply it to random access to detect multiple block-sparse signals over frequency-selective fading channels. By introducing the sparsity variable, the proposed approach decides the presence or absence of the signal in each stage. To mitigate the error propagation, adaptive ordering is also employed as a greedy algorithm. From simulation results, it is shown that the proposed approach performs better than the block orthogonal matching pursuit algorithm, which is a well-known greedy compressive sensing algorithm for compressive random access.

In order to apply downlink nonorthogonal multiple access (NOMA) to delay-sensitive transmissions, we consider NOMA with delay quality of service (QoS) constraints and effective capacity with power control when the channel state information is available in this paper. As the power control problem to maximize the sum effective capacity with delay QoS constraints is not a convex problem, we propose a suboptimal approach based on the partial effective capacity. The resulting power control policy can be seen as a generalization of a well-known approach, which is the truncated channel inversion power control (TCIPC) policy, to NOMA. We can further optimize the NOMA-TCIPC policy with QoS constraints and confirm that the resulting policy can guarantee delay QoS from simulation results.

In this paper, we propose a simple transmit diversity scheme for orthogonal frequency division multiplexing (OFDM) with index modulation (IM) in order to achieve a diversity gain for index detection, which can significantly improve the performance of OFDM-IM at the cost of the spectral efficiency. An optimal approach for active index detection is derived, which has a complexity, growing linearly with the size of OFDM symbol. A salient feature of the proposed transmit diversity scheme is that it can be easily employed in conjunction with a conventional coding scheme for data symbols. Consequently, we can implement coded OFDM-IM together with the proposed transmit diversity scheme in a straightforward manner, which can provide a good performance under a frequency-fading environment.

Nonorthogonal multiple access (NOMA) can effectively improve the spectral efficiency by exploiting the power difference and employing successive interference cancellation (CSI) at receivers. In NOMA, the power allocation is crucial and can be performed with known CSI. It is also possible to carry out the power allocation with statistical CSI to meet target outage probabilities for given transmission rates. In this paper, we consider joint rate and power allocation to minimize the total transmission power with individual throughput constraints in NOMA when statistical CSI is available. An approach to find the optimal solution to the joint rate and power allocation problem is proposed. Based on simulation results, it is shown that NOMA can have a much lower total transmission power than OMA for given target throughput of each user.

In this paper, we study random access for machine-type communications based on a multiple access scheme that exploits different channel state information (CSI) from active devices under frequency-selective fading, which is called channel division multiple access (ChDMA). In ChDMA, in order to use a low-complexity compressive sensing (CS) algorithm for multiuser detection (MUD) with a high transmission rate, we propose to use index modulation. Since the CSI is used as a signature for MUD, the CSI estimation becomes crucial. For the CSI estimation, pilot transmission can be considered where each active device randomly chooses a pilot from a set of pre-determined pilots. Since the CSI estimation suffers from pilot collision in this case, we propose multiple pilot transmissions and show that a low probability of collision can be achieved. A CS-based approach is derived not only to estimate the CSI, but also to detect randomly selected multiple pilot signals by exploiting the sparsity of active devices over frequency-selective fading channels.

In this paper, we study secure hybrid automatic retransmission request (HARQ) over block-fading channels when the channel from a legitimate transmitter (i.e., Alice) to an eavesdropper (i.e., Eve) is less noisy than that to a legitimate receiver (i.e., Bob). In order to have a positive secrecy rate, we exploit the notion of the channel reciprocity in time division duplex mode, where the shared channel state information between Alice and Bob is used as a secret key. The resulting HARQ scheme is referred to as channel-aware secure HARQ (CAS-HARQ) in this paper. In this scheme, in order to keep Eve ignorant of the confidential message from Alice to Bob, deliberative transmissions of random message blocks are considered when the channel to Bob is weak. We derive closed-form expressions for bounds on the probabilities of connection outage and secrecy outage to see reliability and security, respectively, of CAS-HARQ. Based on our analysis, we can see that it is possible to have a positive secrecy rate with certain outage probabilities even if the channel to Eve is less noisy than that to Bob.

We study the secure transmissions for channel-aware (CA) random access based on the notion of physical layer security in this paper, which might be suitable for machine type communications. Thanks to the availability of channel state information in CA random access, opportunistic jamming can be employed to improve the secrecy rate. As a result, a positive secrecy rate can be obtained even if the signal-to-noise ratio at an eavesdropper is higher than that at a legitimate receiver. Since a powerful eavesdropper equipped with multiple antennas can suppress the interference due to opportunistic jamming once jamming users are identified, it is important to know the number of received signals that allows Eve to identify them. Using the notion of unicity distance, we study this issue. Based on analysis, we find that it is necessary to keep the number of jamming users relatively small (compared with the number of ordinary active users) and use a fraction of the coherence time for secure transmissions when the coherence time is long.

In this article, the authors focus on a correlated channel model for secure relay beamforming in the relayeavesdropper network. In this network, a single-antenna sourcedestination pair transmits secure information with the help of a amplify-and-forward (AF) relay equipped with multiple antennas. The relay cannot obtain the instantaneous channel state information (CSI) of the eavesdropper. The relay only have the knowledge of correlation information between the legitimate and eavesdropping channels. Depending on this information, we derived the conditional distribution of the eavesdropping channel. Three beamformers at the relay are studied: the zero-forcing (ZF) beamformer, the generalized match-forward (GMF) beamformer and the general-rank beamformer (GRBF). The authors found that the ZF beamformer is invalid in this system, and the GMF beamformer is the optimal rank-1 beamformer, and the GRBF is the iteratively optimal beamformer. Numerical results are presented to illustrate three beamformers' performance, and the impacts of different parameters, especially the channel correlation, on the system performance are analyzed.

For secure transmissions, we consider direct sequence/spread spectrum (DS/SS) systems where pseudo-random (PN) sequences are used for spreading. To generate PN sequences, linear feedback shift registers (LFSRs) are employed, which are also used to generate keystreams in stream ciphers. For steam ciphers that use LFSRs, there are known attacks including correlation attacks that can regenerate keystreams. Since those attacks can also be used to regenerate spreading sequences, in this paper, in order to mitigate this problem, we consider induced random (chip) flipping of spreading sequences. While perturbed spreading sequences make correlation attacks infeasible, a legitimate receiver also suffers from random flipping. To take advantage of known spreading sequences for good performances, the maximum likelihood (ML) detection can be used, which is, however, computationally prohibitive as the complexity grows exponentially with the processing gain. To avoid this difficulty, we derive an expectation-maximization (EM) algorithm to perform the ML detection with low computational complexity in this paper.

In this paper, we investigate the resource allocation problem for the downlink non-orthogonal multiple access (NOMA) system with imperfect channel state information (CSI). We first derive closed-form expressions for the outage probabilities with and without the estimated instantaneous channel fading coefficient for each user. Upper bounds on both of the probability expressions above are proposed. Based on the outage probability bounds, we propose the power allocation scheme which is combined with a user selection step to minimize the maximum of the outage probability bounds of all the users. Simulation results show that the maximum outage probability can be significantly decreased by exploiting the priori knowledge of the estimated instantaneous channel fading coefficient for each user.

In this paper, we investigate energy-efficient resource management for downlink OFDMA systems with feedback channel capacity constraints. Compared with previous works, quantized channel state information (CSI) is studied by using rate-distortion theory since it can establish an information-theoretic lower bound on the capacity of the feedback channel. Based on the quantized CSI, an effective resource allocation algorithm is proposed to maximize the system energy efficiency employing the generalized fractional programming theory and the Lagrange dual decomposition method. Numerical results show that our proposed algorithm can nearly achieve the optimal solution and imply that the energy efficiency with a limited feedback rate could be close to that with perfect CSI.

Radio resource allocation for multiuser multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM) systems is an important issue to improve overall system performance. Although the achievable rate has been adopted for a performance indicator in most resource allocation schemes, it may not be practical if a nonideal receiver including a suboptimal detector is used instead of optimal one. Under this practical circumstance, we study the subcarrier and power allocation to minimize the average bit error rate (BER) subject to a total power constraint. Different allocation algorithms are proposed for various MIMO detectors such as the maximum likelihood (ML) detector, linear detectors and the successive interference cancellation (SIC) detector. We also propose suboptimal algorithms to reduce the complexity. Based on the simulation results, we can confirm that the proposed suboptimal algorithm for each detector can achieve a comparable performance with the optimal allocation with a much lower complexity.

For iterative detection and decoding (IDD) in multiple‐input multiple‐output systems, the maximum a posteriori probability (MAP) detector would be ideal in terms of the performance. However, due to its high computational complexity, various suboptimal low‐complexity approximate MAP detectors have been studied. In this study, a lattice reduction (LR)‐based detector is considered for a near‐optimal performance for IDD. The authors improve further the performance by employing a partial bit‐wise minimum mean square error (MMSE) approach with randomised sampling, which has a lower complexity than that of the full bit‐wise MMSE method. Moreover, the list of candidate vectors obtained by randomised sampling is extended using a MAP‐aided integer perturbation algorithm for a better performance with low additional complexity. Through simulation results, it is shown that a near‐optimal performance can be obtained which is better than that of the LR‐based randomised successive interference cancellation and the full bit‐wise MMSE methods.

In this study, the authors propose a lattice reduction (LR)‐based doubly iterative receiver for joint channel estimation and detection in multiple‐input–multiple‐output (MIMO) bit‐interleaved coded modulation systems. For the inner iteration loop of the receiver, LR‐based randomised sampling detection is employed to enjoy the tradeoff between performance and complexity while for the outer iteration loop, the expectation–maximisation (EM)‐based iterative channel estimation using sampling results is proposed to achieve the maximum likelihood channel estimation performance. Besides, a modified computational efficient EM‐based channel estimation approach is also derived to reduce the complexity further. Simulation results demonstrate that the proposed doubly MIMO iterative receiver can have comparable bit‐error rate performance with a reasonable computational complexity.

In order to detect space shift keying (SSK) signals, we propose to use the Monte Carlo (MC) expectation maximization (EM) algorithm in this paper. First, we formulate the EM algorithm for the maximum likelihood detection of SSK signals. Then, we derive a Gibbs sampler to approximate the E-step. Through simulation results, we can see that the performance of the derived MCEM algorithm is better than that of the orthogonal matching pursuit algorithm, which was adopted to detect SSK signals with low complexity by Yu et al.

In this paper, we consider an encryption scheme based on compressive sensing for multicarrier systems, where artificial noise is selectively transmitted together with a sparse message signal in the frequency domain to make an adversary's attack difficult. In order to minimize the impact of artificial noise on the performance of a legitimate receiver, the channel state information at the transmitter is exploited under the assumption of channel reciprocity in time division multiplexing mode. We consider attack to estimate the measurement matrix using the maximum likelihood approach and find the probability of correct recovery (or successful attack) under certain assumptions which might be favorable for the adversary.

In this paper, a low-complexity approach for the large-scale (underdetermined) multiple-input multiple-output (MIMO) detection is proposed using the Markov chain Monte Carlo (MCMC) algorithm in conjunction with blockwise sampling. Klein's algorithm is employed in each sub-system to draw multidimensional samples for an MCMC detector in iterative detection and decoding (IDD). From analysis, we find that the lattice reduction (LR) technique cannot improve the performance of the proposed MCMC-based approach under low-correlated channel environment. In addition, due to blockwise sampling, the proposed method exhibits a faster convergence speed when running a Markov chain and provides a near-optimal performance for the detection of underdetermined MIMO systems. Complexity analysis and simulation results show that the proposed approach outperforms the conventional LR-based Klein randomized successive interference cancellation (SIC) detection with a relatively low complexity.

