We propose Vision-Language Feature-based Multimodal Semantic Communication (VLF-MSC), a unified system that transmits a single compact vision-language representation to support both image and text generation at the receiver. Unlike existing semantic communication techniques that process each modality separately, VLF-MSC employs a pre-trained vision-language model (VLM) to encode the source image into a vision-language semantic feature (VLF), which is transmitted over the wireless channel. At the receiver, a decoder-based language model and a diffusion-based image generator are both conditioned+ on the VLF to produce a descriptive text and a semantically aligned image. This unified representation eliminates the need for modality-specific streams or retransmissions, improving spectral efficiency and adaptability. By leveraging foundation models, the system achieves robustness to channel noise while preserving semantic fidelity. Experiments demonstrate that VLF-MSC outperforms text-only and image-only baselines, achieving higher semantic accuracy for both modalities under low SNR with significantly reduced bandwidth.

Federated Learning (FL) is a powerful framework for privacy-preserving distributed learning. It enables multiple clients to collaboratively train a global model without sharing raw data. However, handling noisy labels in FL remains a major challenge due to heterogeneous data distributions and communication constraints, which can severely degrade model performance. To address this issue, we propose FedEFC, a novel method designed to tackle the impact of noisy labels in FL. FedEFC mitigates this issue through two key techniques: (1) prestopping, which prevents overfitting to mislabeled data by dynamically halting training at an optimal point, and (2) loss correction, which adjusts model updates to account for label noise. In particular, we develop an effective loss correction tailored to the unique challenges of FL, including data heterogeneity and decentralized training. Furthermore, we provide a theoretical analysis, leveraging the composite proper loss property, to demonstrate that the FL objective function under noisy label distributions can be aligned with the clean label distribution. Extensive experimental results validate the effectiveness of our approach, showing that it consistently outperforms existing FL techniques in mitigating the impact of noisy labels, particularly under heterogeneous data settings (e.g., achieving up to 41.64% relative performance improvement over the existing loss correction method).

Federated learning (FL) is a distributed training paradigm that enables collaborative learning across clients without sharing local data, thereby preserving privacy. However, the increasing scale and complexity of modern deep models often exceed the computational or memory capabilities of edge devices. Furthermore, clients may be constrained to use heterogeneous model architectures due to hardware variability (e.g., ASICs, FPGAs) or proprietary requirements that prevent the disclosure or modification of local model structures. These practical considerations motivate the need for model-heterogeneous FL, where clients participate using distinct model architectures. In this work, we propose Generative Model-Aided Federated Learning (GeFL), a framework that enables cross-client knowledge sharing via a generative model trained in a federated manner. This generative model captures global data semantics and facilitates local training without requiring model homogeneity across clients. WhileGeFLachieves strong performance, empirical analysis reveals limitations in scalability and potential privacy leakage due to generative sample memorization. To address these concerns, we proposeGeFL-F, which utilizes feature-level generative modeling. This approach enhances scalability to large client populations and mitigates privacy risks. Extensive experiments across image classification tasks demonstrate that bothGeFLandGeFL-Foffer competitive performance in heterogeneous settings. Code is available at [1].

Federated Learning (FL) is increasingly being adopted in military collaborations to develop Large Language Models (LLMs) while preserving data sovereignty. However, prompt injection attacks-malicious manipulations of input prompts-pose new threats that may undermine operational security, disrupt decision-making, and erode trust among allies. This perspective paper highlights four vulnerabilities in federated military LLMs: secret data leakage, free-rider exploitation, system disruption, and misinformation spread. To address these risks, we propose a human-AI collaborative framework with both technical and policy countermeasures. On the technical side, our framework uses red/blue team wargaming and quality assurance to detect and mitigate adversarial behaviors of shared LLM weights. On the policy side, it promotes joint AI-human policy development and verification of security protocols.

Federated Learning (FL) allows multiple clients to collaboratively train shared models without exchanging raw data, thereby preserving privacy. However, FL systems are vulnerable to malicious participants known as free-riders who exploit the collaborative nature without providing genuine data contributions. To expose this critical security threat, we introduce a novel stealth free-rider attack that leverages pre-trained forecasting models to generate highly realistic synthetic time-series data. Our approach enables malicious clients to deceive FL systems while obtaining benefits from fair participants' contributions, thereby undermining the integrity of federated networks. Numerical results on EEG-based sleep stage classification demonstrate that our attack maintains comparable performance with free-rider ratios up to 70% while causing catastrophic degradation when all clients are free-riders.

Given sufficient data from multiple edge devices, federated learning (FL) enables training a shared model without transmitting private data to the central server. However, FL is generally vulnerable to Byzantine attacks from compromised edge devices, which can significantly degrade the model performance. In this work, we propose an intuitive plugin that seamlessly embeds Byzantine resilience into existing FL methods. The key idea is to generate virtual data samples and evaluate model consistency scores across local updates to effectively filter out compromised updates. By utilizing this scoring mechanism before the aggregation phase, the proposed plugin enables existing FL methods to become robust against Byzantine attacks while maintaining their original benefits. Numerical results on blood cell classification task demonstrate that the proposed plugin provides strong Byzantine resilience. In detail, plugin-attached FedAvg achieves over 89.6% test accuracy under 30% targeted attacks (vs. 19.5% w/o plugin) and maintains 65–70% test accuracy under untargeted attacks (vs. 17–19% w/o plugin).

Predictive beam tracking is regarded as one of the most promising solutions to support vehicle-to-infrastructure (V2I) communication with requirements of low latency and high data rates. Especially in millimeter-wave (mmWave) V2I scenarios, vision-aided beam tracking based on artificial intelligence (AI) effectively mitigates critical delays caused by beam alignment. This study builds upon previous work on vision-aided beam tracking in mmWave V2I scenarios by enhancing alignment accuracy through occlusion-aware processing. We propose a visual semantic-aided predictive beam tracking (VS-PBT) method, utilizing a Sequence-to-Sequence (Seq2Seq) model and binary mask sequences for multi-frame beam prediction. Furthermore, using a deep learning-based approach, our method accurately detects the target vehicle in multi-user environments, ensuring precise beam alignment in complex scenarios. Simulation results show that the proposed VS-PBT achieves near 90% Top-1 beam tracking accuracy, achieving a 4% gain over single-frame prediction and exceeding modality benchmarks by over 20%. Even in visually occluded scenarios, VS-PBT maintains robust Top-$k$beam tracking and near-optimal transmission rate ratio (TRR) performance.

Diffusion transformers (DiTs) combine transformer architectures with diffusion models. However, their computational complexity imposes significant limitations on real-time applications and sustainability of AI systems. In this study, we aim to enhance the computational efficiency through model quantization, which represents the weights and activation values with lower precision. Multi-region quantization (MRQ) is introduced to address the asymmetric distribution of network values in DiT blocks by allocating two scaling parameters to sub-regions. Additionally, time-grouping quantization (TGQ) is proposed to reduce quantization error caused by temporal variation in activations. The experimental results show that the proposed algorithm achieves performance comparable to the original full-precision model with only a 0.29 increase in FID (Fréchet Inception Distance) at W8A8 (8-bit weights and activations). Furthermore, it outperforms other baselines at W6A6, thereby confirming its suitability for low-bit quantization. These results highlight the potential of our method to enable efficient real-time generative models.

Federated learning (FL) enables distributed training while preserving data privacy, but stragglers—slow or incapable clients can significantly slow down the total training time and degrade performance. To mitigate the impact of stragglers, system heterogeneity, including heterogeneous computing and network bandwidth, has been addressed. While previous studies have addressed system heterogeneity by splitting models into submodels, they offer limited flexibility in model architecture design, without considering potential inconsistencies arising from training multiple submodel architectures. We proposenested federated learning (NeFL), a generalized framework that efficiently divides deep neural networks into submodels using both depthwise and widthwise scaling. To address theinconsistencyarising from training multiple submodel architectures, NeFL decouples a subset of parameters from those being trained for each submodel. An averaging method is proposed to handle these decoupled parameters during aggregation. NeFL enables resource-constrained devices to effectively participate in the FL pipeline, facilitating larger datasets for model training. Experiments demonstrate that NeFL achieves performance gain, especially for the worst-case submodel compared to baseline approaches (7.63% improvement on CIFAR-100). Furthermore, NeFL aligns with recent advances in FL, such as leveraging pre-trained models and accounting for statistical heterogeneity.

Extended reality-enabled Internet of Things (XRI) provides new user experiences and a sense of immersion by adding virtual elements to the real world through Internet of Things (IoT) devices and emerging sixth-generation (6G) technologies. However, computational-intensive XRI tasks are challenging for energy-constrained small-size XRI devices to cope with, and moreover certain data require centralized computing that needs to be shared among users. To this end, we propose a cache-assisted space-air-ground integrated network mobile edge computing (SAGIN-MEC) system for XRI applications consisting of two types of edge servers mounted on an unmanned aerial vehicle (UAV) and low Earth orbit (LEO) satellite equipped with a cache and multiple ground XRI devices. For system efficiency, four different offloading procedures of XRI data are considered according to the type of information, i.e., shared data and private data, as well as the offloading decision and the caching status. Specifically, private data can be offloaded to either UAV or LEO satellite, while the offloading decision of shared data to the LEO satellite can be determined by the caching status. With the aim of maximizing the energy efficiency of the overall system, we jointly optimize UAV trajectory, resource allocation and offloading decisions under latency constraints and UAV’s operational limitations by using the alternating optimization (AO)-based method along with the Dinkelbach algorithm and successive convex approximation (SCA). Via numerical results, the proposed algorithm is verified to have superior performance compared to conventional partial optimizations or processes without a cache.

