This paper proposes FAS-LLM, a novel large language model (LLM)–based architecture for predicting future channel states in Orthogonal Time Frequency Space (OTFS)-enabled satellite downlinks equipped with fluid antenna systems (FAS). The proposed method introduces a two-stage channel compression strategy combining reference-port selection and separable principal component analysis (PCA) to extract compact, delay–Doppler–aware representations from highdimensional OTFS channels. These representations are then embedded into a Low Rank Adaptation (LoRA)-adapted LLM, enabling efficient time-series forecasting of channel coefficients. Performance evaluations demonstrate that FAS-LLM outperforms classical baselines including GRU, LSTM, and Transformer models, achieving up to 10 dB normalized mean squared error (NMSE) improvement and up to threefold root mean squared error (RMSE) reduction across prediction horizons. Furthermore, the predicted channels preserve key physical-layer characteristics, enabling near-optimal performance in ergodic capacity, spectral efficiency, and outage probability across a wide range of signal-to-noise ratios (SNRs). These results highlight the potential of LLM-based forecasting for delay-sensitive and energy-efficient link adaptation in future satellite IoT networks.

Federated learning (FL) is a decentralized approach for collaborative model training on edge devices. This distributed method of model training offers advantages in privacy, security, regulatory compliance, and cost efficiency. Our emphasis in this research lies in addressing statistical complexity in FL, especially when the data stored locally across devices is not identically and independently distributed (non-IID). We have observed an accuracy reduction of up to approximately 10%–30%, particularly in skewed scenarios where each edge device trains with only 1 class of data. This reduction is attributed to weight divergence, quantified using the Euclidean distance between device-level class distributions and the population distribution, resulting in a bias term$(\delta _{k})$. As a solution, we present a method to improve convergence in FL by creating a global subset of data on the server and dynamically distributing it across devices using a dynamic data queue-driven FL (DDFL). Next, we leverage Data Entropy metrics to observe the process during each training round and enable reasonable device selection for aggregation. Furthermore, we provide a convergence analysis of our proposed DDFL to justify their viability in practical FL scenarios, aiming for better device selection, a non-suboptimal global model, and faster convergence. We observe that our approach results in a substantial accuracy boost of approximately 5% for the MNIST dataset, around 18% for CIFAR-10, and 20% for CIFAR-100 with a 10% global subset of data, outperforming the state-of-the-art (SOTA) aggregation algorithms.

The remarkable success of transformers across various fields such as natural language processing and computer vision has paved the way for their applications in automatic modulation classification, a critical component in the communication systems of Internet of Things (IoT) devices. However, it has been observed that transformer-based classification of radio signals is susceptible to subtle yet sophisticated adversarial attacks. To address this issue, we have developed a defensive strategy for transformer-based modulation classification systems to counter such adversarial attacks. In this paper, we propose a novel vision transformer (ViT) architecture by introducing a new concept known as adversarial indicator (AdvI) token to detect adversarial attacks. To the best of our knowledge, this is the first work to propose an AdvI token in ViT to defend against adversarial attacks. Integrating an adversarial training method with a detection mechanism using AdvI token, we combine a training time defense and running time defense in a unified neural network model, which reduces architectural complexity of the system compared to detecting adversarial perturbations using separate models. We investigate into the operational principles of our method by examining the attention mechanism. We show the proposed AdvI token acts as a crucial element within the ViT, influencing attention weights and thereby highlighting regions or features in the input data that are potentially suspicious or anomalous. Through experimental results, we demonstrate that our approach surpasses several competitive methods in handling white-box attack scenarios, including those utilizing the fast gradient method, projected gradient descent attacks and basic iterative method.

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In addition to enhancing wireless communication coverage quality, reconfigurable intelligent surface (RIS) technique can also assist in positioning. In this work, we consider RIS-assisted superimposed pilot and data transmission without the assumption availability of prior channel state information and position information of mobile user equipments (UEs). To tackle this challenge, we design a frame structure of transmission protocol composed of several location coherence intervals, each with pure-pilot and data-pilot transmission durations. The former is used to estimate UE locations, while the latter is time-slotted, duration of which does not exceed the channel coherence time, where the data and pilot signals are transmitted simultaneously. We conduct the Fisher Information matrix (FIM) analysis and derive Cram´er-Rao bound (CRB) for the position estimation error. The inverse fast Fourier transform (IFFT) is adopted to obtain the estimation results of UE positions, which are then exploited for channel estimation. Furthermore, we derive the closed-form lower bound of the ergodic achievable rate of superimposed pilot (SP) transmission, which is used to optimize the phase profile of the RIS to maximize the achievable sum rate using the genetic algorithm. Finally, numerical results validate the accuracy of the UE position estimation using the IFFT algorithm and the superiority of the proposed SP scheme by comparison with the regular pilot scheme.

As we transition from Narrow Artificial Intelligence towards Artificial Super Intelligence, users are increasingly concerned about their privacy and the trustworthiness of machine learning (ML) technology. A common denominator for the metrics of trustworthiness is the quantification of uncertainty inherent in DL algorithms, and specifically in the model parameters, input data, and model predictions. One of the common approaches to address privacy-related issues in DL is to adopt distributed learning such as federated learning (FL), where private raw data is not shared among users. Despite the privacy-preserving mechanisms in FL, it still faces challenges in trustworthiness. Specifically, the malicious users, during training, can systematically create malicious model parameters to compromise the models’ predictive and generative capabilities, resulting in high uncertainty about their reliability. To demonstrate malicious behaviour, we propose a novel model poisoning attack method named Delphi which aims to maximise the uncertainty of the global model output. We achieve this by taking advantage of the relationship between the uncertainty and the model parameters of the first hidden layer of the local model. Delphi employs two types of optimisation, Bayesian Optimisation and Least Squares Trust Region, to search for the optimal poisoned model parameters, named as Delphi-BO and Delphi-LSTR. We quantify the uncertainty using the KL Divergence to minimise the distance of the predictive probability distribution towards an uncertain distribution of model output. Furthermore, we establish a mathematical proof for the attack effectiveness demonstrated in FL. Numerical results demonstrate that Delphi-BO induces a higher amount of uncertainty than Delphi-LSTR highlighting vulnerability of FL systems to model poisoning attacks.

In this paper, we study the problem of optimizing statistical beamforming design for each user in a two-antenna downlink multiple-input single-output (MISO) non-orthogonal multiple access (NOMA) system. The transmitter only possesses statistical information in the form of covariance matrices for each user’s link. The statistical beamforming designs are derived in maximizing the ergodic sum rate considering both low and high signal-to-interference-plus-noise ratio (SINR) extreme scenarios. In addition to the analytical investigations, we also conduct MonteCarlo simulations in comparison and to affirm the accuracy of the derived ergodic sum rate expressions. Results show that the ergodic sum rate increases with the total transmit power and decreases with the power coefficient of near users. Comparing the optimized MISO-NOMA system with conventional MISO-OMA scheme, it is demonstrated that MISO-NOMA can significantly improve the ergodic sum rate.

In this paper, we investigate channel estimation in a reconfigurable intelligent surface (RIS) assisted multi-user network while taking the mobility of users into consideration. Based on a time-varying channel model, we utilize Kalman filter (KF) that is able to exploit temporal correlation to track cascaded channel. In order to maintain a relatively low pilot overhead, we present a multiple sub-phases based transmission protocol where the number of pilot sequences in each sub-phase is less than the number of users, i.e., pilot contamination exists. For the sake of practicality, we directly utilize discrete Fourier transform (DFT) matrix as phase shift matrix. We analyze normalized mean square error and provide some asymptotic results. A more practical scenario with hardware impairments (HWI) at the transceiver and the RIS is also considered. Since HWI is also part of the measurement matrix and is unknown to the base station, we propose a joint estimation of the channel and HWI. Under this joint estimation framework, the underlying state space model becomes nonlinear. We develop an extended KF (EKF) algorithm to tackle the nonlinearity through which the model can be linearized. Numerical results show that the proposed KF and EKF algorithms outperform benchmark schemes under various scenarios.

In this work, we employ a constrained risk-sensitive deep reinforcement learning (CRS-DRL) approach for joint scheduling in a dynamic multiplexing scenario involving enhanced mobile broadband (eMBB) and ultra-reliable low-latency communications (URLLC). Our scheduling policy minimizes the adverse impact of URLLC puncturing on eMBB users while satisfying URLLC requirements. Conventional DRL-based algorithms for eMBB/URLLC scheduling prioritize maximizing the expected return. However, for URLLC mission-critical applications, it is crucial to explicitly avoid catastrophic scheduling failures associated with the long tail of the reward distribution. Therefore, robust management of such uncertainties and risks is imperative. Our proposed CRS-DRL algorithm incorporates the conditional Value-at-Risk (CVaR) as the risk criterion for optimization. A URLLC queuing mechanism is considered to decrease the URLLC drops and increase eMBB throughput compared to the instant scheduling policy. Our architecture is based on the actor-critic model but considers a transfer function to obtain feasible solutions of the unconstrained actor network, and the critic predicts the entire distribution over future returns instead of simply the expectation. Numerical results indicate that our CRS-DRL algorithm, under varying CVaR levels, achieves similar expected returns but reduces long-tail behavior for long-term rewards compared to the risk-neutral approach.

For wireless federated learning (FL), this work proposes an adaptive model pruning-based FL (AMP-FL) frame-work, where the edge server dynamically generates sub-models by pruning the global model to adapt devices' heterogeneous computation capabilities and time-varying wireless channel conditions. To mitigate the negative effect of different structures of sub-models on learning convergence, this work designs a new compensating strategy for the pruned regions of sub-models via historical gradients. Since the freshness of gradients dominates the convergence speed, this work also defines an age of information (AoI) metric to characterize the staleness of the regions of the local gradients. Based on the compensating strategy, we formulate a joint device scheduling, model pruning, and resource block allocation optimization problem to minimize the average AoI for local gradients. To solve this problem, we theoretically derive an optimal model pruning scheme. After that, we transform the original problem into equivalent linear programming that can be solved with polynomial time complexity. Simulation results on the CIFAR-IO dataset show that the proposed AMP-FL outperforms the benchmark schemes with faster convergence speed and over 7% learning accuracy improvement.

