Cell-free massive multiple-input multiple-output (CF-mMIMO) is a promising architecture for 6G wireless networks through distributed access point (AP) cooperation. In large-scale deployments where user demand exceeds system capacity, effective user admission control (UAC) is essential to select users while meeting quality-of-service (QoS) requirements. The UAC problem in CF-mMIMO is inherently challenging, involving both discrete user selection and continuous power allocation variables. To address this challenge, we propose a Graphormer-enhanced Monte Carlo Tree Search (GE-MCTS) framework that integrates a Graphormer-based neural network (NN) with Monte Carlo Tree Search (MCTS). This framework leverages the Graphormer’s capability to model the graph-structured AP–user topology and MCTS’s planning proficiency to efficiently explore the vast decision space. Furthermore, to accommodate users initially unadmitted due to system constraints, we introduce a complementary AP deployment problem. By adapting the GE-MCTS framework, we optimize the placement of additional APs to achieve full user admission with the minimal number of new APs required. Simulation results demonstrate the effectiveness of our proposed framework. For UAC, with low computational complexity, GE-MCTS consistently admits 26.3–41.7% more users compared to baseline methods across various network scales. For AP deployment, our framework requires 45–73% fewer additional APs to achieve full user admission, highlighting its efficiency and scalability.

Blockchain-based steganography enables data hiding via encoding the covert data into a specific blockchain transaction field. However, previous works focus on the specific field-embedding methods while lacking a consideration on required field-generation embedding. In this paper, we propose a generic blockchain-based steganography framework (GBSF). The sender generates the required fields such as amount and fees, where the additional covert data is embedded to enhance the channel capacity. Based on GBSF, we design a reversible generative adversarial network (R-GAN) that utilizes the generative adversarial network with a reversible generator to generate the required fields and encode additional covert data into the input noise of the reversible generator. We then explore the performance flaw of R-GAN. To further improve the performance, we propose R-GAN withCounter-intuitive data preprocessing andCustom activation functions, namelyCCR-GAN. The counter-intuitive data preprocessing (CIDP) mechanism is used to reduce decoding errors in covert data, while it incurs gradient explosion for model convergence. The custom activation function named ClipSigmoid is devised to overcome the problem. Theoretical justification for CIDP and ClipSigmoid is also provided. We also develop a mechanism named T2C, which balances capacity and concealment. We conduct experiments using the transaction amount of the Bitcoin mainnet as the required field to verify the feasibility. We then apply the proposed schemes to other transaction fields and blockchains to demonstrate the scalability. Finally, we evaluate capacity and concealment for various blockchains and transaction fields and explore the trade-off between capacity and concealment. Experimental results demonstrate that R-GAN and CCR-GAN are able to enhance the channel capacity effectively and outperform state-of-the-art works.

Covert communication over public blockchains has emerged as a promising approach for secret data transmission. However, existing methods often suffer from high communication costs, low communication efficiency, and the risk of permanent data exposure. To overcome the above challenges, we propose FastBOC, a hybrid covert communication framework that integrates blockchain and onion networks. In FastBOC, the blockchain is employed as a covert signal channel to transmit lightweight signals, while the onion network handles high-capacity secret data transmission. This decoupling significantly reduces communication costs and avoids permanent data exposure on the blockchain. We further design an address-based encoding scheme and a dynamic port activation mechanism to enhance concealment. We implement FastBOC on the Ethereum testnet and conduct experiments to evaluate its concealment, efficiency, and cost. The results demonstrate that FastBOC (1) achieves strong concealment and (2) can transmit 1-Megabyte (MB) data within 20.11 seconds and reduce communication cost by 5–7 orders of magnitude compared to prior blockchain-based covert communication schemes.

As a new communications architecture, the Space-Air-Ground integrated network (SAGIN) integrates satellites, airborne platforms, and terrestrial networks to enhance global connectivity and support robust and flexible communication capabilities. Efficient task offloading and resource allocation are crucial for SAGIN to meet the quality of service (QoS) requirements at low cost. In this paper, we formulate task offloading and resource allocation as a time-sequential decision-making problem, aiming to maximize task completion within available communication and computational resources. We propose an online approach referred to as graph fusion deep reinforcement learning (GF-DRL). GF-DRL incorporates a graph feature extraction network that utilizes a graph convolutional network (GCN) to extract features from both the task graph and user equipment (UE) graph, along with two attention mechanisms (hard and soft) to merge the two graphs. We also propose an action encoding and mapping network to generate both discrete (offloading) and continuous (allocation) decisions in an end-to-end manner. Simulation results validate the effectiveness of our proposed GF-DRL compared to state-of-the-art task offloading resource allocation approaches.

Blockchain-based covert communication (BCC) provides high reliability and anonymity by embedding secret data into blockchain transactions. However, existing BCC approaches still face three fundamental limitations: (i) data mining risk, since transactions containing the secret data are permanently recorded on-chain and may be detected perpetually; (ii) limited efficiency, as only small payloads (e.g., 256 bits) can be carried per transaction; and (iii) private key leakage, where receivers often need access to the sender’s private key and may incur private key exposure. To address these issues, we propose a novel covert communication model with transaction withdrawal (BCC-TW) and a multisig-based data embedding scheme (MUL-DE). BCC-TW prevents covert transactions from being confirmed by constructing higher-fee double-spend transactions, thereby ensuring that secret data only exists temporarily in the mempool. MUL-DE encodes data into redundant public keys of Bitcoin multisig addresses, thus enabling higher efficiency and avoiding private key exposure. We implement a prototype on Bitcoin testnet and evaluate its concealment and efficiency. Experimental results demonstrate that the proposed approach achieves strong indistinguishability against statistical and deep-learning-based detectors, improves communication efficiency up to 251 bits per public key, and significantly reduces cost compared with state-of-the-art baselines.

The nonintrusive load monitoring (NILM) technique, through its use of various deep neural networks (DNNs), is capable of learning residential appliances’ usage patterns from networked smart meters. However, such learned information may pose a serious privacy risk to users. In response to this privacy concern, in this article, we introduce an innovative adversarial attack. This attack can effectively restrict the NILM models’ ability to dissect power signals while maintaining accurate electricity charges for users. Given that previous adversarial attacks—which are designed for image classifiers and regressors with one-time output—cannot adequately handle NILM models and regressors with time-series output, we formally present the attack objective by leveraging the unique characteristics of regression and time-series data. Our proposed solution algorithms for this attack objective can generate imperceptible perturbations, effectively misleading the prediction of NILM models. To further ensure accurate billing calculation, we refine the attack objective to a practical version and propose a post-process that can iteratively remove the added perturbation in a certain period without compromising attack effectiveness. Experimental results on two real-world datasets, REDD and UK-DALE, demonstrate the effectiveness, transferability, and practicality of our proposed adversarial attack scheme.

The Internet of Things (IoT) system gathers data through diverse smart devices and sensors to make thorough decisions tailored to specific needs. Yet, in intricate IoT setups, privacy infringement occurs through various means like data collection, initial data handling, and data sharing. Therefore, the concealment of data during transmission should receive sufficient attention. The communication approach that merges blockchain technology with covert communication has shown progress in addressing the aforementioned issues. However, this integration has also led to challenges, such as low-data embedding rates and distinctive features in blockchain transactions containing covert data. To seek a solution with high-embedding rates that do not make generated transactions stand out distinctly, this article analyzes the Ethereum transaction field formats, identifies the input data field with high concealment and large capacity as the embedding target, then proposes a data covert transmission scheme based on hybrid embedding in contract fields. This scheme utilizes LSB steganography to embed high-capacity covert data in images, and embeds the URL of the image into the input data field of the Ethereum smart contract transaction, thereby increasing the embedding rates. Subsequently, to further enhance the concealment of this scheme, a data embedding method based on contract relationships is proposed. Through this technique, for the first time, covert data transmission is achieved solely through the invocation relationships of smart contracts within the blockchain covert communication environment, instead of directly embedding covert data into transactions. This method results in transactions that are theoretically indistinguishable from regular transactions, greatly enhancing the security of the scheme. Finally, an evaluation of undetectability, embedding rate, and scalability was conducted for the proposed schemes, concluding that the schemes presented in this article have significant advantages in all three areas.

The rapid development of the Internet of Things has increased the importance of IoT data privacy. Traditional encryption and access control mechanisms are insufficient for ensuring privacy. Blockchain-based covert communication offers enhanced concealment, anonymity, and immutability for secure information exchange over open networks. However, existing blockchain-based schemes face limitations in point-to-point communication and low screening efficiency and concealment, as well as challenges when extended to group scenarios, such as the existence of leakers. To address these issues, we propose a Blockchain-based Group Covert Communication (BGCC) scheme. BGCC leverages broadcast encryption to revoke leakers and introduces an efficient covert filtering mechanism based on the decisional ℓ-BDHE assumption. We prove its concealment through security reduction, statistical tests, and machine learning test. Experimental results demonstrate that BGCC outperforms existing schemes.

Space-Air-Ground integrated network (SAGIN) is a crucial component of the 6G, enabling global and seamless communication coverage. This multi-layered communication system integrates space, air, and terrestrial segments, each with computational capability, and also serves as a ubiquitous computing platform. An efficient task offloading and resource allocation scheme is key in SAGIN to maximize resource utilization efficiency, meeting the stringent quality of service (QoS) requirements for different service types. In this paper, we introduce a dynamic SAGIN model featuring diverse antenna configurations, two timescale types, different channel models for each segment, and dual service types. We formulate a problem of sequential decision-making task offloading and resource allocation. Our proposed solution is an innovative online approach referred to as graphic deep reinforcement learning (GDRL). This approach utilizes a graph neural network (GNN)-based feature extraction network to identify the inherent dependencies within the graphical structure of the states. We design an action mapping network with an encoding scheme for end-to-end generation of task offloading and resource allocation decisions. Additionally, we incorporate meta-learning into GDRL to swiftly adapt to rapid changes in key parameters of the SAGIN environment, significantly reducing online deployment complexity. Simulation results validate that our proposed GDRL significantly outperforms state-of-the-art DRL approaches by achieving the highest reward and lowest overall latency.

