Accurate beam alignment is a critical challenge in extremely large-scale multiple-input multiple-output (XL-MIMO) systems, especially in the near-field regime, where conventional far-field assumptions no longer hold. While 2D polar-domain codebooks can capture near-field effects, they incur high time and computational complexity. To address this, we propose a novel line-of-sight (LoS) near-field beam alignment scheme that reuses the conventional discrete Fourier transform (DFT) codebook, commonly employed in far-field systems, ensuring backward compatibility without modifying the beam set. By introducing a new method to analyze the energy spread effect, we define the concept of an ϵ-approximated signal subspace, spanned by DFT vectors that exhibit significant correlation with the near-field channel vector. Building on this analysis, the proposed hybrid scheme integrates model-based principles with data-driven techniques, using DFT properties for efficient coarse alignment and a deep neural network (DNN)-aided fine alignment in a reduced search space. Extensive evaluations demonstrate that the proposed scheme attains higher alignment accuracy than existing methods, while providing substantial reductions in both computational load and neural network model size. These advantages make it well suited for practical large-scale near-field deployments.

This paper addresses the challenge of channel estimation in extremely large-scale multiple-input multiple-output (XL-MIMO) systems, pivotal for the advancement of 6G communications. XL-MIMO systems, characterized by their vast antenna arrays, necessitate accurate channel state information (CSI) to leverage high spatial multiplexing and beamforming gains. However, conventional channel estimation methods for near-field XL-MIMO encounter significant computational complexity due to the exceedingly high parameter quantization levels needed for estimating the parametric near-field channel. To address this, we propose a low-complexity two-stage on-grid channel estimation algorithm designed for near-field XL-MIMO systems. The first stage focuses on estimating the LoS channel component while treating the NLoS paths as interference. This estimation is accomplished through an alternating subarray-wise array gain maximization (ASAGM) approach based on the piecewise outer product model (SOPM). In the second stage, we estimate the NLoS channel component by utilizing the sensing matrix refinement-based orthogonal matching pursuit (SMR-OMP) algorithm. This approach helps reduce the high computational complexity associated with large-dimensional joint sensing matrices. Simulation results demonstrate the effectiveness of our proposed low-complexity method, showcasing its significant superiority over existing near-field XL-MIMO channel estimation techniques, particularly in intermediate and high SNR regimes, and in practical scenarios involving arbitrary array placements.

In this paper, we introduce a novel approach to user-centric association and feedback bit allocation for the downlink of a cell-free massive MIMO (CF-mMIMO) system, operating under limited feedback constraints. In CF-mMIMO systems employing frequency division duplexing, each access point (AP) relies on channel information provided by its associated user equipments (UEs) for beamforming design. Since the uplink control channel is typically shared among UEs, we take account of each AP’s total feedback budget, which is distributed among its associated UEs. By employing the Saleh-Valenzuela multi-resolvable path channel model with different average path gains, we first identify necessary feedback information for each UE, along with an appropriate codebook structure. This structure facilitates adaptive quantization of multiple paths based on their dominance. We then formulate a joint optimization problem addressing user-centric UE-AP association and feedback bit allocation. To address this challenge, we analyze the impact of feedback bit allocation and derive our proposed scheme from the solution of an alternative optimization problem aimed at devising long-term policies, explicitly considering the effects of feedback bit allocation. Numerical results show that our proposed scheme effectively enhances the performance of conventional approaches in CF-mMIMO systems.

Recent breakthroughs in reconfigurable intelligent surface (RIS) technology are reshaping next-generation wireless networks. In multi-RIS deployments, however, the received signal is a superposition of numerous single- and double-reflection paths, some arrive from different angles while others overlap at the same angle, which makes channel estimation challenging. Conventional approaches attempt to separate these paths by sequentially turning each RIS on and off, but the required switching overhead grows rapidly with the number of channels and becomes impractical. To overcome this limitation, we propose a two-stage channel estimation method for multi-RIS systems. In the first stage, the received signal is projected onto angle subspaces to separate components from different directions. In the second stage, the remaining components that share the same angle are resolved by exploiting the reflection coefficient design of the RISs. Both stages operate on the same superposed received signal, enabling path separation without exhaustive switching. The method further extends beyond the two-RIS case to an arbitrary number of RISs. Simulation results demonstrate that the proposed scheme maintains estimation accuracy while reducing switching overhead, thereby providing a practical solution for large-scale multi-RIS deployments in future wireless networks.

Research in semantic communication has garnered considerable attention, particularly in the area of image trans-mission, where joint source-channel coding (JSCC)-based neural network (NN) modules are frequently employed. However, these systems often experience performance degradation over time due to an outdated knowledge base, highlighting the need for periodic updates. To address this challenge in the context of training JSCC modules for image transmission, we propose a federated learning (FL) algorithm with semantic feature reconstruction (FR), named FedSFR. This algorithm more efficiently utilizes the available communication capacity by allowing some of the selected FL participants to transmit smaller feature vectors instead of local update information. Unlike conventional FL methods, our approach integrates FR at the parameter server (PS), stabilizing training and enhancing image transmission quality. Experimental results demonstrate that the proposed scheme significantly enhances both the stability and effectiveness of the FL process compared to other algorithms. Furthermore, we mathematically derive the convergence rate to validate the improved performance.

In this paper, we propose an optimally structured gradient coding scheme to mitigate the straggler problem in distributed learning. Conventional gradient coding methods often assume homogeneous straggler models or rely on excessive data replication, limiting performance in real-world heterogeneous systems. To address these limitations, we formulate an optimization problem minimizing residual error while ensuring unbiased gradient estimation by explicitly considering individual straggler probabilities. We derive closed-form solutions for optimal encoding and decoding coefficients via Lagrangian duality and convex optimization, and propose data allocation strategies that reduce both redundancy and computational load. We also analyze convergence behavior for $\lambda$-strongly convex and $\mu$-smooth loss functions. Numerical results show that our approach significantly reduces the impact of stragglers and accelerates convergence compared to existing methods.

Low Earth Orbit (LEO) satellite communication has recently gained significant attention from the industry as it enables fast and seamless connectivity. However, as the number of satellites in LEO networks increases, making the network denser and more dynamic, a scalable routing algorithm is required to efficiently find paths. Many existing routing algorithms are primarily designed for small-scale networks or terrestrial IP networks and may not be suitable for large-scale LEO networks. To address this challenge, we propose an efficient routing algorithm that leverages the grid-like topology by clustering satellites into sub-mesh groups and selecting major routing directions. To accommodate the continuously changing and dynamic topology, we introduce an update period determination method based on a devised correlation metric. Simulation results demonstrate that the proposed algorithm efficiently discovers near-optimal paths in LEO networks designed with various distance-based cost metrics while significantly reducing computational overhead.

Split Learning (SL) has emerged as a promising paradigm for distributed model training in resource-constrained Internet-of-Things (IoT) environments by partitioning deep neural networks between lightweight client devices and a powerful central server. However, SL suffers from substantial communication overhead due to the frequent transmission of high-dimensional intermediate activations. In this paper, we propose a novel sparsification framework based on Gradient-weighted Class Activation Mapping (Grad-CAM) to alleviate this bottleneck. By leveraging the Grad-CAM, our approach quantifies the importance of activations and selectively transmits only the most discriminative intermediate activations. To overcome the limitation that clients lack direct access to Grad-CAM scores during the current forward pass, we utilize the channel importance vectors computed at the server from the previous iteration as surrogates, thereby enabling effective sparsification without incurring additional computational overhead. Extensive experiments demonstrate that our method significantly reduces communication cost compared to baseline schemes. Furthermore, our results reveal a trade-off between mini-batch size and sparsification ratio in SL, emphasizing the importance of careful activation selection for robust learning under communication constraints.