Metaverse is envisioned to be a human-centric framework, and provide a new concept of living by offering comprehensively immersive experience for users in education, medicine and entertainment domain. Since a large amount of private data is generated at each user for accessing Metaverse, the emerging federated learning (FL) provides an effective solution to address the potential privacy leakage of data sharing by adopting the mechanism of local training and global model aggregation. However, the model aggregation is susceptible to adversarial poisoning attacks. This imposes critical issues for the privacy-preserving mechanism in Metaverse. In this research, we develop two poisoning attacks in order to emulate the behaviour of adversaries possibly existing in practical Metaverse scenarios. First, we develop a data poisoning attack using Bayesian optimisation to search for the optimal parameters of generating adversarial examples to conduct reversed adversarial training. Second, we develop a model poisoning attack where we apply layer optimisation using Bayesian optimisation to search the optimal weights for the convolutional layer in order to induce uncertainty in the classification. Numerical results show that both attack schemes can cause attacks that can not be recognised by the FL server, and layer optimisation is a stronger poisoning attack.

Due to great success of transformers in many applications, such as natural language processing and computer vision, transformers have been successfully applied in automatic modulation classification. We have shown that transformer-based radio signal classification is vulnerable to imperceptible and carefully crafted attacks called adversarial examples. Therefore, we propose a defense system against adversarial examples in transformer-based modulation classifications. Considering the need for computationally efficient architecture particularly for Internet of Things (IoT)-based applications or operation of devices in an environment where power supply is limited, we propose a compact transformer for modulation classification. The advantages of robust training such as adversarial training in transformers may not be attainable in compact transformers. By demonstrating this, we propose a novel compact transformer that can enhance robustness in the presence of adversarial attacks. The new method is aimed at transferring the adversarial attention map from the robustly trained large transformer to a compact transformer. The proposed method outperforms the state-of-the-art techniques for the considered white-box scenarios, including the fast gradient method and projected gradient descent attacks. We have provided reasoning of the underlying working mechanisms and investigated the transferability of the adversarial examples between different architectures. The proposed method has the potential to protect the transformer from the transferability of adversarial examples.

This paper proposes an edge-assisted crowdsourced live video transcoding approach where the transcoding capabilities of the edge transcoders are unknown and dynamic. The resilience and trustworthiness of highly unstable transcoders in decision making are characterized with mean-variance-based measures to avoid making highly risky decisions. The risk level of each device’s situation is assessed and two upper confidence bounds of the variance of transcoding performance are presented. Based on the derived bounds and by leveraging the contextual information of devices, two risk-aware contextual learning schemes are developed to efficiently estimate the transcoding capabilities of the edge devices. Combining context awareness and risk sensitivity, a novel transcoding task assignment and viewer association algorithm is proposed. Simulation results demonstrate that the proposed algorithm achieves robust task offloading with superior network utility performance as compared to the linear upper confidence bound and the risk-aware mean-variance upper confidence bound-based algorithms. In particular, an epoch-based task assignment strategy is designed to reduce the task switching costs incurred in assigning the same transcoding task to different transcoders over time. This strategy also reduces the computational time needed. Numerical results confirm that this strategy achieves up to 86.8% switching costs reduction and 92.3% computational time reduction.

Integrating reconfigurable intelligent surface (RIS) into cell-free massive multiple-input multiple-output (MIMO) is a promising approach to enhance the coverage quality, spectral efficiency (SE), and energy efficiency. In this paper, an RIS-assisted cell-free massive MIMO downlink system suffering from the transceiver hardware impairments (T-HWIs) is investigated. To improve the accuracy of the direct estimation (DE) scheme, a modified ON/OFF estimation (MOE) with moderate pilot overhead is proposed. Relying on the knowledge of imperfect channel state information, we derive closed-form expressions of the lower-bound achievable SE with T-HWIs under both DE and MOE schemes. The closed-form results facilitate the investigation of how RIS improves the downlink SE under various system settings and allow us to explore the trade-off strategies between using more hardware-impaired APs and low-cost RISs in terms of the downlink SE and power consumption. Numerical results validate the theoretical analysis and show that the proposed MOE scheme outperforms the DE scheme in terms of the downlink SE. Moreover, the benefits of introducing RIS into hardware-impaired cell-free massive MIMO systems are also illustrated.

This paper studies the allocation of shared resources between ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) in the emerging 5G and beyond cellular networks. In this paper, we design a unique queuing mechanism for the joint eMBB/URLLC system. The aim is to flexibly schedule URLLC traffic to enhance the total eMBB throughput and the reliability of URLLC packets (i.e., the probability of not dropping URLLC packets in each mini-slot) while maintaining a satisfactory transmission latency as per the 3GPP requirements. Precisely, by deriving the steady-state probabilities of URLLC queue backlog analytically, we formulate a stochastic optimization problem to maximize the total normalized eMBB throughput and the URLLC utility. Due to the stochastic nature of the objective function, it is expensive to evaluate it for any set of inputs, and thus the Bayesian optimization is applied to obtain the optimal results of such a black-box objective function. Numerical results demonstrate that the proposed queuing mechanism never violates the latency requirement of the URLLC services but improves the reliability. It also enhances the total normalized eMBB throughput as compared to the method without queuing.

Deep Learning has attracted significant interests in wireless communication design problems. However, recent studies discovered that the deep neural network is vulnerable to adversarial attacks in the sense that a carefully designed and imperceptible perturbation to the input of the neural network could mislead the prediction of the neural network. In this paper, motivated by attractive classification performance of the transformer based neural networks, we analyse the vulnerability and robustness of the transformer against adversarial attacks in modulation classification scenarios. Using real datasets, we demonstrate that the transformer can achieve higher accuracy as compared to a convolutional neural network in the presence of adversarial attacks.

For block fading channels with uncertainty in channel distribution knowledge, we propose and optimize a statistical measure as a way to surely assess reliability in finite-block communications regime. In particular, the confidence level in guaranteeing average block-error rate lower than a specific target is introduced and maximized to find the optimal blocklength, aiming to meet the strict requirements of ultra-reliable low latency communications (URLLC). In order to compute the confidence level, non-parametric learning algorithms are employed for channel modeling with a limited number of training samples. Bayesian optimization, i.e., the tool for black-box optimization, is applied to solve the problem in the absence of the closed form of the confidence level.

The Internet of Things (IoT) allows physical devices to be connected over the wireless networks. Although device-to-device (D2D) communication has emerged as a promising technology for IoT, the conventional solutions for D2D resource allocation are usually computationally complex and time consuming. The high complexity poses a significant challenge to the practical implementation of wireless IoT networks. A graph neural network (GNN)-based framework is proposed to address this challenge in a supervised manner. Specifically, the wireless network is modeled as a directed graph, where the desirable communication links are modeled as nodes and the harmful interference links are modeled as edges. The effectiveness of the proposed framework is verified via two case studies, namely the link scheduling in D2D networks and the joint channel and power allocation in D2D underlaid cellular networks. Simulation results demonstrate that the proposed framework outperforms the benchmark schemes in terms of the average sum rate and the sample efficiency. In addition, the proposed GNN approach shows potential generalizability to different system settings and robustness to the corrupted input features. It also accelerates the D2D resource optimization by reducing the execution time to only a few milliseconds.

Advantages of deep learning over traditional methods have been demonstrated for radio signal classification in the recent years. However, various researchers have discovered that even a small but intentional feature perturbation known as adversarial examples can significantly deteriorate the performance of the deep learning based radio signal classification. Among various kinds of adversarial examples, universal adversarial perturbation has gained considerable attention due to its feature of being data independent, hence as a practical strategy to fool the radio signal classification with a high success rate. Therefore, in this paper, we investigate a defense system called neural rejection system to propose against universal adversarial perturbations, and evaluate its performance by generating white-box universal adversarial perturbations. We show that the proposed neural rejection system is able to defend universal adversarial perturbations with significantly higher accuracy than the undefended deep neural network.

As an important service for 5G communications, ultra-reliable low-latency communications (URLLC) support emerging mission-critical applications, such as factory automation and autonomous driving. For such applications, the probability of failing to successfully transmit URLLC packets should be below a certain threshold. However, in the case of limited knowledge of the channel distribution, achieving such a reliability target requires precise channel modeling. In this paper, we study applying a non-parametric statistical learning approach (i.e. kernel density estimation (KDE)) to estimate the information of the wireless transmission environment (i.e. the probability density function of the channel distribution). Based on the estimated cumulative distribution function, a transmission rate control technique has been developed and the corresponding reliability has been investigated using two measures representing the average performance and the confidence level. Moreover, this paper compares the performance of KDE and traditional empirical estimation scheme. The results show that KDE achieves a high level of confidence in guaranteeing the reliability constraint despite of the limited number of training data when choosing a suitable kernel bandwidth.

Abstract Scarcity of frequency spectrum is one of the main issues in wireless communications. Cognitive radio networks (CRNs) have been considered as an effective way of improving the spectrum efficiency by opportunistically using the spectrum resources through appropriate cooperation between primary and secondary networks. Exploitation of content caching in CRNs can enhance the system performance and reduce the backhaul cost and delay. In this paper, we propose a combined caching strategy and base station coordination in CRNs to achieve a proper balance between the signal cooperation gain and the content diversity gain. Depending on the availability and placement of the requested content, we propose a zero‐forcing coordinated beamforming technique to simultaneously transmit the most popular contents that are cached in every secondary base station and achieve the signal cooperation multiplexing gains, also we propose a maximum ratio transmission technique to deliver the less popular contents which are cached in different secondary base stations and achieve the caching diversity gain. Enumeration of the solution space search is used to obtain the optimal cache solution. Numerical results show that our proposed solution outperforms the cooperative caching and transmission solution proposed where only a maximum ratio transmission technique is used.