Non-orthogonal multiple access (NOMA) can exploit the power difference between the users to achieve a higher spectral efficiency. Thus, the power allocation plays a crucial role in NOMA. In this paper, we study the power allocation for hybrid automatic repeat request (HARQ) in NOMA with two users. For the power allocation, we consider the error exponents of the outage probabilities in HARQ with incremental redundancy (IR) and derive them based on large deviations. While a closed-form expression for the error exponent (or rate function) without interference is available, there is no closed-form expression for the error exponent with interference. Thus, we focus on the derivation of a lower bound on the error exponent in this paper. Based on the error exponents, we formulate a power allocation problem for HARQ-IR in NOMA to guarantee a certain low outage probability for a given maximum number of retransmissions. From the simulation results, we can confirm that it is possible to guarantee a certain outage probability by the proposed power allocation method.

We consider multiple access with re-transmission diversity (RTxD) based on a hybrid automatic request protocol with incremental redundancy (IR) in this paper. In order to mitigate multiple access interference, we employ successive interference cancellation at a receiver and derive conditions that all the signals from active users can be successfully decoded within a certain number of re-transmissions of IR blocks. We consider two special cases, which we derive from two multichannel random access schemes that have sub-channels in the power and rate domains. Through the analysis and simulations, we can show that the proposed RTxD random access schemes can outperform other existing similar RTxD random access schemes.

We consider a well-known beamforming approach that induces jamming signals to avoid eavesdropping from a transmitter equipped with an antenna array in this paper. With this beamforming, which is called masked beamforming, in order to guarantee a certain instantaneous secrecy rate, we formulate robust beamforming problems with a constraint that deals with the eavesdropper channel's uncertainty and find the solutions. Unlike a guaranteed ergodic secrecy rate, this guaranteed instantaneous secrecy rate allows us to use codes of short codewords for secure communications with masked beamforming over slow-fading channels. In addition, since the optimal solutions are found in closed form, we do not need to resort to any convex optimization solvers.

In this paper, we consider a Markov chain Monte Carlo (MCMC) algorithm using Gibbs sampling for signal detection in multiple input multiple output (MIMO) or code division multiple access (CDMA) systems. It is shown that the MCMC detector can be seen as a stochastic linear system solver. Based on this point of view, we generalize the MCMC detector using a relaxation factor that can improve the convergence rate. For the MCMC detector, since a faster convergence rate implies a lower computational complexity, it is important to have a faster convergence rate especially for large MIMO systems, which can also be achieved by optimizing the temperature parameter. While the optimal temperature parameter heavily depends on the signal-to-noise ratio (SNR), it is shown that a good relaxation factor can be found for wide range of the SNR and other parameters.

In this paper, we study optimal power allocation for multiple-input multiple-output (MIMO) nonorthogonal multiple access (NOMA) systems when a layered transmission scheme is employed. An approach to maximize the sum rate of MIMO-NOMA with layered transmissions is proposed once we show that the sum rate is concave in allocated powers to multiple layers of users. We also derive a closed-form expression for the average sum rate when statistical channel state information (CSI) is available at a transmitter, which allows us to allocate powers to multiple layers for the maximization of the average sum rate. We find lower- and upper-bounds on the average sum rate, from which it is shown that the scaling property of MIMO-NOMA with layered transmissions also holds as conventional MIMO does (i.e., the average sum rate grows linearly with the number of antennas).

In this paper, we study the energy efficiency of channel-aware random access with multiple parallel access channels (PACs) under a collision channel model. Bounds on the energy efficiency are derived. An asymptotic relationship between the energy efficiency and total bandwidth is studied, which shows that the relationship depends on the energy consumption properties of sensors.

In this paper, we propose a sparse signal modulation (SSM) method for precoded orthogonal frequency division multiplexing (OFDM) systems and study the signal detection. Although a receiver is able to exploit a path diversity gain with random precoding in OFDM, the complexity of the receiver is usually high as the orthogonality is not retained due to precoding. However, with SSM, we can derive a low-complexity detector that can provide reasonably good performances with a low sparsity ratio based on the notion of compressive sensing (CS). An important feature of a CS detector is that it can estimate SSM signals with a small fraction of the received signals over sub-carriers. This feature can allow us to build a low cost receiver with a small number of demodulators.

For fifth generation (5G) cellular systems, various approaches are studied including millimeter-wave (mm-wave) communications and heterogeneous networks (HetNets) with small cells. In mm-wave bands, since a much wider bandwidth is available, higher data rates can be achieved. However, mm-wave transmissions suffer from blockage. In this paper, in order to take advantage of a wide bandwidth in mm-wave bands with mitigating the blockage problem, we consider a HetNet consisting of micro-wave base stations (BSs) for macro cells and mm-wave BSs for small cells and study its energy efficiency. We show that the energy efficiency of the considered HetNet can be maximized by choosing the optimal communication range of mm-wave BSs.

Most previous researches on the rate analysis of the power allocation (PA) schemes in two-way decode-and- forward (DF) relay systems typically focus on maximizing the instantaneous objective rates. However, the ergodic rate analysis of the optimized rates is not well considered. In this paper, we investigate the ergodic rates of the PA schemes in a two-way DF relay system, where the physical-layer network coding (PNC) protocol is adopted. Specifically, under a total power constraint, we consider three heuristic PA schemes, in which the optimization objectives are: 1) maximizing the sum rate; 2) maximizing the sum rate with an additional rate fairness constraint; and 3) maximizing the minor rate of the two directions of the system. The cumulative distribution functions (CDFs) of the equivalent system SNR are approximately obtained and the analytical expressions for the calculations of the ergodic rates are theoretically derived. Numerical results show that the derived analytical rates are converged to the simulated results in high SNR regions.

In this paper, we investigate the power allocation (PA) schemes in a two-way relay system, where the relay adopts physical layer network coding (PNC) based on decode-and-forward (DF) protocol. The objective is to determine the optimal power allocation (OPA) scheme at both the source nodes and at the relay that minimizes the end-to-end symbol error probability (SEP) performance under a total power constraint. The perfect channel state information (CSI) is assumed to be available at both the source nodes and at the relay. Since numerical methods (e.g., exhaustive search) are required to find the solution of the OPA scheme, which might be infeasible for practical systems, based on upper and lower bounds on the optimal solution, we propose a near optimal power allocation (NOPA) scheme. The asymptotic SEP expression of the proposed NOPA scheme is also derived. By both the analytical and simulated results, it is shown that the proposed NOPA scheme provides the asymptotic SNR gains of about 1.76dB and 2.22dB over two well-known schemes in high SNR. Furthermore, simulation results show that the performance of proposed NOPA scheme is quite close to that of the OPA scheme.

In this study, the authors examine the power allocation (PA) in a two‐way relay system, where the relay adopts physical layer network coding based on decode‐and‐forward protocol. In the PA, the optimisation objective is to minimise the end‐to‐end symbol error probability (SEP) under a total power constraint. Since the optimal power allocation (OPA) scheme requires numerical methods (for exhaustive search) to find the solution, which might be infeasible for practical systems, the authors propose a near optimal power allocation (NOPA) scheme that has a closed‐form solution by studying the properties of the OPA scheme. The asymptotic SEP expression of the proposed NOPA scheme is also derived. Simulation results show that the performance of proposed NOPA scheme is quite close to that of the OPA scheme. Furthermore, it is shown that the NOPA scheme can provide asymptotic SNR gains of 1.76 dB and 2.22 dB over two well‐known schemes.

In this study, the authors have studied downlink multiuser non‐cooperative beamforming in a time‐division duplexing‐based multicell system with a non‐linear Jacobi‐like iterative algorithm, where each base station is to minimise the interference leakage to adjacent cells in forming beams with signal‐to‐interference‐plus‐noise ratio constraints for users in its cell. The resulting approach is referred to as altruistic non‐cooperative beamforming (ANB). To reduce the computational complexity for ANB per cell, a low‐dimensional beamforming approach, which can be easily solved as it has a standard downlink multiuser beamforming formulation, has also been considered at the expense of degraded performance.

In this paper, we study the impact of outdated channel state information (CSI) on the error rate performance in multiple-antenna two-way decode-and-forward (DF) relay systems, where physical-layer network coding (PNC) is adopted. We consider two variations of relay selection, namely, single best relay selection (S-RS) and multiple successfully-participated relay selection (MSP-RS). Under perfect and outdated CSI assumptions, closed-form expressions for the end-to-end system symbol error probability (SEP) are derived for the S-RS scheme, along with the asymptotic SEP expressions in high SNR regions for the MSP-RS scheme. By both analytical and simulation results, it is clearly shown that a full cooperative diversity gain can be achieved with perfect CSI. The results also manifest that the S-RS scheme can achieve only the transmit diversity gain with outdated CSI. On the other hand, the MSP-RS scheme can achieve a full cooperative diversity gain even if the CSI is outdated at the expense of high complexity.

Multicast beamforming with superposition coding (SC) is studied for multiresolution broadcast where both data streams of high priority (HP) and low priority (LP) are to be transmitted for a user close to a base station (BS), while only data stream of LP is to be transmitted to a user not close to the BS (e.g., a cell-edge user). Using SC, a minimum total transmission power beamforming problem has been formulated to find beamforming vectors and powers for both users. For given normalized beamforming vectors, a closed-form expression for the optimal power allocation is derived from which an iterative algorithm is considered to find beamforming vectors. The proposed multicast beamforming with SC is applied to nonorthogonal multiple access (NOMA) systems to support multiple users as a two-stage beamforming method.

In this paper, we consider the uncertainty of channel reciprocity due to radio frequency (RF) frontend impairments for downlink multiuser beamforming in time division duplexing (TDD) based systems. In particular, we focus on conversion gain error (CGE) in analog RF frontends that results in independent in-phase and quadrature-phase (IQ) imbalances for each RF chain. A parameter of conversion gain factors for each RF chain is considered for the compensation of downlink beamforming as it can be easily estimated by a base station (BS) itself. With the set of these parameters, we study downlink multiuser beamforming for given uplink channel vectors that are available through uplink training phases. Based on performance analysis, we observe that the CGE in receive RF frontend can result in more performance degradation than those in transmit RF frontend.

In this paper, we study beam selection for millimeter-wave (mm-wave) multiuser multiple input multiple output (MIMO) systems where a base station (BS) and users are equipped with antenna arrays. Exploiting a certain sparsity of mm-wave channels, a low-complexity beam selection method for beamforming by low-cost analog beamformers is derived. It is shown that beam selection can be carried out without explicit channel estimation using the notion of compressive sensing (CS). Due to various reasons (e.g., the background noise and interference), some users may choose the same BS beam, which results in high inter-user interference. To overcome this problem, we further consider BS beam selection by users. Through simulations, we show that the performance gap between the proposed approach and the optimal beamforming approach, which requires full channel state information (CSI), becomes narrower for a larger number of users at a moderate/low signal-to-noise ratio (SNR). Since the optimal beamforming approach is difficult to be used due to prohibitively high computational complexity for large antenna arrays with a large number of users, the proposed approach becomes attractive for BSs and users in mm-wave systems where large antenna arrays can be employed.