From the perspective of joint source-channel coding (JSCC), there has been significant research on utilizing semantic communication, which inherently possesses analog characteristics, within digital device environments. However, a single-model approach that operates modulation-agnostically across various digital modulation orders has not yet been established. This article presents the first attempt at such an approach by proposing a universal joint source-channel coding (uJSCC) system that utilizes a single-model encoder-decoder pair and trained vector quantization (VQ) codebooks. To support various modulation orders within a single model, the operation of every neural network (NN)-based module in the uJSCC system requires the selection of modulation orders according to signal-to-noise ratio (SNR) boundaries. To address the challenge of unequal output statistics from shared parameters across NN layers, we integrate multiple batch normalization (BN) layers, selected based on modulation order, after each NN layer. This integration occurs with minimal impact on the overall model size. Through a comprehensive series of experiments, we validate that the modulation-agnostic semantic communication framework demonstrates superiority over existing digital semantic communication approaches in terms of model complexity, communication efficiency, and task effectiveness.

This paper addresses the intricate task of hybrid-field channel estimation in extremely large-scale MIMO (XL-MIMO) systems, critical for the progression of 6G communications. Within these systems, comprising a line-of-sight (LoS) channel component alongside far-field and near-field scattering channel components, our objective is to tackle the channel estimation challenge. We encounter two central hurdles for ensuring dependable sparse channel recovery: the design of pilot signals and channel estimators tailored for hybrid-field communications. To overcome the first challenge, we propose a method to derive optimal pilot signals, aimed at minimizing the mutual coherence of the sensing matrix within the context of compressive sensing (CS) problems. These optimal signals are derived using the alternating direction method of multipliers (ADMM), ensuring robust performance in sparse channel recovery. Additionally, leveraging the acquired optimal pilot signal, we introduce a two-stage channel estimation approach that sequentially estimates the LoS channel component and the hybrid-field scattering channel components. Simulation results attest to the superiority of our co-designed approach for pilot signal and channel estimation over conventional CS-based methods, providing more reliable sparse channel recovery in practical scenarios.

This paper introduces a novel privacy-enhanced over-the-air Federated Learning (OTA-FL) framework using client-driven power balancing (CDPB) to address privacy concerns in OTA-FL systems. In recent studies, a server determines the power balancing based on the continuous transmission of channel state information (CSI) from each client. Furthermore, they concentrate on fulfilling privacy requirements in every global iteration, which can heighten the risk of privacy exposure as the learning process extends. To mitigate these risks, we propose two CDPB strategies—CDPB-n (noisy) and CDPB-i (idle)—allowing clients to adjust transmission power independently, without sharing CSI. CDPB-n transmits noise during poor conditions, while CDPB-i pauses transmission until conditions improve. To further enhance privacy and learning efficiency, we show a mixed strategy, CDPB-mixed, which combines CDPB-n and CDPB-i. Our experimental results show that CDPB outperforms traditional approaches in terms of model accuracy and privacy guarantees providing a practical solution for enhancing OTA-FL in resource-constrained environments.

This paper investigates a joint active and passive beamforming design for distributed simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) assisted multi-user (MU)- mutiple input single output (MISO) systems, where the energy splitting (ES) mode is considered for the STAR-RIS. We aim to design the active beamforming vectors at the base station (BS) and the passive beamforming at the STAR-RIS to maximize the user sum rate under transmitting power constraints. The formulated problem is non-convex and nontrivial to obtain the global optimum due to the coupling between active beamforming vectors and STAR-RIS phase shifts. To efficiently solve the problem, we propose a novel graph neural network (GNN)-based framework. Specifically, we first model the interactions among users and network entities using a heterogeneous graph representation. A heterogeneous graph neural network (HGNN) implementation is then introduced to directly optimizes beamforming vectors and STAR-RIS coefficients with the system objective. Numerical results show that the proposed approach yields efficient performance compared to the previous benchmarks. Furthermore, the proposed GNN is scalable with various system configurations.

The Internet of Things (IoT) and massive IoT systems are key to sixth-generation (6G) networks due to dense connectivity, ultra-reliability, low latency, and high throughput. Artificial intelligence, including deep learning and machine learning, offers solutions for optimizing and deploying cutting-edge technologies for future radio communications. However, these techniques are vulnerable to adversarial attacks, leading to degraded performance and erroneous predictions, outcomes unacceptable for ubiquitous networks. This survey extensively addresses adversarial attacks and defense methods in 6G network-assisted IoT systems. The theoretical background and up-to-date research on adversarial attacks and defenses are discussed. Furthermore, we provide Monte Carlo simulations to validate the effectiveness of adversarial attacks compared to jamming attacks. Additionally, we examine the vulnerability of 6G IoT systems by demonstrating attack strategies applicable to key technologies, including reconfigurable intelligent surfaces, massive multiple-input multiple-output (MIMO)/cell-free massive MIMO, satellites, the metaverse, and semantic communications. Finally, we outline the challenges and future developments associated with adversarial attacks and defenses in 6G IoT systems.

We investigate Age of Information (AoI) in an Internet of Things (IoT) sensor network where a single relay terminal connects multiple IoT sensors to their corresponding destination nodes. In order to minimize average weighted sum AoI, joint optimization of sampling and updating policy of a relay is studied. For error-free and symmetric case where weights are identical, the necessary and sufficient condition for optimal policy is figured out. We also obtain the minimum average sum AoI in a closed-form expression which can be interpreted as the fundamental limit of sum AoI in a single relay network. Moreover, we prove that the greedy policy is optimal for minimizing the average sum AoI at the destination nodes in the error-prone symmetric network. For general case where weights are arbitrarily given, we propose a scheduling policy obtained via deep reinforcement learning.

We investigate whether randomized likelihood (RL) decoding based on sampling techniques can completely replace the compressed sensing (CS) process for sparse recovery. For a Gaussian signal model, we propose a novel iterative Markov chain Monte Carlo (MCMC) sampling-based RL decoding method tailored to the attributes of sparse recovery, termed MCMC-RLD-SR. The proposed iterative MCMC-RLD-SR algorithm incorporates two stages, i.e., rough estimation and fine estimation. The rough estimation is a process of figuring out support candidates for the sparse signal via the Metropolis-Hastings (MH) sampling method, which prevents a nonconvergence issue inherent in the CS problem when applying sampling. The fine estimation is a process of acquiring an estimate of the sparse signal through the Gibbs sampling method based on the support candidates from the rough estimation stage. We prove that the proposed algorithm converges by favor of the proposed iterative two-stage sampling structure, and analyze the signal recovery error by the proposed algorithm. Our analysis and simulation results show that the proposed MCMC-RLD-SR algorithm can effectively solve CS problems with much less computational complexity than conventional CS algorithms. Furthermore, even when the signal is not sparse, the proposed algorithm is shown to achieve a reliable signal recovery performance.

This work deals with a heterogeneous semantics-native communication (SNC) problem. When agents do not share the same communication context, the effectiveness of contextual reasoning (CR) is compromised calling for agents to infer other agents’ context before communication. This article proposes a novel framework for solving the inverse problem of CR in SNC using two Bayesian inference methods, namely: Bayesian inverse CR (iCR) and Bayesian inverse linearized CR (iLCR). The first proposed Bayesian iCR method utilizes Markov Chain Monte Carlo (MCMC) sampling to infer the agent’s context while being computationally expensive. To address this issue, a Bayesian iLCR method is leveraged which obtains a linearized CR (LCR) model by training a linear neural network. Experimental results show that the Bayesian iLCR method requires less computation and achieves higher inference accuracy compared to Bayesian iCR. Additionally, heterogeneous SNC based on the context obtained through the Bayesian iLCR method shows better communication effectiveness than that of Bayesian iCR. Overall, this work provides valuable insights and methods to improve the effectiveness of SNC in situations where agents have different contexts.