The classical multi-armed bandit (MAB) framework studies the exploration-exploitation dilemma of the decisionmaking problem and always treats the arm with the highest expected reward as the optimal choice. However, in some applications, an arm with a high expected reward can be risky to play if the variance is high. Hence, the variation of the reward should be considered to make the arm-selection process risk-aware. In this letter, the mean-variance metric is investigated to measure the uncertainty of the received rewards. We first study a risk-aware MAB problem when the reward follows a Gaussian distribution, and a concentration inequality on the variance is developed to design a Gaussian risk aware-upper confidence bound algorithm. Furthermore, we extend this algorithm to a novel asymptotic risk aware-upper confidence bound algorithm by developing an upper confidence bound of the variance based on the asymptotic distribution of the sample variance. Theoretical analysis proves that both proposed algorithms achieve the O(log(T)) regret. Finally, numerical results demonstrate that our algorithms outperform several risk-aware MAB algorithms.

With the rapid development of social networks and high-quality video sharing services, the demand for delivering large quantity and high quality contents under stringent end-to-end delay requirement is increasing. To meet this demand, we study the content caching problem modelled as a Markov decision process in the network edge server when the popularity profiles are unknown and time-varying. In order to adapt to the changing trends of content popularity, a context-aware popularity learning algorithm is proposed. We prove that the learning error of this scheme is sublinear in the number of requests. In light of the learned popularities, a reinforcement learning-based caching scheme is designed on top of the state-action-reward-state-action algorithm with a function approximation. A reactive caching algorithm is also proposed to reduce the complexity. The time complexities of both the caching schemes are studied to demonstrate their feasibility in real time systems and a theoretical analysis is performed to prove that the cache hit rate of the reactive caching algorithm asymptotically converges to the optimal cache hit rate. Finally the simulations are presented to demonstrate the superiority of the proposed algorithms.

Abstract We address the problem of spectrum sensing in decentralized cognitive radio networks using a parametric machine learning method. In particular, to mitigate sensing performance degradation due to the mobility of the secondary users (SUs) in the presence of scatterers, we propose and investigate a classifier that uses a pilot based second order Kalman filter tracker for estimating the slowly varying channel gain between the primary user (PU) transmitter and the mobile SUs. Using the energy measurements at SU terminals as feature vectors, the algorithm is initialized by a K -means clustering algorithm with two centroids corresponding to the active and inactive status of PU transmitter. Under mobility, the centroid corresponding to the active PU status is adapted according to the estimates of the channels given by the Kalman filter and an adaptive K -means clustering technique is used to make classification decisions on the PU activity. Furthermore, to address the possibility that the SU receiver might experience location dependent co-channel interference, we have proposed a quadratic polynomial regression algorithm for estimating the noise plus interference power in the presence of mobility which can be used for adapting the centroid corresponding to inactive PU status. Simulation results demonstrate the efficacy of the proposed algorithm.

This paper studies the problem of trajectory design for unmanned aerial vehicle (UAV)-assisted emergency communications, where the ground base station (BS) may be no longer functioning and the UAV acts as an aerial BS to provide emergency communication services to the ground users. In the event of emergency situations, the user distribution and the geographical features of the target area may have changed dramatically while urgent demand for communications are raised by the surviving ground users. In this paper, we model UAV trajectory design problem as a deep reinforcement learning (DRL) process and propose to adopt transfer learning to leverage previously learned knowledge so as to boost up the learning procedure. Simulation results validate that with limited interactions with the environment, the UAV can rapidly and effectively adapt its trajectory to the new environment and achieve much faster convergence speed than DRL based design.

This paper investigates a secrecy energy efficiency (SEE) optimization problem for a multiple-input multiple-output non-orthogonal multiple access network. In particular, a multi-antenna transmitter intends to send two integrated service messages: a confidential message for the stronger user and a broadcast message for both stronger and weaker users. It is assumed that both users are equipped with multi-antennas. In this secure wireless network, we consider the transmit covariance matrices design of confidential and broadcast message, under broadcast energy efficiency (BEE) constraint. In addition, it is assumed that the weaker user might turn out to be a potential eavesdropper due to the broadcast nature of wireless transmission. We formulate this transmit covariance matrices design as an SEE maximization problem which is non-convex in its original form due the non-linear fractional objective function and constraints. To realize the solution for this problem, we utilize non-linear fractional programming and difference of concave (DC) functions approach which facilitate to reformulate it into a tractable form. Based on the Dinkelbach's algorithm and DC approximation method, we propose iterative algorithms to determine a solution to the original SEE maximization problem. Numerical results are provided to demonstrate the performance of the proposed transmit covariance matrices design to maximize the SEE.

The authors investigate physical layer security design, which employs a random linear network coding with opportunistic relaying and jamming to exploit the secrecy benefit of both source and relay transmissions. The proposed scheme requires the source to transmit artificial noise along with a confidential message. Moreover, in order to further improve the dynamical behaviour of the network against an eavesdropping attack, aggregated power controlled transmissions with optimal power allocation strategy is considered. The network security is accurately characterised by the probability that the eavesdropper will manage to intercept a sufficient number of coded packets to partially or fully recover the confidential message.

Caching is a promising solution for the cloud radio access network (Cloud‐RAN) to mitigate the traffic load problem in the fronthaul links. Multiuser downlink beamforming plays an important role in efficient utilisation of spectrum and transmission power while satisfying the user's quality of service requirements. When the number of users exceeds the serving capacity of the network, certain users will have to be dropped or rescheduled. This is normally achieved by appropriate admission control mechanisms. Introducing local storage or cache at the remote radio heads where some popular contents are cached, the authors propose beamforming and admission control techniques for cache‐enabled Cloud‐RAN in the downlink. This minimises the total network cost including power and fronthaul cost while admitting as many users as possible. They formulate this multi‐objective optimisation problem as a single objective optimisation problem. The original problem, which is a mixed‐integer non‐linear programme, is first converted to the mixed‐integer second‐order cone programming form. The branch and bound algorithm is then used to determine the optimal and suboptimal solutions. A simulation study has been conducted to assess the performance of both methods.

In this treatise, we derive tractable closed-form expressions for the outage probability of the single cell multi-carrier non-orthogonal multiple access (MC-NOMA) downlink, where the transmitter side only has statistical CSI knowledge. In particular, we analyze the outage probability with respect to the total data rates (summed over all subcarriers), given a minimum target rate for the individual users. The calculation of outage probability for the distant user is challenging, since the total rate expression is given by the sum of logarithmic functions of the ratio between two shifted exponential random variables, which are dependent. In order to derive the closed-form outage probability expressions both for two subcarriers and for a general case of multiple subcarriers, efficient approximations are proposed. The probability density function (PDF) of the product of shifted exponential distributions can be determined for the near user by the Mellin transform and the generalized upper incomplete Fox's H function. Based on this PDF, the corresponding outage probability is presented. Finally, the accuracy of our outage analysis is verified by simulation results.

This paper studies the problem of joint edge cache placement and content delivery in cache-enabled small cell networks in the presence of spatio-temporal content dynamics unknown a priori. The small base stations (SBSs) satisfy users' content requests either directly from their local caches, or by retrieving from other SBSs' caches or from the content server. In contrast to previous approaches that assume a static content library at the server, this paper considers a more realistic non-stationary content library, where new contents may emerge over time at different locations. To keep track of spatio-temporal content dynamics, we propose that the new contents cached at users can be exploited by the SBSs to timely update their flexible cache memories in addition to their routine off-peak main cache updates from the content server. To take into account the variations in traffic demands as well as the limited caching space at the SBSs, a user-assisted caching strategy is proposed based on reinforcement learning principles to progressively optimize the caching policy with the target of maximizing the weighted network utility in the long run. Simulation results verify the superior performance of the proposed caching strategy against various benchmark designs.

This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-input-single-output systems where each transmit antenna at the base station has its own power constraint. We focus on the signal-to-interference-plus-noise ratio (SINR) balancing problem which is quasi-convex but there is no efficient solution available. We first design a fast subgradient algorithm that can achieve near-optimal solution with reduced complexity. We then propose a deep neural network structure to learn the optimal beamforming based on convolutional networks and exploitation of the duality of the original problem. Two strategies of learning various dual variables are investigated with different accuracies, and the corresponding recovery of the original solution is facilitated by the subgradient algorithm. We also develop a generalization method of the proposed algorithms so that they can adapt to the varying number of users and antennas without re-training. We carry out intensive numerical simulations and testbed experiments to evaluate the performance of the proposed algorithms. Results show that the proposed algorithms achieve close to optimal solution in simulations with perfect channel information and outperform the alleged theoretically optimal solution in experiments, illustrating a better performance-complexity tradeoff than existing schemes.

In this paper, we analyze the outage probability of the multi-user multiple-input single-output (MISO) downlink system by combining the non-orthogonal multiple access (NOMA) scheme. We derive tractable closed-form outage expressions given a minimum target rate for the individual users for the case of two antennas, by modeling cumulative distribution function (CDF) of received signal-to interference plus noise ratio (SINR). Simulation results illustrate the outage performance for different power allocation scenarios and verify the accuracy of our outage probability analysis.

This paper focuses on joint edge cache placement and content delivery problem at a base station (BS) in the presence of spatio-temporal unknown content dynamics, where the BS can satisfy its users' content demands either directly from its local cache or by fetching from the content server. Unlike the previous works that assume a static content library, we consider a more realistic non-stationary scenario, where new contents are emerging over time at the content library and might be cached at users. We propose that the new contents cached at local users can be utilized by the BS to timely update its flexible portion of cache memory in addition to its routine off-peak main cache update from the content server. We model the caching problem as a non- stationary bandit problem and introduce a user-aided caching algorithm that accounts for the traffic demand variations and the limited caching space at the BS. The proposed algorithm progressively improves the caching policy, with the target of maximizing the weighted content delivery rate to the users in the long run. Simulation results validate that the proposed strategy outperforms various benchmark designs.