Multiple base stations (BSs) equipped with large antenna arrays can perform cooperative downlink transmissions using beamforming for cell-edge users to provide high data rate services. However, due to an excessive overhead for the channel state information (CSI) exchange between BSs, the cooperation with full CSI may not be practical for large antenna arrays. Thus, based on an existing precoding approach, two different precoding approaches with partial cooperation, where no CSI exchange is required, are proposed. One of them (called Type III precoding) has a better performance, and its performance can be further improved by user allocation, which becomes possible by deriving a closed-form expression for transmission power. For low-complexity implementations, an algorithm is derived for the user allocation based on the greedy algorithm. Under certain channel conditions, closed-form expressions for average transmission powers are also derived to see asymptotic performance. Based on them, we can show that the average transmission power becomes Q times higher than that with full cooperation if precoding is carried out with local CSI only, where Q is the number of BSs in cooperation, which would be considered as a penalty due to partial cooperation. Through simulations, it is shown that this penalty (i.e., the increase of transmission power) can be reduced by user allocation.

Downlink beamforming plays a key role in massive multiple-input multiple-output (MIMO) systems to provide reasonable signal-to-interference-plus-noise ratio (SINR) in the presence of intercell interference (ICI). Due to an excessive backhaul overhead, limited cooperative beamforming is usually desirable. In this paper, we consider distributed beamforming that can exploit macro diversity with limited cooperation for cell-edge users where multiple base stations (BSs) transmit signals to those users simultaneously. Bounds on the SINRs are derived to see the impact of the spatial correlation. Since distributed beamforming itself cannot guarantee quality of service (QoS) requirements for given target SINRs with limited cooperation, based on derived closed-form expressions for bounds on SINRs, joint power control is considered with two different approaches. One is optimal, but central processing is required. The other approach is suboptimal, while it can be implemented as a distributed power control algorithm.

In this paper, we derive an iterative method for physical-layer multicast beamforming based on alternating minimization. A salient feature of the proposed iterative method is that beams can be found using simple matrix-vector multiplications without resorting to any optimization solver. Thus, it becomes attractive for hardware-based implementations. Furthermore, since the complexity of the proposed method mainly depends on the number of users in a multicast group regardless of the number of antenna elements in an array, it has a computational advantage over successive convex approximation (SCA) methods and semidefinite relaxation (SDR) approaches with randomization when the number of antennas is larger than the number of users. Through simulations, we see that the proposed iterative method can perform better than the conventional method based on SDR with randomization, while its performance is slightly worse than that of SCA. For millimeter-wave (mm-wave) channels of limited scattering, the proposed iterative method is also applied to physical-layer multicast beamforming by hybrid beamformers in order to find a sparse solution.

We present a secret key agreement (SKA) protocol for a multi-user time-division duplex system where a base-station (BS) with a large antenna array (LAA) shares secret keys with users in the presence of non-colluding eavesdroppers. In the system, when the BS transmits random sequences to legitimate users for sharing common randomness, the eavesdroppers can attempt the pilot contamination attack (PCA) in which each of eavesdroppers transmits its target user's training sequence in hopes of acquiring possible information leak by steering beam towards the eavesdropper. We show that there exists a crucial complementary relation between the received signal strengths at the eavesdropper and its target user. This relation tells us that the eavesdropper inevitably leaves a trace that enables us to devise a way of measuring the amount of information leakage to the eavesdropper even if PCA parameters are unknown. To this end, we derive an estimator for the channel gain from the BS to the eavesdropper and propose a rate-adaptation scheme for adjusting the length of secret key under the PCA. Extensive analysis and evaluations are carried out under various setups, which show that the proposed scheme adequately takes advantage of the LAA to establish the secret keys under the PCA.

In this paper, we consider the collaborative use of amplify-and-forward relays to form a beamforming system and provide physical layer security for a wireless network with multiple eavesdroppers. In this paper, we investigate the relay transmit power minimization under a secrecy rate constraint via secure beamforming. To minimize the relay transmit power, we design an approximate relay power minimization (RPM) beam-forming scheme, in which an iterative algorithm combining the semidefinite relaxation (SDR) technology and the gradient-based method is devised by studying the convexity of the RPM problem. By relaxing the constraints of the RPM problem, we propose a virtual eavesdropper based RPM (VE-RPM) beamforming scheme, which transforms the multivariate RPM problem into a problem of a single variable, and thus obtain an analytical solution. Our proposed beamforming designs can work well even if the number of eavesdroppers is larger than that of relays, while the existing schemes, e.g., the null-space beamforming approaches, can not work under this condition. Simulation results are presented to demonstrate the performance improvement with the RPM beamforming schemes. It is also showed that the virtual eavesdropper approaches significantly reduce the complexity with acceptable performance degradation.

Massive multiple-input multiple-output (MIMO) has been actively studied for next generation wireless systems since it can provide high data rate and an improved energy efficiency in multiuser systems using large antenna arrays at base stations (BSs). While massive MIMO has various advantages, its perfor- mance is known to be limited by pilot contamination. To mitigate the intercell interference resulting from pilot contamination, pilot contamination precoding (PCP) was proposed for downlink transmissions. For PCP, slow-fading coefficients need to be estimated. In this paper, we study the estimation of slow-fading coefficients. We show that slow-fading coefficients cannot be estimated using the conventional uplink training. Thus, we have proposed amplitude modulated pilot sequences, which allow us to estimate slow-fading coefficients without requiring additional orthogonal pilot sequences.

In this paper, we consider the minimum power downlink beamforming problem for a multi-cell system using the decomposition method. Since this problem has coupled constrained, it may not be easy to solve. However, the dual problem can be decomposed and allows to derive a distributed iterative beamforming algorithm for a time-division duplexing (TDD) based multi-cell system. The resulting algorithm does not require any backhaul transmissions between base stations (BSs) through iterations. As a result, it can be seen that the iterative algorithm for distributed beamforming consists of per- cell beamforming and closed-loop power control from users to BSs in a multi-cell system.

Spatial modulation (SM) has been studied extensively to exploit multiple transmit antennas over a multiple-input multiple-output (MIMO) channel. Since one transmit antenna is used at a time in SM, there is no inter-channel interference (ICI), which results in a low detection complexity, while additional bits can be transmitted by the antenna selection. In this paper, we propose the signal dependent antenna (SDA) mapping approach for SM to achieve a mapping gain (at the cost of the throughput decrease) without any conventional channel coding schemes. Based on the performance analysis for SM, we derive design criteria for SDA mapping, and based on them, propose two specific mapping rules for 4 and 8 transmit antennas, which can provide asymptotic SNR gains of 4.77 and 6.98 dB, respectively.

Device-to-device (D2D) communication enables us to spread information in the local area without infrastructure support. In this paper, we focus on information spreading in mobile wireless networks where all nodes move around. The source nodes deliver a given information packet to mobile users using D2D communication as an underlay to the cellular uplink. By stochastic geometry, we derive the average number of nodes that have successfully received a given information packet as a function of the transmission power and the number of transmissions. Based on these results, we formulate a redundancy minimization problem under the maximum transmission power and delay constraints. By solving the problem, we provide an optimal rule for the transmission power of the source node.

Iterative channel estimation and detection (ICED) can provide a better performance as the channel estimation can be improved through iterations. For a multiple‐input multiple‐output channel, owing to a large size of the signal alphabet, ICED becomes less practical unless a low‐complexity detector such as a lattice reduction‐based detector is employed. However, since the lattice basis reduction has to be carried out for each iteration, the resulting complexity can be still high. In this study, a computationally efficient technique was proposed to perform the lattice basis reduction within ICED over both static and slowly time‐varying block‐fading channels, where orthogonal defect of basis, as well as error probability are considered to decide whether or not basis reduction is needed for each iteration.

For iterative detection and decoding (IDD) in multiple‐input–multiple‐output (MIMO) systems, although the maximum a posteriori (MAP) detector can achieve an optimal performance, because of its prohibitively high computational complexity, various low‐complexity approximate MAP detectors are studied. Among the existing MIMO detectors for the non‐IDD receivers, lattice reduction (LR)‐aided detectors can provide a near maximum‐likelihood (ML) detector's performance with reasonably low complexity, and they could be modified to be used in the IDD receivers. In this study, the authors propose a bit‐level LR‐aided MIMO detector whose performance can approach that of the MAP detector, where a priori information is taken into account for soft‐decisions. Furthermore, the proposed method can be extended to large dimensional MIMO systems by channel matrix decomposition and successive interference cancellation, by which a significant complexity reduction can be achieved. Through simulations and complexity analysis, it is shown that a near‐optimal performance is obtained by the authors proposed low‐complexity bit‐level LR‐aided detector for the IDD in the MIMO systems.

In this work, we consider high-rate error-control systems for storage devices using multi-level per cell (MLC) NAND flash memories. Aiming at achieving a strong error-correcting capability, we propose error-control systems using block-wise parallel/serial concatenations of short Bose-Chaudhuri-Hocquenghem (BCH) codes with two iterative decoding strategies, namely, iterative hard-decision decoding (IHDD) and iterative reliability based decoding (IRBD). It will be shown that a simple but very efficient IRBD is possible by taking advantage of a unique feature of the block-wise concatenation. For tractable performance analysis and design of IHDD and IRBD at very low error rates, we derive semi-analytic approaches. The proposed error-control systems are compared with various error-control systems with well-known coding schemes such as a product code, multiple BCH codes, a single long BCH code, and low-density parity-check codes in terms of page error rates, which confirms our claim: the proposed error-control systems achieve good tradeoffs between error-performance and complexity as compared to the traditional schemes and is also very favorable for implementation.

Hierarchical modulation (HM) enables unequal priority transmissions using a signal constellation of non-uniformly spaced constellation points, which is an important property for broadcast systems. Using bit interleaved coded modulation (BICM) and iterative decoding (ID), the performance of HM systems can be further improved. In this paper, we study HM system with BICM-ID or HM-BICM-ID. Since the performance of BICM-ID depends on signal mapping rules, we derive a mapping rule based on distance properties to minimize the bit error rate (BER). Furthermore, we perform the BER performance analysis for HM-BICM-ID based on binary erasure channel (BEC) modeling, which provides BER prediction without time-consuming simulations. Based on the derived BER prediction scheme, we can also perform system design and optimization for applications of HM-BICM-ID. For example, we are able to optimize the constellation priority parameter in a broadcast system to maximize the throughput.

We consider secret key agreement (SKA) over time division duplex for a multi-user wireless system, in which a base station (BS) equipped with a large antenna array remotely generates different secret keys for multiple users in the system. The BS can have the SKA over downlink broadcast channels with precoding based on uplink training from the multiple users. In this case, unfortunately, an eavesdropper can effectively perform a pilot contamination attack (PCA) by transmitting a targeted user's training sequence for the purpose to steer beam direction toward the eavesdropper. As the beam direction becomes under the eavesdroppers control, this PCA can result in information leakage from the BS to the targeted user. In order to fend off this PCA, we derive PCA detectors based on generalized likelihood ratio test and propose a countermeasure. For the performance analysis, we consider the outage probability and show that it decreases exponentially with the number of antennas at the BS, which cannot be achieved by the conventional SKA.

In the past decades, physical layer security has been extensively studied to exploit fundamental capabilities of physical layer such as randomness in wireless channels, signal-to-noise ratio gap, intended jamming, etc., for secure wireless communications. The notion of secrecy rate plays a crucial role in quantifying the transmission rate for secure communications in the presence of an eavesdropper. Wireless sensor networks (WSNs) often require secure communications as the transmissions from sensors to a fusion center are vulnerable to eavesdropping. Although the application of cryptography schemes has been considered in WSNs, most schemes would be too expensive for sensors in terms of computation and energy cost. Thus, it might be necessary to devise physical layer security schemes that exploit properties of wireless channels to avoid eavesdropping with much less computation and energy cost. In this paper, we discuss physical layer security techniques for the WSNs that perform distributed detection. Since the notion of secrecy rate may not be useful in WSNs, we employ the notion of the maximum equivocation in distributed detection to see whether or not perfect secrecy is achievable.