Space-Air-Ground integrated network (SAGIN) is a crucial component of the 6G, enabling global and seamless communication coverage. An efficient task offloading and resource allocation scheme is key in SAGIN to maximize resource utilization efficiency, meeting the stringent quality of service (QoS) requirements for different service types. In this paper, we introduce a dynamic SAGIN model featuring diverse antenna configurations, two timescale types, different channel models for each segment, and dual service types. We formulate a problem of sequential decision-making task offloading and resource allocation. Our proposed solution is an innovative online approach referred to as graphic deep reinforcement learning (GDRL). This approach utilizes a graph neural network (GNN)-based feature extraction network to identify the inherent dependencies within the graphical structure of the states. Simulation results validate that our proposed GDRL significantly outperforms state-of-the-art deep reinforcement learning (DRL) approaches by achieving the highest reward and lowest overall latency.

Mobile edge computing (MEC) enables mobile devices (MDs) to offload their computational tasks to the network edge, significantly reducing transmission delay and energy consumption. In this paper, we develop a novel partial NOMA (PNOMA) based task offloading scheme in a multi-layer mobile computing network (MD-MEC-Cloud). PNOMA combines the high throughput of NOMA with the low interference of OMA for efficient, low-latency transmission. Furthermore, the PNOMA-based multi-layer collaborations enable rapid task processing across various computing requirements. We formulate a non-convex mixed-integer optimization problem aimed at minimizing the average delay across all MDs. To address this challenging problem, we propose an algorithm called reincarnating proximal policy optimization (RPPO), which uses online inference solutions to significantly reduce complexity. In addition, we incorporate accumulated apriori information into RPPO for fast retraining and design both a reward function and an evaluation phase to ensure the communication/computation constraints are met with a high probability. Simulation results demonstrate that the proposed task offloading scheme outperforms existing methods.

Blockchain-based steganography enables data hiding via encoding the covert data into a specific blockchain transaction field. However, previous works focus on the specific field-embedding methods while lacking a consideration on required field-generation embedding. In this paper, we propose GBSF, a generic framework for blockchain-based steganography. The sender generates the required fields, where the additional covert data is embedded to enhance the channel capacity. Based on GBSF, we design R-GAN that utilizes the generative adversarial network (GAN) with a reversible generator to generate the required fields and encode additional covert data into the input noise of the reversible generator. We then explore the performance flaw of R-GAN and introduce CCR-GAN as an improvement. CCR-GAN employs a counter-intuitive data preprocessing mechanism to reduce decoding errors in covert data. It incurs gradient explosion for model convergence and we design a custom activation function. We conduct experiments using the transaction amount of the Bitcoin mainnet as the required field. The results demonstrate that R-GAN and CCR-GAN allow to embed 11-bit (embedding rate of 17.2%) and 24-bit (embedding rate of 37.5%) covert data within a transaction amount, and enhance the channel capacity of state-of-the-art works by 4.30% to 91.67% and 9.38% to 200.00%, respectively.

Blockchain-based censorship-resistant systems enable the user to access the blocked content through a covert channel while avoiding a suspicious network connection between the user and the proxy. However, state-of-the-art blockchain-based censorship-resistant schemes cannot satisfy both low communication fees and unobservability, and their method of identifying transactions with covert data may inadvertently expose the covert channel. In this paper, we present Hades, a blockchain-based covert channel framework that aims to circumvent censorship. Hades allows users to encode covert data as a transaction field, and identify transactions with covert data by using another transaction field as a label. We also present the security model for Hades, which defines the unobservability of Hades as the indistinguishability of transactions with covert data from normal transactions. We further propose two cost-friendly and unobservable instantiations of Hades: the basic RDSAC and the improved DDSAC. RDSAC uses private keys to encode covert data and utilizes random factors in the signing process as labels, while incurring a communication delay. DDSAC avoids the delay by encoding covert data into random factors and sampling a transaction amount from normal transactions as the label. We implement a prototype system of Hades and evaluate its performance. Experiment results show that our Hades prototype is unobservable, robust, and efficient. RDSAC and DDSAC can identify 1,654 transactions in 6.054 seconds and 0.071 seconds, respectively. Hades supports 1KB data transfer at $0.44 on the Bitcoin mainnet and cost-free data transfer on the Bitcoin testnet.

Covert channels in blockchain networks achieve undetectable and reliable communication, while transactions incorporating secret data are perpetually stored on the chain, thereby leaving the secret data continuously susceptible to extraction. MTMM (IEEE Transactions on Computers 2023) is a state-of-the-art blockchain-based covert channel. It utilizes Bitcoin network traffic that will not be recorded on the chain to embed data, thus mitigating the above issues. However, we identify a distinctive pattern in MTMM, based on which we propose a comparison attack to accurately detect MTMM traffic. To defend against the attack, we present an improvement named ORIM, which exploits the permutation of transaction hashes within inventory messages to transmit secret data. ORIM leverages a pseudo-random function to obscure the transaction hashes involved in the permutation to ensure unobservability. The obfuscated values, rather than the original transaction hashes, are utilized to encode the confidential data. Furthermore, we introduce a variable-length encoding scheme predicated on complete binary trees. This scheme considerably amplifies the bandwidth and facilitates efficient encoding and decoding of secret data. Experimental results indicate that ORIM maintains unobservability and that ORIM’s bandwidth is approximately$3.7\times $of MTMM.

Network slicing is a key enabler of 5G and beyond networks to satisfy the diverse quality of service (QoS) requirements of different services simultaneously. In network slicing, radio access network (RAN) slicing is essential to establish a functional network slice by connecting mobile devices and mapping virtualized resource units to different slices. This requires a highly efficient resource allocation scheme to maximize resource utilization efficiency and meet the diverse QoS requirements. In this paper, we propose a dynamic RAN slicing model that incorporates multiple distributions to accommodate different user request types and diverse priorities among traffic types in the same slice, where the total available resources are dynamically changing over time. We formulate resource allocation as a time-sequential dynamic optimization problem that takes into account system stability, resource limitation, different timescales, long-term system performance, and user priority. We propose a deep reinforcement learning-based (DRL-based) approach referred to as prediction-aided weighted DRL (PW-DRL) to online infer the power allocation and user acceptance decisions that can maximize a predefined reward function. Additionally, a prediction network is formulated to capture the correlation between current and future states. Simulation results validate that our proposed PW-DRL significantly outperforms state-of-the-art approaches by achieving the highest long-term reward and fastest convergence.

The ever-increasing number of ubiquitous Internet of Things (IoT) applications entails a high demand for scarce communication and network resources. To meet this stringent requirement, mobile edge computing (MEC) is envisioned as a transformative technique to significantly streamline the existing network operations. Recently, device-to-device (D2D) communication has been proposed as a promising technology in 5G and beyond networks with a significantly increased transmission efficiency, especially suitable for small-packet task exchanges. In this paper, we incorporate D2D communication into the multi-layer computing network and propose a full-dimensional task offloading scheme by jointly optimizing task offloading decisions and computation/communication resource allocation. We formulate it as mixed-integer nonlinear programming (MINLP) problem, where the optimal branch-and-bound (B&B) algorithm with the full strong branching (FSB) variable selection policy features an extremely high complexity. To address this challenge, we propose inverse reinforcement learning with graph neural networks (GIRL) to generate a new variable selection policy that closely matches the FSB variable selection. Without sacrificing the global optimality, the GIRL can directly infer the variable selection with a much lower complexity, significantly accelerating the original B&B algorithm. Simulation results show that the GIRL achieves a lower complexity without sacrificing the global optimality. Furthermore, our proposed full-dimensional task offloading scheme achieves better performance than the existing schemes in terms of average delay for all mobile devices (MDs).

Network slicing is key to enabling 6G and beyond networks to simultaneously meet the diverse quality of service (QoS) requirements of various services. In network slicing, radio access network (RAN) slicing is essential to establish a functional network slice by connecting mobile devices and mapping virtualized resource units to different slices. This demands an efficient resource allocation scheme that maximizes resource utilization while meeting diverse QoS requirements. In this paper, we propose a new dynamic resource allocation framework that encompasses three types of services. We formulate a dynamic resource allocation problem that features a mixed action space and has both long-term power and instantaneously available resource unit constraints. We propose a deep reinforcement learning (DRL)-based approach referred to as prediction-aided weighted DRL (PW-DRL), which infers the power allocation and user acceptance decisions to maximize a predefined reward function. Additionally, we propose a prediction network that significantly improves the DRL learning process under limited resources by supplying future state information. Simulation results validate that our proposed PW-DRL significantly outperforms state-of-art DRL approaches by achieving the highest long-term reward and fastest convergence.

Cell-free massive multiple-input multiple-output (MIMO) deploys a large number of distributed access points (APs) without cell edges, offering seamless connectivity with significantly increased spectral efficiency and system capacity, but suffering degraded energy efficiency. In this paper, we develop a green energy scheme by simultaneously optimizing power allocation and AP selection. We formulate it as a non-convex mixed-integer nonlinear programming problem (MINLP), which is NP-hard. To address this challenging problem, we propose a learning-based algorithm that embeds non-convex optimization into contemporary deep reinforcement learning (DRL), referred to as optimization-embedded soft actor-critic with graph transformer networks (OSAC-G). OSAC-G enjoys the benefits of directly online inferring solutions for the non-convex problem with a much lower computational complexity compared to conventional non-convex optimization. Simulation results demonstrate that the green energy scheme significantly decreases energy consumption compared to the existing ones.