Under frequency-selective multi-path channel environment, delayed copies of the transmitted symbols are summed up to form a received signal. In order to remove this intersymbol interference (ISI), linear minimum mean-square error (LMMSE) equalizer can be applied to the received signal to reconstruct the transmitted symbols. While being an optimal linear filter, the LMMSE equalizer ideally requires infinite length of the received signal, which is infeasible in practice. In order to mitigate this limitation of linear filters, we propose to utilize neural networks for equalization, referred to as neural filters. Numerical results verify that, given with enough pilot data, the proposed neural filter outperforms the optimal LMMSE equalizer that uses perfect knowledge on the channel realization vector.

Federated learning (FL) aims at optimizing a shared global model over multiple edge devices without transmitting (private) data to the central server. While it is theoretically well-known that FL yields an optimal model – centrally trained model assuming availability of all the edge device data at the central server – under mild condition, in practice, it often requires massive amount of iterations until convergence, especially under presence of statistical/computational heterogeneity. This paper utilizes cyclic learning rate at the server side to reduce the number of training iterations with increased performance without any additional computational costs for both the server and the edge devices. Numerical results validate that, simply plugging-in the proposed cyclic aggregation to the existing FL algorithms effectively reduces the number of training iterations with improved performance.

Increasing communication bandwidth is a highly effective method for improving communication system throughput. However, the sub-6GHz frequency band is already heavily utilized, which has led to the exploration of higher frequency bands such as sub-terahertz (sub-THz) communication as a key technology for the upcoming 6G era. Unfortunately, sub-THz channels tend to have only line-of-sight (LoS) components due to reflection losses, making it challenging to achieve multiplexing gain through spatial dimension. This paper investigates the practical implications of using orthogonal angular momentum (OAM) signals for multiplexing in LoS scenarios. While being a well-known fact that independent transmit signals conveyed on orthogonal OAM modes keep independence at the receiver side under a perfectly aligned system, here we focus on misaligned system in which inter-mode-interference (IMI) breaks such independence. Inspired by recent advancements in MIMO detection, particularly deep soft interference cancellation (DeepSIC), we propose a hybrid model-based/data-driven detector for misaligned OAM system that aims at achieving minimal symbol error rate via mitigating IMI. Experimental results demonstrate that the proposed detector, named OAM-DeepSIC, outperforms conventional detectors with minimal computational cost.

Marine Internet of Things (IoT) systems have grown substantially with the development of nonterrestrial networks (NTNs) via aerial and space vehicles in the upcoming sixth-generation (6G), thereby assisting environment protection, military reconnaissance, and sea transportation. Due to the unpredictable climate changes and the extreme channel conditions of maritime networks, however, it is challenging to efficiently and reliably collect and compute a huge amount of maritime data. In this article, we propose a hybrid low-Earth orbit (LEO) and unmanned aerial vehicle (UAV) edge computing method in space–air–sea integrated networks for marine IoT systems. Specifically, two types of edge servers mounted on UAVs and LEO satellites are endowed with computational capabilities for the real-time utilization of a sizable data collected from ocean IoT sensors. Our system aims at minimizing the total energy consumption of the battery-constrained UAV by jointly optimizing the bit allocation of communication and computation along with the UAV path planning under latency, energy budget, and operational constraints. For availability and practicality, the proposed methods are developed for three different cases according to the accessibility of the LEO satellite, “Always On,” “Always Off,” and “Intermediate Disconnected,” by leveraging successive convex approximation (SCA) strategies. Via numerical results, we verify that significant energy savings can be accrued for all cases of LEO accessibility by means of joint optimization of bit allocation and UAV path planning compared to partial optimization schemes that design for only the bit allocation or trajectory of the UAV.

Whenever wireless communication link becomes a unique communication means, ensuring security is just as important as achieving fast and reliable communication especially for military and medical systems. A common approach for secure wireless communication is via encryption of the information but this may fail to provide complete security (e.g., vulnerable to side-channel attack) especially when it comes with computation-limited scenarios such as Internet of Things (IoT) case. A simpler, yet powerful alternative technique to improve security is by preventing eavesdroppers from knowing correct modulation and coding (MCS) scheme. In traditional wireless communication systems, this MCS information can be secured based on sophisticate design of mutually cooperative strategies between the transmitter and the receiver. However, for deep learning (DL)-based wireless communication systems, this cooperation can be significantly simplified by just adding a well-designed adversarial signal to the transmitted signal. In this paper, we propose a novel, transferable adversarial signal design that simultaneously prevents non-cooperative eavesdroppers from achieving correct modulation scheme while ensuring the intended, cooperative receiver's correct acquisition of the modulation scheme. Extensive numerical results validate secrecy of the proposed scheme along with transferability.

Abstract Cooperation of co‐located mobile network operators can provide potential benefits for the capacity expansion without further densification of radio nodes. However, such benefits need to be scrutinised for coexisting cloud radio access network (C‐RAN) operators because the inter‐operator cooperation may compromise the superb interference coordination capability that each C‐RAN has. Furthermore, altering C‐RAN infrastructure for the cooperation incurs a high investment cost. In this paper, quantitative gain of inter‐operator coordination strategies is evaluated to provide the C‐RAN operators with a guideline on their cooperation decisions. The coordination strategies encompass dynamic user association and the spectrum sharing which aim at maximising the total user throughput. A heuristic algorithm is proposed that reduces the computational burden of the coordination significantly. Numerical results suggest that the inter‐operator cooperation is beneficial particularly when the network size of each operator tends to be highly asymmetric. It is also verified that the users who belong to smaller network attain more coordination gains.

It has been a challenging task to derive the complementary cumulative distribution function (CCDF) of aggregate interference as closed-form in Poisson point process (PPP). In this paper, we consider wireless communication networks where transmitters are all distributed according to homogeneous PPP and study the CCDF of the interference. Mainly, we derive generalized bounds on the CCDF of aggregate interference from whole homogeneous PPP based on discretization of interference, which considerably enhance tightness compared to the previous bounds. These bounds can be used to the analysis requiring the distribution for aggregate interference, such as deployment and activation of BSs, massive MIMO, and millimeter wave communication networks. As examples of utilizing the derived bounds, we derive tractable coverage probabilities of uplink non-orthogonal multiple access (NOMA) users and downlink users in cellular networks under line-of-sight(LoS)/non-line-of-signt (NLOS) channels, respectively. In NOMA, we derive the closed-form bounds on coverage probability when successive interference cancellation is adopted for NOMA signal demodulation. Using the proposed bounds, we also obtain the bounds on coverage probability of a cellular system with LoS/NLOS channels. The tightness of bounds in this work is shown via numerical comparison. Moreover, we mathematically prove the convergence of bounds to exact CCDF.

Recently, unmanned aerial vehicles (UAVs) have been the subject of much attention, and UAV swarms are a promising solution to resolve the limitations of a single UAV. In terms of aerodynamics, fixed-wing UAV swarms fly in a V-formation to reduce their energy usage via upwash. UAVs in the V-formation are typically in close proximity, reducing power consumption aerodynamically. However, these formations lack spatial separability, degrading the performance of communication employing non-orthogonal multiple access schemes. In this paper, to tackle this problem, the formation of UAV swarms considering both aerodynamics and communications employing the beam division multiple access (BDMA) scheme is examined. Problems related to the minimization of power consumption of UAVs and the ground control station (GCS) are formulated separately, after which we optimize the beamforming vector, power, and formation of the UAV swarm and propose a novel UAV swarm formation, termed the O-formation, that is power-efficient for communications. Numerical results show that formation of the UAV swarm affects the power consumption related to communications and that the proposed O-formation improves the power efficiency compared to the V-formation and a random formation.

Cooperative training methods for distributed machine learning are typically based on the exchange of local gradients or local model parameters. The latter approach is known as Federated Learning (FL). An alternative solution with reduced communication overhead, referred to as Federated Distillation (FD), was recently proposed that exchanges only averaged model outputs. While prior work studied implementations of FL over wireless fading channels, here we propose wireless protocols for FD and for an enhanced version thereof that leverages an offline communication phase to communicate "mixed-up" covariate vectors. The proposed implementations consist of different combinations of digital schemes based on separate source-channel coding and of over-the-air computing strategies based on analog joint source-channel coding. It is shown that the enhanced version FD has the potential to significantly outperform FL in the presence of limited spectral resources.

When a channel model is available, learning how to communicate on fading noisy channels can be formulated as the (unsupervised) training of an autoencoder consisting of the cascade of encoder, channel, and decoder. An important limitation of the approach is that training should be generally carried out from scratch for each new channel. To cope with this problem, prior works considered joint training over multiple channels with the aim of finding a single pair of encoder and decoder that works well on a class of channels. As a result, joint training ideally mimics the operation of non-coherent transmission schemes. In this paper, we propose to obviate the limitations of joint training via meta-learning: Rather than training a common model for all channels, meta-learning finds a common initialization vector that enables fast training on any channel. The approach is validated via numerical results, demonstrating significant training speed-ups, with effective encoders and decoders obtained with as little as one iteration of Stochastic Gradient Descent.