In order to support multiple users simultaneously in a multi-antenna downlink system, the zero-forcing (ZF) precoding and dirty paper coding (DPC) approaches have been proposed. It is well-known that the user selection plays a crucial role in improving the performance of multiuser precoding with ZF and DPC. While the user selection criteria based on the sum-rate maximization are extensively studied for ZF and DPC, it is relatively not well investigated for the vector perturbation (VP) approach, which is known to be near optimal. In this paper, we derive a user selection criterion for VP and a greedy algorithm for the user selection in a multiuser system.

In this paper, we investigate the energy-delay tradeoff (EDT) of data transmissions in wireless butterfly networks consisting of two source nodes, a relay node and two destination nodes. Two network coding (NC) techniques, namely physical-layer NC (PLNC) and analog NC (ANC), are exploited to reduce the number of transmissions at the relay by half. Additionally, hybrid automatic repeat request with incremental redundancy (HARQ-IR) is applied to assure the reliability of data transmissions in wireless network-coded butterfly networks (WNCBNs). By deriving the EDT of both PLNC and ANC schemes in WNCBNs, we evaluate not only their energy efficiency, but also the effectiveness of the relay positions in WNCBNs. It is found that the PLNC scheme is more energy efficient than both the ANC and direct transmission (DT) schemes when the relay is located either at the centre or close to the destinations. In addition, it is shown that both the PLNC and ANC schemes are shown to be less energy efficient than the DT scheme when the relay is located near the sources.

In a multicell scenario, two competitive beamforming design schemes are proposed to maximise the worst signal‐to‐interference‐plus‐noise ratio (SINR) in each cell for downlink transmissions. The first design adopts interference control. The authors use different interference control weighting factors to suppress the inter‐cell interference effectively as each cell may experience different level of inter‐cell interference. The other design is based on power control, where the authors can adjust the power of each base station to improve the system performance. Both the two improved designs keep the distributive nature of the competitive design as well as improve the worst SINR of the system. The existence of Nash equilibrium of the proposed designs is proved and the system overhead is analysed for the comparison with other distributive algorithms. Simulation results show that both the proposed competitive designs have improved performances, and the competitive design with power control outperforms that with the interference control as the transmission power increases.

This study considers successive orthogonal beamforming (SOBF) for multicell downlink transmissions. A group of base stations (BSs) are in cooperation for effective downlink transmissions to cell‐edge users, which is usually referred to as cooperative multi‐point. As the number of cell‐edge users is small and varying, conventional beamforming schemes, for example, zero‐forcing beamforming (ZFBF), are not suitable for the multicell scenario. In the SOBF scheme considered here, the beamforming vectors are found in a successive manner when a new user comes in, so that the beamforming vectors of the existing users remain unchanged. With limited channel state information at the group of BSs, a new successive beam allocation (SBA) scheme is proposed for SOBF. In SBA, the candidate beams are broadcasted to all the users through downlink before the index of the best beam is sent back to the BS by each user. The sum rate performance of a system with two users is studied analytically. The numerical results are presented to show that the proposed SOBF with SBA outperforms ZFBF with conventional feedback strategy.

Recent studies suggest that relay communications can be a major application to extend the range of wireless communications by forwarding the signal from the sender to the receiver. Although fixed or robust distributed relay schemes for single receiver have been previously investigated, the distributed relay beamforming (DRBF) for multiple receivers is studied here. The authors propose an algorithm to design the DRBF weights for maximising the worst received signal‐to‐noise ratio subject to two different types of relay power constraints, which are the total relay power constraints and individual relay power constraints. The authors also prove that the proposed algorithm with relay power constraints results in a quadratic programming optimisation problem, which can be optimally solved by using semidefinite relaxation technology. The simulation results demonstrate an effective gain of the proposed optimal scheme over other intuition schemes.

Energy delay tradeoff (EDT) is a fundamental tradeoff that plays a crucial role in understanding the energy efficiency of various transmission schemes. In particular, for hybrid automatic repeat request (HARQ) protocols, the EDT can be well defined. In this paper, in order to understand the EDT for a wireless multiuser system, we consider various downlink transmission schemes where the HARQ with incremental redundancy (HARQ-IR) protocol is employed for reliable transmissions to users. For a given total power, the impact of different transmission schemes in conjunction with power allocation on EDT is extensively studied. Among the transmission schemes, it is shown that the threshold-based transmission (TBT) scheme efficiently lowers the minimum energy per bit (EB) in spite of limited prior channel state information (CSI) feedback. The study is further extended to a more plausible scenario where users have different channel statistics. In doing so, we consider a proportional fairness scheduling based on relative channel gain that can be easily accommodated in the proposed TBT scheme, and study its EDT behaviors. Performance evaluations show that the schemes incorporating proportional fairness achieve good EDT performances while guaranteeing the fairness among users.

For iterative detection and decoding (IDD) in multiple-input multiple-output (MIMO) systems, the log-likelihood ratio (LLR) of each coded bit can be found by an optimal bit-wise maximum a posteriori probability (MAP) detector. However, since this MAP detector requires a prohibitively high computational complexity, low-complexity suboptimal detectors are desirable. In this paper, lattice reduction (LR)-based MIMO detection is investigated to derive a low-complexity detector that can achieve near MAP performance for IDD. In order to approximate LLR values incorporating the extrinsic information provided by a soft-input soft-output (SISO) decoder, bit-wise LR-based minimum mean square error (MMSE) filters are derived. Furthermore, in order to minimize the performance degradation due to quantization (or rounding) errors in the LR-based detection, a low-complexity integer perturbed list generation method is proposed, where no tree search is used by taking advantage of a near orthogonal channel basis obtained by LR. Through a complexity analysis and simulations, it is shown that the proposed approach achieves near optimal performance, while the complexity is comparable with that of the MMSE soft cancellation method, which is known to be computationally efficient. As a bit-wise detector, a parallel implementation of the proposed method would be straightforward, which lowers the detection delay.

For iterative detection and decoding (IDD) in multiple-input multiple-output (MIMO) systems, although the maximum a posteriori probability (MAP) detector is desirable in terms of performance, it is difficult to be employed due to its prohibitively high complexity as an exhaustive search is used. In this paper, a lattice reduction (LR)-based MIMO detection method is studied to achieve near MAP performance with reasonably low complexity for IDD. The a priori information (API), which is available from a soft-input soft-output (SISO) decoder, is taken into account to generate a list with a randomized successive interference cancellation (SIC) method. More specifically, a joint Gaussian distribution is used to convert the API into the LR domain and a modified sampling distribution, which was originally adopted for near optimal LR-based detection in non-IDD MIMO systems, is derived for random sampling to build a list of candidate vectors of high a posteriori probability (APP) with low complexity. It is shown that the IDD receiver with the proposed method outperforms those with the conventional LR-based methods, where no API is taken into account to build a list. Furthermore, the trade-off between performance and complexity is exploited with varying list length.

Due to increasing energy cost and various environmental issues, energy efficiency becomes an important issue in wireless communications where spectral efficiency has been considered as a key performance measure. If transmission schemes can be flexible to allow varying average transmission delay (or throughput), the energy-delay tradeoff (EDT) can be exploited. In general, the longer (average) transmission delay, the better energy efficiency can be achieved. In this paper, we study various transmission schemes with limited channel state information (CSI) feedback that can have flexible transmission delay in wireless communications, including hybrid automatic repeat request (HARQ) protocols, and derive EDT characteristics over fading channels. An opportunistic transmission scheme that relies on prior CSI feedback is studied for comparisons with HARQ protocols in terms of EDT. In order to improve the energy efficiency of HARQ, a modified transmission scheme that employs prior CSI feedback is proposed and analyzed to understand its EDT characteristics.

Using relays, wireless relay systems can provide reliable transmissions when the distance between a source node (SN) and a destination node (DN) is sufficiently long and the transmission power is limited. In this paper, we study the application of the hybrid automatic repeat request (HARQ) protocol with incremental redundancy (HARQ-IR) to wireless relay systems for energy efficient reliable packet transmissions when a delay constraint is not stringent. The energy-delay tradeoff (EDT) is discussed for one-way and two-way relay systems. It is shown that direct transmissions without a relay node (RN) can be more energy efficient under a short delay constraint (or in the high power regime). On the other hand, in the low power regime (or under a long delay constraint), we can show that decoding at an RN plays a crucial role in improving energy efficiency. In particular, if decoding is performed at an RN in both one-way and two-way relay systems, relay-aided transmissions become more energy efficient than direct transmissions by a factor of greater than or equal to 8 (with a path loss exponent of 3 over Rayleigh fading channels) as the transmission power approaches 0.

Hybrid automatic repeat request (HARQ) protocols can be employed for reliable transmissions of delay tolerant traffics over fading channels. Relay-aided transmissions can offer various advantages over direct transmissions in wireless communications including transmission power saving. In this paper, we consider the energy efficiency of a relaying protocol that is based on the HARQ protocol with incremental redundancy (HARQ-IR) when distributed cooperative beamforming (DCB) is employed in a wireless relay system. It is assumed that multiple relays between a source node (SN) and a destination node (DN) are available for relaying and some of them are selected for DCB. In the proposed relaying protocol, the signal transmission consists of two phases and each phase can have an independent HARQ-IR protocol. It is shown in this paper that although the performance can be improved when the number of selected relays for DCB increases as the diversity gain increases, it is not true in terms of the energy efficiency. For Rayleigh fading channels, closed-form expressions are derived for the average numbers of transmissions for each phase. From them, under a certain energy efficiency criterion, we can find the optimal number of multiple selected relays for DCB to forward signals from relays to DN.

Recently collaborative relay systems have been studied to increase the spectral efficiency and extend the range of wireless communications. In this paper, we consider the collaborative relay beamforming (CRBF) for multiuser systems. In the proposed method, we select base station (BS) antennas for each user and optimize their CRBF weights under two different types of power constraints, which are the total relay power constraint and individual relay power constraints, by exploiting the notion of the maximization of the worst-case received signal-to-interference-and-noise ratio (SINR). The optimization problem of the CRBF weights can be efficiently solved using a semidefinite relaxation (SDR) technique.We derive an iterative CRBF scheme to jointly select the BS antennas and optimize the CRBF weights to maximize the worst SINR among all users. Simulation results validate our theoretical analysis and demonstrate the tradeoffs between the number of BS antennas and the number of relays in the CRBF system.

In this work, we propose a scheme to extract soft information from hard-decision outputs in multi-level per cell (MLC) flash memory based on a cell-to-cell interference model. It will be shown that the soft information extracted in the form of log-likelihood ratio (LLR) by taking into account a dominant cell-to-cell interference term provides significant performance improvements when the error-control system is designed with an error-correcting code with iterative decoding algorithm, e.g. low-density parity-check (LDPC) code with the Belief-Propagation (BP) algorithm. To confirm the claims, we design error-control systems with a conventional Bose-Chaudhuri-Hocquenghem (BCH) code and an LDPC code with/without the soft information extraction. Performances of the systems are extensively evaluated and compared, which clearly shows that the soft information extraction based on the cell-to-cell interference model leads to a considerably better performance.