The rapid development of Internet of Things (IoT) applications requires efficient computing and communication resource allocation strategies to streamline the existing network operations. These strategies could be formulated as mixed-integer nonlinear programming (MINLP) problems, where the optimal branch-and-bound (B&B) with the full strong branching (FSB) variable selection policy features an extremely high complexity. We propose inverse reinforcement learning with graph neural networks (GNNIRL) to generate a new variable selection policy that closely matches the FSB variable selection. Without sacrificing the optimality, the GNNIRL can directly infer the variable selection with a significantly lower complexity, which is also verified by simulation.

Covert channels serve the construction of cyberspace security. By realizing the secure transmission of data, it is widely used in political and financial fields. Blockchain covert channels have higher reliability and concealment compared to traditional network-based covert channels. However, existing blockchain covert storage channels need to create a large number of transactions to transmit covert information. Creating transactions requires a transation fee, which means that the implementation of blockchain covert storage channels requires a high cost. Besides, created transactions remain on-chain permanently, leading to the threat of covert information being detected. To overcome these limitations, we propose a blockchain covert timing channel framework. Specifically, we utilize inv and getdata messages in the Bitcoin transaction broadcast as carriers and propose three modulation modes to achieve covert channels without cost and leaving no trace. We evaluate the concealment of our modes by K-S, KLD tests, and machine learning approaches. Experimental results show the indistinguishability between traffic carrying covert information and normal traffic. Our channels promise a capacity of 2.4 bit/s.

The booming of the industrial Internet of Things (IIoT) brings an exponential increase in industrial devices, calling for more flexible and low-cost communications. The fifth generation and beyond (B5G) communication technologies provide a dedicated solution by supporting two industry-targeted technologies: Massive machine-type communications (mMTC) and ultra reliable low-latency communications (URLLC). In this article, we design a B5G-aided quality test system in advanced manufacturing, where various sensors are connected to the base station (BS) and send contextual information via mMTC. The BS and quality test machine transmit short length commands and small size feedback to each other, respectively, via URLLC. We formulate a long-term optimization problem to improve the product qualification rate by maximizing the expected average reward with limited testing capacity and changing configurations. To address this problem, we develop a novel context-aware combinatorial quality test (CC-QT) algorithm based on bandit learning (BL), which integrates contextual information to predict the product quality, and a combinatorial method to decrease the complexity of the BL process. Furthermore, we derive a performance upper bound of the proposed CC-QT and analyze its computational complexity. Experimental results illustrate the performance of CC-QT and substantiate its superiority over the existing algorithms.

Integrating mobile edge computing (MEC) in the ultra-dense network (UDN) is a key enabler to meet the service demand by allowing smart devices to perform uninterrupted task offloading via densely deployed MEC servers. In most cases, the smart devices randomly move around the whole network. Consequently, the popular ‘`MEC-centralized decision’' offloading approach could be inapplicable, as joint decision-making among multiple MEC servers becomes difficult due to time synchronization and information exchange overhead. In this paper, we take a user-centric approach to minimize a long-term delay for a given task duration under a price budget constraint. To address this problem, we develop a novel contextual sleeping bandit learning (CSBL) algorithm, which integrates contextual information and sleeping characteristic to accelerate the learning convergence and leverage Lyapunov optimization to deal with the price budget constraint. Furthermore, we extend to a multiple offloading scenario where multiple MEC servers can be selected in each offloading round and propose a CSBL-multiple (CSBL-M) algorithm to address the exponential increase of the offloading selections. For both CSBL and CSBL-M, we derive the upper bounds of learning regret and provide rigorous proofs that they asymptotically approach the Oracle algorithm within bounded deviations for finite task duration.

The industrial Internet of Things (IIoT) interconnects an exponential number of industrial devices, and more flexible and low-cost communications are widely in demand. The fifth-generation (5G) communication provides two industrial-target technologies, massive machine-type communications (mMTC) and ultra-reliable low-latency communications (URLLC), to meet the demand. We design a 5G-aided quality test system, where various sensors are connected to the base station (BS) and send contextual information via mMTC. The BS and quality test machine transmit short-length commands and small-size feedback to each other via URLLC. The problem is formulated as a long-term optimization one with the purpose of improving the product qualification rate. We develop a novel contextual combinatorial quality test (CC-QT) algorithm to solve the problem. We further derive a performance upper bound of the proposed CC-QT and analyze its computational complexity. Experimental results illustrate the performance of CC-QT and substantiate its superiority over the existing algorithms.

The integration of mobile edge computing (MEC) into an ultra-dense network (UDN) can provide ubiquitous task offloading services to computation-demanding users leveraging densely deployed micro base stations. The conventional multi-user task offloading strategies are performed centrally, where a central node makes global task offloading decisions on server selection and resource allocation. In practice, the deployment becomes prohibitively complex with the increasing number of users as it involves high communication overhead and complex global optimization operations. In this paper, we develop a novel decentralized task offloading strategy in UDN, enabling users to independently make local task offloading decisions. We formulate the associated optimization problem to minimize the long-term average task delay among all users. On this basis, we develop a novel calibrated contextual bandit learning (CCBL) algorithm, where users can learn the computational delay functions of micro base stations and predict the task offloading decisions of other users in a decentralized manner. The convergence of the proposed CCBL algorithm is verified via the approachability theory. Moreover, we transfer the target of calibrated learning from all micro base stations to a single user and propose a user-oriented CCBL algorithm to further decrease the computational complexity and increase the convergence rate. Simulation results illustrate that our proposed algorithm outperforms the existing decentralized algorithms and approaches the centralized one.

The rapid development of 5G and Internet-of-Things catalyzes ever-increasing computation-intensive and delay-sensitive applications demanding ubiquitous computation services. Integrating mobile edge computing (MEC) in the ultra-dense network (UDN) is a key enabler to meet the service demand by allowing smart devices to perform uninterrupted task offloading via densely deployed MEC servers. In this paper, we take a user-oriented approach to minimize a long-term delay for a given task duration under a price budget constraint. To address this problem, we develop a novel contextual sleeping bandit learning (CSBL) algorithm, which integrates context information and sleeping bandit theory to handle the fast changing environment and leverages Lyapunov optimization to deal with the price budget. We derive the upper bound of learning regret and provide a rigorous proof that CSBL asymptotically approaches the Oracle algorithm within bounded deviations for finite task duration. Simulation results illustrate that CSBL significantly outperforms existing algorithms.

Envisioned as a promising component of the future wireless Internet-of-Things (IoT) networks, the non-orthogonal multiple access (NOMA) technique can support massive connectivity with a significantly increased spectral efficiency. Cooperative NOMA is able to further improve the communication reliability of users under poor channel conditions. However, the conventional system design suffers from several inherent limitations and is not optimized from the bit error rate (BER) perspective. In this article, we develop a novel deep cooperative NOMA scheme, drawing upon the recent advances in deep learning (DL). We develop a novel hybrid-cascaded deep neural network (DNN) architecture such that the entire system can be optimized in a holistic manner. On this basis, we construct multiple loss functions to quantify the BER performance and propose a novel multi-task oriented two-stage training method to solve the end-to-end training problem in a self-supervised manner. The learning mechanism of each DNN module is then analyzed based on information theory, offering insights into the explainable DNN architecture and its corresponding training method. We also adapt the proposed scheme to handle the power allocation (PA) mismatch between training and inference and incorporate it with channel coding to combat signal deterioration. Simulation results verify its advantages over orthogonal multiple access (OMA) and the conventional cooperative NOMA scheme in various scenarios.

Mobile computing network is envisioned as a powerful framework to support the growing computation-intensive applications in the era of the Internet of Things (IoT). In this paper, we exploit the potential of a multi-layer network via a two-dimensional (2-D) task offloading scheme, which enables horizontal cooperations among the edge nodes. To minimize the average task offloading delay for all the mobile users, we formulate a mixed non-linear programming (MINLP) by jointly optimizing the 2-D offloading decisions and communication/computational resource allocation. To address this very challenging problem, we exploit the unique algorithmic structure of the optimal branch-and-bound (B&B) algorithm, and propose a novel Gaussian process imitation learning (GPIL) method to learn how to discover the shortcut for node searching in the B&B enumeration tree and significantly accelerate the B&B algorithm. When the network key parameters change, we further propose a novel recursive GPIL (RGPIL) method to agilely adapt to the new scenario with a fast policy update, where the new posterior distribution can be recursively updated based on a few new training data. Our simulation results show that the proposed method can achieve a near optimal solution with a significantly reduced complexity (e.g., a reduction of 98.7% in the number of searched nodes for a typical case). On this basis, the advantage of 2-D offloading scheme over the conventional schemes is also verified.

The cooperative relay network is a type of multi-terminal communication system. We present in this paper a Neural Network (NN)-based autoencoder (AE) approach to optimize its design. This approach implements a classical three-node cooperative system as one AE model, and uses a two-stage scheme to train this model and minimize the designed losses. We demonstrate that this approach shows performance close to the best baseline in decode-and-forward (DF), and outperforms the best baseline in amplify-and-forward (AF), over a wide range of signal-to-noise-ratio (SNR) values. It is also shown that training at a list of mixed SNR values can improve the error performance compared to training at a fixed SNR value. Moreover, to verify the robustness of the trained AE model, we test it under the effect of impulse-noise.

Mobile-edge computing (MEC) is a promising technology to support computation-intensive and delay-sensitive applications at smart devices by offloading their local tasks to the network edge. In this paper, we propose a novel index based real-time task offloading policy for an asynchronous large-scale MEC system. We first formulate the policy design as a restless multi-armed bandit (RMAB) to capture the stochasticity and criticality in tasks. Based on the Whittle index theory, we then rigorously establish the indexability of our RMAB and derive a closed-form solution, making it scalable to the number of users and extremely simple to implement in practice. Simulation results show that the propose policy can achieve a significant performance improvement in term of the accumulative reward and completion ratio, compared with some existing policies.