The federated learning is a promising machine learning technique to bring about advanced services and application for future industries. It has been known that the federated learning secures the privacy of the participants well so far. However, various attacks appear recently which are possible to extract the private information of them in the federated learning systems. Consequently, development of privacy preserving schemes for the federated learning is paramount. In this paper, we consider the over-the-air computation based federated learning system, and adopt the concept of differential privacy to prevent the private information leakage. During the training process, when a sum of local gradients is received via over-the-air computation, they conceal each other and appear to be random to the parameter server. Motivated by this fact, the differential privacy of the over-the-air computation based federated learning is analyzed by considering the inherent randomness of the local gradients. We analytically quantify required amount of the artificial noise to be added to preserve privacy. Furthermore, a parameter estimation based algorithm is proposed which is applicable in real scenarios. The simulation results show the efficacy of the proposed algorithm for preserving privacy.

Despite its vital role in intelligent IoT networks, distributed computing was not studied well in wireless networks. Notably, the distortions introduced by wireless channels and noise can significantly undermine the accuracy of distributed computations. Unfortunately, existing distributed computing schemes inadequately address this critical issue. In this paper, we aim at resolving the numerical stability issue associated within wireless networks and building a new computationally accurate design framework which concretely integrates distributed computing and communication. To this end, we first explore the previous group algebra based coded matrix computation scheme, suitable for numerically stable computation in noisy network, in orthogonal multiple access channel and perform a thorough analysis of computational errors. Furthermore, to leverage the inherent communication structure constructed in distributed computing and thus more efficiently utilize limited wireless resources, we propose to combine the Compute-and-Forward scheme with coded matrix computation. Furthermore, we devise a novel transmission scheme for distributed computing called the Broadcast-and-Compute scheme, in which a master node broadcasts the same information, not transmitting differently encoded submatrices to worker nodes. We analyze the computation errors of Compute-and-Forward and Broadcast-and-Compute based coded matrix computation. The superiority of the proposed schemes is validated by various simulations.

The requirement for massive connectivity in 5G and beyond 5G systems pose new challenges to random access protocol design. As a promising technique to afford massive short packet connections, unsourced random access (URA) gains traction. However, the existing URA schemes were developed for static channels and not implementable in fast fading channels. In this paper, we propose a novel URA strategy tailored to time-varying Rayleigh fading channels when the system loads and instantaneous channels are unknown to the receiver. With a time slotted transmission framework, we devise a low-complexity error correcting code and a decoding algorithm leveraging successive interference cancellation (SIC). The receiver corrects errors across slots by using SIC to decode the transmitted messages from highly interfering superposed signals. The asymptotic error rate is derived and a trade-off between the minimum required energy required for reliable communication and the user density is analyzed. Our simulation results verify that the proposed scheme achieves a near-optimal energy efficiency performance when the system load is in the moderate system load regime and a significant performance gain over the existing URA schemes in time varying fading channels.

This paper investigates double RIS-assisted MIMO communication systems over Rician fading channels with practical propagation conditions. Firstly, we derive the statistical channel information in closed form, which reveals the various influences of the system and the environment. Next, we study the active and passive beamforming designs for enhancing communication reliability. In particular, we propose a novel end-to-end design, where each 1-dimensional convolutional neural network (1D-CNN) represents a system entity that minimizes the symbol error rate by controlling the transceiver and RISs' phase shifts. Numerical results validate our analysis and demonstrate the superior improvements of phase shift designs to boost system performance. It is demonstrated that the proposed design can encode multiple data symbols simultaneously over different channel realizations and improve the SER performance without requiring channel information at the transmitter.

In conventional federated learning (FL), dataset at the parameter server (PS) is not usually considered but may enhance the performance of FL if available. The benefit of leveraging the dataset at PS can be multiplied in over-the-air aggregation-based FL where combating local gradient update distortion against channel fading is inordinately consequential. In this paper, an over-the-air aggregation framework for communication efficient FL is investigated in cache-enabled wireless edge networks where not only edge devices but also a base station (BS) has its own local dataset. The proposed framework leverages the BS dataset to reduce the number of channel uses necessary for the model convergence and to avoid the overhead incurred by power scale coordination and global channel state information (CSI) acquisition at BS. We present a sufficient condition for convergence to a stationary point without convexity assumption on the objective function. Based on the sufficient condition, a power control method is optimized to facilitate the model convergence without assumptions on power scale coordination and global CSI at BS. Our simulation results validate that BS dataset is beneficial to reduce the number of channel uses for the model convergence and the developed power control method outperforms the conventional method in terms of both convergence rate and converged test accuracy.

Despite high utility in distributed networks, federated learning entails enormous communication overhead due to the requirement of trained model exchange at every global iteration. When the communication resources are limited, as in wireless environments, learning performance can be severely degraded by the communication overhead. On this account, communication efficiency is one of the primary concerns in federated learning. In this paper, we put forth a communication-efficient federated learning system based on the projection of local model updates. Leveraging the correlation of consecutive local model updates, we devise a novel local model update compression scheme based on the projection onto the selected subspace. Furthermore, to avoid error propagation over global iterations and thus improve learning performance, we also develop novel criteria for deciding whether to compress the local model updates or not. The convergence of the proposed algorithm is also mathematically proved by deriving an upper bound on the mean square error of the global parameter. The merits of the proposed algorithm over the state-of-the-art benchmark schemes are verified by various simulations.

This paper investigates double RIS-assisted MIMO communication systems over Rician fading channels with finite scatterers, spatial correlation, and the existence of a double-scattering link between the transceiver. First, the statistical information is driven in closed form for the aggregated channels, unveiling various influences of the system and environment on the average channel power gains. Next, we study two active and passive beamforming designs corresponding to two objectives. The first problem maximizes channel capacity by jointly optimizing the active precoding and combining matrices at the transceivers and passive beamforming at the double RISs subject to the transmitting power constraint. In order to tackle the inherently non-convex issue, we propose an efficient alternating optimization algorithm (AO) based on the alternating direction method of multipliers (ADMM). The second problem enhances communication reliability by jointly training the encoder and decoder at the transceivers and the phase shifters at the RISs. Each neural network representing a system entity in an end-to-end learning framework is proposed to minimize the symbol error rate of the detected symbols by controlling the transceiver and the RISs’ phase shifts. Numerical results verify our analysis and demonstrate the superior improvements of phase shift designs to boost system performance.

This paper proposes an over-the-air aggregation framework for federated learning (FL) in broadband wireless networks where not only edge devices but also a base station (BS) has its own local dataset. The proposed framework leverages the BS dataset to improve communication efficiency of FL by reducing the number of channel uses required for the model convergence as well as avoiding the signaling overhead incurred by power scale coordination among edge devices. We analyze the convergence to a stationary point without convexity assumption on the objective function. The analysis result reveals that the utilization of BS dataset improves the convergence rate and the update distortion caused by the limited power budget is a crucial factor hindering the model convergence. To facilitate the convergence, we develop an optimized power control method by solving the distortion minimization problem without assumptions on power scale coordination and global CSI at BS. Our simulation results validate that BS dataset is beneficial to reducing the number of channel uses for the model convergence and the developed power control method outperforms the conventional method in terms of both convergence rate and converged test accuracy. Furthermore, we identify some scenarios where the compression of local update can be helpful to reduce communication resources for model training.