This paper studies the coordinated multicell downlink transmission to cope with the severe inter-cell interference (ICI) in future cellular systems. We propose an iterative scheme to jointly optimize the base station (BS) and relay beamforming weights to maximize the worst-case signal-to-interference-and-noise ratio (SINR) with perfect channel state information (CSI), under two different types of BS and relay power constraints, respectively, which are the total power constraints and individual power constraints. Using the semidefinite relaxation (SDR) technology, the optimization problems for BS and relay beamforming weights can be converted into semidefinite programming (SDP) problems, which can be effectively solved by interior-point methods. Simulation results demonstrate that the proposed iterative scheme can achieve near-optimal performance within a few iterations and provide a good tradeoff between performance and cost (i.e. the number of BS antennas and relays) in the multicell systems.

Energy delay tradeoff (EDT) is a fundamental tradeoff that can play a crucial role in understanding the energy efficiency of various transmission schemes. For hybrid automatic repeat request (HARQ) protocols, EDT can be well defined. In this paper, in order to understand the EDT for wireless multiuser systems, we consider downlink channels where the HARQ with incremental redundancy (HARQ-IR) protocol is employed for reliable transmissions to users. For a given total power, the power allocation is studied and its impact on EDT is investigated. It is shown that the multiuser diversity (MD)-based power allocation becomes preferable when a long effective delay is allowed.

The performance of downlink transmissions can be improved by using relays that can form a distributed virtual multiple-input multiple-output (MIMO) beamforming system. In this paper, we study a collaborative downlink transmission scheme for a cellular system which consists of a base station (BS), multiple amplify-and-forward (AF) relay nodes, and multiple users. We propose a new algorithm to determine the signal forwarding weights for both transmit power and signal phase with an objective of minimizing power consumption of the relay nodes while guaranteeing the receive signal-to-noise ratio (SNR) at multiple users for the collaborative relay beamforming. We prove that the beamforming solution obtained by a semidefinite relaxation (SDR) technique is optimal with the proposed algorithm. Numerical results validate our theoretical analysis and demonstrate that the proposed optimal scheme can effectively reduce the power consumption of relay nodes compared with other beamforming approaches.

In this paper, an iterative distributed approach is studied for the signal amplitude optimization when the distributed detection is carried out with a symmetric signaling constraint in a wireless sensor network (WSN) consisting of a fusion center (FC) and multiple sensors. In the conventional distributed detection, if sensors'' characteristics change due to aging problems or varying operating conditions, they have to be available at the FC for a best combining result. On the other hand, in the proposed approach, each sensor can adjust its signal amplitude using a type-based multiple access (TBMA) technique to provide a best performance at the FC through an iterative approach without sending their individual characteristics.

We study the energy efficiency for a wireless sensor network, in which multiple sensors having identical information cooperatively transmit signals to a fusion center. To facilitate the cooperation, we propose a transmission scheme consisting of four phases: channel state information acquisition phase, sensor selection phase, beamforming phase, and cooperative transmission phase. Analyses show that there is tradeoff between the energies for sensor selection plus beamforming phases and cooperative transmission phase in deciding the number of sensors to be selected. This observation is captured by numerical and simulation results, which can provide a design guideline for energy saving and prolonging of network lifetime.

In the future cellular communication systems, multiple relay nodes can be used for distributed virtual multiple-input multiple-output (MIMO) beamforming to improve the performance of downlink transmissions. In this paper, we study a distributed relay scheme in cellular systems which consists of a base station (BS), multiple amplify-and-forward (AF) relay nodes, and multiple users. We propose an optimal distributed relay beamforming scheme that maximizes the worst-case received signal-to-noise ratio (SNR) under two different types of power constraints, which are the total relay transmit power constraint and individual relay transmit power constraints. We show that the distributed relay beamforming solution for multiusers can be obtained optimally by solving second-order cone programming (SOCP) problems. Numerical results validate our theoretical analysis and demonstrate an effective gain of the proposed scheme over other conventional schemes.

We consider an overlay mode of an orthogonal frequency division multiple access (OFDMA) based cellular system using orthogonal beamforming to provide broadband wireless access for rural users. In overlay mode, since the signals transmitted to rural users should not interfere with mobile users of higher priority in the OFDMA-based cellular system, the beamforming vectors to rural users have to be orthogonal to the channel vectors to mobile users, which becomes a beamforming constraint for rural users. It is shown that the signal to interference-plus-noise ratio (SINR) can be reasonably high for rural users in overlay mode despite the beamforming constraint and the proposed overlay mode can offer broadband access to rural users.

This paper is concerned with block acknowledgement (ACK) mechanisms in wireless regenerative relay networks. In an N-relay network, a total of (2N+1) block ACK packets is required to acknowledge the data transmission between source and destination nodes via the N-relay nodes. In this paper, the authors propose a block ACK scheme based on network coding (NC) to significantly reduce the ACK overheads by N block ACK packets. In addition, this achieves a reduction of N(N–1) computational operators. Particularly, we derive the error probability of the determination of the packets to be retransmitted at the source and relays, which shows that the NC-based scheme also improves the reliability of block ACK transmissions. Furthermore, asymptotic signal-to-noise (SNR) scenarios for forward links are considered and a general expression of error probability in multi-relay networks is derived for each SNR scenario. Finally, simulation results are presented to verify the analytical findings and demonstrate a lower number of data retransmissions for a higher system throughput.

Multipath routing can offer various advantages for wireless multihop networks. Among those advantages, in this paper, we focus on an advantage of multipath routing for security using the notion of information-theoretic security. We formulate an optimization problem that maximizes the transmission rate from a source node to a destination node with information-theoretic security constraints to avoid eavesdropping that can be carried out at some nodes within a wireless multihop network. Binary erasure channel (BEC) modeling is employed to model wireless links that suffer from fading and interference. It is shown that the proposed throughput maximization problem is a linear optimization problem and the optimal rate allocation can be found by standard optimization techniques. We also study performance analysis including an asymptotic analysis to see whether or not multipath routing (with equal rate allocation) could be secure in terms of an information-theoretic security point of view. Through performance analysis, we can show that multipath routing can provide not only more secure routing against eavesdropping, but also a higher throughput as more multipaths are available.

We introduce a binary erasure wiretap channel of type II in which the number of eavesdropped bits μ becomes available a posteriori. We aim at achieving perfect secrecy over such a channel model. The most appropriate application is a secret key agreement scheme. We present a secret key agreement scheme that adopts the formulation S = HX of Wyner-Ozarows's linear coset coding. The scheme is based on the following simple observation: even if some information on a secret message leaked out, I(S; Xμ) >; 0, where Xμis a binary sequence of length μ, it is still possible to have perfect secrecy I(SJ; Xμ) = 0 for some subsequence SJof S. Our secret key agreement scheme achieves perfect secrecy by taking only those subsequences SJthat are independent of the eavesdropped bits Xμ. Our secret key agreement scheme naturally leads to defining a security measure DH(μ) for parity-check matrices such that the eavesdropper gets zero information on SJas long as the length of SJis less than DH(μ). We study basic properties of DH(μ) and prove the perfect secrecy of our key agreement scheme. For parity-check matrices of small sizes, we perform an exhaustive search for matrices maximizing DH(μ).

ABSTRACT Multiuser diversity beamforming (MDB) is investigated for downlink channels of multiuser multiple input multiple output (MIMO) systems. Since the use of multiple beams simultaneously results in a poor performance of MDB when the number of users is not sufficiently large, the use of single‐beam at a time is considered to avoid performance degradation due to the interference from other beams. Furthermore, in order to share a common precoder by multiple users, modulation division multiplexing (MDM) with a higher order modulation for MDB is discussed. Performance criteria for precoder and user selection are derived under the assumption that the maximum likelihood (ML) detector is used. Copyright © 2011 John Wiley & Sons, Ltd.

Soft-decisions from a multiple input multiple output (MIMO) detector are desirable if iterative detection and decoding (IDD) is employed at receivers in MIMO systems. The minimum mean square error (MMSE)-soft cancellation (SC) detector has an excellent trade-off between performance and complexity. By noting that the MMSE-SC detector is an approximate maximum a posteriori probability (MAP) detector, another approximate MAP detector that uses sequential processing is proposed in this paper. The proposed sequential detector can provide a better performance than the MMSE-SC detector, while its complexity is comparable with that of the MMSE-SC detector. An interesting feature of the proposed sequential detector is that the trade-off between complexity and performance can be exploited by a single parameter, which could be an important control parameter in practical implementations.

Opportunistic beamforming is a promising technique for downlink channels in cellular systems. When opportunistic beamforming is to support multiple users at a time, beams should be formed to avoid interference and this requires the feedback of channel state information (CSI) from users to a base station (BS). With limited feedback, to minimize the impact of interference, the best beamforming matrix can be selected from a set of multiple beamforming matrices. Thus, when a different set of users are to be supported by a scheduler, a new beamforming matrix has to be selected, which may require excessive complexity. To avoid this problem, in this paper, we propose an opportunistic beamforming approach with single beamforming matrix. In addition, we show that the proposed approach suits for a virtual antenna array where multiple BS units form a transmit antenna array.

In this paper, we study the power allocation for multiband coded OFDM systems. With limited feedback, we propose an effective power allocation method across OFDM bands to maximize the throughput and achieve the quality of service target. To facilitate the proposed method, two optimization algorithms based on greedy and dynamic programming principles are discussed. The trade-off between performance and complexity is provided. Simulation results show that the proposed power allocation mechanism allows a signal to noise ratio gain of 2 dB at a goodput of 2.5 bit per second per Hz over the multiband OFDM systems with equal power allocation.

This paper considers block acknowledgement (BACK) mechanisms in wireless regenerative relay networks. Conventionally, a total of (2N+1) BACK packets are required in wireless regenerative N-relay networks to acknowledge the data transmission between the source and destination via the relays. In this paper, based on network coding (NC) we propose an efficient BACK scheme to significantly reduce the required acknowledgement load by N BACK packets. Importantly, by deriving the error probability of the determination of the packets to be retransmitted at the source or relays, it is found that the utilised NC helps improve the reliability of BACK transmissions. Furthermore, some asymptotic scenarios of the forward links are considered and a general expression of error probability in multi-relay networks is derived for each scenario. Finally, simulation results are shown to verify the analytical findings.

Application of network coding in wireless communications has enabled the development of physical layer network coding (PLNC). Applying the principle of PLNC to wireless cooperative networks for spectral efficiency improvement has recently received tremendous attention from the research community. The best relay selection is extensively investigated in the literature for PLNC cooperative networks with multiple relays. However, the authors study the problem of signal beamforming and relay selection for a cooperative bi-directional relay network using PLNC and consider signal beamforming and relay selection for (i) multiple relays each equipped with single antenna and (ii) multiple relays with multiple antennas on uncoded amplify-and-forward scheme for PLNC transmission. The authors derive the optimal design criteria and provide low-complexity sub-optimal solutions for relay selection and beamforming to minimise the symbol error probability.

In bit-interleaved coded modulation with iterative decoding (BICM-ID), the extrinsic information transfer (EXIT) function of the demapper can be parameterised. Using this parameterisation, in conjunction with the EXIT function of the decoder, a symbol mapping design criterion based on the flatness of the gap between the two EXIT functions (called the maximum flatness criterion) is proposed in this study. Simulation results confirm that a good performance can be achieved in terms of convergence rate for fast fading channels and outage probability for slow fading channels.