With the rapid development of vehicular networks, various applications that require high computation resources have emerged. To efficiently execute these applications, vehicular edge computing (VEC) can be employed. VEC offloads the computation tasks to the VEC node, i.e., the road side unit (RSU), which improves vehicular service and reduces energy consumption of the vehicle. However, communication environment is time-varying due to the movement of the vehicle, so that finding the optimal offloading parameters is still an open problem. Therefore, it is necessary to investigate an optimal offloading strategy for effective energy savings in energy-limited vehicles. In this paper, we consider the changes of communication environment due to various speeds of vehicles, which are not considered in previous studies. Then, we jointly optimize the offloading proportion and uplink/computation/downlink bit allocation of multiple vehicles, for the purpose of minimizing the total energy consumption of the vehicles under the delay constraint. Numerical results demonstrate that the proposed energy-efficient offloading strategy significantly reduces the total energy consumption.

In a conventional Cloud Radio Access Network (CRAN) system, due to geographical and cost problems, it is difficult to install additional remote radio head (RRH) with fronthaul links once after the initial CRAN deployment is completed. To solve this problem, we present a new type of CRAN architecture which has simple supplementary radio nodes termed w-RRHs straightly linked to the adjacent existing RRHs, not to a centralized baseband unit (BBU) pool. This paper quantitatively evaluates the potential benefits from the use of extra w-RRHs. Specifically, we provide a quantified guideline on whether or not to permit the usage of w-RRHs from the value of achievable spectral efficiency with regard to the additional delay caused by w-RRHs. Simulation results clearly demonstrate the trade-off between the advantage and penalty of additional w-RRHs, and emphasize the impact of network size on the system performance.

In this paper, we investigate the sum secrecy rate of cloud and edge processing in multi-cell downlink system by adopting the artificial noise precoding (ANP). To elaborate, in cloud processing, we consider the outdated channel state information (CSI) inevitably induced by fronthaul link which connects the cloud and radio units in return for centralized processing. In contrast, the radio unit (RU) in edge processing only manages its cell using timely but local CSI. Furthermore, under per-antenna power constraint, we obtain a sub-optimal precoder to maximize the sum-secrecy rate for both processing. With simulation results, we compare the secrecy rate of the cloud and edge processing, and show that the sub-optimal precoder performs well.

Distributed computing platforms typically assume the availability of reliable and dedicated connections among the processors. This work considers an alternative scenario, relevant for wireless data centers and federated learning, in which the distributed processors, operating on generally distinct coded data, are connected via shared wireless channels accessed via full-duplex transmission. The study accounts for both wireless and computing impairments, including interference, imperfect Channel State Information, and straggling processors, and it assumes a Map-Shuffle-Reduce coded computing paradigm. The total latency of the system, obtained as the sum of computing and communication delays, is studied for different shuffling strategies revealing the interplay between distributed computing, coding, and cooperative or coordinated transmission.

Cooperative training methods for distributed machine learning typically assume noiseless and ideal communication channels. This work studies some of the opportunities and challenges arising from the presence of wireless communication links. We specifically consider wireless implementations of Federated Learning (FL) and Federated Distillation (FD), as well as of a novel Hybrid Federated Distillation (HFD) scheme. Both digital implementations based on separate source-channel coding and over-the-air computing implementations based on joint source-channel coding are proposed and evaluated over Gaussian multiple-access channels.

We investigate the physical layer security for multiple-input single-output (MISO) multigroup multicast system in the presence of multiple eavesdroppers (Eves). To elaborate, we aim to tackle the secrecy rate maximization (SRM) problem of the multicast system by proposing an algorithm for secure beam-forming. Here, we adopt the concept of artificial noise (AN) which is used for disturbing Eves by sending noise deliberately. Thus, we simultaneously design beamforming vectors and AN vector to maximize the secrecy rate of the system. We formulate the SRM problem in the form of non-convex max-min fair problem which is hard to solve due to its non-convexity. Therefore, in order to tackle this problem, we first reformulate this SRM problem to epigraph form, and we utilize the minorization maximization (MM) algorithm and rank-1 reduction technique. Simulation results show that our proposed algorithm improves the secrecy rate of given system model and outperforms the conventional algorithm for secure beamforming.

In this paper, for cloud-radio access network (C-RAN) architecture which consists of the cloud and base stations (BSs) via dedicated fronthaul link, we investigate the energy efficiency for cloud processing at the cloud and edge processing at each BS. For cloud processing, we reflect the outdated channel state information (CSI) and additional power consumption for the centralized processing. For edge processing, each RRH is allowed to perform baseband processing based on local, but timely CSI. Thus, we reflect the degradation of spectral efficiency induced by inter-cell interference to our energy efficiency model. Furthermore, we propose an RRH selection algorithm for cloud and edge processing to enhance the energy efficiency. Simulation results show that there certainly exists the regime that the edge processing outperforms than the cloud processing. Moreover, we observe that, by means of our proposed algorithm, the energy efficiency is enhanced, especially in cloud processing.

This paper presents a multi-mode transmission technique that can increase the T-R distance of UCA OAM systems. The proposed technique uses dual polarized UCA antennas to increase the number of simultaneously transmitable modes at a specific T- R distance by exploiting the two basis of the polarized dipole antenna elements, and further extends the reaching distance of the multi-mode signals by using the inter-mode spreading between the OAM modes belonging to the same polarization domain. Simulation results show that the proposed scheme improves the T-R distances by at least 2 times as much as the conventional scheme in transmitting the same number of OAM modes with the same symbol error rate, and also that the proposed technique can transmit twice as many modes as the conventional method with the SNR loss of less than 3 dB at the same T-R distance.

Taking the positions of pico-cell base stations (PBSs) into consideration, a scheme of cell range expansion (CRE) for maximum sum rate is addressed in heterogeneous multi-input multi-output multi-user wireless networks. The optimal CRE bias obtained numerically by the proposed CRE scheme with intercell interference coordination (ICIC) allows us to maximize the sum rate while successfully maintaining the load balance between the macro-cell base station and PBSs. Numerical results confirm that the proposed CRE scheme with ICIC can provide a higher sum rate than conventional schemes while maintaining balanced load.

Massive full-dimensional multiple-input multiple-output (FD-MIMO) base stations (BSs) have the potential to bring multiplexing and coverage gains by means of three-dimensional (3D) beamforming. The key technical challenges for their deployment include the presence of limited-resolution front ends and the acquisition of channel state information (CSI) at the BSs. This paper investigates the use of FD-MIMO BSs to provide simultaneously high-rate data communication and mobile 3D positioning in the downlink. The analysis concentrates on the problem of beamforming design by accounting for imperfect CSI acquisition via time division duplex-based training and for the finite resolution of analog-to-digital converter and digital-to-analog converter at BSs. Both unstructured beamforming and a low-complexity Kronecker beamforming solution are considered, where for the latter the beamforming vectors are decomposed into separate azimuth and elevation components. The proposed algorithmic solutions are based on the Bussgang theorem, rank-relaxation, and successive convex approximation (SCA) methods. Comprehensive numerical results demonstrate that the proposed schemes can effectively cater to both data communication and positioning services, providing only minor performance degradations compared to the conventional cases in which either only the data communication or only positioning is implemented. Moreover, the proposed low-complexity Kronecker beamforming is seen to guarantee a limited performance loss in the presence of a large number of BS antennas.

Fog-aided network architectures for 5G systems encompass wireless edge nodes, referred to as remote radio systems (RRSs), as well as remote cloud center (RCC) processors, which are connected to the RRSs via a fronthaul access network. RRSs and RCC are operated via network functions virtualization, enabling a flexible split of network functionalities that adapts to network parameters such as fronthaul latency and capacity. This paper focuses on uplink communications and investigates the cloud-edge allocation of two important network functions, namely, the control functionality of rate selection and the data-plane function of decoding. Three functional splits are considered: 1) distributed radio access network, in which both functions are implemented in a decentralized way at the RRSs; 2) cloud RAN, in which instead both functions are carried out centrally at the RCC; and 3) a new functional split, referred to as fog RAN (F-RAN), with separate decentralized edge control and centralized cloud data processing. The model under study consists of a time-varying uplink channel with fixed scheduling and cell association in which the RCC has global but delayed channel state information due to fronthaul latency, while the RRSs have local but more timely CSI. Using the adaptive sum-rate as the performance criterion, it is concluded that the F-RAN architecture can provide significant gains in the presence of user mobility.

This paper concerns the feasibility of full-duplex large-scale multiple-input-multiple-output cellular systems. We first derive the analytic model of the ergodic achievable sum-rate for cell-boundary users. The model is derived by applying a simple linear filter, i.e., matched filter or zero-forcing filter, to the base-station (BS). In the analytic model, we consider large-scale fading, pilot contamination, transmitter noise, and receiver distortion. In addition, to solve the critical pilot overhead problem induced by self-interference channel estimation, we propose a pilot transmission scheme-the simultaneous pilot transmission (SPT)-and assess its performance, in terms of the ergodic sum-rate. In considering two multicell scenarios, cooperative and non-cooperative multicell systems, we obtain the ergodic achievable sum-rate by reflecting the characteristic of each scenarios, such as limited front-haul capacity and procedures of channel estimation. With all derived results, to investigate the feasibility, we observe the tradeoffs between the full- and half-duplex systems, between the SPT and conventional scheme, and between the two multicell scenarios with respect to various system parameters and environment. In the end, we confirm the tightness of our analytic model and advantages of full-duplex, SPT, and cooperation of BSs in our system model.