Emerging recently as a novel concept in communication system design, end-to-end learning introduces deep neural networks (NNs) to represent the transmitter and receiver functions. Consequently, the whole system can be interpreted as an autoencoder (AE), which can be optimized from a holistic approach through a data-driven training method. Until now, the AE technique is mainly developed for point-to-point communication scenarios. In this paper, we aim to develop a novel NN-based AE scheme for relay-assisted cooperative communication systems. Specifically, three NN components are constructed to learn the behavior of the transmitter, relay node, and receiver, respectively. As the conventional end-to-end training is inapplicable, a novel two-stage training approach is proposed to indirectly solve the end-to-end training problem. The implicit approximations involved are analytically expressed based on information theory, offering insights on the achievable performance with the proposed training method. The proposed AE model eliminates the need for channel state information and noise variance of any link, and is adaptive to the variation in the input block length. Simulation results verify its advantages over the conventional decode-and-forward (DF) and amplify-and-forward (AF) schemes in various scenarios.

Opportunistic spectrum access (OSA) is envisioned to support the spectrum demand of future- generation wireless networks. In practice, primary channels are usually correlated and network dynamics is unknown a-priori. This entails a great challenge on sensing policy design, and conventional model-based methods are generally inapplicable. In this paper, we propose a novel Gaussian process reinforcement learning (GPRL) based model-free solution to enable the fast sensing policy optimization in OSA. In essence, Gaussian process is embedded in RL framework as a Q-function approximator to efficiently utilize the past learning experience. A novel kernel function is first tailor designed to measure spectrum data correlation. Then a covariance-based exploration strategy is developed to strike a better trade-off between the exploration and exploitation in RL. Our simulation results show that the proposed GPRL can obtain a near-optimal policy with significantly reduced learning period compared with deep reinforcement learning.

Multi-parameter cognition in a cognitive radio network provides a potential avenue to more efficient spectrum usage. In this paper, we propose a two-stage spectrum sharing strategy, where the primary user operates with multiple transmit power levels. Different from the conventional approaches, our method does not require any prior knowledge of the primary transmitter (PT) power characteristics. In the first stage, we use a conditionally conjugate Dirichlet process Gaussian mixture model to capture the multi-level power characteristics inherent in the PT signals, and design a Bayesian inference method to infer the model parameters. In the second stage, we propose a secondary transmitter (ST) prediction-transmission method based on reinforcement learning, which adapts to the PT power variation and strike an excellent tradeoff between the secondary network throughput and the interference to the primary network. The simulation results show the effectiveness of the proposed strategy.

Spectrum sensing in heterogeneous networks is very challenging as it usually requires a large number of static secondary users (SUs) to capture the global spectrum states. In this paper, we tackle the spectrum sensing in heterogeneous networks from a new perspective. We exploit the mobility of multiple SUs to simultaneously collect spatial-temporal spectrum sensing data. Then, we propose a new non-parametric Bayesian learning model, referred to as beta process hidden Markov model to capture the spatio-temporal correlation in the collected spectrum data. Finally, Bayesian inference is carried out to establish the global spectrum picture. Simulation results show that the proposed algorithm can achieve a significant spectrum sensing performance improvement in terms of receiver operating characteristic curve and detection accuracy compared with other existing spectrum sensing algorithm.

Generalized spatial modulation (GSM) activates 'it out of Nt (1 ≤ 'it <; Nt) available transmit antennas, and information is conveyed through 'it modulated symbols as well as the index of the 'it activated antennas. GSM strikes an attractive tradeoff between spectrum efficiency and energy efficiency. Linear precoding that exploits channel state information at the transmitter enhances the system error performance. For GSM with 'it = 1 (the traditional SM), the existing precoding methods suffer from high computational complexity. On the other hand, GSM precoding for 'it ≥ 2 is not thoroughly investigated in the open literature. In this paper, we develop a unified precoding design for GSM systems, which universally works for all 'it values. Based on the maximum minimum Euclidean distance criterion, we find that the precoding design can be formulated as a large-scale nonconvex quadratically constrained quadratic program problem. Then, we transform this challenging problem into a sequence of unconstrained subproblems by leveraging augmented Lagrangian and dual ascent techniques. These subproblems can be solved in an iterative manner efficiently. Numerical results show that the proposed method can substantially improve the system error performance relative to the GSM without precoding and features extremely fast convergence rate with a very low computational complexity. I'idex Terms-

Wideband spectrum sensing for cognitive radio is highly challenging because it needs to locate multiple active spectrum subbands (channels) across a large bandwidth. The high-speed Nyquist sampling involved is either technically infeasible or very expensive in implementation. In this paper, we draw on the recent development in Bayesian machine learning, and propose a new high-resolution wideband spectrum sensing method, referred to as sub-Nyquist assisted matrix sparse Bayesian learning (M-SBL). We first use multicoset sampling to significantly reduce the sampling rate. Then we develop a M-SBL method that carries out Bayesian inference from received spectrum data to learn and iteratively reconstruct a latent variable, whose significant peaks can be used to locate multiple active spectrum subbands. Simulation results indicate that the proposed method significantly outperforms conventional ones in sensing accuracy, especially at low signal-to-noise ratios or with a small number of cosets.

In this paper, we investigate linear precoding for spatial modulation (SM) over multiple-input-multiple-output (MIMO) fading channels. With channel state information avail- able at the transmitter, our focus is to maximize the minimum Eu- clidean distance among all candidates of SM symbols. We prove that the precoder design is a large-scale non-convex quadratically constrained quadratic program (QCQP) problem. However, the conventional methods, such as semi- definite relaxation and it- erative concave-convex process, cannot tackle this challenging problem effectively or efficiently. To address this issue, we leverage augmented Lagrangian and dual ascent techniques, and transform the original large-scale non-convex QCQP problem into a sequence of subproblems. These subproblems can be solved in an iterative manner efficiently. Numerical results show that the proposed method can significantly improve the system error performance relative to the SM without precoding, and features extremely fast convergence rate with very low computational complexity.

Carrier-frequency offset (CFO) estimation for uplink orthogonal frequency-division multiplexing access (OFDMA) systems is very challenging as it requires the estimation of multiple CFOs. In this paper, we propose a new framework referred to as sparse blind CFO estimation for interleaved uplink OFDMA. The proposed framework first discretizes the potential frequency offset ranges into discrete grid points, and formulates the original CFO estimation into a sparse signal recovery problem. Then, a novel two-stage matrix Bayesian compressive sensing-based CFO estimation method is proposed to solve the formulated problem. In the first stage, we employ a relatively large grid interval, and iteratively reconstruct a hyperparameter vector to generate a coarse estimation of multiple CFOs. The second stage reduces the grid interval, and the refined CFOs are estimated one by one through a novel low-complexity one-dimension searching algorithm. Numerical results show that the proposed method significantly outperforms conventional ones in terms of estimation accuracy, especially in the scenarios, such as low signal-to-noise ratios, large CFOs, and a large number of users.

Interference from unknown devices makes time-of-arrival (ToA) estimation using conventional signal processing methods unreliable. In this paper, we propose new ToA estimation techniques based on Bayesian compressive sensing (BCS) to improve the accuracy of the ToA estimation under the interference scenario. Our proposed BCS based ToA estimation schemes maximize the posterior probability of the channel impulse response (CIR) with given frequency domain received signals. Simulation results show that proposed BCS based ToA estimations exhibit significantly improved ToA detection accuracy and mean-squared error performance in interference scenarios. We also demonstrate a practical example of the ToA estimation using real measured indoor channels.

Distributed data storage (DDS) provides a promising approach to the reliable recovery of the whole sensor readings in a wireless sensor network (WSN) by visiting a small subset of sensor nodes. Various DDS schemes based on compressive sensing (CS) have been proposed to reduce the number of transmission/receptions to improve network's area energy efficiency. However, these schemes assume that sensor readings are compressible in the discrete cosine transformation (DCT) domain, whereas our experimental results validate that this assumption cannot be established in a real WSN scenario and the performance of the practical sensor readings recovery will be significantly degraded. To address this problem, this paper proposes a novel DDS scheme termed as practical spatiotemporal compressive network coding (P-STCNC). Our idea is to adaptively train the sparse dictionaries to sparsify practical sensor readings as well as optimize corresponding measurement matrices in both spatial and temporal domains to guarantee accurate data recovery. Simulation results based on real datasets demonstrate the effectiveness of the proposed scheme.

The availability of channel state information (CSI) at the transmitter is crucial to multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems to suppress interference with precoding techniques. In a MIMO-OFDM frequency division duplex (FDD) system, the amount of CSI required by the transmitter increases linear with the number of subcarriers, and therefore becomes prohibitive for a large bandwidth configuration. Conventional schemes based on broadband analog CSI feedback can reduce the overhead, but has limitations to achieve higher spectral efficiency and feedback robustness. In this paper we first propose a novel broadband analog CSI feedback framework for MIMO-OFDM systems based on the distributed compressive sensing (DCS) theory. Then, we further optimize this framework to maximize its feedback benefits. By exploiting the sparse common support (joint sparsity) inherent in MIMO channels and taking advantage of the high efficiency of DCS in encoding original MIMO channels, the proposed framework is capable of significantly reducing the overhead and enhancing the robustness. The numerical results validate the effectiveness of the proposed scheme.

This paper addresses adaptive modulation and power allocation for maximizing the practicable sum capacity (sum of the uncoded throughputs of the users) of a multiuser multiple-input single-output MU-MISO) system. Since the optimal solution, dirty paper encoding (DPC), is complicated to implement, we use the simpler linear precoding based on signal- to-leakage-plus-noise ratio (SLNR). For different choices of constellation sizes, the maximum practicable sum capacities and their corresponding optimal power allocations are obtained via individual non-convex optimization. The best constellation sizes for each user are then identified through a search for the highest maximized practicable sum capacity. Simulations demonstrate the significant performance improvement from this approach compared to existing power allocation schemes. Finally, a selection procedure between different constellation sets is presented to obtain the highest practicable sum capacity while maintaining the instantaneous BER of each user below a target value. This approach allows management of the trade-off between the capacity and error performances.