Wireless sensor networks (WSNs) have been demonstrated to enhance parameter estimation performance for target tracking. In this paper, a prior information based quantizer design framework is proposed for target tracking for WSNs. In the proposed framework, the imperfect wireless channels between local sensors and the fusion center are considered. To make full use of the historical states and measurements embedded into the Bayesian tracking methodology, the quantizer design is suggested to be implemented with considering the prior state information. To this end, a channel-aware posterior Cramér-Rao lower bound (PCRLB) is derived based on the state prediction and further used as the performance indicator for quantizer design. Regarding target tracking, we model the quantizer design problem as a non-convex and highly nonlinear optimization problem that is intractable in general. We split the problem in terms of different scenarios, and for one-bit quantizer design based on a binary symmetric channel (BSC), we find that the optimal solution can be analytically computed. While for the general fading channel-based quantizer design problems, we propose two polynomial-time algorithms to find the solutions. Meanwhile, an approximation-based channel-aware particle filter (A-CAPF) is proposed to improve the implementation efficiency of state filtering. Simulation results demonstrate the enhanced performance and execution efficiency of the proposed algorithms in the context of the BSC and Rayleigh fading channel.

This paper considers reconfigurable intelligent surface (RIS)-assisted point-to-point multiple-input multiple-output (MIMO) communication systems, where a transmitter communicates with a receiver through an RIS. Based on the main target of reducing the bit error rate (BER) and therefore enhancing the communication reliability, we study different model-based and data-driven (autoencoder) approaches. In particular, we consider a model-based approach that optimizes both active and passive optimization variables. We further propose a novel end-to-end data-driven framework, which leverages the recent advances in machine learning. The neural networks presented for conventional signal processing modules are jointly trained with the channel effects to minimize the bit error detection. Numerical results demonstrate that the proposed data-driven approach can learn to encode the transmitted signal via different channel realizations dynamically. In addition, the data-driven approach not only offers a significant gain in the BER performance compared to the other state-of-the-art benchmarks but also guarantees the performance when perfect channel information is unavailable.

Cell-free massive multiple-input multiple-output (CF-mMIMO) systems have been developed primarily in time-division duplex (TDD) systems to provide uniformly good service to all users in network area. However, frequency division duplex (FDD) systems still prevail in the communication systems to date. In FDD systems, there is always an issue of channel state information (CSI) acquisition at access points (APs) due to channel feedback overhead and this issue becomes even more prominent when the FDD systems are applied to CF-mMIMO systems. To overcome this limitation, we propose a Rician$K$-adaptive feedback method that significantly reduces feedback overhead by selecting paths with small Rician$K$-factors in multipath Rician fading channel and feeding back only the channels for the selected paths. In addition, we propose a channel-mixed angle-based zero-forcing (CM-AZF) beamforming technique that mixes angle information from angle-reciprocity and the quantized channel information from the channel feedback. The performance of our Rician$K$-adaptive feedback method and CM-AZF beamforming technique has been validated through simulation results and shows a higher average achievable rate for the same amount of feedback compared to the conventional leakage power-based path selection method.

Due to the rapid paradigm shift in Internet of Things networks from wired and centralized to flexible wireless and decentralized networks, building effective and reliable distributed consensus mechanisms over wireless is becoming essential. Especially, since the performance of consensus over communication endpoints in a large-scale wireless network is limited by their communication capability, it requires a careful co-design of communication and consensus to attain a fast and scalable distributed wireless consensus mechanism with high resiliency against faulty nodes. Within this context, this article addresses such problem by designing two wireless consensus mechanisms that well-suit in large-scale wireless networks. On the one hand, as a reinterpretation of the conventional referendum consensus (RC) in a large-scale wireless network, gossip-broadcasting-based RC (GB-RC) is proposed. On the other hand, to overcome the scalability issue of the GB-RC, cooperative-broadcast-based electoral-college consensus (CB-EC) is proposed. By mathematically analyzing the performance of both of the consensus mechanisms, in terms of consensus latency and resiliency against the faulty nodes, we show that the GB-RC outperforms the conventional RC, while the CB-EC significantly reduces the consensus latency compromising the stochastic resiliency. We further evaluate their performance numerically to show their effectiveness and feasibility under realistic large-scale wireless environments.

Wireless sensor networks (WSNs) are demonstrated to be the increasingly essential systems for various Internet of Things (IoT)-based sensing applications. This article proposes a cost-aware dynamic sensor scheduling (CADSS) framework for WSNs with multiple tasks. At its core, a system cost function is designed to quantify the expenses of the WSNs due to task executions, and a task quality function is modeled to indicate the performance of the corresponding tasks. The proposed CADSS is further formulated as an optimization problem to minimize the system cost while maintaining the desired task qualities. In this way, a comprehensive task utility evaluation methodology for self-organized WSNs is constituted. Furthermore, by modeling the posterior Cramér–Rao lower bound (PCRLB) as the task quality function and a weighted sum of the communication and sensor scheduling cost as the system cost, the CADSS is instantiated into a multitarget tracking (MTT) application. It is shown that the formulated CADSS is a nonconvex optimization problem involving two coupled binary variables that, respectively, correspond to the scheduling of sensor and cluster head. We then propose a parallel convex relation approach to solve it effectively. Numerical results verify the effectiveness of the proposed CADSS by comparing it with state-of-the-art strategies.

This work studies the cooperative inference of deep neural networks (DNNs), in which a memory-constrained end device performs a delay-constrained inference process with an aid of an edge server. Although several works considered the cooperative inference of DNNs in the literature, it was assumed in those works that the memory footprints at end devices are unlimited, which is in practice not realistic. To address this issue, in this work, a memory-aware cooperative DNN inference is proposed. Specifically, we propose to adopt knowledge distillation to obtain high-performing lightweight DNNs. To minimize the inference delay, we first analyze the end-to-end delay required for processing the proposed cooperative DNN inference, and then we minimize the delay by jointly optimizing the DNN partitioning point and the intermediate data transmission rate. Also, a dynamic DNN selection scheme is developed by fully exploiting the available memory resource in order to maximize the performance of the inference task in terms of inference accuracy. Experimental results demonstrate that the proposed cooperative DNN inference considerably outperforms the comparable schemes while satisfying both the delay constraint and the memory constraint.

Wireless distributed machine learning is envisaged to facilitate advanced learning services and applications in wireless networks consisting of devices with limited computing capability. Distributed machine learning algorithms are more vulnerable in wireless systems since information exchange in learning is limited by wireless resources and channel conditions. Moreover, their performance can be significantly degraded by attacks of the Byzantine devices, and information distorted by channel fading can be treated as Byzantine attacks. Consequently, protection of wireless distributed machine learning from Byzantine devices is paramount. Leveraging over-the-air computation, we put forth a novel wireless distributed stochastic gradient descent system which is resilient to Byzantine attacks. The proposed learning system is underpinned by two novel and distinct features which enable more accurate and faster distributed machine learning resilient to Byzantine attacks:(1)collecting training data in the PS to obtain its own training results and(2)grouping the distributed devices. We derive upper bounds of the mean square error of the global parameter when the proposed algorithms are used in the cases with and without Byzantine devices, and prove the convergence of the proposed algorithms with the derived bounds. The effectiveness of the algorithms is validated by showing the accuracy and convergence speed.

Coded computation has attracted great interests as a promising technique to cope with straggling computing nodes in mobile edge computing (MEC) networks. Contrary to the existing coded computation schemes developed with a fixed network topology, this paper studies a MDS coded computation for random networks. Specifically, we put forth maximum distance separable (MDS) coded task offloading and investigate its MDS coded computing gain by deriving the average successful retrieval probability with stochastic geometry in random wireless edge computing networks, where it encodes the original task into multiple equal and small sized MDS coded sub-tasks and offloads their subset to edge computing nodes for computation. We also identify a tradeoff between the latency in processing a sub-task at an edge node and the minimal number of edge nodes required to retrieve the original task output, according to the size of MDS coded sub-tasks. To efficiently control the tradeoff, we determine the desirable size of MDS coded sub-tasks in a semi-closed form to maximize the average successful retrieval probability for regime 1 and regime 2 networks, which correspond to the cases that communication latency is negligible compared to computation latency and that computation latency is negligible compared to communication latency, respectively, and develop an efficient algorithm with low search complexity for a general environment. Our numerical results reveal that the proposed scheme outperforms the other conventional task offloading schemes such as partial task offloading and replication task offloading in terms of average successful retrieval probability.