In this paper, we consider the multiple-input single-output (MISO) wiretap channel with a cooperative jammer (CJ). Motivated by the fact that corrupting pilot signals can effectively degrade the channel estimation at the eavesdropper (Eve), we emphasize that the physical-layer security can be enhanced by introducing jamming signals in pilot phase. Therefore, we propose a cooperative jamming scheme which delivers artificial noise (AN) both in pilot and data phases, unlike the conventional schemes that transmit AN only in data phase. More specifically, we optimize the jamming power allocation between pilot phase and data phase, along with designing the beamforming vector at the CJ. Furthermore, considering the limitation of energy budget at the CJ, the energy efficiency of the proposed scheme is investigated based on the circuit power consumption model. Finally, numerical results verify the performance of the proposed scheme, in terms of secrecy rate and energy efficiency.

We propose a user selection algorithm for the downlink multi-user millimeter wave systems. Our design aims to classify favorable users in terms of the achievable rate based on the channel state information. The phased-zero-forcing scheme is introduced to find the analog precoder gain easily under the multi-user environment. In order to maximize the overall sum-rate of selected users, we then establish an appropriate criterion, which quantifies the fidelity to each sum-rate maximization under interference condition. Some numerical results show that the performance of the proposed algorithm is very close to that of the conventional full search scheme. It is also observed that the complexity of proposed algorithm can be further reduced by setting the proper value of threshold, while it still exhibits almost the same performance as that of the full search.

In this paper, we propose a user selection algorithm for full-duplex cellular system. In full-duplex cellular system, besides self-interference (SI), the user-equipment (UE)-UE interference at downlink (DL) users is induced by adjacent uplink (UL) users thereby degrading the performance severely. Thus, we alleviate the UE-UE interference and improve the achievable rate of UL users at the same time by means of our proposed user selection algorithm. Specifically, we first adopt semiorthogonal user selection (SUS) algorithm [13] with ZF precoder for DL users and then we select UL users considering the trade-off between increasing the rate of UL users and decreasing the UE-UE interference. Moreover, we also propose a mode selection based on our proposed algorithm. As a result, we provide the improved achievable sum-rate of full-duplex system as guaranteeing better achievable rate for both DL and UL users, compared with those of users in half-duplex system. In addition, we analyze the complexity of the proposed user selection algorithm along with analysis of full-search algorithm. In the end, we observe that the numerical results show the improved achievable sum-rate of the proposed user selection algorithm.

In this paper, a new subband-filtered and pulse-shaped multicarrier (SBF-PS-MC) scheme is introduced and investigated in a generalized framework. The main motivation behind is to capture inter-user quasi-synchronicity (QS) and channel spread in 5G massive machine-type communication (mMTC) use case where uplink (UL) synchronization and other control procedures need to be simplified or even dropped to reduce signaling/protocol overhead. Based on downlink synchronization, UL transmissions from geographically distant users can entail severe relative symbol time and carrier frequency offsets by fractional amounts of multicarrier (MC) symbol interval and subcarrier spacing, respectively (i.e., interuser QS). SBF-PS-MC scheme can take advantage both of subband filtering and pulse shaping, whereby, on the one hand, other-subband (or other-user) interference (OBI) is effectively suppressed by subband filtering and, on the other hand, intra-subband and inter-symbol interference is reduced and OBI even further mitigated by pulse shaping. A main contribution is to provide a generalized framework for SBF-PS-MC scheme to handle each of the interference terms analytically dependent on the used subband filter and prototype pulse. Eventually, SBF-PS-MC is numerically evaluated over inter-user QS in time and delay spread channel that per-subband average signal to interference plus noise ratio (SINR) of SBF-PS-MC is superior to those of conventional cyclic prefix orthogonal frequency division multiplexing (OFDM), weighted overlap-add OFDM, universal filtered OFDM, and subband-filtered OFDM.

Fronthaul limitations in terms of capacity and latency motivate the growing interest in the wireless industry for the study of alternative functional splits between cloud and edge nodes in Cloud Radio Access Network (C-RAN). This work contributes to this line of work by investigating the optimal functional split of control and data plane functionalities at the edge nodes and at the Remote Cloud Center (RCC) as a function of the fronthaul latency. The model under study consists of a two-user time-varying uplink channel in which the RCC has global but delayed channel state information (CSI) due to fronthaul latency, while edge nodes have local but timely CSI. Adopting the adaptive sum-rate as the performance criterion, functional splits whereby the control functionality of rate selection and the decoding of the data-plane frames are carried out at either the edge nodes or at the RCC are compared, demonstrating the potential advantages of implementing control functions at the edge.

In this paper, we investigate the total delivery latency across the fronthaul and wireless segments of a Fog Radio Access Network (F-RAN) under the assumption that cloud processor and edge nodes (ENs) are connected by a multicast fronthaul link. The total delivery latency is assessed via the Normalized Delivery Time (NDT) metric which provides a high signal-to- noise ratio (SNR) measure of the relative delivery worst-case latency with respect to an interference-free system. We derive upper and lower bounds on the achievable NDT for a F-RAN with two ENs and two users as a function of cache and fronthaul resources. The upper bound is obtained by studying the NDT achieved by delivery strategies that encompass both coded and uncoded multicast strategies on the fronthaul. The lower bound is instead derived by leveraging information theoretic converse arguments. Upper and lower bounds are shown to coincide, hence characterizing the minimum NDT, for a large range of problem parameters. Among the conclusions of this study, we demonstrate that coded multicasting is not useful for reducing the NDT for the mentioned range of parameters.

Mobile cloud computing relieves the tension between computation‐intensive mobile applications and battery‐constrained mobile devices by enabling the offloading of computing tasks from mobiles to a remote processors. This study considers a mobile cloud computing scenario in which the ‘cloudlet’ processor that provides offloading opportunities to mobile devices is mounted on unmanned aerial vehicles (UAVs) to enhance coverage. Focusing on a slotted communication system with frequency division multiplexing between mobile and UAV, the joint optimisation of the number of input bits transmitted in the uplink by the mobile to the UAV, the number of input bits processed by the cloudlet at the UAV and the number of output bits returned by the cloudlet to the mobile in the downlink in each slot is carried out by means of dual decomposition under maximum latency constraints with the aim of minimising the mobile energy consumption. Numerical results reveal the critical importance of an optimised bit allocation in order to enable significant energy savings as compared with local mobile execution for stringent latency constraints.

In this paper, we consider the cooperative spectrum sensing (CSS) in the non-time-slotted full duplex cognitive radio (FDCR) networks. In the non-time-slotted cognitive radio (CR) networks, the primary user can change its activity during the transmission duration in contrast with the time-slotted CR networks. We analytically derive the collision probability and the outage probability for the two operation modes of the secondary user under the CSS in non-time-slotted FDCR networks. We then turn to determine the optimum decision threshold for the energy detector at each secondary user to maximize the average throughput subject to a constraint on the outage probability of primary user. In numerical results, we demonstrate and analyze the impact of CSS in the non-time-slotted FDCR networks.

In this paper, we investigate a two-hop full-duplex wireless-powered relaying system consists of a source, a relay, and a destination in the presence of a passive eavesdropper. The relay assists the transmission of confidential information from the source to the destination, while simultaneously harvesting the energy with time switching scheme by the radio-frequency (RF) signals. Our goal is to maximize the physical-layer security under harvested energy constraints by designing the full-duplex wireless-powered relay, whose two relaying strategies are considered, namely amplify-and-forward (AF) and decode-and-forward (DF). The relay beamforming vector design is proposed for AF protocol, and is jointly optimized with the time ratio parameter in the case of DF protocol to maximize the physical-layer security under harvested energy constraints. Moreover, for the simultaneous energy and secure message transfer at the relay, a two-phase method is provided, which enables the relay to avoid the self-interference caused by full-duplex operation, and also to harvest the energy from the self-interference. The proposed algorithmic solutions leverage the rank relaxation, Majorization-Minimization (MM) programming, and line search method. Numerical results show that the proposed full-duplex relaying system outperforms the half-duplex relaying system in energy harvesting. Moreover, the trade-off between AF and DF protocols according to the occurrence probability of non-zero secrecy rate can be observed in terms of physical-layer security.

In the downlink of the Cloud-Radio Access Network (C-RAN) cellular architecture, complex baseband signals are transmitted from a central unit (CU) in the “cloud” over digital fronthaul links to distributed radio units (RUs). The standard design of digital fronthauling is based on quantization that operates separately over each fronthaul link. In this paper, a fronthaul quantization scheme is proposed that, unlike conventional schemes, implements a joint quantization mapping across all fronthaul links that is adapted to the current channel conditions. As compared to the current standard approach, the proposed multivariate quantization (MQ) scheme only requires additional processing at the CU, while no modification is needed at the RUs. The algorithm is extended to enable variable-length compression, and is compared via numerical results to a related approach based on the information-theoretic technique of multivariate compression.