This paper studies the problem of max-min fairness power allocation in distributed small cell networks operated under the same frequency bandwidth. We introduce a calibrated learning enhanced time division multiple access scheme to optimize the transmit power decisions at the small base stations (SBSs) and achieve max-min user fairness in the long run. Provided that the SBSs are autonomous decision makers, the aim of the proposed algorithm is to allow SBSs to gradually improve their forecast of the possible transmit power levels of the other SBSs and react with the best response based on the predicted results at individual time slots. Simulation results validate that in terms of achieving max-min signal-to-interference-plus-noise ratio, the proposed distributed design outperforms two benchmark schemes and achieves a similar performance as compared to the optimal centralized design.

Intrusion prediction is a key task for forecasting network intrusions. Intrusion detection systems have been primarily deployed as a first line of defence in a network, however; they often suffer from practical testing and evaluation due to unavailability of rich datasets. This paper evaluates the detection accuracy of determining all states (AS), the current state (CS), and the prediction of next state (NS) of an observation sequence, using the two conventional Hidden Markov Model (HMM) training algorithms, namely, Baum Welch (BW) and Viterbi Training (VT). Both BW and VT were initialised using uniform, random and count-based parameters and the experiment evaluation was conducted on the CSE-CIC-IDS2018 dataset. Results show that the BW and VT count-based initialisation techniques perform better than uniform and random initialisation when detecting AS and CS. In contrast, for NS prediction, uniform and random initialisation techniques perform better than BW and VT count-based approaches.

The authors propose a semi‐blind channel estimation (semi‐BCE) and precoding/decoding technique for frequency selective (FS) multiple‐input multiple‐output (MIMO) channels. A FS MIMO channel can be represented using a matrix whose elements are polynomials; hence their method is based on polynomial matrix decomposition. Polynomial eigenvalue decomposition (PEVD) and polynomial QR decomposition (PQRD) are the generalisation of eigenvalue decomposition and QR decomposition; they are suitable for decoupling and precoding of FS MIMO channels. As the coding of communication channels requires reliable estimation of the channel, a semi‐BCE scheme, coupled with PQRD/PEVD‐based MIMO‐channel decomposition, is attractive since this reduces training overhead (pilot transmission) considerably, resulting in higher spectral efficiency. The proposed semi‐BCE algorithm is a generalisation of a recently developed single‐input single output BCE method to MIMO systems. A new class of PQRD algorithms is introduced, which is based on the recently‐developed sequential matrix diagonalisation (SMD). The decoders produced by the proposed SMD‐based PQRD algorithm are shown to be more suitable (efficient) for MIMO‐channel equalisation than those generated by the prior art. Computer simulations show that the proposed MIMO‐channel coding strategy compares favourably to state‐of‐the‐art MIMO systems, in terms of bit error rate performance, while reducing the overhead.

The nonorthogonal multiple access (NOMA) well improves the spectrum efficiency which is particularly essential in the Internet of Things (IoT) system involving massive number of connections. It has been shown that applying buffers at relays can further increase the throughput in the NOMA relay network. This is however valid only when the channel signal-to-noise ratios (SNRs) are large enough to support the NOMA transmission. While it would be straightforward for the cooperative network to switch between the NOMA and the traditional orthogonal multiple access (OMA) transmission modes based on the channel SNR-s, the best potential throughput would not be achieved. In this paper, we propose a novel prioritization-based buffer-aided relay selection scheme which is able to seamlessly combine the NOMA and OMA transmission in the relay network. The analytical expression of average throughput of the proposed scheme is successfully derived. The proposed scheme significantly improves the data throughput at both low and high SNR ranges, making it an attractive scheme for cooperative NOMA in the IoT.

Non-orthogonal multiple access (NOMA) has been envisioned as a promising multiple access technique for 5G and beyond wireless networks due to its significant enhancement of spectral efficiency. In this paper, we investigate a robust energy efficiency design for multi-user multiple-input single-output (MISO) NOMA systems, where the imperfect channel state information is available at the base station (BS). A clustering algorithm is applied to group the users into different clusters, and then, the NOMA technique is employed to share the available resources fairly among the users in each cluster. To remove the interference between clusters, two different types of zero-forcing (ZF) designs, namely, hybrid-ZF and full-ZF, are employed at the BS. The full-ZF scheme completely removes the interference leakage at the cost of more number of antennas, and the hybrid-ZF scheme partially mitigates the interference leakage. To solve the problem, Dinkelbach’s algorithm is employed to convert the non-linear fractional programming problem into a simple subtractive form. Finally, simulation results reveal that hybrid-ZF outperforms the full-ZF scheme with a few clusters, while full-ZF shows a better performance with higher number of clusters. Numerical results confirm that our proposed robust scheme outperforms the non-robust scheme in terms of the rate-satisfaction ratio at each user.

This paper introduces a combined calibrated learning and bandit approach to online distributed power control in small cell networks operated under the same frequency bandwidth. Each small base station (SBS) is modelled as an intelligent agent who autonomously decides on its instantaneous transmit power level by predicting the transmitting policies of the other SBSs, namely the opponent SBSs, in the network, in real-time. The decision making process is based jointly on the past observations and the calibrated forecasts of the upcoming power allocation decisions of the opponent SBSs who inflict the dominant interferences on the agent. Furthermore, we integrate the proposed calibrated forecast process with a bandit policy to account for the wireless channel conditions unknowna priori, and develop an autonomous power allocation algorithm that is executable at individual SBSs to enhance the accuracy of the autonomous decision making. We evaluate the performance of the proposed algorithm in cases of maximizing the long-term sum-rate, the overall energy efficiency and the average minimum achievable data rate. Numerical simulation results demonstrate that the proposed design outperforms the benchmark scheme with limited amount of information exchange and rapidly approaches towards the optimal centralized solution for all case studies.

The combination of non-orthogonal multiple access (NOMA) and transmit antenna selection (TAS) techniques has recently attracted significant attention due to the low cost, low complexity, and high diversity gains. Meanwhile, random linear coding (RLC) is considered to be a promising technique for achieving high reliability and low latency in multicast communications. In this paper, we consider a downlink system with a multi-antenna base station and two multicast groups of single-antenna users, where one group can afford to be served opportunistically, while the other group consists of comparatively low-power devices with limited processing capabilities that have strict quality of service (QoS) requirements. In order to boost reliability and satisfy the QoS requirements of the multicast groups, we propose a cross-layer framework, including NOMA-based TAS at the physical layer and RLC at the application layer. In particular, two low-complexity TAS protocols for NOMA are studied in order to exploit the diversity gain and meet the QoS requirements. In addition, RLC analysis aims to facilitate heterogeneous users, such that sliding window-based sparse RLC is employed for computational restricted users, and conventional RLC is considered for others. Theoretical expressions that characterize the performance of the proposed framework are derived and verified through simulation results.

The problem of optimum distribution of the available spatial degrees of freedom among two sparse antenna subarray beamfomers in shared aperture receiver is investigated. The two subarrays, forming a full array, co-exist on the same platform and could perform separate RF sensing and communications tasks. The sparsity and cardinality of the subarray configurations are joint optimization variables which considerably affect the output signal-to-interference plus noise ratios (SINR) of the two beamformer outputs. A minimum output SINR figure value is imposed to guarantee minimum performance. We solve this problem by utilizing Taylor series approximation to reformulate the initial non-convex problem to a convex one. Simulation results validate the effectiveness of the proposed method.

In this paper, we study power allocation for downlink multi-carrier non-orthogonal multiple access (MC-NOMA)systems and examine the effects of residual cancellation errors resulting from imperfect successive interference cancellation (SIC) on the system performance. In the presence of random SIC errors, we study outage probability of minimum reserved rate for individual user and formulate outage-constrained robust optimization to minimize the total transmit power. Since the problem is non-convex due to probabilistic constraints, complementary geometric programming (CGP) and arithmetic geometric mean approximation (AGMA) technique are employed to transform it into a convex form. An efficient iterative algorithm with low computational complexity is developed to solve the optimization problem. Simulation results demonstrate the performance of robust MC-NOMA with imperfect SIC and compare that to non-robust MC-NOMA and orthogonal multiple access (OMA) schemes.

We study a resource allocation problem for the uplink of a virtualized massive multiple-input multiple-output system, where the antennas at the base station are priced and virtualized among the service providers (SPs). The mobile network operator (MNO) who owns the infrastructure decides the price per antenna, and a Stackelberg game is formulated for the net profit maximization of the MNO, while the minimum rate requirements of SPs are satisfied. To solve the bi-level optimization problem of the MNO, we first derive the closed-form best responses of the SPs with respect to the pricing strategies of the MNO, such that the problem of the MNO can be reduced to a single-level optimization. Then, via transformations and approximations, we cast the MNO's problem with integer constraints into a signomial geometric program (SGP), and we propose an iterative algorithm based on the successive convex approximation (SCA) to solve the SGP. Simulation results show that the proposed algorithm has performance close to the global optimum. Moreover, the interactions between the MNO and SPs in different scenarios are explored via simulations.

We thoroughly investigate the downlink beamforming problem of a two-tier network in a reversed time-division duplex system, where the interference leakage from a tier-2 base station (BS) toward nearby uplink tier-1 BSs is controlled through pricing. We show that soft interference control through the pricing mechanism does not undermine the ability to regulate interference leakage while giving flexibility to sharing the spectrum. Then, we analyze and demonstrate how the interference leakage is related to the variations of both the interference prices and the power budget. Moreover, we derive a closed-form expression for the interference leakage in an asymptotic case, where both the charging BSs and the charged BS are equipped with a large number of antennas, which provides further insights into the lowest possible interference leakage that can be achieved by the pricing mechanism.