Designing distributed, fast, and reliable wireless consensus protocols is instrumental in enabling mission-critical decentralized systems, such as robotic networks in the Industrial Internet of Things (IIoT), drone swarms in rescue missions, and so forth. However, chasing both low-latency and reliability of consensus protocols is a challenging task. The problem is aggravated under wireless connectivity that may be slower and less reliable, compared to wired connections. To tackle this issue, we investigate fundamental relationships between consensus latency and reliability through the lens of wireless connectivity, and co-design communication and consensus protocols for low-latency and reliable decentralized systems. Specifically, we propose a novel communication-efficient distributed consensus protocol, termed random representative consensus (R2C), and show its effectiveness under gossip and broadcast communication protocols. To this end, we derive a closed-form end-to-end (E2E) latency expression of the R2C that guarantees target reliability, and compare it with a baseline consensus protocol, referred to as referendum consensus (RC). The result shows that the R2C is faster compared to the RC and more reliable compared when co-designed with the broadcast protocol compared to that with the gossip protocol.

Leveraging the idea of group theory, we explore the distributed matrix multiplication problem posed in on-device edge computing. We first revisit how to embed matrix multiplication into group structure and then propose a distributed matrix multiplication scheme using the cyclic group. We identify the condition for the perfect reconstruction with the proposed scheme, and exhibit that the proposed scheme has better error performance than uncoded scheme in a noisy channel owing to the diversity gain of the proposed scheme.

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.

The fundamental limits of private information retrieval (PIR) with unknown cache prefetching at the user are investigated in this paper. To this end, a novel random linear combination (RLC)-based PIR scheme that can solve the basic PIR problem and its variation is proposed. The proposed scheme is based on random coding approach and achieves the capacity of the basic PIR asymptotically. Then, we investigate PIR with unknown cache prefetching (PIRC) problem at different cache-to-file size ratio. Specifically, we propose RLC-based PIRC method, which prefetches RLC-based side information and leverages them to retrieve desired information at small download cost. Furthermore, by applying time and memory sharing on the proposed RLC-based PIRC, RLC-based basic PIR and some other known approach in literature, we derive the achievable normalized download cost bound of PIRC. The derived achievable bound outperforms the existing bound in literature and the case study provides numerical results that verifies it.

Mobile edge caching, that prefetches the frequently requested contents at edge nodes, is envisaged to play the leading role in reducing network traffic and latency. Limited storage space at each edge node necessitates efficient caching strategies of what and how to store in advance, and storing segmented content based on maximum distance separable (MDS) code enables to utilize the limited storage space more efficiently. This paper designs a strategy of probabilistic content caching based on MDS code to minimize the help from a macro base station and thus to lessen backhaul traffic. Desirable length of an MDS coded segment and the probability of caching an MDS coded segment are determined to maximize the amount of information obtained from neighboring edge nodes. Numerical results demonstrate that the proposed caching strategy enable to lessen backhaul load effectively.

This paper studies a receive beamwidth controlling method in vehicle-to-infrastructure (V2I) wireless communication system using millimeter wave (mm-wave) band. We use a triangular beam pattern to model and characterize a mm-wave receive beam pattern. First of all, channel coherence time for line-of-sight (LoS) downlink transmission is derived under the given vehicular scenario. Then, we derive an attainable data rate for the time varying vehicular channel, by supposing that the beam is realigned whenever the channel coherence time is elapsed. In addition, the optimal receive beamwidth, which achieves the maximum point of the derived attainable data rate, is obtained. The effectiveness and feasibility of the proposed receive beamwidth controlling method is underpinned by both analytic and numerical simulation results. The results are also compared with a uniform linear array (ULA) beam pattern model and show that the triangular beam pattern model can well characterize the practical antenna model.

In this paper, we propose a resource management method based on deep learning, which controls both the transmit power and the power splitting ratio to maximize the sum rate with low computational complexity in D2D networks with energy harvesting requirements. The introduction of the energy harvesting requirements to D2D networks makes it hard to design an effective resource management solution since the treatment of interference signals should be completely different from the conventional resource management focusing only on the rate maximization. To deal with drawbacks of the conventional deep learning-based approach, we propose a new training algorithm suitable for our resource management problem. Numerical simulations show that the proposed learning-based method outperforms the benchmark methods, which are derived from some relevant works, in most situations and achieves performances comparable to an exhaustive search in terms of the sum rate and energy outage probability. Although the conventional optimization-based method is derived to achieve the asymptotic optimal performance for a large network, the proposed deep learning method is shown to achieve almost the same performance with much lower computational complexity. Furthermore, simulation results offer new insights to the impact of the energy harvesting requirements on the behaviour of the optimal resource management.

Besides maximizing multiplexing gain, improving diversity is important for reliable communications in the presence of interference. In this context, on-off switched interference alignment (IA) is investigated, where IA is intermittently utilized by switching IA on/off in order to improve diversity gain in an interference channel and hence to maximize diversity multiplexing tradeoff (DMT). This paper adopts Alamouti coding based IA for the on-off switched IA, which requires only local channel state information at the transmitter. Optimizing the portion of IA utilization of the proposed scheme in closed form, we derive the achievable DMT and show that the intermittent utilization of IA with simultaneous non-unique decoding can improve DMT in the 2-user X-channel with two antennas. The proposed scheme, to the best of our knowledge, surpasses any other existing schemes for the 2-user X-channel with two antennas and is closed to the ideal DMT.

In Compressed Sensing based Random Access CHannel (CS-RACH) protocol, a base station leverages compressed sensing technique to detect the active users in the cell coverage and estimate the channel gain between the users and the base station. In a real communication scenario, activity of a specific user usually varies time to time and thus can be seen as a random variable following ON/OFF distribution. Meanwhile, the performance of compressed sensing technique is dependent on the sparsity of the estimating vector, which is closely related to the user activity in CS-RACH scenario. In this perspective, we analyze the condition of the user activity for the stable operation of the protocol. Particularly, we use the least absolute shrinkage and selection operator (LASSO) approach, which gives a closed form expressions of the sparsity condition for the successful active user detection in an asymptotic manner. As a result, we obtain the condition of the user activity for stable operation of CS-RACH and verify the result with numerical simulations.

This paper proposes a compressed sensing-based random access protocol (CS-RACH), which is suitable for servicing a large number of machine-type communication devices in Internet of Things (IoT) network. In CS-RACH, we utilize a larger number of unique preambles compared to conventional LTE-RACH, however, the compressed sensing technique makes it possible to simultaneously detect the users with high accuracy. Compared to the user detection in conventional LTE-RACH, the proposed user detection can get rid of preamble collisions and decrease the collision probability, thereby the overall access latency is significantly reduced. To prove the benefits of the proposed CS-RACH, we mathematically analyze and compare access latency performance of LTE-RACH and CS-RACH. In particular, based on the least absolute shrinkage and selection operator approach, we derive a normalized throughput, access success probability, and average access latency. Our simulation results also exhibit that the proposed CS-RACH considerably reduces the access latency under reasonable conditions in IoT environments.