The unmanned ground system (UGS) demands the high data rate and link reliability in battlefield and hazardous environment. Also, more radio resources should be allocated in the uplink channel for the surveillance data than in the downlink channel, unlike the commercial communication system where the utilization of resources is biased toward the downlink to provide the multimedia service to the multiple users. In this paper, considering the traffic unbalance between the uplink and downlink of UGS, we propose an appropriate frame structure for UGS based on the orthogonal frequency division multiplexing (OFDM) and time division duplex (TDD). In addition, a power control method for UGS is proposed to guarantee the stable communication link for the downlink, uplink, and ad-hoc channel. Finally, in order to evaluate the performance of the proposed system, we design and implement the asymmetric MIMO system with space time block code (STBC), spatial multiplexing method and modified maximum likelihood (MML) decoding scheme. We perform the field test in the long-range and line-of-sight (LoS) environment. It is verified that implemented system for UGS can support the high- speed data transmission and maintain the robust link.

Cloud radio access network (C‐RAN) is a prominent architecture for fifth generation wireless cellular system that is based on the centralisation of baseband processing for multiple distributed radio units (RUs) at a control unit (CU). In this study, it is proposed to leverage the C‐RAN architecture to enable the implementation of direct localisation of the position of mobile devices from the received signals at distributed RUs. With ideal connections between the CU and the RUs, direct localisation is known to outperform traditional indirect localisation , whereby the location of a source is estimated from intermediary parameters estimated at the RUs. However, in a C‐RAN system with capacity limited fronthaul links, the advantage of direct localisation may be offset by the distortion caused by the quantisation of the received signal at the RUs. In this study, the performance of direct localisation is studied by accounting for the effect of fronthaul quantisation with or without dithering. An approximate maximum likelihood localisation is developed. Then, the Cramér–Rao bound on the squared position error of direct localisation with quantised observations is derived. Finally, the performance of indirect localisation and direct localisation with or without dithering is compared via numerical results.

A key practical constraint on the design of Hybrid automatic repeat request (HARQ) schemes is the modem chip area that needs to be allocated to store previously received packets. The fact that, in modern wireless standards, this area can amount to a large fraction of the overall chip has recently highlighted the importance of HARQ buffer management, that is, of the use of advanced compression policies for storage of received data. This work tackles the analysis of the throughput of standard HARQ schemes, namely Type-I, Chase Combining and Incremental Redundancy, under the assumption of a finite-capacity HARQ buffer by taking an information-theoretic standpoint based on random coding. Both coded modulation, via Gaussian signaling, and Bit Interleaved Coded Modulation (BICM) are considered. The analysis sheds light on questions of practical relevance for HARQ buffer management such as on the type of information to be extracted from the received packets and on how to store it.

Coordinated multi-point (CoMP) in heterogeneous network (HetNet) has been a promising solution to meet the unprecedented growth of data traffic. In this paper, we revisit the conventional energy efficiency (EE) for the downlink of HetNet CoMP by considering the fronthaul power consumption. To this end, we propose a new power consumption model for the practical use, in which two methods of fronthaul transmission are investigated, namely “After-precoding (AP)” and “Before-precoding (BP)”. In order to further improve the EE, the sleep mode of small cells is applied. Based on the EE formulation via the proposed power consumption model, we verify the EE performances of CoMP and Non-CoMP, and present the impact of the fronthaul power on the EE in HetNet CoMP system through numerical results.

Channel state information (CSI) is known to play crucial role for downlink transmissions of multi-user multiple-input multiple-output (MU-MIMO) systems. The existing works have been widely researched on the CSI feedback problem for system throughput aspects. With limited available radio resources, system throughput and fairness are usually in conflict with each other, balancing system throughput and fairness with high resource efficiency is necessary. Also, the analysis of feedback-rate allocation considering these two performance measures is needed. Therefore, in this paper, we propose the non-cooperative feedback-rate control game for improving throughput fairness in MU-MIMO system. Based on the game theory, the utility function of each user is considered both throughput fairness and total throughput. Moreover, we investigate the existence of the Nash-equilibrium of the proposed game and outage probability in terms of CSI feedback-rate. Simulation results show that the proposed game improves throughput fairness, and thus achieves better outage performance, as compared to the conventional equal-sized feedback scheme.

A key practical constraint on the design of hybrid automatic repeat request (HARQ) schemes is the size of the on-chip buffer that is available at the receiver to store previously received packets. In fact, in modern wireless standards such as LTE and LTE-A, the HARQ buffer size is one of the main drivers of the modem area and power consumption. This has recently highlighted the importance of HARQ buffer management, that is, of the use of buffer-aware transmission schemes and of advanced compression policies for the storage of received data. This work investigates HARQ buffer management by leveraging information-theoretic achievability arguments based on random coding. Specifically, standard HARQ schemes, namely Type-I, Chase Combining, and Incremental Redundancy, are first studied under the assumption of a finite-capacity HARQ buffer by considering both coded modulation, via Gaussian signaling, and Bit Interleaved Coded Modulation (BICM). The analysis sheds light on the impact of different compression strategies, namely the conventional compression log-likelihood ratios and the direct digitization of baseband signals, on the throughput. The optimization of coding blocklength is also investigated, highlighting the benefits of HARQ buffer-aware transmission scheme.

In the simultaneous wireless information and power transfer (SWIPT) system, the security becomes a major concern. This is because the energy harvesting (EH) operates with much higher received power than conventional information decoding (ID), which leads the environment vulnerable to eavesdropping. In this work, we propose a joint transceiver design to maximize physical-layer security while guaranteeing the required minimum of the harvested energy. The system under consideration consists of a transmitter (Tx), a receiver (Rx), a cooperative jammer (CJ) and a passive eavesdropper (Eve). The Rx is assumed to be energy-constrained in terms of the operation time or the limited power of a battery, where an EH Rx and an ID Rx are co-located. Based on an employment of the CJ, we jointly optimize the beam-forming vector for the CJ and the limited-resource allocation for the Rx to achieve the system objective. For the structure of the co-located EH and ID Rx, two schemes are investigated such as time switching (TS) and power splitting (PS) whose limited resources are the operation time and power, respectively. The algorithmic solutions are proposed for both TS and PS schemes based on rank-relaxation and Minorization-Maximization (MM) approach. Finally, the numerical results show that the secrecy rate in the SWIPT system can be improved by using the proposed algorithm while guaranteeing the requirement of the harvested energy.

Although orbital angular momentum (OAM) was first introduced for optical communications, it is drawing attention as a new transmission technique for millimeter wave communication. Due to the additional orthogonal transmit dimension by photon OAM, the theoretical throughput can increase dramatically with various OAM modes. In practice, since OAM by uniform circular arrays (UCAs) has the implementation problem such as twisted axis between transmitter and receiver, the performance degradation happens frequently. Hence, this paper investigates the imperfect receiver condition and the effect of this problem. In particular, the off-axis and non-parallel location problems are focused. Here, the capacity with inter-mode interference (IMI) of OAM is first considered and the receiver compensation method is proposed. Numerical results show that the throughput after compensation is better than that without compensation.

In network MIMO cellular systems, subsets of base stations (BSs), or remote radio heads, are connected via backhaul links to central units (CUs) that perform joint encoding in the downlink and joint decoding in the uplink. Focusing on the uplink, an effective solution for the communication between BSs and the corresponding CU on the backhaul links is based on compressing and forwarding the baseband received signal from each BS. In the presence of ergodic fading, communicating the channel state information (CSI) from the BSs to the CU may require a sizable part of the backhaul capacity. In a prior work, this aspect was studied by assuming a Compress-Forward-Estimate (CFE) approach, whereby the BSs compress the training signal and CSI estimation takes place at the CU. In this work, instead, an Estimate-Compress-Forward (ECF) approach is investigated, whereby the BSs perform CSI estimation and forward a compressed version of the CSI to the CU. This choice is motivated by the information theoretic optimality of separate estimation and compression. Various ECF strategies are proposed that perform either separate or joint compression of estimated CSI and received signal. Moreover, the proposed strategies are combined with distributed source coding when considering multiple BSs. "Semi-coherent" strategies are also proposed that do not convey any CSI or training information on the backhaul links. Via numerical results, it is shown that a proper design of ECF strategies based on joint received signal and estimated CSI compression or of semi-coherent schemes leads to substantial performance gains compared to more conventional approaches based on non-coherent transmission or the CFE approach.

We consider the problem of designing transmit beamformer and power for downlink cooperative base-station (BS) system with a large antenna arrays. Since the design of the beamforming vector at the transmitter requires high computational complexity, in a large antenna arrays, we utilize the zero-forcing transmit beamformer, which is the simplest form and the optimal performance in a large antenna arrays. Therefore, this paper focuses on the design of power allocation with fixed transmit beamformer for minimizing the transmit power while meeting target signal-to-interference-and-noise-ratio (SINR) of each user and power constraints. We consider two scenarios according to the power constraints of cooperative BSs. One scenario is the sum power constraint on the cooperative base-stations. In this case, the cooperative BSs share the total available transmit power. However, each BS exists a maximum available transmit power in practical implementations. Thus, we consider a more realistic per BS power constraints.We proposed the solution strategies for both scenarios: For the sum power constraint case, a simple intuitive solution, where the power is allocated without regard to the power constraint until the SINR constraints is satisfied, is presented. For the per BS power constraints case, we use the properties of a large antenna arrays to find the solution of closed form. We also demonstrate, via numerical simulation, the performance of proposed strategy is convergent to the optimal performance which is achieved by using the iterative algorithm.