In this paper, we propose a combined full duplex (FD)- and half duplex (HD)-based transmission and channel acquisition model for an open-loop multiuser multiple-input multiple-output (MIMO) systems. Assuming residual self-interference at the base station (BS), the idea is to utilize the FD mode during the uplink (UL) training phase in order to achieve simultaneous downlink (DL) data transmission and UL CSI acquisition. More specifically, the BS begins serving a user when its CSI becomes available, while at the same time, it also receives UL pilots from the next scheduled user. We investigate both zero-forcing (ZF) and maximum ratio transmission MIMO beamforming techniques for the DL data transmission in the FD mode. The BS switches to the HD mode once it receives the CSI of all users and it employs ZF beamforming for the DL data transmission until the end of the transmission frame. Furthermore, we derive closed-form approximations for the lower bounded ergodic achievable rate relying on the proposed model. Our numerical results show that the proposed FD-HD transmission and channel acquisition approach outperforms its conventional HD counterpart and achieves higher data rates.

This paper focuses on edge caching in dense heterogeneous cellular networks, in which small base stations (SBSs) with limited cache size store the popular contents, and massive multiple-input multiple-output (MIMO)-aided macro base stations provide wireless self-backhaul when SBSs require the non-cached contents. Our aim is to address the effects of cell load and hit probability on the successful content delivery (SCD) and present the minimum required base station density for avoiding the access overload in an arbitrary small cell and backhaul overload in an arbitrary macrocell. The achievable rate of massive MIMO backhaul without any downlink channel estimation is derived to calculate the backhaul time, and the latency is also evaluated in such networks. The analytical results confirm that hit probability needs to be appropriately selected in order to achieve SCD. The interplay between cache size and SCD is explicitly quantified. It is theoretically demonstrated that when non-cached contents are requested, the average delay of the non-cached content delivery could be comparable to the cached content delivery with the help of massive MIMO-aided self-backhaul, if the average access rate of cached content delivery is lower than that of self-backhauled content delivery. Simulation results are presented to validate our analysis.

With dual connectivity, a mobile user can be served by a macro base station (MBS) and a pico base station (PBS) simultaneously. In this paper, we address the problem of optimizing user-PBS association and power allocation in the uplink such that the network can serve the users' demand at the minimum cost, where the PBSs are subject to backhaul capacity limitations and minimum rate requirements of users. We show that this non-convex problem can be formulated as a signomial geometric programming (SGP) whose solution can be found by solving a series of geometric programming (GP) problems. Simulation results are provided to demonstrate traffic offloading trend to PBSs for different cost and backhaul capacity settings, confirming the effectiveness of the proposed iterative algorithm. They also show that the output of the proposed algorithm closely matches the global optimal solution with affordable complexity.

In this paper, we study a downlink wireless network consisting of wireless powered communication (WPC) system and a simultaneously wireless information and power transfer (SWIPT) systems. The SWIPT system simultaneously serves one information receiver (IR) while transferring power to a wireless device (WD). The wireless powered system consists of the WD and its IR. Both systems operate on the same frequency band. The WD is therefore able to take advantage of the wireless energy transfer from the SWIPT basestation (BS), interference power from the BS due to transmission of signals to IRs and the recycled power for energy harvesting. We aim to minimize the total transmitted power of the SWIPT BS subject to the signal-to-interference-and-noise ratio (SINR) target at the information receivers. In order to preserve the secrecy of the information transmitted by BS to IRs on the BS, we introduce a set of constraints SINR less than one.

The authors propose a game‐theoretic approach for the downlink beamformer design for a multiuser multi‐cell wireless network under a mixed quality of services (QoS) criterion. The network has real time users (RTUs) that must attain a specific set of signal‐to‐interference‐plus‐noise ratios (SINRs), and non‐RTUs whose SINRs should be balanced and maximised. They propose a mixed QoS strategic non‐cooperative game wherein base stations determine their downlink beamformers in a fully distributed manner. In the case of infeasibility, they have proposed a fallback mechanism which converts the problem to a pure max–min optimisation. They further propose the mixed QoS bargain game to improve the Nash equilibrium operating point through Egalitarian and Kalai–Smorodinsky bargaining solutions. They have shown that the results of bargaining games are comparable to that of the optimal solutions.

We propose an auction‐based beamforming and user association algorithm for a wireless network consisting of a macrocell and multiple small cell access points (SCAs). The SCAs compete for serving the macrocell base station (MBS) users (MUs). The corresponding user association problem is solved by the proposed bid‐wait auction method. The authors considered two scenarios. In the first scenario, the MBS initially admits the largest possible set of MUs that it can serve simultaneously and then auctions off the remaining MUs to the SCAs, who are willing to admit guest users in addition to their commitments to serve their own host users. This problem is solved by the proposed forward bid‐wait auction. In the second scenario, the MBS aims to offload as many MUs as possible to the SCAs and then admits the largest possible set of remaining MUs. This is solved by the proposed backward bid‐wait auction. The proposed algorithms provide a solution that is very close to the optimum solution obtained by using a centralised global optimisation.

We investigate a pricing based approach to control interference from a tier-2 base station (BS) to a tier-1 BS in reversed time division duplex (TDD) multi-antenna systems. The tier-2 BS is being charged for causing interference to the tier-1 BS. Also, the tier-2 BS has to satisfy the signal-to-interference-plus-noise ratio (SINR) targets of its downlink users under a maximum transmission power constraint. Analytical and simulation studies are carried out to understand the behavior of the tier-2 BS for different charges and for different power budgets. Observations from the analyses suggest that the tier-1 BS can perform interference control and/or profit maximization without knowing the downlink channels of the tier-2 BS.

The authors propose a coordinated multi‐cell beamforming technique for signal‐to‐interference plus noise ratio (SINR) balancing under multiple base station power constraints. Instead of balancing SINR of all users in all cells to the same level, the authors’ propose a new approach to balance SINR of users in various cells to different maximum possible values. This has the ability to allow users in cells with relatively more transmit power or better channel condition to achieve a higher balanced SINR than that achieved by users in the worst‐case cells. This multi‐level SINR balancing problem is solved using SINR constraints based SINR balancing criterion and subgradient method. The simulation results support the optimality of the results through comparison with semi‐definite programming‐based optimisation.

Facilitated by advanced information and communication technologies (ICT) infrastructure and optimization techniques, smart grid has the potential to bring significant benefits to the energy consumption management. This paper presents a game theoretic consumption scheduling framework based on the use of mixed integer programming (MIP) to schedule consumption plan for residential consumers. In particular, the optimization framework incorporates integration of locally generated renewable energy in order to minimize dependency on conventional energy and the consumption cost. The game theoretic model is designed to coordinatively manage the scheduling of appliances of consumers. The Nash equilibrium of the game exists and the scheduling optimization converges to an equilibrium where all consumers can benefit from participating in. Simulation results are presented to demonstrate the proposed approach and the benefits of home demand management.

We propose a coordinated multicell beamformer design method based on the signal-to-interference-plus-noise ratios (SINRs) balancing technique within the context of mixed quality of services (QoS). Instead of attaining an overall balance of SINRs to all users in all cells, the proposed algorithm allows a specific subset of users in each cells to achieve certain target SINRs while the SINRs of the remaining users in all cells are balanced subject to the total transmission power. The uplink-downlink duality is used for converting the downlink problem into the uplink beamformer design problem. The semidefinite programming (SDP) method is used to check the optimality.

We propose a robust formulation for the noncooperative rate-maximization game in MIMO Gaussian interference channels under bounded channel uncertainty. The proposed robust game needs little additional computation and requires no additional information exchange among users when compared to the nominal game and thus maintains the low-complexity and distributed nature of the MIMO waterfilling algorithm. The robust rate-maximization game is shown to be equivalent to the nominal game with modified direct-channel matrices. The equilibrium solution of the robust rate-maximization game and the required iterative algorithm to obtain the solution are presented. Sufficient conditions for the uniqueness of the equilibrium and the convergence of the algorithm are also presented. Simulation results indicate that the robust solution in the presence of channel uncertainty performs better than the nominal solution with zero uncertainty, due to the users being more conservative in their power allocation when there is channel uncertainty.

We solve a mixed quality of services (QoS) requirement problem for a multiple-input-multiple-output (MIMO) network with multiple linear transmit covariance constraints. Specifically, we design linear transceivers to satisfy the data rate requirements for a set of users while the rates of the remaining users are balanced. In addition, the design will ensure a set of multiple linear transmit covariance constraints are satisfied. The coupled structure of the transmit filters makes the original problem difficult to solve in the broadcast channel (BC). Hence, we propose an iterative algorithm to solve this mixed QoS problem based on stream-wise mean square error (MSE) duality and alternating optimization framework where the optimization problem is switched between the virtual multiple access channel (MAC) and the BC by exploiting stream-wise MSE duality. The proposed iterative algorithm solves the rate balancing problem by modifying the target rates of the users. In each iteration, a quadratically constrained quadratic programming (QCQP) is solved to obtain the virtual MAC receiver filters by incorporating multiple linear transmit covariance constraints, where the downlink receiver filters are obtained by minimizing each layer MSE. The power allocation in the virtual MAC is determined by solving a geometric programming (GP) where the product of layer MSEs of each user is balanced with total transmit power constraint. Simulation results for an underlay MIMO cognitive radio network demonstrate the convergence of the proposed algorithm.

The authors investigate a spectrum sharing peer‐to‐peer relay network where multiple source nodes with multiple antennas communicate with their desired destination nodes with multiple antennas through a multiple‐input and multiple‐output (MIMO) relay. The authors establish the duality between uplink and downlink peer‐to‐peer MIMO channels with any number of antennas at each node and demonstrate mean square error (MSE) of a downlink peer‐to‐peer network can be achieved in a virtual uplink network with the same total network transmission power constraint. By applying this result, the authors develop an iterative algorithm to optimise the source, relay and receiver processing matrices such that the weighted MSE of the retrieved signal at the receivers is minimised. The simulation results demonstrate satisfactory performance of the proposed algorithm.