At an energy harvesting relay, securing residual harvested energy, net remaining energy after each receiving and forwarding cycle, is of importance for sustainable operation. However, there exists a tradeoff between the achievable rate and residual harvested energy, whereby understanding this tradeoff concretely is crucial for energy harvesting relay system design. This paper analyzes the rate-energy (R-E) region for achievable rate and residual harvested energy in two-way decode-and-forward (DF) relay systems with a power splitting based energy harvesting relay. In particular, we characterize R-E regions for multiple access broadcast (MABC) and time division broadcast protocols. Moreover, we propose a new energy harvesting relaying protocol, namely, information and energy signals multiple access broadcasts (IEBC), to improve the achievable R-E region. The boundary of the R-E regions is obtained by optimizing a power splitting factor in each protocol. Moreover, to have better analytic comparisons and useful insights on performance, we derive approximated R-E regions of all protocols for high and low signal-to-noise ratio cases. Based on the approximated R-E region, it is shown that if the required residual energy is large, the IEBC outperforms the others, but if the required residual energy is small, either the IEBC or MABC is preferred.

This paper studies secure transmissions in a bursty interference channel constructed by opportunistic user scheduling. To improve the physical layer security, we propose a signal-to-noise-plus-interference ratio (SINR) based opportunistic transmission scheme and mathematically analyze the ergodic secrecy rate per transmitter-receiver pair. In this scheme, a subset among$K$transmitter-receiver pairs is selected opportunistically, but each transmitter determines its activation state based on limited feedback of the SINR from its designated receiver only, without any information from other transmitters. For comparison, we also analyze the ergodic secrecy rates per transmitter-receiver pair for two benchmark schemes: non-opportunistic transmission and random transmission. Using derived analytical expressions, the achievable ergodic secrecy rates per transmitter-receiver pair are obtained by optimizing the activation probability for each scheme. To solve the non-convex optimization problem, we first show that the objective function is a sum of quasi-concave functions. Then, leveraging this fact, we propose a two-level iterative algorithm based on the damped Newton method. Furthermore, asymptotic behaviors of the achievable ergodic secrecy rate per transmitter-receiver pair are analyzed to offer insights behind the mathematical expressions. Our analytical and numerical results exhibit the gain of the proposed SINR based opportunistic transmission scheme compared to the benchmark schemes.

To improve diversity gain in an interference channel and hence to maximize diversity multiplexing tradeoff (DMT), we propose an on-off switched interference alignment (IA) where IA is intermittently utilized by switching IA on/off. For onoff switching, either IA with symbol extension or IA with Alamouti coding is adopted in this paper. Deriving and analyzing DMT of the proposed schemes, we reveal that the intermittent utilization of IA with simultaneous non-unique decoding can improve DMT in the 2-user X-channel with two antennas. Both proposed schemes are shown to achieve a diversity gain of 4 and DoF per user of 3. In particular, the on-off switched IA with Alamouti coding, to the best of our knowledge, surpasses any other existing schemes for the 2-user X-channel with two antennas and nearly approaches the ideal DMT.

We investigate how the maximum-distance separable (MDS) coding can be incorporated into probabilistic caching to utilize the limited storage space efficiently. In a user-centric clustered wireless network, each caching helper node probabilistically caches a segment of MDS coded sequence of each file. The segment size is optimized to maximize the cache hit probability or successful file retrieval probability. We reveal that the best way of storing files is determined by the condition whether the average amount of MDS coded information stored for the requested file within a user's cluster exceeds the amount required for file retrieval or not. In terms of the cache hit probability maximization, if the condition is not fulfilled, it is proved that storing the complete file with a low probability is optimal. Otherwise, storing either a segment as small as possible with a high probability or a complete file with a low probability, according to a given environment, is shown to be desirable. We also analyze the successful retrieval probability, which accounts for both a cache hit event and successful transmissions from multiple caching helper nodes. Since the successful retrieval probability is in an intractable form, to find the desirable segment size, the theoretically driven algorithms with low search complexity are developed.

This paper considers a single-input single-output broadcast channel with receiver side information. We find the optimal clique cover index code and the optimal transmission time allocation that minimize outage probability when total transmission time is limited. As our problem is NP-hard, we first find the optimal time allocation for a given index code. Then, we describe a brute-force algorithm that finds the set of all decodable index codes and chooses the optimal one adopting the optimal time allocation. To reduce the computational complexity of the brute-force algorithm, we propose a pruning algorithm which solves the same problem using the Hasse diagram but does not harm the optimality. Our analysis reveals that the optimal index code is dependent on the channel conditions, not simply on the number of required transmissions, which implies that the index coding-channel coupling improves the outage performance. It is also shown that this claim is still valid for general scalar linear index coding. Our simulation results verify that our proposed schemes effectively reduce the outage probability compared to other reference schemes, and our pruning algorithm considerably reduces the computational complexity required for the brute-force algorithm.

User mobility has a large effect on optimal content placement in device-to-device (D2D) caching networks. Since a typical user can communicate neighboring users who stay in the D2D communication area of the typical user, the optimal content placement should be changed according to the user mobility. Under consideration of randomness of incoming and outgoing users, we formulate an optimization problem to minimize the average data load of a BS. It is proved that minimization of the average data load of a BS can be transformed to maximization of a monotonic submodular function with a matroid constraint, for which a greedy algorithm can find near-optimal solutions. Moreover, when motions of neighboring users are rapid, the optimal content placement is derived in closed-form, aided by reasonable approximation and relaxation. In the high mobility regime, the optimal content placement is shown to cache partial amounts of the most popular contents.

We propose a novel scheme that achieves the capacity of the PIR, which is the maximum number of bits of the desired messages that can be privately retrieved by a single bit information download from the databases. The proposed scheme is based on the stochastic approach, where the queries are generated randomly and the downloading information is the random linear combinations of the message bits. Consequently, we claim that one-shot query generation, based on the stochastic approach, can guarantee the privacy of the user and the correctness of the retrieved information while achieving the capacity, when the number of answering bits are sufficiently large.

In this paper, we find the optimal index coding scheme with transmission time allocation that minimizes overall outage probability in a single-input single-output broadcast channel. In our system model, a transmitter has a set of files (i.e., a file library), and each user requests a single file in the library. Meanwhile, each user has a memory so that may already know some files. When the transmitter can only use limited transmission time to serve the users, the overall outage probability varies according to the index code and transmission time allocation. To find the optimal index code and time allocation, we first derive the optimal time allocation for a given index code. Then, we propose the optimal index code searching algorithm. We also propose a pruning algorithm, which reduces the searching complexity while maintaining the optimality. Our simulation results show that our proposed scheme well minimizes the overall outage probability, and our proposed pruning algorithm significantly reduces the complexity of our proposed scheme.

When simultaneous wireless information and power transfer is carried out, a fundamental tradeoff between achievable rate and harvested energy exists because the received power is used for two different purposes. The tradeoff is well characterized by the rate-energy region, and several techniques have been proposed to improve the achievable rate-energy region. However, the existing techniques still have a considerable loss in either energy or rate and thus the known achievable rate-energy regions are far from the ideal one. Deriving tight upper and lower bounds on the rate-energy region of our proposed scheme, we prove that the rate-energy region can be expanded almost to the ideal upper bound. Contrary to the existing techniques, in the proposed scheme, the information decoding circuit not only extracts amplitude and phase information but also combines the extracted information with the amplitude information obtained from the rectified signal. Consequently, the required energy for decoding can be minimized, and thus the proposed scheme achieves a near-optimal rate-energy region, which implies that the fundamental tradeoff in the achievable rate-energy region is nearly eliminated. To practically account for the theoretically achievable rate-energy region, we also present practical examples with an M-ary multi-level circular QAM with Gaussian maximum likelihood detection.