It was shown that the required transmit power to support a target achievable rate is inversely proportional to the number of antennas in massive multiple-input multiple-output (MIMO) systems [1]. However, the consumed power of the massive MIMO systems should include not only transmit power but also the fundamental power for operating the circuit at the transmitter, because the effect of circuit power consumption is more serious when the transmitter is equipped with massive number of antennas. Hence, to analyze the exact power consumption of massive MIMO systems, we investigate the energy efficiency for multiple cellular systems with large-scale antenna arrays under a consideration of circuit power consumption of each antenna. In particular, we propose a new power consumption model that considers not only transmit power on the power amplifier but also circuit power dissipated by analog devices and residually lossy factors in base stations (BSs). Through new energy efficiency formulation based on the proposed power consumption model, we analyze the tendency of the energy efficiency as the number of antennas increases and can see that the energy efficiency becomes a quasi-concave function with respect to the number of antennas. Finally, from the derived function of the energy efficiency, we determine the number of antennas to provide the maximum energy efficiency.

In this paper, we investigate the cognitive radio (CR) network, in which the secondary link works as a helper node to improve the physical layer secrecy rate of primary user. Depending upon the operation of secondary link, we consider two operation modes: (i) Spectrum-spatial leasing (SSL), which is introduced in the previous work, and (ii) Spectrum-time leasing (STL), which we propose. In guaranteeing the given secrecy rate and ensuring the throughput of the secondary link, the SSL considers both simultaneously, whereas the STL considers them separately. Therefore, there exists a performance gap between SSL and STL in terms of secrecy rate. We analyze both modes under the constraint of target physical layer secrecy rate compared with no helper node case. Since the performance of both modes has changed over different channel conditions, we propose a mode selection algorithm to increase the expected secondary throughput. We demonstrate that our analysis is in good agreement with the simulation results.

It is generally agreed that channel state information (CSI) is of great importance for downlink and interference channels. In practice, however, since the CSI is imperfect due to finite-rate channel feedback, the secondary transmitter is hard to control the interferences to the primary users and among the secondary users in cognitive radio network. In this paper, we propose an algorithm to efficiently control the interferences for the secondary transmitter under imperfect CSI. The proposed algorithm determines the secondary transmit power considering the maximum channel error between actual and feedback channel, therefore, it can perfectly control the interferences to the primary users. Furthermore, we propose the efficient power allocation algorithm based on water-filling for the secondary users. To verify the performance of the proposed algorithm, we derive the lower and upper bounds of the secondary user's sum-rate. We show that the theoretically derived bounds are in good agreement with the simulation results.

In this letter, we propose a non-cooperative feedback control game for secondary transmitter, where we assume that secondary users are rational and thus selfish, i.e., they will choose the feedback rate to maximize their own utility functions. The utility function of each user is defined as the downlink data-rate minus a linear price function of the channel state information (CSI) feedback rate. The existence of the Nash-equilibrium of the proposed game is analyzed. Also, in order to satisfy the primary user's interference threshold, power control algorithms are proposed. Simulation results show that the sum-rate of the proposed game is better than that of the equally distributed feedback-size scheme.

In spectrum leasing, primary users (PUs) lease the part of their spectral resources to secondary users (SUs) in exchange for appropriate remuneration. In this paper, we consider spectrum leasing via cooperation for physical-layer security that the secondary cooperation exists for improving the primary secrecy rate while maintaining its quality of service (QoS). Unlike the previous researches with the perfect channel state information (CSI) at transmitter (CSIT) assumption, we study when no information regarding the eavesdropper is available at the transmitter. This imperfect CSIT makes the SU's transmission strategy limited. To find the optimal transmission technique for the SU, we formulate a problem appropriate for the imperfect CSIT case. By using the proposed problem, we design the optimal transmit beamforming for the SU. Also, we analyze the ergodic rate of the secondary link at high signal-to-noise ratio (SNR) when the secondary cooperation focuses only on maximizing the primary secrecy rate for the viable choice of the secondary QoS level. The numerical result shows that the primary secrecy rate by the proposed transmit beamforming is comparable to that based on the perfect CSIT.

A common model for transmission over wireless links is that of a multiantenna system affected by an additive interfering signal. In some scenarios of interest, such as when the interferer is located close to the transmitter and performs retransmission, interference may be learned by the transmitter, but remain unknown at the receiver. In this case, it is well known that, if transmitter and receiver have perfect channel state information (CSI), then a technique called Dirty Paper Coding (DPC) is able to fully mitigate the interference. This paper studies the impact of imperfect CSI on a multiple-input multiple-output (MIMO) system with interference and compares the performance of DPC with that of a scheme where interference is decoded at the receiver, which we refer to as beamforming with joint decoding (BF-JD). Unlike DPC, which models the interference as an "unstructured" random process, BF-JD exploits the fact that the interfering signal is a codeword of the interferer's codebook. It is demonstrated by analysis and numerical results that BF-JD provides advantages over DPC when CSI is imperfect at the transmitter but perfect at the receiver, whereas this is not true for the case of imperfect CSI at both transmitter and receiver.

Spectrum leasing via cooperation refers to the possibility for primary users to lease part of the spectral resources to secondary users in exchange for cooperation. This paper proposes a novel implementation of this concept in which secondary cooperation aims at improving the secrecy of the primary link. In particular, a secondary transmission with multiple antennas creates interference on both primary and eavesdropping receivers but an appropriately designed beamformer may impair more the eavesdropper's reception and thus enhance primary secrecy. Design of the secondary beamformer that maximizes the primary secrecy rate while guaranteeing a minimal secondary rate is studied. It is proved that the problem can be solved in a domain that includes only two real numbers irrespective of the number of antennas. Numerical results show that the proposed spectrum leasing strategy increases the primary secrecy rate compared to the case of no spectrum leasing for a wide range of secondary minimum rate constraints.

In this paper, we study an energy efficient two-way amplify-and-forward (AF) relay network equipped with multiple antennas at both sources and relay. To minimize the transmit power while satisfying quality of service (QoS) of both sources, transmit beamformers and receive combiners are designed with ZF-based relay processing matrix. Due to the non-convexity of the optimization problem, the original problem is decomposed into two simpler optimization problems which can be solved through iteration algorithm. As an alternative, eigen-beamforming based technique is also provided to reduce the complexity of the iterative solution at the expense of the power usage. Numerical results show that the proposed solution efficiently reduces the additional power usage of the system compared to the eigenbeamforming based technique while meeting the QoS constraint of each source. Moreover, we can see the proposed algorithm converges to the steady-state value in a reasonable number of iterations.

In this paper, we present a low-complexity joint estimation algorithm of the direction of arrival (DOA) and time of arrival (TOA) for mobile location system, which is based on a multiple signal classification (MUSIC) algorithm. To reduce the computational overhead of conventional joint DOA/TOA estimation algorithms, the proposed approach employs two complexity reduction schemes: separation of the signal vector for individually extracting DOA/TOA and pseudo-spectrum computation with discrete Fourier transform (DFT). In addition, the loss of accuracy caused by the separation process is compensated by summing multiple pseudo-spectrums. Compared with the conventional two-dimensional (2-D) MUSIC algorithm, the proposed low-complexity algorithm not only substantially reduces the computational complexity but also provides better performance, especially in a low signal-to-noise ratio (SNR). The effectiveness of the proposed algorithm is verified by simulation results.

In this paper, we propose a computationally efficient decoding algorithm for space-time trellis codes in slow Rayleigh fading channels. The proposed scheme is based on a stack algorithm with two key ideas: (i) a variable stack size depending upon the signal-to-noise ratio to avoid the exhaustive search of paths and (ii) a normalized metric, which is defined as each cumulative path metric divided by its own length in the stack, to provide an appropriate comparison of the paths with different lengths. Simulation results demonstrate that the proposed algorithm achieves near-ML performance with significant reduction in complexity, compared with the conventional Viterbi algorithm.

We propose transceiver algorithms in cognitive radio networks where the cognitive users are equipped with multiple antennas. Prior work has focused on the design of precoding matrices to suppress interference to the primary receivers. This work considers designs of precoding and decoding matrices for spatial sensing to achieve two objectives: (i) to prevent interference to the primary receivers and (ii) to remove the interference, due to primary transmissions, at the secondary receiver. With single antenna primary terminals and two antenna cognitive terminals, a linear transceiver design has been introduced under a global channel state information (CSI) assumption . In this letter, multiple antenna primary and cognitive terminals and three different CSI scenarios depending upon the amount of CSI are studied: (i) local CSI, (ii) global CSI, and (iii) local CSI with side information. When local CSI is available, we leverage prior work and employ the projected-channel singular value decomposition (P-SVD). In the global CSI scenario, we propose a joint transmitter-receiver design under the assumption of full CSI of all the users at the secondary transceiver. To reduce the feedback overhead, we also propose a new iterative algorithm that exploits only local CSI with side information. In this algorithm, the secondary transmitter and receiver iteratively update precoding and decoding matrices based on the local CSI and side information (precoding/decoding matrices at the previous iteration step) to maximize the rate of the secondary link while maintaining the zero-interference constraint. Convergence is established in the special case of single stream beamforming. Numerical results confirm that the proposed joint design and the iterative algorithm show better achievable rate performance than the P-SVD technique at the expense, respectively, of CSI knowledge and side information.