Distributed beamforming using multiple relay nodes is an effective means to provide power efficient transmission with diversity gain. Various approaches have been proposed to decide relay weights for each relay so that they can cooperatively relay signals. The maximum signal-to-noise ratio (MSNR) has been widely adopted as a performance measure in deciding relay weights. In this paper, we adopt the minimum mean square error (MMSE) to decide relay weights as the MMSE criterion can easily allow distributed implementation with local channel state information (CSI). We also discuss the channel estimation at each relay for distributed beamforming and the impact of the channel estimation error.

We consider data confidentiality in a distributed detection scenario with a type-based multiple-access (TBMA) protocol where a large set of sensors sends local measurements to an ally fusion center (FC) over an insecure wireless medium called the main channel. Then, the ally FC makes a final decision to the physical environment. Although many wireless sensor networks are mission-specific and need data confidentiality due to the broadcast nature of wireless transmission, it can be easily wiretapped by unauthorized enemy FCs through eavesdropping channels. We propose a novel TBMA protocol called secure TBMA which provides data confidentiality by taking advantage of inherent properties of wireless channels, namely randomness and independence of the main and eavesdropping channels. In particular, the secure TBMA activates sensors having strong and weak main channel gains and makes the sensors follow different reporting rules based on the magnitudes of their channel gains. The reporting rules are carefully designed to confuse the enemy FC. The proposed secure TBMA delivers unconditional/perfect secrecy and does not assume any superiority of the ally FC over the enemy FC in terms of computational capability, secret key, and so on. For Rayleigh fading channels, we analyze the performance of the secure TBMA at both enemy and ally FCs by investigating conditions for perfect secrecy and an error exponent of detection error probability, respectively. On the one hand, the analysis at the enemy FC provides a design criterion of the reporting rules to achieve perfect secrecy. On the other hand, the analysis of the error exponent carried out with a Gaussian approximation shows that perfect secrecy is achievable at a marginal cost in detection error performance. All our claims are also verified with simulation results which have good matches with the analysis.

We investigate the secrecy capacity of an ergodic fading wiretap channel when the main and eavesdropper channels are correlated. Assuming that the transmitter knows the full channel state information (CSI) (i.e., the channel gains from the transmitter to the legitimate receiver and eavesdropper), we quantify the loss of the secrecy capacity due to the correlation and investigate the asymptotic behavior of the secrecy capacity in the high signal-to-noise ratio (SNR) regime. While the ergodic capacity of fading channels grows logarithmically with SNR in general, we have found that the secrecy capacity converges to an upper-bound (a closed-form expression is derived) that will be shown to be a function of two channel parameters; the correlation coefficient and the ratio of the main to eavesdropper channel gains. From this, we are able to see how the two channel parameters affect the secrecy capacity and conclude that the excessively large signal power does not help to improve the secrecy capacity and the loss due to the correlation could be significant especially when the ratio of the main to eavesdropper channel gains is low.

Abstract Medium access control (MAC) protocols making use of multipacket reception (MPR) capability achieve better throughput than conventional MAC protocols. When a wireless network operates with MPR capable nodes and non‐MPR nodes, the MAC protocols must not only utilise the MPR capability to maximise throughput, but must also enable the co‐existence with these two types of nodes. We propose a new MPR MAC protocol to achieve the co‐existence requirement by adopting a request‐to‐send (RTS)/clear‐to‐send (CTS) mechanism in IEEE 802.11 MAC standards. This MPR MAC protocol also improves throughput by allowing additional data transmissions to use the MPR capability fully. We analyse the system throughput of the co‐existence of different link characteristics of nodes, and optimise its throughput by adjusting contention window sizes with respect to certain throughput requirements of the nodes. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, we derive user selection criteria based on the error probability for an actually employed detector for multiuser multiple-input multiple-output (MIMO) systems. We propose a low complexity sequential user selection scheme when a lattice reduction (LR) based MIMO detector is used. We also analyze the diversity gain for combinatorial user selection approaches with a given LR-based detector. From simulation results, we can confirm that the proposed sequential user selection approach can provide a comparable performance to the combinatorial ones with much lower complexity.

We investigate the maximum outage throughput for Orthogonal Frequency Division Multiple Access (OFDMA) systems in the presence of feedback channel capacity constraints. We attempt to establish an information-theoretic lower bound on the capacity of feedback channel and build the corresponding test channel that achieves this lower bound. Based on the derived test channel, we formulate the outage throughput maximization problem with the quantized CSI. Since the optimal solutions require an exponential time complexity, we propose a low complexity suboptimal approach that separately performs subcarrier and power allocation. Numerical results show that the proposed suboptimal approach can achieve a near optimal performance, and the outage throughput with a limited feedback of CSI can be close to that with perfect CSI by exploiting correlation properties of downlink CSI for quantization.

Energy saving is the one of the most critical issues in wireless sensor networks (WSNs) due to limited battery power of sensor nodes. In this paper, we propose a channel-aware type-based multiple access (TBMA) scheme for longer lifetime of WSNs. In an effort to minimize data transmission which is the most energy consuming operation in a sensor device, the proposed scheme allows a subset of sensors to be opportunistically activated in TBMA when their channel gains are higher than a threshold broadcasted by the fusion center to satisfy a given detection error performance (DEP). With the assumption of the channel reciprocity in a narrowband time-division duplexing system, the activated sensors by the threshold exploit channel state information and control their transmit power in a way to maximize an error exponent of the DEP. Here, the broadcasted threshold plays a role in minimizing the number of activated sensors, and thereby it is expected to prolong the lifetime of WSNs. We analyze the DEPs of the proposed and a random selection schemes, and our results show that the proposed scheme provides significant energy saving as compared to the random selection scheme especially in the low signal-to-noise ratio regime.

Since wireless sensor networks (WSNs) are vulnerable to threats and attacks due to the nature of wireless communications between sensors and fusion center (FC), it is often necessary to secure transmissions from any possible eavesdropping. In this paper, we study the eavesdropping issue when sensors are over-deployed and the channels between sensors and the ally FC are modeled as time-varying Rayleigh fading channels. We propose a transmission scheme in which only the sensors of strong and weak channel gains report their decisions in a predetermined way based on type-based multiple access protocol. By taking advantage of random behaviors of wireless channels in the form of the multiuser diversity and carefully design different roles of sensors of strong and weak channel gains to the ally FC, the proposed scheme can confuse the enemy FC in making a decision. Eventually, it guarantees the perfect secrecy promised by an information theoretic measure. We also analyze detection error probability and equivocation at the ally and enemy FCs, respectively.

We consider the information exchange between two source nodes with the help of a relay node in a two-way relay network (TWRN). Based on physical-layer network coding (PNC), at the relay node, Viterbi or BCJR algorithms based on a full-state trellis can be used for decoding. Since the relay node only needs to decode XORed message rather than two individual messages in PNC, we show that the decoding can be performed using a reduced-state trellis which results in a lower complexity. It is proved that if the source nodes use identical encoders, based on the reduced-state trellis, the diversity order is kept unchanged, which is then confirmed by simulation results.

This paper attempts to address the effectiveness of physical-layer network coding (PNC) on the capacity improvement for multi-hop multicast in random wireless ad hoc networks (WAHNs). While it can be shown that there is a capacity gain by PNC, we can prove that the per session throughput capacity with PNC is θ (nR(n))-1), where n is the total number of nodes, R(n) is the communication range, and each multicast session consists of a constant number of sinks. The result implies that PNC cannot improve the capacity order of multicast in random WAHNs, which is different from the intuition that PNC may improve the capacity order as it allows simultaneous signal reception and combination.

In distributed detection for wireless sensor networks, multiple access schemes and channel conditions affect the performance of data fusion at a fusion center together with communication constraints (bandwidth and transmission energy). For an efficient channel use, the type-based multiple access (TBMA) has been proposed by Mergen et al. Since the performance of TBMA is not satisfactory for zero-mean fading channels, it could be extended to exploit multichannel diversity for better performance, which is called TBMA with multichannel diversity (TBMA-MD) in this paper. We study the performance of TBMA-MD over Rayleigh fading channels and show that the error probability decays exponentially in the multichannel diversity gain (not in the number of sensors). Error exponents are also derived for binary hypotheses with closed-form expressions for some cases.

We consider a contention based random access protocol for networks with multiple users sharing the same transmission medium. Specifically, communication channel between the base station and each user is modeled by a simplified finite-state Markov model to capture the correlation of the channel evolution in time. Under this scenario, we study a reservation-type protocol in channel aware ALOHA context. We formulate the Markovian model necessitates a state description consisting the outcome of the transmission in each slot. The average throughput of the scheme is obtained using the Markov Analysis technique, and we show that reservation help improve the system performance by reducing the number of collision in transmission. Numerical results show that the proposed scheme always improves the system performance when the user channels are more correlated over the time.

Lattice reduction (LR) based detectors has been investigated for multiple input multiple output (MIMO) systems. For most LR aided detectors, it is assumed that the channel matrix is a square or tall matrix. However, practically there are many cases that the channel matrix is fat which is referred to as the underdetermined/rank-deficient MIMO system. In this paper, we employ the pre-voting cancellation approach and propose the LR-based detectors for underdetermined MIMO systems. It can be shown that the proposed detectors can exploit a full receive diversity. Furthermore, the pre-voting vector selection criteria for the proposed detectors are taken into account to improve performance further.

Multiuser diversity beamforming (MDB) is investigated for downlink channels of multiuser multiple input multiple output (MIMO) systems. In general, the multiple-beam approach for MDB results in a poor performance when the number of users is not sufficiently large. In this paper, we consider a single-beam approach where a common precoder is shared by multiple users using modulation division multiplexing (MDM) of a higher order modulation. Performance criteria for precoder and user selection are derived under the assumption that the maximum likelihood (ML) detector is used.

In this paper, we consider user selection criteria for various multiple input multiple output (MIMO) detectors to exploit the multiuser diversity. It is shown that the user selection criterion plays a crucial role in exploiting both multiuser and receive (or spatial) diversity. We also show that the maximum likelihood (ML) and even some low complexity suboptimal detectors (based on the lattice reduction (LR)) can achieve a full multiuser and receive diversity when the user selection criterion is properly chosen.

We propose a channel-aware type-based multiple access (TBMA) scheme for longer lifetime of WSNs. In an effort to minimize data transmission, the proposed scheme allows a set of sensors to be opportunistically activated in TBMA when their channel gains are higher than a threshold broadcasted by the fusion center. In each transmission, the activated sensors control their transmit power in a way to maximize an error exponent of the detection error performance. We analyze the DEPs of the proposed scheme and show that it provides significant energy saving as compared to the random selection scheme especially in the low signal-to-noise ratio regime where most of power-limited sensors are working.

We study the application of convolutional codes to two-way relay networks (TWRNs) with physical-layer network coding (PNC). When a relay node decodes coded signals transmitted by two source nodes simultaneously, we show that the Viterbi algorithm (VA) can be used by approximating the maximum likelihood (ML) decoding for XORed messages as two-user decoding. In this setup, for given memory length constraint, the two source nodes can choose the same convolutional code that has the largest free distance in order to maximize the performance. Motivated from the fact that the relay node only needs to decode XORed messages, a low complexity decoding scheme is proposed using a reduced-state trellis. We show that the reduced-state decoding can achieve the same diversity gain as the full-state decoding for fading channels.

Abstract In this paper, we investigate multi‐group linear turbo equalization using single antenna interference cancellation (SAIC) techniques to mitigate the intercell interference for multi‐carrier code division multiple access (MC‐CDMA) cellular systems. It is important for the mobile station to mitigate the intercell interference as the performance of the users close to cell edge is mainly degraded by the intercell interference. The complexity of the proposed iterative detector and receiver is low as the one‐tap minimum mean square error (MMSE) equalizer is employed for mitigating the intracell interference, while a simple group interference canceller is used for suppressing the intercell interference. Simulation results show that the proposed iterative detector and receiver can mitigate the intercell interference effectively through iterations for both uncoded and coded signals. Copyright © 2009 John Wiley & Sons, Ltd.