In this paper, we consider a cellular network, where a macro BS serves a single user with the aid of helper nodes, and find the optimal way to save a file in the helper nodes. Storing a file into distributed storage affects both hitting probability and successful recovery of the data at a user. With MDS coding, cache equipped helper nodes can store small fragments to improve the hitting probability. When a file is relatively important, we show that the optimal way to save a file is to save either the whole file or the smallest MDS fraction constrained on the amount of fragments. For less important file, we show that the conventional replica storing is optimal in terms of hitting probability.

This paper studies a two-user single-input single- output (SISO) interference channel with a cognitive relay (ICCR) under delayed feedback. We consider three types of feedback: delayed channel state information at transmitter (CSIT), delayed output feedback, and no feedback. For each feedback information, we derive the optimal degrees of freedom (DoF) region of the two-user ICCR where delayed feedback information is available at all transmitters. We show that while a cognitive relay with no feedback cannot improve the sum DoF beyond 1 in the two-user interference channel, delayed feedback can increase the sum DoF to 4/3.

This paper studies the interference channel with a cognitive relay under delayed feedback. Three types of delayed feedback are studied: delayed channel state information at the transmitter, delayed output feedback, and delayed Shannon feedback. Outer bounds are derived for the degrees of freedom (DoF) region of the two-user multiple-input multiple-output interference channel with a cognitive relay with delayed feedback as well as without feedback. For the single-input single-output scenario, optimal schemes are proposed based on retrospective interference alignment. It is shown that while a cognitive relay without feedback cannot improve the sum-DoF in the two-user single-input single-output interference channel, delayed feedback in the same scenario can increase the sum-DoF to 4/3. For the multiple-input multiple-output case, achievable schemes are obtained via extensions of retrospective interference alignment, leading to the DoF regions that meet the respective upper bounds.

Depending on what and how caching helpers cache content in their finite storage, the caching helpers can offer either a content diversity gain by serving diverse content or a cooperative gain by jointly transmitting the same content. This paper identifies a tradeoff between the content diversity gain and the cooperative gain according to content placements and proposes a probabilistic content placement to optimally balance the tradeoff. Using stochastic geometry, we quantify this tradeoff by deriving the cache hit rate and the rate coverage probability. To efficiently control the tradeoff, we determine the near-optimal caching probabilities that maximize the average content delivery success probability with the cooperative caching helpers. Our analysis and numerical results reveal that our proposed content placement outperforms the conventional caching schemes, such as caching with uniform probabilities, caching the most popular contents, and caching the content maximizing the cache hit, in terms of the average content delivery success probability.

This paper investigates optimal content placement for wireless femto-caching network. The average bit error rate (BER) is formulated as a function of content placement under wireless fading. To minimize the average BER, we propose a greedy algorithm finding optimal content placement with low-computational complexity. Exploiting the property of the optimal content placement which we derive, the proposed algorithm can be performed over considerably reduced search space. Contrary to the optimal content placement without consideration of wireless fading aspects, we reveal that optimal content placement can be reached by balancing a tradeoff between two different gains: file diversity gain and channel diversity gain. Moreover, we also identify the conditions that the optimal placement can be found without running the proposed greedy algorithm and derive the corresponding optimal content placement in closed form.

From the perspective of a single user, occupying all available radio access technologies (RAT) is always beneficial for increasing data rate. However, due to the interactions among the users, occupying multiple RATs at each user might not be optimal in a network view point. This paper studies the optimal association strategy to maximize the system throughput in multi-RAT cellular networks. To this end, with stochastic geometry, we derive the network sum rates by considering two association modes; single RAT association (altruistic association) and multiple RATs association (selfish association). By comparing the network sum rates for the two association modes, we show that optimally balancing the number of users between two association modes is necessary to improve the performances. With some numerical examples, we investigate how various system parameters, such as the number of carrier components, user density, and density of access points, etc, affect on the optimal portion of the users between two association modes.

This paper studies two-user cognitive multiple-input multiple-output (MIMO) interference channel with delayed channel state information at transmitter (CSIT), where a transmitter possesses noncasual knowledge of data originating at the other user. We first derive an upper bound of degrees-of-freedom (DoF) region and then propose DoF bound achieving schemes for various antenna configurations. Since interference condition and interference suppression capability vary with antenna configurations, the proposed DoF optimal schemes differently utilize cognitive transmission based on delayed CSIT to suppress interference and thus differ in the required numbers of time slots and transmit symbols, although the key idea of swapping interfering signals between users is common. Comparing with the DoF regions of relevant channels, we identify and analyze the contributions of cognitive transmission and delay CSIT toward enlarging the DoF region. Our analysis specifies antenna configurations where delayed CSIT and cognitive transmission are useful, respectively.

Content delivery success in wireless caching helper networks depends mainly on cache-based channel selection diversity and network interference. For given channel fading and network geometry, both channel selection diversity and network interference dynamically vary according to what and how the caching helpers cache at their finite storage space. We study probabilistic content placement (or caching placement) to desirably control cache-based channel selection diversity and network interference in a stochastic wireless caching helper network, with sophisticated considerations of wireless fading channels, interactions among multiple users, such as interference and loads at caching helpers, and arbitrary memory size. Using stochastic geometry, we derive optimal caching probabilities in the closed form to maximize the average success probability of content delivery and propose an efficient algorithm to find the solution in a noise-limited network. In an interference-limited network, based on a lower bound of the average success probability of content delivery, we find near-optimal caching probabilities in the closed form to control the channel selection diversity and the network interference. We numerically verify that the proposed content placement is superior to other comparable content placement strategies.

From the perspective of a single user, concurrent utilization of all available radio access technologies (RATs) is always beneficial for increasing individual data rate. However, given the interactions among users, occupying multiple RATs at each user might not be optimal from a network-wide viewpoint. In this paper, we explore the answer to the posed question what is the optimal access strategy in multi-RAT cellular networks, solving a distributed RAT access control problem for maximizing network throughput. With stochastic geometry, we analytically evaluate network throughputs for two different access modes: 1) single RAT access (altruistic access) and 2) simultaneous multiple RATs access (selfish access). Comparing the network throughputs for the two modes, we first show that the network throughput can be maximized by properly mixing the two access modes and then derive the optimal portions of each mode in a network, which motivates a distributed RAT access control in a probabilistic sense. The optimal mixture of the two modes controls scheduling contention and interference among users to maximize the network throughput. We also analyze the effects of various system parameters, such as the number of frequency sub-bands for each RAT, user density, and access point density, on the optimal portions of the two access modes.

Each video clip has different degrees of popularity and a user can cache some popular videos in preparation for future requests. The cached data work as a buffer against the playback delay due to unstable wireless channels. Since the storage space for caching is limited while the number of video clips is tremendously large, a memory efficient caching strategy is important for maximizing the caching gain. We propose a user prefix caching scheme in a downlink network where each user caches an initial part (i.e., prefix) of each popular video to minimize the average playback delay. We derive closed-form expressions of the optimal prefix size and the corresponding average playback delay. Our results show the effects of system parameters on the average playback delay and provide an insightful guideline on memory-efficient caching. Moreover, in the scenarios where a constraint on tolerable delay is given, we derive the minimum storage space required at each user and the maximum number of supportable users at a base station in wireless caching networks, which provide a useful insight on designing wireless caching networks.

In wireless cloud storage systems, the recovery failure probability depends on not only wireless channel conditions but also storage size of each distributed storage node. For an efficient utilization of limited storage capacity and the performance characterization of allocation strategies, we asymptotically analyze the recovery failure probability of a wireless cloud storage system with a sum storage capacity constraint for both high signal-to-noise ratio (SNR) regime and low SNR regime. Then, we find the optimal storage allocation strategy across distributed storage nodes in terms of the asymptotic recovery failure probability. Our analysis reveals that the maximal symmetric allocation is optimal for high SNR regime and the minimal allocation (with ⌊T⌋ complete storage nodes and an incomplete storage node) is optimal for low SNR regime, where T is the sum storage capacity. Based on the numerical investigation, we also show that in intermediate SNR regime, a balance allocation between the minimal allocation and the maximal symmetric allocation would not be required if we select one between them according to SNR.