The authors propose two algorithms to solve sum mean‐square error (MSE) minimisation and mixed quality of service (QoS) requirement problems for a multiuser multiple‐input‐multiple‐output system with multiple linear transmit covariance constraints. These original problems in the downlink are complicated because of the coupled structure of the transmitter filters. To overcome this issue, MSE duality proposed in the literature is extended at different levels for a general linear transmit covariance constraint. Exploiting the general sum‐MSE duality and subgradient method, the sum‐MSE minimisation algorithm is proposed first for multiple linear transmit covariance constraints. Secondly, a novel algorithm is proposed to solve mixed QoS requirement problem, where multiple linear transmit covariance constraints are incorporated in the design of the receiver filters in the equivalent multiple access channel. This algorithm is developed based on stream‐wise MSE duality and alternating optimisation framework. Simulation results have been provided to validate the convergence of the proposed algorithms. In addition, the proposed sum‐MSE minimisation algorithm with per‐antenna power constraints outperforms the existing algorithm in terms of achieved sum‐MSE and power consumption at each transmit antenna.

We consider a multiple‐input and multiple‐output (MIMO) cognitive radio (CR) network with a MIMO relay that opportunistically accesses the same frequency band as that of a MIMO primary network. In particular, both interference cancellation and interference alignment techniques have been investigated to enhance the achievable degrees of freedom (DoF) for the MIMO CR network. Based on the number of antennas at the primary network and the secondary network, the authors analytically quantify the maximum achievable DoF of the secondary network by using the proposed techniques. It is shown that the DoF obtained by the CR network in the presence of a MIMO relay is higher than that could be obtained without a relay. The analyses consider both sufficient and insufficient number of antennas at the relay in terms of the ability to separate and decode both the primary and secondary transmitted signals. The simulation results support the analytically quantified achievable DoF results.

The interference alignment (IA) is a promising technique to efficiently mitigate interference and to enhance capacity of a wireless communication network. This paper proposes an interference alignment scheme for a network with multiple cells and multiple multiple-input and multiple-output (MIMO) users under a Gaussian interference broadcast channel (IFBC) scenario. We first extend a grouping method already known in the literature to a multiple-cells scenario and jointly design transmit and receiver beamforming vectors using a closed-form expression without iterative computation. Then we propose a new approach using the principle of multiple access channel (MAC) - broadcast channel (BC) duality to perform interference alignment while maximizing capacity of users in each cell. The algorithm in its dual form is solved using interior point methods. We show that the proposed approach outperforms the extension of the grouping method in terms of capacity and basestation complexity. Finally, a rate balancing technique is introduced to maintain fairness among users.

We investigate a weighted sum rate maximization and rate balancing problem for a spectrum sharing multiple input multiple output (MIMO) based wireless relay network. The aim is to maximize the sum rate of the wireless relay network whilst ensuring the interference leakage to the primary user terminals during two time slots are below a specific value. We solve this problem by asymmetrically allocating the power to different time slots and using the principle of MAC-BC duality. The algorithm in its dual form has been solved using sub-gradient methods. The simulation results demonstrate the convergence of the algorithm and the simultaneous satisfaction of maximum power and the interference constraints.

We propose a two-way relay based spatial multiplexing technique for a cognitive radio relay network (CR). The relay coefficients and the transmission powers are optimized to maximize the worst-case user signal-to interference and noise ratio (SINR), while ensuring interference leakage from the relays to the primary users (PUs) in the network is below a threshold. We solve this problem through an iterative procedure that uses semidefinite and geometric programming along with bisection search method. We evaluate the performance of the proposed scheme in terms of the mean SINR for different number of relays and transmission power at the relays.

This study proposes a new technique for communicating over multiple-input multiple-output (MIMO) frequency selective channels. This approach operates by calculating the QR decomposition of the polynomial channel matrix at the receiver on the basis of channel state information, which in this work is assumed to be perfectly known. This then enables the frequency selective MIMO system to be transformed into a set of frequency selective single-input single-output systems without altering the statistical properties of the receiver noise, which can then be individually equalised. A like-for-like comparison with the orthogonal frequency division multiplexing scheme, which is typically used to communicate over channels of this form, is provided. The polynomial matrix system is shown to achieve improved performance in terms of average bit error rate results, as a consequence of time-domain symbol decoding.

The authors propose a suboptimal algorithm for adaptive subcarrier, bit and power allocation for orthogonal frequency division multiple access-based spectrum-sharing networks. This problem in its original form is non-convex and may be solved using greedy algorithms or integer linear programming (ILP) techniques. However, the computational complexity of the latter techniques is quite high, while the suboptimal greedy algorithms are not very well suited for spectrum-sharing networks because of multiple constraints on the transmitted power, interference leakage and individual user data rate. Therefore the authors propose a novel recursion-based linear optimisation framework that provides a solution that is very close to the optimal one and that has the ability to perform adaptive subcarrier, bit and power allocation for multiple users in the presence of multiple individual user constraints. Owing to the convexity of the proposed algorithm at each recursion, its overall complexity is substantially lower than that of the ILP-based solution.

In this paper, we consider a two-way amplify-and-forward relaying scheme, which consists of multiple transceivers and r relay nodes. Assuming that both the transceivers and the relays are equipped with single antennas, we deploy a signal-to-interference and noise-ratio (SINR) balancing technique, where the smallest of the transceivers SINRs is maximized under a total transmit power constraint. We solve this problem through an iterative procedure that uses semidefinite and geometric programming along with bisection search methods. We evaluate the performance of the proposed scheme in terms of the mean SINR for various relays and power at the relays.

We provide a rate balancing technique with mixed Quality of Services (QoS) requirement for an underlay multiple-input-multiple-output (MIMO) cognitive radio network (CRN). Specifically, we have considered an optimization criterion such that a set of SUs are required to achieve a target data rate whilst the data rates for the remaining SUs are to be balanced. This problem with a mixed QoS requirement in the broadcast channel (BC) cannot be solved directly due to a coupled structure of the transmitted covariance matrices. Hence, we solve an equivalent multiple access channel (MAC) problem using BC-MAC duality and subgradient method. An iterative algorithm is proposed to determine the transmit covariance matrices. The convergence analysis and simulation results are provided to validate the proposed algorithm.

We solve the rate balancing problem in the downlink for a multiuser multiple-input-multiple-output (MIMO) system with multiple linear transmit covariance constraints. In particular, we adopt a linear transceiver structure to maximize the worst-case rate of the user while satisfying multiple linear transmit covariance constraints. The original rate balancing problem in the downlink is more complicated due to the coupled structure of the transmit filters. Hence, this optimization problem is solved in an alternating manner by switching between the virtual uplink and the downlink and exploiting the stream-wise mean square error (MSE) duality. An iterative algorithm has been proposed based on stream-wise MSE duality to obtain transceiver filters. In each iteration, the virtual uplink receiver filter design is formulated into a quadratically constrained quadratic programming (QCQP) by incorporating the multiple linear constraints, where the downlink receiver filters are obtained by minimizing each layer MSE. A geometric programming (GP) is solved to obtain the power allocation in the virtual uplink where the product of layer MSEs of each user is balanced with total transmit power constraint. Simulation results have been provided to validate the performance of the proposed algorithm.

We consider an underlay cognitive radio network, in which the cognitive users (also referred to as secondary users (SUs)) are allowed to access the licensed spectrum simultaneously with the primary users (PUs). Specifically we solve a beamforming and power allocation problem in the downlink with mixed quality-of-service (QoS) requirements where a set of SUs are required to achieve a specific signal-to-interference and noise ratio (SINR) targets whilst the SINRs for the remaining SUs are balanced. This mixed QoS requirement problem is more complicated in the downlink because of the coupled structure of beamformers and power allocations. Hence, we solve an equivalent uplink problem based on the uplink-downlink duality and subgradient method. An iterative algorithm is proposed to determine the optimal beamformers and power allocation. Simulation results are provided to validate the optimality of the result and the convergence of the proposed algorithm.

We propose a suboptimal optimization algorithm for user maximization and resource allocation in an OFDMA based cognitive radio network. The aim is to admit as many secondary users as possible while satisfying quality of services for each admitted secondary user and ensuring the interference leakage to primary network is below a threshold. The original problem which is a combinatorial optimization problem becomes computationally prohibitive as the problem dimension in terms of the number of users seeking access to the network increases. However, our proposed suboptimal algorithm performs very closely to the optimal combinatorial optimization algorithm while keeping the complexity substantially low.

We propose a capacity balancing beamforming technique for a multiple inputs and multiple outputs (MIMO) based cognitive radio (CR) network. The proposed algorithm is based on mean square error (MSE) duality and it is aimed at maximizing the worst case user capacity of multiple secondary users (SUs) by jointly designing their transceiver beamformers while ensuring the interference leakage to multiple primary users (PUs) are below a specific set of thresholds. We use an iterative approach to determine the transceiver beamformers so that the capacities achieved by all SUs are balanced and maximized. The performance of the algorithm is demonstrated through Monte Carlo simulation results.

The problem of decentralized power allocation for competitive rate maximization in a frequency-selective Gaussian interference channel is considered. In the absence of perfect knowledge of channel state information (CSI), a distribution-free robust game is formulated. A robust-optimization equilibrium (RE) is proposed where each player formulates a best response to the worst-case interference. The conditions for existence, uniqueness and convergence of the RE are derived. It is shown that the convergence reduces as the uncertainty increases. Simulations show an interesting phenomenon where the proposed RE moves closer to a Pareto-optimal solution as the CSI uncertainty bound increases, when compared to the classical Nash equilibrium under perfect CSI. Thus, the robust-optimization equilibrium successfully counters bounded channel uncertainty and increases system sum-rate due to users being more conservative about causing interference to other users.