In order to derive low complexity multiple input multiple output (MIMO) detectors, we combine two additional approaches, namely lattice reduction (LR) and list within the framework of the successive interference cancellation (SIC) based detection in this paper. The resulting detector, called the SICList-LR based detector, provides a near maximum likelihood (ML) performance with a significantly reduced complexity. For example, the signal-to-noise ratio (SNR) loss of the proposed detector compared to the ML detector is less than 0.3 dB for a 4 × 4 MIMO systems with 16-quadrature amplitude modulation (QAM) at a bit error rate of 10-3while complexity is about half of that of the conventional LR based detectors.

Modulation Division Multiplexing for Multiuser Diversity Beamforming

In this paper, we propose a feedback based cooperative transmission (FBCT) scheme for multiuser multichannel communication systems. We focus on uplink channels and consider a user, say user 1, who has a weak uplink channel. Another user (who is possibly located somewhere between the base station and user 1), say user 2, can be a relay for cooperative transmission. Using the feedback information of a positive acknowledgment (ACK) or negative acknowledgment (NACK) signaling from the base station, the signal from user 1 can be re-relayed by the relay (i.e., user 2) or re-transmitted by the source (i.e., user 1) in the proposed FBCT scheme. The resulting FBCT scheme can be seen as a hybrid of cooperative transmission and automatic repeat request (ARQ) protocol. We show that a mixed strategy of re-relay and re-transmission of the proposed FBCT scheme can perform better than a pure strategy of re-relay or re-transmission.

In this paper, using diagonal signal repetition with Alamouti code employed as building blocks, we propose a high-rate groupwise space-time block code (GSTBC) which can be effectively decoded by a low-complexity successive interference cancellation (SIC) based receiver. The proposed GSTBC and SIC based receiver are jointly designed such that the diversity repetition in a GSTBC can induce the dimension expansion to suppress interfering signals as well as to obtain diversity gain. Our proposed scheme can be easily applied to the case of large number of antennas while keeping a reasonably low complexity at the receiver. It is found that the required minimum number of receive antennas is only two for the SIC based receiver to avoid the error floor in performance. The simulation results show that the proposed GSTBC with SIC based receiver obtains a near maximum likelihood (ML) performance while having a significant performance gain over other codes equipped with linear decoders.

In this paper, we consider non-orthogonal multiple access (NOMA) over block fading channels where the performance is limited by interference and fading. Re-transmission and interference cancellation techniques can be employed to improve performance. Re-transmissions can provide diversity gain, while successive interference cancellation (SIC) can improve signal to interference ratio (SIR). We can show that the proposed NOMA approach with re-transmission outperforms an orthogonal multiple access (OMA) approach with re-transmission using information outage probability when the signal to noise ratio (SNR) is low. It can also be shown that the outage probability of the NOMA with SIC is lower than that of the OMA when the transmission rate is sufficiently low where SIC can be facilitated.

In this paper, using diagonal signal repetition, we propose a high-rate groupwise space-time block code (GSTBC) which can be effectively decoded by a low-complexity successive interference cancellation (SIC) based receiver. The proposed GSTBC and SIC based receiver are jointly designed such that the diversity repetition in a GSTBC can induce the dimension expansion to suppress interfering signals as well as to obtain diversity gain. Our proposed scheme can be easily applied to the case of large number of antennas while keeping a reasonably low complexity at the receiver. It is found that the required minimum number of receive antennas is only two for the SIC based receiver to avoid the error floor in performance. The simulation results show that the proposed GSTBC with SIC based receiver obtains a near maximum likelihood (ML) performance while having a significant performance gain over other codes equipped with linear decoders.

In multiple input multiple output (MIMO) relay networks, an MIMO node can relay different traffics from multiple MIMO source nodes. We consider the power allocation across multiple layers of MIMO channel when two different traffics, realtime and best-effort traffics, are presented in this paper. Due to different quality of service (QoS) of each traffic, we need to transmit both different traffics separately and simultaneously through multiple parallel channels. We study the power allocation across multiple parallel channels and carry out performance analysis under uncorrelated Rayleigh-fading environments.

For effective transmission over fading channels, the adaptive transmission becomes promising. In particular, the adaptive modulation and coding (AMC) can be used with known channel state information (CSI). In this paper, we consider the stability of an adaptive transmission with AMC and queue. Using the transmission time as a random variable that is characterized by the statistics of fading channel and diversity technique used, we have found that a necessary condition for the stability of the adaptive transmission system is that the diversity order is greater than or equal to three over Rayleigh fading channels.

A signal-to-interference-noise ratio (SINR) constrained power control for code division multiple access (CDMA) and minimum mean square error (MMSE) multiuser detection is investigated. Two scenarios of a CDMA wireless local area network (WLAN) with MMSE multiuser detection are considered, i.e., the centralized power control for the uplink of a cellular WLAN, and the decentralized power control for an ad- hoc WLAN. It is proved that in both scenarios the interference between terminals is standard, so that the convergence of the proposed iterative power control (IPC) algorithm is guaranteed. Assuming asymptotically large number of communicating terminals and asymptotically large processing gain, it is shown that the power control becomes independent of particular spreading sequences, and thus, the random spreading sequences can be assumed. Furthermore, the transmitting powers can be readily adapted while the terminals join or leave the network. Numerical examples confirm that the simple IPC algorithm presented in this paper is very effective for cellular as well as ad-hoc CDMA WLAN's with MMSE multiuser detection for small to medium traffic loads and various SINR's requirements.

The fast retrial random access (FRRA) algorithm has been recently proposed for orthogonal frequency-division multiple access (OFDMA) uplink channels to improve the performance by taking advantage of multiple channels in the frequency domain. In this paper, we study the impact of channel outage on the throughput of FRRA algorithm through extended performance analysis taking into account the effects of both channel outage and packet collisions. Numerical examples show that the channel outage can provide a constructive effect on the throughput of FRRA algorithm.

In this paper, we consider iterative detector/receiver based on single antenna interference cancellation (SAIC) techniques for multicarrier code division multiple access (MC-CDMA) cellular systems. Since the performance of the users close to cell edge is mainly degraded by the intercell interference, it is important to suppress the intercell interference. Using spreading in MC-CDMA, the intercell interfering signals can be detected and cancelled. A better performance can also be achieved if iterative detectors/receivers are used. We propose low complexity iterative detector and receiver using the one-tap minimum mean square error (MMSE) equalizer and a simple group canceller as they are employed at the mobile station.

Transmit antenna diversity without feedback has been widely investigated for various wireless communication systems. Especially, space-time codes are extensively studied to exploit the spatial diversity induced by multiple transmit antennas. Apart from these approaches, there can be different methods that provide a diversity gain using multiple antennas with conventional channel coding. In this paper, diversity eigenbeamforming is studied to exploit the spatial diversity when the channel covariance matrix is available at the transmitter. Diversity eigenbeamforming is based on eigenmode (of the spatial covariance matrix) switching that converts the spatial diversity into the temporal diversity which is exploited by channel coding. Optimized diversity eigenbeamforming is considered to take into account the spatial correlation. In addition, the trade-off between diversity and multiplexing gains is addressed. It is shown that the diversity eigenbeamforming can achieve optimal trade-off for the case of one receive antenna. Although, for simplicity, binary phase shift keying (BPSK) is used to derive diversity eigenbeamforming, any higher-order modulation scheme can also be used with diversity eigenbeamforming.

Among various Bell Labs layered space-time (BLAST) architectures over multiple input multiple output (MIMO) channels, we focus on the horizontal BLAST (H- BLAST) transmission scheme as it can achieve the channel capacity with a simple receiver, namely the successive cancellation (SC) receiver. The power optimization across layers is considered with a short-term power constraint. In addition, for block- fading channels, the rate optimization across layers is studied to maximize the throughput when the instantaneous channel state information (CSI) is not available. Since the CSI is not available, the rates can be greater than the instantaneous channel capacity and error propagation in the SC receiver is inevitable. Thus, the rate optimization plays a key role in reducing the chance of error propagation to maximize the throughput. It is shown that the optimal rates can be obtained recursively and each optimization can be carried out by a one-dimensional search.

In multiple input multiple output (MIMO) relay networks, an MIMO node can relay different traffics from multiple MIMO source nodes. Thus, an MIMO relay node has to allocate the power across multiple channels to relay different traffics. In this paper, we consider the power allocation across multiple layers of MIMO channel when two different traffics, real-time and best-effort traffics, are presented. Generally, realtime traffics have stringent delay constraints and their data streams could not be multiplexed with the data streams from best-effort traffics due to different quality of service (QoS). Since MIMO channels can provide multiple parallel channels, we are able to transmit both different traffics separately (and simultaneously). For efficient power use, we investigate the power allocation across multiple parallel channels in presence of two different traffics with different performance index for each traffic. Performance analysis is carried out for uncorrelated Rayleigh-fading MIMO channels.

For the coherent detection, the detection performance depends on the channel estimation error. To improve the detection performance, we consider the use of the averaged likelihood function to take into account the reliability of the channel estimate. The resulting detection approach, which is called the maximum averaged likelihood (MAL) detection, can be seen as an extension of the generalized likelihood ratio test (GLRT) detection. In this letter, we show the relationship between the MAL detection and other existing detection methods.

Among various Bell Labs layered space-time (BLAST) architectures over multiple input multiple output (MIMO) channels, we focus on horizontal BLAST (H-BLAST) as the channel capacity can be achieved by a simple receiver, which is called the successive interference cancellation (SIC) receiver. For block-fading channels without feedback of channel state information (CSI), which would be a practical case, the rate optimization across layers is investigated to maximize the throughput. Since the error propagation in the SIC receiver can impair the performance, the rate optimization is essential to maximize throughput by properly minimizing the chance of error propagation.

The minimum mean square error with successive interference cancellation (MMSE-SIC) receiver is known to achieve the capacity of multiple-input multiple-output (MIMO) fast fading channels in the presence of knowledge of the channel at the receiver. This paper presents efficient and numerically stable Cholesky decomposition based detection algorithms for MMSE-SIC, exploiting a property of ordering of MMSE-SIC. The proposed algorithms are shown to significantly reduce the computational complexity of existing efficient algorithms for SIC in MIMO flat fading channels.

This paper presents a new ordering criterion for successive interference cancelations (SIC) in MIMO flat fading channels and provides a performance comparison with the original ordering of V-BLAST. Furthermore, an efficient and stable Cholesky decomposition based detection algorithm is proposed to further reduce the computational complexity of existing fast algorithms for SIC in MIMO flat fading channels.

In this paper, we propose a novel iterative detection scheme for multiple-input multiple-output intersymbol interference (MIMO-ISI) channels using soft-decision correlations. We design a soft decision feedback detector (SDFD) combined with an additional probabilistic data association (PDA) detector to exchange the soft decisions in an iterative manner. It is shown that this combined detection scheme outperforms the conventional decision feedback detector (DFD) and approaches the ideal case where feedback decisions are perfectly correct.