We study the analog feedback of channel state information (CSI) in the massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) transmission. In a massive MIMO-OFDM system, the amount of CSI required by the transmitter increases linearly with the number of transmit antennas and subcarriers. To address this challenge, in this paper we propose a novel CSI feedback method for massive MIMO-OFDM systems based on the multidimensional compressive sensing theory. Taking advantage of a mathematical advance, the Tucker tensor decomposition, and spatial and frequency correlation of the channel, we reveal the connection between the tensor decomposition model and CS involving multidimensional signals (tensors). Then by making use of this connection, we exploit the structure contained in all different dimensions of the original channel matrix and compress it in each dimension simultaneously, thereby resulting in a large reduction in amount of CSI to be fed back and enabling a significant enhancement of spectral efficiency. The numerical results validate the effectiveness of the proposed scheme.

With recent advances in energy harvesting technology, practical wireless nano-sensor networks (WNSNs) are coming within reach. An important aspect of these WNSNs is that the charge time is significantly longer than each sensor mote can reliably transmit its data-leading to sparse transmission requests in the time-domain. In this paper, we propose a compressed sensing-based approach for efficient request handling. We show that our scheme can achieve near contention free transmission while ensuring that each sensor mote's queue is stable. This sharply contrasts with the unstable sensor mote queues obtained using the standard round- robin approach. To guide design, we also derive closed-form expressions for the average energy consumption, which show that the average energy state of the battery increases exponentially with the transmit power.

In the cooperative multiple-input/multiple-output (MIMO), multiple access point (AP)-user links form a cluster to increase achievable throughput by cooperatively mitigating inter-user interference within the cluster. In this paper, we propose a clustered cooperative MIMO endowing a constraint on the cluster size such that the clustered MIMO can be implemented as a distributed version of downlink multiuser MIMO in existing standards with minimal modifications. New greedy algorithm and coalition formation algorithm are proposed using matching theory for establishing clusters and allocating frequencies. Simulation results shows that the proposed algorithms achieve almost 20% higher throughput than the fixed cell planning scheme, with very low searching complexity.

In a two-way relay network (TWRN), the optimal joint relay selection (RS) and network coding (NC) (O-RS-NC) scheme, which searches all the relay combinations to select a best relay subset, requires high computational complexity and significant amount of feedback. To address this issue, two joint RS and NC (RS-NC) schemes, referred to as a joint single RS and NC (S-RS-NC) and a joint dual RS and NC (D-RS-NC), are proposed based on decode-and-forward (DF) protocol for error-prone TWRNs. Specifically, for the S-RS-NC scheme, a single relay is selected to minimize the sum bit error rate (BER) of the TWRN. The S-RS-NC scheme is simple to implement, but it suffers from a relatively large signal-to-noise ratio (SNR) loss compared to the O-RS-NC scheme. To reduce the SNR loss, a D-RS-NC scheme is proposed. In the D-RS-NC scheme, one or two relays are selected to minimize the sum BER of the network. Because the source and relay transmission powers (ESand ER) have different impacts on the equivalent SNR of the TWRN, the RS criterion is designed based on different ratios of ESand ER. BER lower-bounds for these schemes are derived and verified by simulations to be tight asymptotically. Both analytical and simulation results show that the proposed RS-NC schemes are superior to the conventional RS without NC scheme when 2ES>ER. In most practical applications, for example, a wireless sensor network, all the nodes transmit at the same power, where the proposed RS-NC schemes perform better than the conventional RS without NC scheme.

Relays enabled with multiuser MIMO techniques have great potential to mining vehicle safety applications. However, they are yet to be practical due to high scheduling overhead in mobile, radio-unfriendly, mining environments. A new decentralized relay-assisted multiuser MIMO approach is proposed, which cuts the overhead by 80% and enables relay-assisted multiuser MIMO to be implemented in practice. This approach is a new distributed participatory downlink transmission method, where both the relays and destinations participate in the scheduling decisions. A new recursive algorithm is also developed to optimally quantize the channel conditions of the vehicles, thereby minimizing the feedback requirement. Analytical results, confirmed by simulations, show that the proposed approach is able to achieve 97.6% of the sum-rate upper bound of the network, using only three bits to characterize the channel condition of each vehicle. In terms of throughput, the proposed decentralized scheme can perform 45.2% better than the existing centralized scheme. The proposed approach is compatible with industrial communication standards and can be implemented with commercial industrial communication systems.

Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband system operating with high mobility is very challenging. This is mainly due to the significant Doppler spread, inherent in a time-frequency doubly-selective (DS) channel. Consequently, a large number of channel coefficients must be estimated, forcing the need for allocating a large number of pilot subcarriers. To address this problem, we propose a novel channel estimation method based on basis expansion models (BEMs) and distributed compressive sensing (DCS) theory. To be specific, we develop a two-stage sparse BEM coefficients estimation method, which can effectively combat the Doppler spread and enable accurate channel estimation with dramatically reduced number of pilot subcarriers. The numerical results reveal that, in a typical LTE system configuration, the proposed scheme can increase the spectral efficiency by 40% and achieve a 6 dB gain in terms of normalized mean square error (NMSE), both compared to the conventional scheme.

Carrier-frequency offset (CFO) estimation for orthogonal frequency-division multiplexing access (OFDMA) systems operating in multiuser uplink transmission is very challenging due to the presence of a multiple-parameter estimation problem. In this paper, we propose a novel blind CFO estimation method for interleaved OFDMA uplink based on distributed Bayesian compressive sensing (DBCS) theory. Considering the received signal structure, the new method first constructs a measurement matrix associated with a sparse signal matrix weight, which sets up the stage for the application of CS theory in tackling the original estimation problem. Then, the DBCS theory that exploits a common sparse profile of the sparse signal matrix weight is employed to distributively estimate a sparse hyperparameter vector, whose significant peaks are linked to the correct estimation of the multiple CFOs. Compared with the existing subspace theory based methods, the proposed scheme offers a significant enhancement in estimation accuracy, in specific in the low signal-to-noise ratio (SNR) region. The numerical results validate the effectiveness of the proposed scheme.

Coordinated Multi-Point (CoMP) for Long Term Evolution Advanced (LTE-A) systems refers to a range of techniques to increase the capacity averaged over the cell, and also at the cell edge where the path loss is usually highest. In this paper, the problem of power allocation is first addressed for maximizing the sum capacity at each point of the coverage area in a CoMP multi-user downlink. Then, based on targeting the minimum value of the maximized sum capacity, a design approach is presented for obtaining an optimal size of the cells in a wireless network. The approach guarantees that the sum capacity at each point of the coverage area is above the target value. The path loss model is pivotal for the outcome of this type of performance analysis and the ensuing system design. Here, a simplistic, standard path loss model is used, but the approach can use other models.

Limited feedback precoding (LFP) significantly improves multiple-input multiple-output (MIMO) spatial multiplexing link reliability with a small amount of feedback from the receiver back to the transmitter. One of the key problems linked to LFP is how to select an optimal precoder from a pre- determined unitary codebook. We find that the conventional precoder selection criteria are not applicable to the stream maximization transmission (SMT) mode with linear receivers, including zero forcing (ZF) and minimum mean square error (MMSE) decoders. To solve this issue, a novel singular value decomposition (SVD) based precoder selection criterion is proposed in this paper. This criterion features a unified structure for all the linear receivers such as ZF and MMSE decoders, and is shown by simulation to provide significant coding gains in various SMT systems. With the same complexity as the conventional one, the proposed criterion could find its applications in next generation systems employing SMT spatial multiplexing, significantly improving system performance with affordable feedback requirement.

Generating and feeding back beamforming vector are very challenging tasks in distributed transmit beamforming (DTB) systems. Phases of DTB nodes may vary rapidly due to residual carrier frequency offset and hence frequent updating of beamforming vector is required. Existing iterative training schemes that only require one bit training and one or two bits feedback in each iteration have low convergence speed and are not robust in noisy channels due to the lack of structure in the training sequences. In this paper, we consider a DTB system where the number of training bits N sent from each node is no more than the number of source nodes M, and propose a concatenated training scheme based on optimal design of training sequences in this case. For spatially uncorrelated channels, we show that the concatenated training scheme can optimally combine the N latest training signals and achieve beamforming gain approximately proportional to N/M. An algorithm which can adaptively determine the length of the combination in time-varying channels is also proposed. Simulation results demonstrate the proposed scheme can work efficiently even at very low signal-to-noise ratio, with the total feedback bits much less than those required in the iterative schemes.

Channel estimation for an orthogonal frequency-division multiplexing (OFDM) broadband system over a doubly selective channel is very challenging. This is mainly due to the significant Doppler shift, which results in a time-frequency doubly-selective (DS) channel. The DS channel features a large number of channel coefficients, which introduces inter-carrier interference (ICI) and forces the need for allocating a large number of pilot subcarriers. To tackle this problem, in this paper we propose a novel channel estimation scheme based on distributed compressive sensing (DCS) theory. Taking advantage of the basis expansion model (BEM) and the channel sparsity in the delay domain, we transform the original DS channel into a novel two-dimensional channel model, where several jointly sparse BEM coefficient vectors become the estimation goal. Then a special decoupling form originating from a novel sparse pilot pattern is designed for such estimation, which results in an ICI-free structure and enables the DCS application to make joint estimation of these vectors accurately. Combined with a smoothing treatment process, the proposed scheme can achieve significantly higher estimation accuracy than the existing ones, although with a much smaller number of pilot subcarriers. Theoretical analysis and simulation results both confirm its performance merits.

Channel matrix inversion, which requires significant hardware resource and computational power, is a very challenging problem in MIMO-OFDM systems. Casting the frequency-domain channel matrix into a polynomial matrix, interpolation-based matrix inversion provides a promising solution to this problem. In this paper, we propose novel algorithms for interpolation based matrix inversion, which require little prior information of the channel matrix and enable the use of simple low-complexity interpolators such as spline and low pass filter interpolators. By invoking the central limit theorem, we show that a Gaussian approximation function well characterizes the power of the polynomial coefficients. Some low-complexity and efficient schemes are then proposed to estimate the parameters of the Gaussian function. With these estimated parameters, we introduce phase shifted interpolation and propose two algorithms which can achieve good interpolation accuracy using general low-complexity interpolators. Simulation results show that up to 85% complexity saving can be achieved with small performance degradation.