We propose a novel signal to interference and noise (SINR) balancing technique for a downlink cognitive radio network (CRN) wherein multiple cognitive users (also referred to as secondary users (SUs)) coexist and share the licensed spectrum with the primary users (PUs) using the underlay approach. The proposed beamforming technique maximizes the worst SU SINR while ensuring that the interference leakage to PUs is below specific thresholds. Due to the additional interference constraints imposed by PUs, the principle of uplink-downlink duality used in the conventional downlink beamformer design cannot be directly applied anymore. To circumvent this problem, using an algebraic manipulation on the interference constraints, we propose a novel SINR balancing technique for CRNs based on uplink-downlink iterative design techniques. Simulation results illustrate the convergence and the optimality of the proposed beamformer design.

We consider a network of cognitive users (also referred to as secondary users (SUs)) coexisting and sharing the spectrum with primary users (PUs) in an underlay cognitive radio network (CRN). Specifically, we consider a CRN wherein the number of SUs requesting channel access exceeds the number of available frequency bands and spatial modes. In such a setting, we propose a joint fast optimal resource allocation and beamforming algorithm to accommodate maximum possible number of SUs while satisfying quality of service (QoS) requirement for each admitted SU, transmit power limitation at the secondary network basestation (SNBS) and interference constraints imposed by the PUs. Recognizing that the original user maximization problem is a nondeterministic polynomial-time hard (NP), we use a mixed-integer programming framework to formulate the joint user maximization and beamforming problem. Subsequently, an optimal algorithm based on branch and bound (BnB) method has been proposed. In addition, we propose a suboptimal algorithm based on BnB method to reduce the complexity of the proposed algorithm. Specifically, the suboptimal algorithm has been developed based on the first feasible solution it achieves in the fast optimal BnB method. Simulation results have been provided to compare the performance of the optimal and suboptimal algorithms.

This paper investigates delay constrained performance of a cognitive radio relay network when the cognitive (secondary) user transmission is subject to satisfying spectrum-sharing restrictions imposed by a primary user. The primary user allows a secondary user to gain access to its allocated spectrum band as long as certain thresholds on the interference power, on the peak or average values, inflicted on the primary receiver are not exceeded by the transmission of the secondary users. In addition, we assume that the secondary transmitter benefits from an intermediate node, chosen from K terminals, to relay its signal to the destination. Considering that the transmission of the secondary user is subject to satisfying a statistical delay quality-of-service (QoS) constraint, we study the maximum arrival rate of the secondary user's relay link while the interference limitations required by the primary user are satisfied. Particularly, we obtain the effective capacity of the secondary network and determine the power allocation policies that maximize the effective capacity of the secondary user's relaying channel. In addition, we derive closed-form expressions for the effective capacity of the channel in Rayleigh block-fading environment under peak or average interference-power constraints. Numerical simulations are provided to endorse our theoretical results.

Cognitive radio networks opportunistically operate in frequency bands that have been licensed to other networks. Therefore, communication between unlicensed users should ensure the interference leaked to the licensed users is kept below an acceptable level while achieving the required quality of services. In this paper, we extend SINR balancing technique to serve multiple cognitive users in the downlink while imposing constraints on interference temperature of primary users. We show that when the set interference temperatures is fixed, the proposed SINR balancing technique will always have a unique solution that is identical to semidefinite programming based optimal solution. The advantages and disadvantages of the SINR balancing technique and semidefinite programming based techniques are also discussed.

In the case of a frequency flat multiple-input multiple-output (MIMO) system, QR decomposition can be applied to reduce the MIMO channel equalization problem to a set of decision feedback based single channel equalization problems. Using a novel technique for polynomial matrix QR decomposition (PMQRD) based on Givens rotations, we extend this work to frequency selective MIMO systems. A transmitter design based on Diagonal Bell Laboratories Layered Space Time (D-BLAST) encoding has been implemented. Turbo equalization is utilized at the receiver to overcome the multipath delay spread and to facilitate multi-stream data feedback. The effect of channel estimation error on system performance has also been considered to demonstrate the robustness of the proposed PMQRD scheme. Average bit error rate simulations show a considerable improvement over a benchmark orthogonal frequency division multiplexing (OFDM) technique. The proposed scheme thereby has potential applicability in MIMO communication applications, particularly for TDMA systems with frequency selective channels.

We propose a cooperative relaying strategy for wireless mesh networks where a set of relay nodes assists forwarding signals from multiple sources to multiple destinations. A semi- definite programming framework is used to ensure target SINRs at the destination are achieved with minimum possible transmit power at the relay layers. The proposed algorithm also considers various level of quantization applied to the messages passed between relays within its optimization framework. The proposed technique has been shown to outperform a non-cooperation based relaying strategy for various values of SINR targets.

The problem of multiuser multiplexing with a MIMO sub system for each individual user is considered. We demonstrate that the capacity performance of the null space based spatial multiplexing schemes can be improved with iterative power allocation within the iterative design process. We considered water-filling based local and global power allocation and demonstrate that both schemes outperform the existing null space based spatial diversity technique in terms of mean capacity and outage capacity.

For a frequency flat multi-input multi-output (MIMO) system the QR decomposition can be applied to reduce the MIMO channel equalization problem to a set of decision feedback based single channel problems. Using a novel technique for polynomial matrix QR decomposition (PMQRD) based on Givens rotations, we show the PMQRD can do likewise for a frequency selective MIMO system. Two types of transmitter design, based on Horizontal and Vertical Bell Laboratories Layered Space Time (H-BLAST, V-BLAST) encoding have been implemented. Receiver processing utilizes Turbo equalization to exploit multipath delay spread and to facilitate multi-stream data feedback. Average bit error rate simulations show a considerable improvement over a benchmark orthogonal frequency division multiplexing (OFDM) technique. The proposed scheme thereby has potential applicability in MIMO communication applications, particularly for a TDMA system with frequency selective channels.

For a narrowband multi-input multi-output (MIMO) system the singular value decomposition has the ability to provide multiple spatial channels for data transmission. We extend this work to obtain spatial diversity techniques for frequency selective MIMO systems using a polynomial matrix decomposition known as the sequential best rotation using second order statistics (SBR2) method. This algorithm diagonalizes a MIMO frequency selective channel yielding various spatial modes for data transmission. We evaluate the diversity performance of the dominant channel provided by the SBR2 based broadband decomposition and compare it with a transmit antenna selection method (TAS) and a MIMO orthogonal frequency-division multiplexing (OFDM) singular value decomposition (SVD) based approach. Simulation results show SBR2 significantly outperforms the average bit error rate (BER) of TAS, making it very suitable for time division multiple access (TDMA) and code division multiple access (CDMA) systems. SBR2 and MIMO-OFDM systems are shown to have identical BER performance, confirming the efficiency of the proposed low delay spatial-temporal scheme.

We propose superimposed training in conjunction with space time turbo equalization for communication over multiple-input-multiple-output (MIMO) wireless links in doubly selective environments. The iterations of the turbo algorithm provide the flexibility to reincorporate data energy along with the pilot energy into channel estimation so that simultaneous data transmission actually aids in channel estimation rather than interfering with it. We demonstrate the iterative method has the ability to improve the BER performance significantly over a comparable non iterative scheme.

We consider the problem of performing spatial multiplexing in a multiuser system with multipath channels. The proposed solution is based on space-time channel shortening, where we extend the uplink-downlink duality known for flat fading channels. We establish that the uplink-downlink duality result for flat fading channels holds for frequency selective channels i.e. both uplink and downlink share the same SINR and normalized MSE region under a sum-power constraint. We then use this result to propose spatial multiplexing schemes based on equalization and channel shortening at the transmitter under a max-min fairness framework i.e. all users achieve the same SINR/MSE targets. The advantage of channel shortening based spatial multiplexing as compared to a complete equalization at the transmitter is demonstrated using simulation results.

We address the problem of transmit beamforming under channel uncertainties for a multiuser MIMO system, where both the transmitter and the receiver are equipped with multiple antennas. In transmit beamforming multi-user multiplexing is performed using spatial diversity techniques so that a base station could serve multiple users in the same frequency band enabling a substantial saving in bandwidth utilization. However, such techniques require nearly perfect knowledge of the channel state information at the transmitter, which is generally not available in practise. In this paper, we propose robust spatial multiplexing schemes based on a worst case performance optimization by incorporating imperfect channel state information. In the simulation, we have examined two scenarios. In the first the channel state information is assumed to have Gaussian distribution errors. In the second scenario, we analyze the performance for errors introduced due to a partial channel state information feedback scheme. In both scenarios, the proposed robust scheme outperforms the conventional scheme.

We propose a minimum mean-square error (MMSE)-based signal forwarding technique for a cooperative relay network. Transmission of information between multiple source-destination pairs through a set of relays is considered. Cooperation between relays has been shown to improve substantially the bit-error rate (BER) performance as compared to noncooperative relays under a total power constraint. A general model for relay cooperation has been considered; however, for a single source-destination scenario, the proposed MMSE relaying strategy has been shown to be a product of receiver and transmitter beamformers at the relay layer.

An iterative frequency offsets (FOs) estimation and correction technique for OFDM is proposed to track multiple frequency offsets due to distinct Doppler shifts associated with multi- paths. The pilot symbols available in an OFDM symbol for channel estimation can be combined with the soft estimates of the data symbols in an iterative manner to track carrier frequency offsets. The main merit of this iterative method is its capability of tracking frequency offsets. The proposed iterative technique has the ability to resolve multipaths, bringing the multiple frequency offset problem into the estimation of distinct frequency offsets. We verify the detection performance using BER curves and the estimation performance through comparison of the variance of estimates with the Cramer-Rao lower bounds (CRLB).