In this paper, we derive an iterative receiver for multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. As MIMO-OFDM is considered for future cellular systems, the iterative receiver is investigated when the co-channel interference (CCI) exists. It is assumed that the CCI's are also OFDM signals. Since a joint detection of the desired signal and CCI is difficult, we only detect the desired signal, while the CCI is assumed to be a (colored) noise. This approach can avoid the channel estimation and detection of the CCI and keep the complexity of the receiver low. The proposed iterative receiver estimates the channel impulse response of the desired signal and the covariance matrix of the CCI for the detection based on a generalized expectation- maximization (GEM) algorithm. Through GEM iterations, we can observe the performance is improved.

In this paper, using the turbo principle, different iterative receiver designs are proposed for orthogonal frequency division multiplexing (OFDM) systems under a time-varying channel environment. In the presence of carrier frequency offset (CFO) error, the iterative receiver can improve the performance as better channel estimation and CFO compensation are available through iterations. The mitigation of the CFO is carried out via the two proposed methods - time domain compensation (TDC) and frequency domain cancellation which uses a decision feedback equalizer (DFE-FDC). The complexity of the DFE-FDC method is higher than that of the TDC approach as more operations are required to update the DFE coefficients. However, our simulation results show that the DFE-FDC outperforms the TDC for each iteration.

In this paper, Rayleigh fading channel with an OFDMA system is modeled as a finite-state Markov channel (FSMC) by partitioning the received signal envelope into several intervals. With the aid of sub-band formation and property of lumpability, the size of feedback information can be reduced. This approach involves reducing an exponentially increased states of Markov channel to multiple lumpable Markov channels. The corresponding state transition probabilities and steady-state probabilities are used to predict channel states information in multiple symbol durations ahead. Some simulation examples are presented to illustrate the capability of the lumpable FSMC in channel prediction.

Precoding is a transmission technique for efficient channel utilization with or without channel knowledge. There are various methods for precoding. Among them, linear precoding is generally investigated as it is easy to implement. In this paper, we consider linear precoding when an iterative receiver is used. Due to limited feedback rate, a codebook approach is employed. A criterion to choose a precoding matrix has been derived in conjunction with an iterative receiver

In this paper, we address some performance issues of multiple input multiple output (MIMO) channels with a nulling and cancellation (N/C) detector, which is based on the QR factorization. We find a theoretical bit error rate (BER). With this, an optimization for the bit energy allocation across multiple transmitter antennas has been considered with the proposed optimization criterion. In addition, the concept of multilevel coding has been adopted for optimizing the performance of a multistage receiver with the N/C detector when a channel code is employed. Through simulations, the performance has been compared for coded signals. Both the code rate and energy optimization across layers are shown to be effective means for improving the performance of MIMO systems when the multistage receiver is employed. In addition, it has been shown that the optimization of code rate for each layer is more effective means than that of bit energy.

For coded multiple-input multiple-output spatial multiplexing (MIMO-SM) systems, the iterative receiver consisting of the MIMO detector and decoding can provide near optimal performance. While the sphere detection (SD) technique can be employed to implement the MIMO maximum likelihood (ML) detection with a lower complexity, some modifications of the SD have been proposed to provide a soft-decision for iterative receivers. In the paper, we propose an alternative approach that is based on a quadratic cost function to find the maximum a-posteriori (MAP) solution for the MIMO detection. Using the proposed approach, the MAP detection with the soft-decision can be straightforwardly implemented by the SD technique. Compared to the existing approach, in the new scheme, the soft-decision is well defined and it avoids a numerical instability in computing a soft-decision (which is an approximation of the log likelihood ratio (LLR)). Through simulation results, it is shown that the performance of the proposed scheme is comparable to that of the existing approach.

The iterative receiver for multiple input multiple output (MIMO) channels is investigated with the bit-level cancellation (BLC) in the paper for a modulation of square constellation. The cancellation can be used to reduce the complexity of the MIMO detection by using the extrinsic information from the channel decoder. We show that the BLC can approximate the maximum a posteriori probability (MAP) detection so that the complexity of the MIMO detection can be significantly reduced. The resulting complexity is similar to that of the conventional nulling and cancellation (N/C) detector, while the performance can be close to that of the iterative receiver which can perfectly cancel the interference for 4-ary quadrature amplitude modulation (QAM).

In this paper, we address some performance issues of multiple input multiple output (MIMO) channels with a nulling and cancellation (N/C) detector which is based on the QR factorization. We find a theoretical bit error rate (BER) expression. The concept of multilevel coding has been adopted for optimizing the performance of a multistage receiver with the N/C detector when a channel code is employed. Through simulations, the performance has been studied for coded signals. It has been shown that the optimization of code rate for each layer is an effective means to improve the performance when the multistage receiver is employed.

We investigate the beamformer design for coded signals when the channel matrix is known by the transmitter for a multiple input multiple output (MIMO) channel. A robust beamformer that minimizes the worst pairwise error probability (PEP) of coded signals is considered. It is shown that the beamformer and channel encoder can be separately designed to achieve channel gain (or diversity gain) and coding gain, respectively, in asymptotic cases (for large number of antennas). It is interesting since a joint design of the beamformer and channel encoder is not required.

This paper proposes an approach for the soft-decision in the coded multiple-input multiple-output (MIMO) iterative receiver. A quadratic cost function approximation for the maximum a-posteriori (MAP) detection is considered to apply the computationally efficient sphere decoding (SD) method and a numerically stable approach for the soft-decision is proposed.

For the code division multiple access (CDMA) downlink channel, we investigate the decision feedback equalizer (DFE) at chip-level to effectively suppress multiple-access interference (MAI) when the spreading sequences are orthogonal. The structure of the receiver with the chip-level DFE has been investigated and the minimum mean-square error solution has been derived. Due to the inherent structure of the chip-level DFE, some iterative techniques with hard and soft decisions have been proposed. It is shown that the proposed receivers with the chip-level DFE can provide satisfactory performance. In comparison with the adaptive chip-level linear equalizer, the number of users can be doubled by using adaptive chip-level DFE at a bit-error rate of 10/sup -3/. Throughout the paper, we assume that scrambled orthogonal codes are used for spreading sequences.

A constrained minimum mean square error (CMMSE)-RAKE receiver for multipath fading channels is developed by extending the CMMSE receiver for flat fading channels. Based on the observation that interpath interference causes a bias of the channel estimator in , a receiver that can remove such a bias is proposed, plus a closed-form expression of the bit-error rate of the receiver is derived. Computer simulation is used to demonstrate that the proposed receiver can outperform existing RAKE receivers.

In commercial wideband code division multiple access (W-CDMA), the transmitted signal in the downlink channel is spread by orthogonal codes. However, frequency selective fading destroys the orthogonality, which causes multiple access interference (MAI). Although the RAKE receiver can exploit path diversity, it does not restore the orthogonality and suppress the MAI. As a result, CDMA becomes an interference limited system. The equalization followed by despreader can be adopted to restore the orthogonality and then to suppress the MAI, without significantly increasing the complexity. In this paper, we propose to apply a chip level decision feedback equalizer (DFE) with the Alamouti transmit diversity scheme to restore the orthogonality of the received spreading sequences. Theoretical and simulation results show significant performance gains compared to the chip level linear equalizer (LE) and the RAKE receiver.

We consider the use of transmitter filters as pre-equalizers at the base station to mitigate the interference in code division multiple access (CDMA) systems over multipath fading channels. It is shown that the transmitter filters can mitigate the. interference and thereby the capacity can be improved. The minimum variance distortionless response (MVDR) beamforming technique in antenna arrays is utilized to find the coefficients of the interference mitigation (IM) transmitter filters. To design the IM transmitter filters, we assume that the channel information is available.

The constrained minimum mean square error (CMMSE)-RAKE receivers for multipath fading channels are analyzed. In particular, it is shown that for known channels the output signal-to-interference and noise ratio (SINR) of the CMMSE-RAKE receiver with a single constraint (Type 1) is greater than or equal to that of the CMMSE-RAKE with multiple constraints (Type 2). For Type 1 CMMSE-RAKE receiver, a maximum likelihood (ML) channel estimate is derived. Computer simulation results indicate that CMMSE receivers improve the bit error rate (BER) as compared with the conventional RAKE and currently available adaptive MMSE receivers, and that Type 1 performs the best.

Adaptive linear minimum mean square error (LMMSE) receivers for frequency selective fading channels are developed by modifying the constrained LMMSE receiver of Kim, Jeong and Choi (see IEEE Trans. Commun., vol.48, p.1793-96, 2000). In particular, additional constraints regarding interpath interference are imposed on the constrained optimization proposed by Kim et al., and simple adaptation rules are derived by converting the constrained MMSE problem into an unconstrained optimization. It is shown that the instantaneous SINR of the proposed receiver is always higher than that of the RAKE receiver. Computer simulation results indicate that the proposed receivers can perform better than the existing RAKE and adaptive LMMSE receivers in multipath fading environments.

With a rate policy, we can achieve buffer-constrained rate control for variable bit rate coder. Since the rate policy emphasizes the stability of the state of the buffer, we can loose the advantage of variable bit rate coders, i.e., a (nearly) constant quality/distortion. To overcome the difficulty, the distortion policy is considered in this paper. Based on both the state of the buffer and the distortion, we can formulate the distortion policy. By choosing a proper weighting factor of the cost function in the distortion policy, the state of the buffer can be stable and/or the variation of the distortion sequence can be kept small. In addition, the properties of the distortion policy are obtained. We also consider switching and adaptive methods for the weighting factor to adjust time-varying characteristics of blocks of images. Using a JPEG-like image coder, the distortion policy is applied to buffer-constrained rate control.

In most coding systems, for image or audio signals, the bit allocation or quantization assignment problem is one of the important issues to minimize the distortion for a given bit budget. By exploiting the empirical distortion-quantization and rate-quantization relations, we propose a model based bit allocation method for JPEG-like coders. The empirical relations have been established using the regression technique which provides a low computational complexity to construct the preprocessing for the bit allocation. From the empirical relations, we can establish the modeling for the distortion-quantization and rate-quantization relations. Using the model, we simplify the problem of the quantization assignment and then obtain an efficient method for solving the simplified problem.

The major drawback of the code excitation linear prediction (CELP) coder is computational complexity that finds the best excitation vector from a stochastic codebook. To provide a synthesized speech signal with reasonable quality, the size of the stochastic codebook should be large. For this reason, the search becomes highly complex. To overcome this difficulty, several methods have been proposed. In this paper, we consider a method that enables us to directly determine the stochastic excitation vector without a codebook search. The stochastic excitation vector has been determined by projection onto a subspace that is obtained using the Karhunen-Loeve (K-L) expansion and the spectral property of the random excitation residual vector. Since the excitation vector can be determined without a codebook search, the computational complexity becomes low. From experimental results, it is shown that the proposed coder provides a synthesized speech signal that is quite comparable in quality to that of the conventional CELP coder with low computational complexity.

We propose a stable feedback control algorithm of the buffer state using the controlled Lagrange multiplier in combined rate-distortion characteristics for a buffer-constrained adaptive quantization. The proposed algorithm is established using feedback control theory and is described by the state equation with nonlinearity in the feedback path. The nonlinearity of the state equation comes from the average distortion-rate curve of the information source. The stability of this buffer control algorithm is shown using Lyapunov stability theory. It is shown that the stability of the algorithm depends on average distortion-rate curve and buffer size, and sufficient conditions for the algorithm to be stable are obtained from the usual average distortion-rate curve. In addition, it is observed from experimental results that the performance of the proposed algorithm and the optimal algorithm is not significantly different. Although the proposed algorithm does not provide optimal performance, the algorithm can be implemented easily with quite low computational complexity, as compared with others.