It is well-known that the OFDM scheme has some tolerance for symbol/frame timing errors (i.e. valid starting position of FFT window). Such tolerance exist for SC-FDE. However, frame timing offset affects the receiver performance due to deviation in frequency domain channel estimation result when fractional channel estimation vector is applied in time domain. To achieve optimized receive performance at all times, symbol/frame timing must be estimated precisely in accordance to the exact starting position of the channel estimation sequence block. In this paper, we propose an efficient frame/frequency synchronization along with channel estimation technique suitable for implementation of SC-FDE based 60 GHz WPAN physical layer systems employing a Frank-Zadoff preamble structure. The proposed method is designed for parallel processing, and is robust against carrier frequency offset in moderately dispersive multipath-fading LOS+NLOS channels.

Block diagonalization (BD) and coordinated beamforming (CBF) are two major signal processing schemes to achieve the sum rate close to the sum capacity of a multiuser multiple-input multiple-output (MU-MIMO) downlink. However, they do not achieve the full spatial multiplexing gain except under a special antenna configuration. In this paper, therefore, we propose a new linear precoding algorithm for achieving the full spatial multiplexing gain in a general antenna configuration. To obtain the full spatial multiplexing gain, the proposed algorithm divides the users into the two groups in terms of the number of data streams transmitted. In addition, to maximize the sum rate, we choose the member of each groups by comparing the eigenvalues of each effective channel. Simulation results confirm that the proposed scheme performs close to the sum capacity and achieves the full multiplexing gain.

In this paper, we propose an adaptive cooperative spectrum-sensing scheme for cognitive radio system. Since our propose scheme chooses an adaptive criterion value which decides the usage of spectrum based on sensing environments such as the number of cooperative secondary users and the target miss-detection probability of cognitive radio system, we can achieve high frequency utilization. We firstly obtain an adaptive criterion value which gratified low false-alarm probability and kept target miss-detection probability using empirical searching. Then, we find a simple linear equation determined an appropriate criterion value quickly based on empirical searching results. Since our propose scheme always keeps the target miss-detection rate within primary user's coverage and provides lower false-alarm rate than any other schemes's false-alarm rate out of the coverage, it expects to contribute to high frequency utilization for cognitive radio systems.

We propose a novel location finding system exploiting the downlink reference signal of the Long Term Evolution (LTE) because of the absence of an efficient location finding system for the LTE. The proposed system is based on the correlation method and Chan's method for location sensing and positioning process, respectively. The conventional correlation method, however, is not matched to the LTE since the orthogonal gold sequence used for reference signal is assigned in frequency domain. Therefore, we estimate Time Difference Of Arrival (TDOA) data by cross-correlating the transmitted signal and received signal in the frequency domain. Simulation results show that the proposed system meets the Federal Communication Commission (FCC) requirement for typical Signal-to-Noise Ratio (SNR) region, and the performance can be further improved as more antennas are implemented in transmitter or receiver.

In this paper, we propose a new limited channel feedback algorithm for Orthogonal Frequency Division Multiple Access (OFDMA) systems. The proposed method reports channel information of relative maximum and minimum points of the channel gain from users to base station (BS). Based on the feedback information, BS reconstructs the channel for all the subcarriers using linear interpolation. Compared with the full CSI reporting, the proposed scheme requires only 1/60 feedback information at the expense of 1% throughput loss. Simulation results show that the proposed scheme provides an attractive tradeoff between uplink feedback amount and downlink system performance compared to the conventional limited feedback techniques.

This paper presents the achievable capacity expressions with analytical closed forms of spatial multiplexing transmission (SMT) and single antenna transmission (SAT) for multicell downlink distributed antenna systems (DAS). Considering that SMT and SAT yield high transmission gains of the center and the edge of the cell, respectively, we also propose switching algorithms depending on a user location. The transmission scheme decides a favorable transmission technique between SMT and SAT through instantaneous or ergodic capacity comparison. Simulation results show that the analytical closed forms well match to the empirical expressions. Furthermore, the proposed transmission schemes are superior to SMT and SAT.

Cooperative multiple input multiple output (MIMO) system is capable in mitigating co-channel interference (CCI) that limits the downlink capacity of cellular network. Among the joint transmission designs for base station (BS) cooperation, the linear joint transmissions with per-base power constraint is preferred due to its computational simplicity and reality. However, since the transmissions with per-base power constraint sometimes make BSs cannot fully utilize their usable power, it leads considerable capacity loss. In this paper, we propose a BS selection technique for MMSE joint transmitter in downlink cooperative MIMO system to relive the inefficient usage of power under per-base power constraint. To find the BS group which yields the highest capacity, we compare the system capacity of all the possible BS groups. Simulation results show that the BS selection significantly improves the system capacity compared to the conventional linear joint transmissions by overcoming the aforementioned power allocation problem.

The stack-based iterative precoding (SBIP) is an efficient algorithm that achieves a tradeoff between bit error rate (BER) performance and computational complexity in multi-user MIMO downlink systems. In this paper, we propose an adaptive SBIP algorithm with variable candidate set and statistically derived threshold for pruning. By analyzing the upper bound of accumulated metric, the range of candidate set is calculated. The probability distribution is also derived for statistical pruning of the tree search. Applying these ideas, the number of undesirable node visit is significantly reduced. Monte-Carlo simulations show that the proposed algorithm considerably reduces the computational complexity whereas the performance is close to that of sphere encoder (SE).

In this paper, a computationally efficient iterative detection algorithm (IDA) for D-STTD systems is presented. To find an optimal sequence by iterative searching, the proposed scheme consists of two stages. Firstly, the IDA establishes the candidate sequences. Then, it iteratively finds an optimal sequence by examining the candidate sequences until the early termination condition is satisfied. The adaptation of the number of candidate sequences makes the proposed IDA more efficient in computational complexity. The proposed detector is shown to achieve the near-ML performance with significantly reduced complexity.

Space-time block coded orthogonal frequency division multiplexing (STBC-OFDM) from coordinate interleaved orthogonal design (CIOD) is considered to be an effective space- time code (STC) to achieve full-rate and full-diversity with a simple maximum-likelihood (SML) decoder. However, the SML decoder cannot prevent the performance degradation completely due to time-varying effects such as co-channel interference (CCI). In this paper, we propose a decoding scheme to reduce the CCI after comparing the performance of the various STCs in time-varying environment. To fully exploit the special characteristic of the CIOD, the proposed decoding scheme consists of two stages: two symbols are decoded in advance and then used for decoding of the rest symbols in the form of interference cancellation. Simulation results demonstrate that the proposed receiver achieves significantly enhanced performance over time-varying channel compared to that of the SML. The complexity analysis of the proposed scheme and the conventional decoding schemes is also delivered.

In this paper, we propose a computationally efficient stack-based iterative precoding (SBIP) algorithm that achieves a performance comparable to the sphere encoder for multi-user MIMO downlink systems. The proposed algorithm is basically based on stack algorithm aided lattice reduction in the closest lattice point search of vector precoding. The first full expansion is tried by selecting the first level node with a minimum metric, constituting a reference metric. To find the best sequence, it iteratively visits each node and terminates the expansion by comparing node metrics with the calculated reference metric. In addition, we derive the range of candidate set size by using a characteristic of vector precoding. All things considered, the number of undesirable node visit is significantly reduced. Monte-Carlo simulations show that the proposed algorithm considerably reduces the computational complexity whereas the performance is close to that of sphere encoder.

V-BLAST (Vertical Bell Labs Layered Space-Time) system is considered as a key enabling technology for achieving high spectral efficiency in wireless communication. To meet the requirements of low complexity and high constant throughput in practical V-BLAST system design, we propose a computationally efficient stack-based tree searching algorithm (SBSA) from a hardware implementation point of view. To reduce the complexity, we introduce sub-space division method, which divides the constellation search space into four or nine sub- spaces. Stack size is adapted to eliminate redundant tree searching and early termination is employed to ensure a constant throughput. Consequently, the SBSA increases the system throughput with significant complexity reduction while achieving near-ML performance, compared with ML detector.

In the multiple-antenna broadcast channel, each user is not allowed to cooperate with other users, so the efficient and well-designed preceding scheme to reduce other users' interferences is required. Recently, the use of lattice basis reduction(LR) has been shown to be a very effective method for preceding in multiuser multiple antenna systems. Lattice basis reduction helps to reduce the average transmitted energy by modifying the region which includes the constellation points. Even though the LR precoding method achieves a full diversity order, the performance loss caused by quantization error still remains. In this paper, we propose a simple precoding technique to reduce the quantization error. The proposed scheme establishes a lattice list to provide more candidates for transmission power reduction based on the analysis of the patterns of the error. Simulation results show that the proposed scheme achieves the BER performance as good as that of more complex precedents (such as the vector perturbation using sphere-encoding) with significant saving in complexity.remains.