Distributed transmit beamforming (DTB) can significantly boost the signal-to-noise ratio (SNR) of a wireless communication system. To realize the benefits of DTB, generating and feeding back beamforming vector are very challenging tasks. Existing schemes have either enormous overhead or weak robustness in noisy channels. In this paper, we investigate the design of training sequences and beamforming vector estimators in DTB systems. We consider an under-determined case, where the length of training sequence N sent from each node is smaller than the number of source nodes M. We derive the optimal estimation of the beamforming vector that maximizes the beamforming gain and show that it can be well approximated as the linear minimum mean square error (LMMSE) estimator. Based on the LMMSE estimator, we investigate the optimal design of training sequences and propose efficient DTB schemes. We analytically show that these schemes can achieve approximately N times increased SNR in uncorrelated channels, and even higher gain in correlated ones. We also propose a concatenated training scheme which optimally combines the training signals over multiple frames to obtain the beamforming vector. Simulation results demonstrate that the proposed DTB schemes can yield significant gains even at very low SNRs, with total feedback bits much less than those required in the existing schemes.

In a wireless multi-hop relay network, the optimal routing scheme with exhaustive path search entails high computational complexity and large storage requirement, and is impractical for a large number of hops. In this paper, we propose a suboptimal path selection scheme, based on amplify-and-forward (AF) protocol, that has outage performance close to the optimal routing scheme, but with much less complexity. The proposed scheme draws on the analogy between the node distribution of a commonly used relay network model and the trellis of a convolutional code, and applies the Viterbi algorithm in selecting a path to maximize the end-to-end signal-to-noise ratio (SNR). In specific, the relay network topology is first mapped to the trellis diagram of a convolutional code. In the trellis, the branch metric is defined as the inverse of the instantaneous SNR of the channel connecting two relays in two adjacent clusters. Consequently, the path metric is equal to the inverse of the equivalent SNR of the path. Then, the sliding window Viterbi algorithm is used to select a path from the source to the destination. Simulation results show that when the window size is five times the total encoder memory or more, the proposed routing scheme achieves near optimal outage performance. The proposed scheme has a polynomial complexity and low communication overhead. Therefore, it is very efficient for relay networks with a large number of hops.

Channel matrix inversion, which requires significant hardware resource and computational power, is a very challenging problem in MIMO-OFDM systems. Casting the frequency-domain channel matrix into a polynomial matrix, interpolation-based matrix inversion provides a promising solution to this problem. In this paper, by showing that the polynomial coefficients can be well approximated by a Gaussian function, we propose an efficient algorithm, which relaxes the requirement for knowing the maximum multipath delay spread and enables the use of simple low-complexity interpolators by introducing a phase shift term to the signal to be interpolated. Simulation results show that significant complexity saving can be achieved with little equalization performance degradation.

Multi-user multiple-input multiple-output (MU-MIMO) has a potential to realize cost effective high data rate internet access to the homes in rural areas. We propose the novel Ngara Access system where the central access point (AP) is equipped with a uniform circular array (UCA) installed on a high tower while each UT is equipped with a directional antenna free of clutter, providing predominantly line-of-sight (LoS) channel environment. Using a three dimensional geometric optics based channel model, we provide bit error probability simulation results which show that the spectral efficiency of the proposed system can be improved linearly as a function of the number of antenna elements at AP, without increasing the total transmitting power, provided a half wavelength antenna spacing is maintained and user groups of four or more are used to avoid ill-conditioned channel. Hardware demonstrators based on the proposed system have achieved the system spectral efficiency of 20 bits/s/Hz in an actual rural environment and of 67 bits/s/Hz in a laboratory environment at a lower UHF band.

Channel prediction, which predicts a future channel gain based on current and past observations, is very useful for power control and resource optimization in wireless communication systems. However, a low-complexity predictor with trustworthy prediction accuracy is yet to be developed. This paper proposes a linear predictor and two linear Markov predictors, which achieve a good balance between complexity and accuracy.

We investigate a channel-coded physical-layer network coding (CPNC) scheme for binary-input Gaussian two-way relay channels. In this scheme, the codewords of the two users are transmitted simultaneously. The relay computes and forwards a network-coded (NC) codeword without complete decoding of the two users' individual messages. We propose a new punctured codebook method to explicitly find the distance spectrum of the CPNC scheme. Based on that, we derive an asymptotically tight performance bound for the error probability. Our analysis shows that, compared to the single-user scenario, the CPNC scheme exhibits the same minimum Euclidean distance but an increased multiplicity of error events with minimum distance. At a high SNR, this leads to an SNR penalty of at most ln2 (in linear scale), for long channel codes of various rates. Our analytical results match well with the simulated performance.

In this paper, we analyze the error performance of the mutual information based forwarding (MIF) scheme for a memoryless parallel relay network in additive white Gaussian noise (AWGN) channels. The analytical expression for soft noise variance is first derived. Note that in the literature, the exact soft noise variance could only be evaluated by Monte Carlo simulation due to the lack of its analytical form. The derived soft noise variance expression only relies on the transmit signal-to-noise ratio (SNR), without the need to have the knowledge of actual or estimated information bits. With the expression of the soft noise variance, we derive an approximate bit error rate (BER) expression for a parallel relay network employing MIF scheme. The derived soft noise variance and system BER expressions are shown to be in tight match with Monte Carlo simulation results.

In this paper, we investigate two different multi-hop bi-directional relay transmission schemes based on amplify-and-forward (AF) protocol and analogue network coding (ANC). In the first scheme, referred to as the AF-ANC-Central scheme, AF is employed at each of the intermediate nodes, while ANC is only utilized at the central relay. In the second scheme, referred to as the AF-ANC-Even scheme, the even relays perform ANC and the odd relays only perform subtraction and AF. For reference purpose, the AF-No-ANC scheme is also considered, where the intermediate relays only perform AF and no ANC is employed. Bit error rate (BER) lower bounds of these three schemes are obtained, and are verified by Monte Carlo simulations to be asymptotically tight ones at high signal-to-noise ratios (SNRs). It is shown that the combination of AF and ANC is able to significantly improve system throughput when compared with the AF-No-ANC scheme. The BER performance of the AF-ANC- Central and AF-No-ANC schemes are of the same at high SNR region under the same transmission powers and system configuration, but the AF-ANC-Central scheme is able to double the system throughput. The AF-ANC-Even is able to further increase the system throughput with an SNR loss upper bounded by 1.76 dB when compared to the AF-ANC-Central and AF-No-ANC schemes.

We investigate joint channel and physical layer network coding (CPNC) for Gaussian two-way relay channels. The two users' messages are encoded using the same binary linear code and are transmitted simultaneously with equal power. At the relay node, the network-coded message is recovered directly from the received signal sequence, and is then broadcast to the users. We propose a new methodology to explicitly find the distance spectrum of the coding scheme. Based on that, we analyze the error probability at the relay and derive an asymptotically tight performance bound (for high SNRs). We show that, with a general binary linear code, the CPNC scheme is subject to an asymptotic SNR loss of approximately ln 2 relative to the single-user case, regardless of the coding rate. Numerical results show that our analysis matches very well with the performance of the CPNC scheme.

In wireless mesh networks, it is desirable to utilize overlapping coverage of multiple parallel relays to assist the source-destination transmission. In this letter, we consider selection diversity (SD) to select the strongest link amongst the direct and N amplify-and-forward (AF) relay links. We derive new closed-form expressions for the symbol error rate (SER) in independent but not necessarily identically distributed (i.n.d.) Rayleigh fading relay channels. Our results are given as both lower bound and asymptotic expressions based on an accurate upper bound on the signal-to-noise ratio (SNR) of the relay links. Our asymptotic results provide key performance parameters such as the array gain and diversity order, which prove that a full N+1 diversity order is achieved. We show that SD can offer an array gain advantage over maximal-ratio combining which entails all the relays to transmit. Numerical results are shown to validate the analysis.

In wireless dual-hop decode-and-forward (DF) relaying networks, multiple relay stations (RS) and users construct a DF broadcast channel (DFBC). Due to unpredictable decoding failure, scheduling in the DFBC should depend on not only channel qualities but also the availability of error-free data at individual RSs. Based on the reception qualities of RSs and channel quality information (CQI), we propose a new centralized scheduling scheme to maximize the sum rate of the DFBC. An exact closed-form expression for the sum rate is derived to analyze the new scheme. To further facilitate the analysis, the bounds of the sum rate are derived as a computationally effective alternative to the exact expression. Simulations demonstrate that the derived closed-form expression quantifies the proposed scheduling method accurately. It is also revealed that the analytical bounds are able to characterize the performance of the new scheduling method with acceptable accuracy.

In this paper, we analyze the bit error rate (BER) performance of a single relay selection with network coding (S-RS-NC) scheme in two-way relay channels. In this scheme, two source nodes first broadcast their information to the relays sequentially. A single relay which optimizes the system performance is selected. The selected relay decodes the received signals from two sources, performs network coding on two symbol estimates and then forwards them to two source nodes. Equivalent signal-to-noise-ratio (SNR) of the whole source-relay-destination link is analyzed and its high SNR approximation is derived. Closed form BER expressions are then derived and the results are verified through Monte-Carlo simulations which show that the derived analytical expressions offer a tight bound for the average BER. It is shown that this S-RS-NC scheme can achieve a full diversity order as if all relays were used. Simulation results show that the S-RS-NC scheme can bring considerable gains over the all-participation relaying scheme when the source transmission power is larger than or equal to the relay transmission power. However, when the relay transmission power is greater than the source power, the single relay selection scheme could be inferior to the all-participation relaying scheme. This is quite different from the conventional relay selection scheme in one way relay network, where the relay selection always outperforms the all participation relaying scheme.