In this paper we propose a generalization of extended orthogonal space-time block codes (EO-STBCs) for MIMO (multi-input/multi-output) channels using four transmit antennas for quasi-static flat fading channels. Since full rate and complex orthogonal space-time block codes (STBCs) do not exist for more than two transmit antennas, we propose a feedback based STBC scheme. In this scheme, phases of certain symbols are rotated according to the feedback from the receiver which is equivalent to rotating the phases of the corresponding channel coefficients. Simulation results show that this rotation phase feedback method achieves a satisfactory performance and outperforms the previous closed-loop space-time block codes, even when the feedback is quantized.

We proposes an integrated voice and data service for a multiple input multiple output (MIMO) system that assigns one subchannel for voice transmission and the remaining subchannels for data transmission. Two approaches are considered. In the first approach, voice is allocated on the subchannel associated with the largest singular value, while data is allocated on the remaining subchannels. In the second approach, voice and data are allocated on any of the unordered singular value channels. Based on the eigenvalue distribution of random Wishart matrices, closed form expressions for rate and power control are obtained to investigate the performance of the proposed schemes. The system performance is analyzed based on appropriate quality of service (QoS) indicators for voice and data services. Finally the analytical expressions are verified using Monte-Carlo experimental results.

Reliable communication over a doubly selective wireless channel is a challenging problem, mainly due to poor estimation of the channel parameters in the presence of limited training. We therefore propose turbo equalization in conjunction with Doppler domain channel estimation techniques for communication over a channel that is selective over both the time and frequency dimensions. It is shown that joint techniques to address the problems of channel estimation and symbol detection along with iterative equalization and decoding constitute a powerful technique with overwhelming performance gains over separate channel estimation and decoding in doubly selective environments. Such techniques can provide the flexibility required to improve the estimate with limited training.

We propose an iterative channel estimation and data detection technique for multiple-input multiple-output (MIMO) frequency selective channels with distinct frequency offsets (FOs) for each multipaths. The pilot symbols are generally inadequate to obtain an accurate estimate of the frequency offsets due to the limitation on the frequency resolution of the estimator. Therefore we initially use the pilot sequence for the estimation and equalization of the channel without consideration to frequency offsets, however we then use the soft estimates of the transmitted signal as a long pilot sequence to determine multiple frequency offsets and to refine the channel estimates iteratively. In the iterative channel estimator, the MIMO frequency selective channel is decoupled into multiple single-input single-output (SISO) flat fading sub-channels through appropriately cancelling both intersymbol-interference (ISI) and the inter-user-interference (IUI) from the received signal. The refined channel estimates and the corresponding frequency offset estimates are then obtained for each resolved MIMO multipath taps. We verify the detection performance using the BER curves and the estimation performance through comparison of the variance of the estimates with the corresponding Cramer-Rao lower bounds (CRLB).

The performance of multi-user detectors in the presence of multiple frequency offsets under a Rayleigh fading channel environment is analysed, and techniques to estimate and remove multiple frequency offsets (FOs) for successive interference cancellation (SIC) and parallel interference cancellation (PIC) receivers are also proposed. The closed form expressions derived for bit error rate (BER) of SIC and PIC schemes in the presence of multiple FOs have been verified using extensive simulation results. The PIC is shown to be less sensitive to frequency offsets as compared to SIC. It is demonstrated through analytical and simulation results that the proposed frequency offset estimation and correction techniques provide approximately 8 dB gain in the BER performance over conventional SIC and PIC schemes in the presence of multiple frequency offsets.

We propose an iterative channel estimation and data detection technique for frequency selective channels with multiple frequency offsets (FOs). The pilot symbols are generally inadequate to obtain an accurate estimate of the FOs due to limitation on the frequency resolution of the estimator. Therefore, we initially use the pilot sequence for the estimation and equalization of the channel without consideration to FOs. However, we then use the soft estimate of the transmitted signal as a long pilot sequence to determine multiple FOs and to refine channel estimates iteratively. The proposed iterative technique also has the ability to resolve multipaths, bringing the multiple FO problem into estimation of distinct harmonics. We verify the detection performance using the bit-error-rate curves and the estimation performance through comparison of the variance of estimates with the Crameacuter-Rao lower bounds

In this paper, we generalize the algorithms of our previously proposed Bayesian estimation based variable rate variable power multilevel quadrature amplitude modulation (VRVP-MQAM) system to incorporate for the first time a maximum a posteriori (MAP) channel predictor and a MQAM scheme adopted with practical constellation sizes. Based on a pilot symbol assisted modulation (PSAM) scheme, we evaluate the performance of our proposed VRVP-MQAM system over a Rayleigh flat-fading channel. We demonstrate in our simulation results that the proposed rate and power algorithms that are derived based on a Bayesian bit error rate (BER) estimation and the second order statistical characterization of the channel state information (CSI) outperforms in terms of spectral efficiency and average BER. This improvement is confirmed by comparison with an alternative rate and power algorithm which exploits an ideal CSI assumption

Multiple antennas at the transmitter and the receiver have the potential to either increase the data rate through spatial multiplexing or enhance the quality of transmission through exploitation of diversity. In this paper, we address the problem of multi-user multiplexing using spatial diversity techniques for a MU-MIMO-OFDM system so that a basestation could serve multiple users in the same frequency band making huge saving in bandwidth utilization. In particular, we have proposed various techniques to improve substantially the performance of a recently proposed signal-to-leakage maximization based algorithm. Our simulation results reveal a lower error floor and improvement in BER performance and system throughput for various coding rates.

Orthogonal frequency division multiplexing (OFDM) requires an expensive linear amplifier at the transmitter due to its high peak-to-average power ratio (PAPR). Single carrier with cyclic prefix (SC-CP) is a closely related transmission scheme that possesses most of the benefits of OFDM but does not have the PAPR problem. Although in a multipath environment, SC-CP is very robust to frequency-selective fading, it is sensitive to the time-selective fading characteristics of the wireless channel that disturbs the orthogonality of the channel matrix (CM) and increases the computational complexity of the receiver. In this paper, we propose a time-domain low-complexity iterative algorithm to compensate for the effects of time selectivity of the channel that exploits the sparsity present in the channel convolution matrix. Simulation results show the superior performance of the proposed algorithm over the standard linear minimum mean-square error (L-MMSE) equalizer for SC-CP.

The problem of joint transmitter and receiver design for multi-input multi-output (MIMO) channel shortening for frequency-selective fading channel is addressed. A frequency domain approach is followed which is equivalent to infinite length time-domain channel shortening equalizers (TEQ). A practical joint space and frequency waterfilling algorithm is also provided for optimum transmit power loading. It is demonstrated that the finite length TEQ suffers from a flooring effect on the compression ratio performance, whereas the proposed method overcomes this disadvantage. The noise amplification and the compression performance of the proposed joint transceiver method is found to be better than both finite and infinite length receiver-only designs, with a gain of order of 3dB for a 2/spl times/2 MIMO channel.

We consider estimation of frequency offset (FO) and equalization of a wireless communication channel, within a general framework which allows for different frequency offsets for various multipaths. Such a scenario may arise due to different Doppler shifts associated with various multipaths, or in situations where multiple basestations are used to transmit identical information. For this general framework, we propose an approximative maximum-likelihood estimator exploiting the correlation property of the transmitted pilot signal. We further show that the conventional minimum mean-square error equalizer is computationally cumbersome, as the effective channel-convolution matrix changes deterministically between symbols, due to the multiple FOs. Exploiting the structural property of these variations, we propose a computationally efficient recursive algorithm for the equalizer design. Simulation results show that the proposed estimator is statistically efficient, as the mean-square estimation error attains the Crame/spl acute/r-Rao lower bound. Further, we show via extensive simulations that our proposed scheme significantly outperforms equalizers not employing FO estimation.

The problem of underdetermined blind source separation is addressed. The sparse assumption which is commonly required in the current underdetermined blind source separation literature is relaxed. By introducing an advanced clustering technique based upon self-splitting competitive learning, the time-frequency plane is partitioned into appropriate blocks where the number of active sources is no more than the number of sensors, resulting in a novel robust block based algorithm. Simulation studies are presented to support the proposed approach for the separation of GMSK sources.

Quasi-orthogonal space-time block codes (QO-STBCs) achieve full code rate at the expense of loss in diversity gain. We propose two feedback methods for QO-STBCs to achieve full diversity and full code rate. In the first method, signals radiated from various antennas are rotated by phasors according to feedback from the receiver, whereas the second method is based upon antenna weighting/selection. For high to moderate feedback error rates, it is demonstrated that the proposed methods outperform the quantized transmit beamformer. The performance improvement is also investigated for these closed-loop methods when the transmitted signal is error control coded.

Orthogonal space-time block coding is a transmit diversity method that has the potential to enhance forward capacity. For a communication system with a complex alphabet, full diversity and full code rate space-time codes are available only for two antennas, and for more than two antennas full diversity is achieved only when the code rate is lower than one. A quasi-orthogonal code could provide full code rate, but at the expense of loss in diversity, which results in degradation of performance. We propose a closed loop feedback scheme for quasi-orthogonal codes which provides full diversity while achieving the full code rate. We investigate, in particular, the performance of this scheme, when the feedback information is quantised and when the fading of the channel is frequency-selective.

We propose a new family of mixed constant modulus algorithms for the elimination of local minima associated with the fractionally spaced constant modulus algorithm in the presence of channel noise. A special case of this family is the leaky constant modulus algorithm (L-CMA). We show that L-CMA aims to minimise jointly the intersymbol interference (ISI) and the noise gain introduced by the equalizer. Moreover, we derive a suitable range of leakage factors for which all local minima due to large noise amplification are eliminated.

The performance of the constant modulus algorithm can suffer because of the existence of local minima with large mean squared error (MSE). This paper presents a new way of obtaining the optimum MSE over all delays using a second equalizer under a mixed constant modulus and cross correlation algorithm (CM-CCA). Proof of convergence is obtained for the noiseless case. Simulations demonstrate the potential of the method.