In this paper, we propose a novel orthogonal space- time-frequency block codes using Alamouti scheme for OFDM systems (STFBC-OFDM) employing two transmit antennas. Space-time block coded OFDM (STBC-OFDM) systems and space-frequency block coded OFDM (SFBC-OFDM) systems face problems in time-selective fading (i.e., time-varying channel) and frequency-selective fading environments, respectively. In the proposed STFBC-OFDM, the Alamouti code is applied over two successive OFDM symbols and neighboring subcarriers simultaneously with scaling factor to reduce the performance degradation from channel selectivities. Although the repetition of coding decreases the code rate, a SNR gain obtained by scaling can nearly compensate for the loss of code rate. The simulation results show that the proposed STFBC-OFDM outperforms STBC-OFDM in time-selective fading channels and SFBC-OFDM in frequency-selective fading channels.

An orthogonal frequency division multiplexing (OFDM) system combined with space division multiple access (SDMA) is considered as a spectral efficient transmission scheme. However, the computational effort is very intensive to achieve the near ML performance. In this paper, we propose a computationally efficient sequential multiuser detector using a stack algorithm for uplink OFDM/SDMA systems. The proposed scheme has a structure with three interrelated procedures, namely preprocessing, stack-based tree searching, and optimal sequence selection. In order for complexity to be reduced with achieving appropriate performance, the proposed scheme is designed to search branches with constrained stack size. Simulation results show that the proposed algorithm yields the average bit error rate (BER) performance close to that of sphere decoding (SD) applied to OFDM/SDMA systems with significant saving in complexity.

In this paper, we propose iterative biased minimum-mean-squared-error (BMMSE) detection schemes for double space-time transmit diversity (DSTTD)-orthogonal frequency division multiplexing (OFDM) systems over time-varying multipath channels. The time-varying effects cause intercarrier interference (ICI) and destroy the orthogonality of each STTD unit, considerably degrading system performance. To mitigate these impairments, we first estimate all transmitted symbols temporarily using sorted-QR-decomposition (SQRD)-based decision-feedback detection (DFD) considering the partial ICIs. Next, the significantly dominant ICI terms are eliminated with the help of the estimated symbols in the previous step. Finally, the SQRD-based DFD with the only desired subcarriers is performed. In each detection steps, we employ a biased MMSE criterion, which considers ICIs as well as noise. Numerical results demonstrate that the system performance of the proposed algorithms is greatly enhanced in comparison to the conventional schemes.

Channel state information (CSI) at the transmitter in multiple-input multiple-output (MIMO) downlink systems is indispensable to beamforming or precoding to take advantage of MIMO capacity. In time-division duplex (TDD) systems, CSI for downlink can be obtained from uplink channel using reciprocity. However, the CSI from uplink is not accurate enough to keep track of the continuously varying channel characteristic in downlink period. In this paper, a MIMO-OFDM downlink channel prediction technique based on Kalman filter is proposed for IEEE802.16e systems. The proposed method consists of three procedures: MMSE channel estimation, Kalman filtering and prediction, and linear interpolation. Kalman filter is employed to filter the estimated channel and to predict the next channel sample to determine the precoding weights. Simulation results demonstrate that the proposed method improves the bit error rate (BER) performance significantly.

This paper proposed partial linear and sorted QR decomposition (SQRD)-based decision feedback detection (DFD) schemes for double space time transmit diversity (DSTTD)-orthogonal frequency division multiplexing (OFDM) systems to overcome severely time-varying multipath channels. The rapid channel variation deteriorates BER performance due to the introduction of intra-STTD-unit interference and intercarrier interference (ICI). The partial linear approaches mitigate the ICI effects with banded submatrices, instead of full channel frequency response (CFR) matrix. For further improvement of BER performance, the partial SQRD-based DFD with a modified ordering criterion is developed, which effectively removes above two types of interferences including inter-STTD-unit interference simultaneously. Computer simulation results show that the performance of the partial SQRD-based DFD is superior to those of the other linear approaches, with comparable complexity.

In IEEE 802.15.4a, location awareness is a mandatory function in most applications. The exact location-finding highly depends on the ranging accuracy. In order to reach the higher ranging accuracy, less than 1 meter distance measurement error, most of the previous ranging algorithms require the extremely high sampling rate for ranging process. However, it should increase the hardware complexity and implementation cost. In this respect, we present a low-complex and cost-efficient ranging algorithm, still satisfying the reliably high ranging accuracy comparing with the previous works. This novel scheme is based on utilizing two clock pulses with a slightly different frequency in one device to measure the relative distance between two devices. The simulation results show the effect of possible several factors on the ranging accuracy.

A sub-optimal but computationally efficient modified Fano detection (MFD) algorithm for DSTTD system is presented. The proposed algorithm utilizes the sequential detection scheme based on tree searching in order to find the optimal symbol sequence. For more reliable signal detection and complexity reduction, the decoder is designed to move backward for the specified value at the end of the tree and to compute the reduced branch metrics. Simulation results show that the performance of MFD is comparable to that of ML detector while reducing the computational effort of ML method significantly.

In real wireless environments, perfect channel state information is not available at the receiver. Thus, to obtain an accurate channel information, channel estimation is an important issue in the wireless communication systems. In general, pilot symbols are used to obtain channel information. In this paper, we propose a channel estimation technique exploiting impulse symbol for OFDM systems. Our proposed technique estimates the channel impulse response directly at the receiver in time-frequency-selective fading channels and yields better bit error rate (BER) performance than existing channel estimation methods

This paper presents iterative sequential detection (ISD) scheme using the modified Fano algorithm (MFA) for a double-STTD system. The proposed scheme can be decomposed into three interrelated steps to overcome the limitation of sequential detection for MIMO systems, which are preprocessing, iterative tree searching, and optimal decision. At the first step, preprocessing is performed to decide the number of iterations and to enumerate the candidate symbols. Then, the tree searching step is followed, where a modified version of Fano algorithm is performed to find the candidate symbol sequences. Finally, the optimal sequence is selected among the candidate sequences. In particular, the variable number of candidate symbols determined in the first and second steps plays an important role of reducing the receiver complexity significantly. Simulation results show that the proposed algorithm has a significant reduction in complexity with a small performance degradation

Over the past decade, much research effort has been devoted to the study of communication using chaotic basis functions. Due to the low complexity in hardware implementation and low power consumption in chaotic signal generation, the chaotic signal is one of the possible candidate signals, applicable to both low rate Ultra-WideBand (UWB) systems and sensor networks. Among the modulation schemes using the chaotic signal, Differential Chaos Shift Keying (DCSK) is a robust noncoherent technique with respect to the implementation issue. However, Inter Symbol Interference (ISI) can induce the crucial performance degradation and synchronization error may destroy the orthogonality of the basis functions. Therefore, we present enhanced DCSK scheme and investigate the effect of the synchronization error on the performance by comparison with that of conventional DCSK.

In orthogonal frequency division multiplexing (OFDM) systems, time-frequency-selective, equivalently time-variant multipath fading destroys subcarrier orthogonality, resulting in intercarrier interference (ICI). OFDM systems with guard interval (GI) can prevent intersymbol interference (ISI) caused by frequency-selective fading, but do not combat ICI caused by time-selective fading. To mitigate the effects of ICI, several estimation methods have been proposed. These conventional algorithms are highly complex in general due to the large-sized matrix inversion. In this paper, we propose an efficient OFDM symbol estimation scheme, called iterative sequential neighbor search (ISNS) algorithm, which achieves an enhanced performance with a moderate complexity compared with the conventional methods. The validity of the proposed estimation scheme is demonstrated by computer simulations.

In this paper, we investigate diversity combining schemes for OFDM wireless LAN systems with antenna array. Current standards were designed for diversity selection instead of combining. It is well known that the combining results in a higher receiver gain than the selection. However, the combining requires significantly more complexity than the selection. This paper is focused on implementing a low-complexity diversity combining scheme, which reduces the complexity in vector channel estimation. We demonstrate the performance of the proposed combining scheme via computer simulations.

In this paper, a new bandwidth efficient OFDM transmission scheme is proposed. A bandwidth efficient iterative scheme for OFDM systems without using cyclic prefix was proposed before [3] which works well when a channel does not have a deep spectral null. But such a frequency domain deep null is common in a wireless channel. A bandwidth efficient scheme proposed in this paper works well regardless of the existence of spectral null. In the proposed scheme, OFDM symbols are recovered in two steps. In the first step, only a small portion of the time domain OFDM symbol is recovered by the frequency domain 1-tap equalizer. In the second step, the recovered small portion and the tail part of the previous symbol are used for the cyclic reconstruction and tail cancellation. Simulation experiments are conducted to validate the proposed scheme.

Most recent studies of transmit diversity and space-time coding have focused on random channels which feature rich scattering around both transmitters and receivers. In this paper, we consider a highly structured vector channel in a block fading scenario. Specular propagation and local scattering are assumed at the base-station and mobile station respectively. The channel knowledge required for the transmit design includes: a subspace formed by direction vectors of specular propagation and length of the fading coherent interval. The relatively stationary subspace information reflecting the spatial power spectral distribution is exploited to derive a code design criterion. The optimal design turns out to be a joint coding and beamforming scheme and resistance to fading within the subspace is provided by such code.

We propose an improved fast algorithm for spatial signature estimation in array antenna systems. We provide an analysis for a fast estimation algorithm, which is not fully exploited in the previous work of Kim and Xu (see Proc. of IEEE Vehicular Technology Conference, 1997). Based on the analysis, we derive an improved algorithm which yields more accurate estimates with tolerable increment of computational complexity. The improvement is accomplished by compensation of noise power. Two different estimation methods for the noise power are presented. We investigate the performance of the proposed method via computer simulation.