In wireless dual-hop decode-and-forward (DF) relaying networks, multiple relay stations (RS) and users construct a multiple-in-multiple-out (MIMO) broadcast channel (BC). Due to unpredictable decoding failure, scheduling the transmission for the decode-and-forward broadcast channel (DFBC) should depend on not only channel qualities but also the availability of errorless data at individual RSs. Based on the reception qualities of RSs and channel quality information (CQI), we propose a new centralized scheduling scheme to maximize the sum rate of the DFBC. An exact closed-form expression for the sum rate is derived to analyze the new scheme. Simulations demonstrate that the derived closed-form expression is able to quantify the proposed scheduling method accurately. It is also revealed that extra cooperative diversity can be exploited by employing the increased number of RSs, thereby improving sum rate.

Distributed turbo coding (DTC) has been shown to be able to approach the information-theoretic capacity of wireless relay networks. However, decoding errors at the relay can lead to severe error propagation and hinder the achievement of such capacity. In order to increase the decoding success rate at the relay, thereby enhancing the DTC system performance, we propose a novel DTC scheme, referred to as nested DTC, for a two-hop triangular relay network. In this scheme, we use a turbo code instead of a single convolutional code as does in the conventional DTC. At the source node, the four output streams of the two component convolutional encoders are converted into two streams by adding in modulo-2 addition. An extended 2-D log MAP decoding is used at both relay and destination to retrieve the information symbols. Numerical results show that the error performance of the proposed nested DTC scheme significantly outperforms the existing DTC schemes and performs within only 1 dB of the DTC technique with automatic repeat request (ARQ). The performance superiority of the nested DTC scheme is attributed to the retention of the full information due to the nested encoding structure.

Wireless decode-and-forward (DF) relay networks suffer from severe latency due to multi-hop propagation. When hybrid ARQ (HARQ) is employed, the latency leads to the throughout degradation of individual HARQ processes because of the decreased number of retransmissions of unsuccessful packets in a given time. We propose two new distributed cooperative HARQ protocols to not only reduce latency but also optimize throughput in both forward and reverse DF links. Additional selection diversity from multiple packets is exploited to compensate for the throughput loss stemming from the less powerful source in the reverse DF link. Based on the 1st hop reception quality, each relay station (RS) independently forwards the packet in such a manner that, from the perspective of the destination, one of the three cooperative relaying modes is effectively formed with the highest instantaneous throughput: spatial multiplexing (SM), space-time transmit diversity (STTD) and unicast (UC). The maximal throughput can be achieved with minimal latency. The superiority of the proposed approaches has been demonstrated in terms of the optimal spectral efficiency and significant reduction in latency. Compared to the centralized approaches, the reduction is up to 40% at high signal-to-noise ratio (SNR) and the throughput of individual HARQ processes increases as a result.

When multi-user multiple-input multiple-output (MU-MIMO) is applied to predominantly line-of-sight (LoS) environments, such as in the case of fixed wireless access in rural areas where a central access point (AP) equipped with an antenna array with NAPantenna elements serves NUTuser terminals (UTs) each equipped with a single antenna, the problem of ill-conditioned channels arises. This paper investigates the performance of zero-forcing preceding based MU-MIMO downlink when the AP is equipped with a uniform circular array (UCA) in an LoS environment. The performance is analyzed as a function of the spacing and the number of AP UCA antenna elements for NAP≥ NUT. The analysis reveals a complex yet orderly pattern of the performance nulls indicating different optimal antenna spacing for different number of antenna elements. The performance nulls can be largely eliminated by employing NAP≥ 2NUT.

Decentralized multiple access networks require dynamic spectrum allocation to efficiently and fairly allocate resources among multiple users. In this paper, we consider the problem of spectrum allocation from the standpoint of diversity combining, and in particular as an explicit case of selection combining (SC). General order selection allocation (GOSA) was previously proposed by the authors as a low-complexity spectrum allocation scheme for decentralized multiple access networks. In this paper, noting that previous analytical results on the error performance of GOSA are for independent identically distributed (i.i.d.) Rayleigh fading, we carry out a thorough and exact analysis of GOSA for the i.i.d. Nakagami-m fading scenario. In particular, based on new results on the exact and asymptotic average error probability of the r-th order statistic, we obtain exact and asymptotic closed-form expressions for the error performance of GOSA. Numerical results show that the performance of the algorithm is close to that of the highly complex optimal search method.

In this paper, we consider a general two-hop relay network and propose a distributed turbo coding with selective relaying (DTC-SR) scheme to improve the performance of relayed transmission. In the proposed scheme, each relay adaptively selects an amplify and forward (AAF) or a decode and forward (DAF) protocol based on whether it can decode correctly or not. Among all the relays, a single relay, which has the maximum destination SNR, is selected for transmission. If the selected relay uses the DAF protocol, it decodes the received signals, interleaves, re-encodes and forwards them to the destination. At the destination, the signals directly transmitted from the source and that from the selected relay form a distributed turbo code (DTC). If the selected relay uses the AAF protocol, it just simply amplifies the received signal. The destination then combines the signals transmitted from the source and the relay. Simulation results show that the DTC-SR can take advantages of both distributed turbo coding and relay selection, providing not only a considerable SNR gain contributed from the relay selection, but also a coding gain contributed from the distributed turbo coding. And these gains increase as the number of relay increases.

In this paper, two improved distributed turbo coded (DTC) schemes, namely, superposition DTC scheme and punctured DTC scheme, are proposed for a two-hop relay system using decode-and-forward protocol. The performance of a communication system using relay mainly depends on the successful decoding of source information at the relay. The proposed techniques increase the decoding success rate at the relay thanks to the unique encoding procedures used at the source to relay link. Here, the input information symbols are first encoded using two rate 1/2 parallel concatenated recursive systematic convolutional (RSC) encoders, and the two parity streams of the two RSC encoders are converted into one stream by superposition and puncturing in the proposed superposition DTC scheme and punctured DTC scheme, respectively. Simulation results show that the error performance of the proposed superposition DTC scheme and the punctured DTC scheme are superior to the conventional DTC scheme by around 1.2 and 0.8 dB respectively.

In this paper, the performance of an uncoded multiple-input-multiple-output (MIMO) scheme combining single transmit antenna selection and receiver maximal-ratio combining (the TAS/MRC scheme) is investigated for independent flat Nakagami-m fading channels with arbitrary real-valued m. The outage probability is first derived. Then the error rate expressions are attained from two different approaches. First, based on the observation of the instantaneous channel gain, the binary phase-shift keying (BPSK) asymptotic bit error rate (BER) expression is derived, and the exact BER expression is obtained as an infinite series, which converges for reasonably large signal-to-noise ratios (SNRs). Then the exact symbol error rate (SER) expressions are attained as a multiple infinite sum based on the moment generating function (MGF) method for M-ary phase-shift keying (M-PSK) and quadrature amplitude modulation (M-QAM). The asymptotic SER expressions reveal a diversity order equal to the product of the m parameter, the number of transmit antennas and the number of receive antennas. Theoretical analysis is verified by simulation.

In this paper, we propose and analyze three new transmit antenna selection schemes with reduced feedback rate requirement compared with the conventional scheme. In scheme 1, Ltavailable transmit antennas are divided as equally as possible into two groups with consecutive antennas. The best single antenna within each group is selected. In scheme 2, only the best one among Ltantennas is made known to the transmitter, and the other one is selected at random. In Scheme 3, Ltantennas are divided into multiple subsets each consisting of two adjacent antennas, and the best subset is selected. Bit error rate (BER) expressions for the proposed schemes with Alamouti code are derived for independent flat Rayleigh fading channels. It is found that all the three schemes achieve a full diversity order. The relative merit of each proposed scheme is delineated based on the trade-off between the asymptotic performance loss and feedback reduction, both relative to the conventional scheme. We conclude that Schemes 1 and 3 are more favorable for practical applications, and the appropriate application scenarios are also identified. The proposed schemes enrich the choices for antenna selection system design for various feedback channel bandwidths and different requirements for quality of service.

In this paper, we propose a novel distributed spacetime trellis code (DSTTC) structure, and analyze its error performance in both slow and quasi-slow Rayleigh fading channels. The protocol adopted is decode-and-forward (DAF) with a single relay between the source and destination. Both scenarios with perfect and imperfect decoding at the relay are investigated. For imperfect decoding at the relay node, we consider an equivalent one-hop link model for the source-relay-destination path, and use it to modify the maximum likelihood detection metric by taking into account the equivalent signal-to-noise ratio (SNR) of the link model. The upper bounds of pairwise error probability (PEP) are derived for slow and quasi-slow Rayleigh fading channels, and the DSTTC design criteria are formulated accordingly. Based on the proposed design criteria, new DSTTCs are constructed by computer search. Simulation results demonstrate the superiority of the designed codes.

In this paper, we consider a multiple-relay network. We propose differential modulation at each communication node and selective combining method at the receiver. As both differential modulation and the selective combining do not require the full channel state information, the system design is greatly simplified without compromising much of the system performance. The average BER for this multiple-relay system with estimation-and- forward signaling protocol at the relay nodes is derived. In the analysis, we take into account the effect of imperfect estimation at the relay nodes, by replacing each of the source-relay-destination links with an equivalent relay-destination link. From the performance analysis, we conclude that this multiple-relay system with selective combiner can achieve full diversity. The analysis is also verified by simulation results.

In this paper, we propose an improved relay selection scheme based on a hybrid relaying protocol (RS-HRP). In the proposed scheme, all the relays are included into two groups, referred to as an amplify and forward (AAF) and a decode and forward (DAF) relay groups. The relays which decode successfully are included in the DAF group and the rest of relays, which fail to decode correctly, are included in the AAF group. The best relay, which maximizes the destination SNR, will be selected from all relays in both AAF and DAF relay groups. If it is selected from the AAF group, it will amplify the received signal while if it is selected from the DAF group, it will decode the received signals and re-encode. Results show that the proposed relay selection scheme significantly outperforms the conventional AAF selection scheme and this performance gain considerably grows as the number of relays increases. It also approaches the perfect DAF relay selection as the SNR increases.

In this paper, we propose three different transmit antenna selection schemes with reduced feedback requirement compared with the conventional scheme. In Scheme 1, all the Lt available transmit antennas are divided as equally as possible into two groups. The best single antenna within each group is selected. In Scheme 2, only the best one among Lt antennas is made known to the transmitter, and the other one is selected at random. Scheme 3 is for even Lt, and Lt antennas are divided into multiple subsets each consisting of two adjacent antennas, among which the best subset is selected. Analytical performances of these three schemes with the Alamouti space-time block code (STBC) are derived for flat Rayleigh fading channels. The asymptotic signal-to-noise ratio (SNR) loss of each proposed scheme relative to the conventional transmit antenna selection scheme is quantified. Together with the reduction in feedback requirement, the relative merit of each proposed scheme is delineated. In general, all of the three schemes provide a good trade-off between error performance and feedback requirement. And the application scenario for each scheme is also identified. The results in this paper provide guidance for the design of transmit antenna selection systems with various feedback channel bandwidths, different requirements for quality of service, and specific antenna configuration.

An adaptive coded multiple-input multiple-output (MIMO) system with outdated channel state information (CSI) at the transmitter is proposed and investigated. By incorporating the rate-compatible punctured codes (RCPCs) and bit-interleaved coded modulation (BICM) into the adaptive MIMO system, the proposed adaptive RCPC-BICM MIMO system is shown to achieve both a near-full multiplexing gain and a robust BER against the CSI feedback delay. A comparison among various adaptive MIMO systems shows that the proposed system provides a good trade-off between the spectral efficiency, BER and system complexity.

In this paper, a coded beamforming scheme is considered for frequency-flat multiple-input-multiple-output (MIMO) fading channels. With channel state information (CSI) available at the transmitter, this scheme combines coded modulation (CM) with downlink transmission beamforming to exploit both diversity and coding advantages. In order to identify the appropriate code design criteria, the exact pairwise error probability and bit error rate upper bounds are derived for both frequency-flat slow and fast MIMO Rayleigh fading channels. It is shown that the minimum squared Euclidean distance of the code should be maximised in slow-fading channels, whereas the minimum effective code length and the product distances should be maximised in fast-fading scenarios. This conclusion indicates that the conventional Ungerboeck's trellis-coded modulation codes originally designed for additive white Gaussian noise channels and single-antenna Rayleigh fading channels could be directly utilised in the proposed scheme for slow and fast MIMO fading channels, respectively. This eliminates the need for complicated code design. Simulation results are provided to substantiate the theoretical analysis. For an example, it is demonstrated that a trellis-coded beamforming scheme can outperform the published space-time trellis coded beamforming schemes by up to 4 dB, although it has a simpler encoder structure and requires no specific code design. Interference free is assumed in the analysis, while the impact of the imperfect CSI and antenna correlation on the system error performance is evaluated.

In this paper, the error performance of the Alamouti scheme with transmit antenna selection is investigated in the context of imperfect subset selection. The asymptotic bit error performance is derived for binary phase-shift keying (BPSK) modulation in flat Rayleigh fading channels. It is shown that the transmit diversity order is equal to the larger ordinal number of the antenna within the selected antenna subset, while the smaller ordinal number only determines horizontal location of the error performance curve without an impact on asymptotic diversity order. Simulation results are provided to substantiate the theoretical analysis.

In this paper, we first address the effects of the feedback delay on a variable-rate variable-power adaptive modulation MIMO system that is designed under a perfect channel state information (CSI) assumption. Closed-form expressions for the average bit error rate (BER) and average spectral efficiency (ASE) are derived. Based on that, simple but effective adaptive modulation designs using the outdated instantaneous CSI and time correlation coefficient are proposed based on the signal-to-interference and noise ratio (SINK) and BER analysis. Analytical and simulation results show that these designs provide good trade-offs between the ASE and BER in an adaptive way, and make the adaptive MIMO system much more robust to the CSI imperfection.

We investigate the error performance of the Alamouti scheme with transmit antenna selection. The exact bit error rate (BER) is derived for binary phase-shift keying (BPSK) in flat Rayleigh fading channels. The analysis reveals that this scheme achieves a full diversity order at high SNRs, as if all the transmit antennas were used. Simulation results are provided to substantiate the analysis. It is shown that compared with conventional space-time block codes (STBCs), this scheme incurs much less SNR loss inherent to the transmit-diversity system. Therefore, the Alamouti scheme with transmit antenna selection provides a new general approach to the design of MIMO systems for high-data-rate downlink transmission with a high diversity order.

We propose a layered space time (LST) transceiver in a multiuser code division multiple access (CDMA) mobile communication system. In particular, we examine a multiuser iterative receiver with joint detection, channel estimation and decoding in a frequency selective fading channel. The iterative parallel interference canceller (PIC) with statistics combining is used as the multiuser detection scheme and a least mean square (LMS) adaptive algorithm is applied to estimate the multiple input and multiple output (MIMO) frequency selective fading channel. Simulation results are presented to demonstrate the performance of this receiver. It is shown that the performance of the receiver with iterative channel estimation is close to that of the receiver with perfect channel state information (CSI).

In this paper we investigate the error performance of a multiple-input-multiple-output (MIMO) scheme combining transmit antenna selection (TAS) and space-time trellis codes (STTCs), which is referred to as the TAS/STTC scheme. In this scheme, two transmit antennas, which maximize the total received signal power, are selected to transmit the full-rank baseline STTCs designed for two transmit antennas. An upper bound on the pairwise error probability (PEP) of this scheme is derived in quasistatic flat Rayleigh fading channels. It is shown that, as long as the baseline STTC has a full rank, a full diversity order can be achieved, as if all the transmit antennas were used. This scheme has a fixed low decoding complexity as for the baseline STTC and a full diversity order can be achieved with a small memory order. Therefore, the TAS/STTC scheme provides a new approach to the design of MIMO system achieving a high diversity order based on the existing STTCs designed for a small number of transmit antennas.

The pairwise-error probability upper bounds of space-time codes (STCs) in independent Rician fading channels are derived. Based on the performance analysis, novel code design criteria for slow and fast Rayleigh fading channels are developed. It is found that, in fading channels, the STC design criteria depend on the value of the possible diversity gain of the system. In slow fading channels, when the diversity gain is smaller than four, the code error performance is dominated by the minimum rank and the minimum determinant of the codeword distance matrix. However, when the diversity gain is larger than, or equal to, four, the performance is dominated by the minimum squared Euclidean distance. Based on the proposed design criteria, new codes are designed and evaluated by simulation.

It has been established that the appropriate design parameters for space-time trellis code (STTC) in quasi-static flat Rayleigh fading channels are the rank and determinant criteria or the Euclidean distance criterion, depending on the value of the overall diversity gain. We propose two groups of new 4- and 8-PSK STTCs with two to four transmit antennas based on these two design criteria, respectively. Simulation results show that increasing the number of transmit antennas in general provide large performance improvement.

Multiple antenna systems have the potential to provide a high capacity wireless communication system. The spectral efficiency of space time trellis coding (STTC) is limited by the encoder structure. The layered space time (LST) architecture can overcome this problem. Three different LST schemes are presented. An improved iterative parallel interference canceller (PIC) method is applied at the receiver. A significant performance improvement is achieved compared to the standard PIC. Simulation results of three various layer structures are compared with low density parity check (LDPC) and convolutional codes as component codes.

This paper presents the design of space-time turbo trellis coded modulation (ST turbo TCM). We introduce new recursive space-time trellis coded modulation (STTC) which outperform feedforward STTC proposed by Tarokh, Seshadri and Calderbank (see Trans. Inform. Theory, vol.44, no.2, p.744-65, 1998) and by Baro, Bauch and Hansmann (see IEEE Trans. Commun. Letters, vol.4, no.1, p.20-22, 2000) . A substantial improvement in performance can be obtained by constructing parallel concatenation of recursive STTC and making use of iterative decoding. The new recursive STTCs can be used directly in this scheme. ST turbo TCM outperforms the best known STTC by about 2 dB on slow fading channels and by up to 8 dB on fast fading channels.

It has been established that the appropriate criteria for space-time trellis coded modulation (STTCM) design on slow Rayleigh fading channels are maximization of the minimum rank and the minimum determinant of the distance matrices. We show here that when STTCM is used in systems with a large product of the numbers of the transmit and the receive antennas (>3), the multiple fading subchannels between individual transmit and receive antenna pairs converge to an additive white Gaussian noise (AWGN) channel and the design of codes with maximum coding gain is governed by the minimum trace of the distance matrices, or the minimum Euclidean distance between any two codewords over all transmit antennas. A number of new 4 and 8-PSK codes based on the proposed design criterion were constructed and shown to be superior to other known codes.

Novel code design criteria for space-time codes on slow Rayleigh fading channels are presented. It is shown that the code performance is dominated by the minimum rank r and the minimum determinant of the codeword distance matrix when the product of the minimum rank r and the number of receive antennas n/sub R/ is less than 4. However, when r/spl middot/n/sub R/ is greater than or equal to 4, the code error performance is dominated by the minimum trace of the codeword distance matrix. Furthermore, we present recursive spacetime trellis coded modulation (STTC) which outperforms feedforward STTC on slow and fast fading channels. A substantial increase in performance can be obtained by constructing spacetime turbo trellis coded modulation (ST turbo TCM) which consists of concatenated recursive STTC, decoded by iterative decoding algorithm. The proposed recursive STTC are used as constituent codes in this scheme. The proposed ST turbo TCM significantly outperforms the best known STTC on both slow and fast fading channels.