In this paper, we jointly consider the user selection and feedback design problems in a virtual cellular network (VCN), where multiple base stations (BSs) share a user set. In many practical systems, the uplink feedback channel is generally shared by multiple users. Thus, the feedback budget allocated to unselected users not only wastes the feedback resources, but harms the system throughput by decreasing the available feedback budget for the selected users. We optimize both the user selection and feedback bit allocation based on long-term average channel information of the users. We first analyze the effects of the quantization error on the average achievable rate of the VCN system. Next, we propose a user selection and feedback bit allocation protocol under each BS's sum feedback rate constraint as well as the sparsity constraint on all users' feedback sizes. We show that the joint optimization problem can be decoupled into several NP-hard subproblems, one for each BS. We describe the brute-force searching algorithm for the optimal solution, and propose an efficient algorithm with significantly reduced computational complexity by relaxing the sparsity constraint on the feedback sizes. As a result, only the selected users exploit the uplink feedback budget, and the system performance is improved.

We consider the full-duplex (FD) two-way amplify-and-forward relay system with imperfect cancellation of loopback self-interference (SI) and investigate joint design of relay and receive beamforming for minimizing the mean square error (MSE) under a relay transmit power constraint. Due to loopback channel estimation error and limitation of analog-to-digital converter, the loopback SI cannot be completely canceled. Multiple antennas at the relay can help loopback SI suppression, but beamforming is required to balance between the residual SI suppression and the desired signal transmission. Moreover, the relay beamforming matrix should be updated every time slot, because the residual SI in the previous time slot is amplified by the current beamforming matrix and added to the received signals from the two sources in the current time slot. We derive the optimally balanced relay beamforming and receive beamforming matrices in closed form based on minimum MSE, taking into account the propagation of the residual loopback SI from the first to the current time slot. We also propose beamforming design using only the channels of the $m$ latest time slots, not from the first time slot. Based on our numerical results, we also identify when FD is beneficial and propose selection between FD and half-duplex according to signal-to-noise ratio and interference-to-noise ratio.

This paper models the cooperative transmission via caching helpers (cache-based joint transmission), which cache one of the K most popular files, in wireless cellular networks and analyzes its performance in terms of the file transmission success probability in the stochastic geometry framework. For given caching placement, the caching helpers can either provide a file diversity gain by serving different files or a cooperative gain by jointly transmitting the same files. In order to account for the tradeoff between the file diversity gain and the cooperative gain, we derive the cache hit probability and the rate coverage probability. We also find the optimal caching placement balancing the tradeoff and investigate the effects of various system parameters, such as the cooperative region, the density of caching helpers, and the file popularity exponent, on the optimal caching placement.

A distributed beamforming technique at each user pair (transmitter-receiver) is proposed in a (n, K)-user interference channel where K user pairs are allowed to simultaneously communicate with each other among n user pairs (K ≪ n). Each transmitter sends a single spatial stream and each user pair minimizes generating interference to the scheduled receivers and the received interference from the scheduled transmitters via transmit beamforming and receive beamforming, respectively. We analyze scaling of n to achieve K degrees-of-freedom (DoF) with high probability via the proposed beamforming technique. This results show that the proposed beamforming reduces the required network size (i.e., scaling of n) compared to the previous results on (n, K)-user interference channel.

This paper considers a cell with many caching helpers, such as femto-base stations with memories for caching files. We formulate average bit error rate (BER) as a function of caching placement in a Rayleigh flat fading channel. In order to minimize the average BER, we propose an algorithm which finds a caching placement in greedy way and prove that the caching placement found by the proposed greedy algorithm is optimal. We also show that the proposed greedy algorithm has complexity independent of file library size. It is shown that the optimal caching placement is not just caching all different files; however, for a given environment, the optimal caching placement is to balance between channel diversity gain achieved by caching the same file in multiple helpers and caching diversity gain achieved by caching different files in helpers to enlarge a set of cached-file.

In machine-to-machine communications, several machines located close to one another can form a cluster to leverage spatial reuse gains and energy savings. We investigate a machine selection algorithm where only a machine in each cluster transfers its data to the cellular user via device-to-device communication and then the cellular user forwards it to the base station. Unlike the previous works that focus on data rate or channel condition as the selection metrics, this paper proposes a correlation-aware selection algorithm which maximizes the joint entropy extracted from the data delivered from the selected machines. Our evaluation results show the proposed selection scheme reaps a significant gain and is near-optimal.

This paper studies diversity-multiplexing tradeoff (DMT) of decode-and-forward (DF)-based half-duplex relay networks without a direct path from source to destination. We propose a new multiple relay coded cooperation (MRCC) protocol to overcome the half-duplex loss in DF relay networks and show that the proposed MRCC protocol achieves the MISO/SIMO DMT upper bound as the number of assisting relays increases. The loss of multiplexing gain from the half-duplex constraint is recovered by coded cooperation and successive cancellation. Despite its notable gain in terms of DMT, the proposed MRCC protocol requires only limited feedback information/signaling for relay and operation mode selection. We also derive the achievable DMT of the proposed MRCC protocol when each node has multiple antennas. It is shown that the proposed MRCC protocol achieves K times diversity gain of the point-to-point N × N MIMO case. Compared with existing protocols, the MRCC protocol shows much higher diversity gain for almost all multiplexing gains. Furthermore, our numerical results verify that the proposed MRCC protocol outperforms existing protocols in terms of outage probability for the environments of practical interest.

In this paper, we consider a gateway channel, in which multiple source nodes serve their destination nodes with an aid of a single relay node, and propose power allocation schemes at the relay node to maximize the sum rate. After receiving the signals from the source nodes, the relay node broadcasts the decoded messages to the destination nodes, using zero-forcing beamforming and a proper power allocation. For the power allocation, the relay node should consider not only the channel conditions to the destination nodes, but also the message rates from the source nodes, which are the destination nodes' maximum achievable rates. To develop an efficient power allocation strategy with quantized channel state information (CSI), we first assume perfect CSI at the relay node and find the optimal power allocation scheme named asceiled waterfilling. Then, we modify the ceiled waterfilling taking into account the effects of the quantization errors and develop an iterative power allocation algorithm with quantized CSI. Our numerical results show that our proposed power allocation scheme considerably increases the sum rate with respect to the naive equal power allocation at the relay node in the gateway channel for all limited feedback scenarios.

This paper studies the symmetric two-user Gaussian interference channel where the transmitters harvest randomly arrived energies and share the harvested energy with each other for energy cooperation. We characterize the achievable average rate region of the interference channel with time-varying available power when the simplified Han-Kobayashi scheme, known as a near-optimal transmission strategy for the conventional two-user Gaussian interference channel, is employed. We prove that each corner point on the average rate region is on a sum rate bound with appropriate power allocation. Based on the proof, we find the optimal strategy of energy cooperation and power allocation between the two transmitters to achieve the boundary points on the average rate region. It is shown that the energy cooperation can enlarge the average rate region compared to that of the conventional interference channel. We also show that the energy cooperation yields almost the same average rate region regardless of the interference channel condition because it enables to effectively change the given interference channel condition into a more favorable one by flexibly controlling the transmit powers.

This paper considers random multiple access in a network where only a small portion of users have data to forward and transmit packets in each time slot because the user activity ratio is not high in practice. For this reason, the access point (AP) has to not only identify the users who transmitted but also decode the received data codewords. Exploiting the sparsity of transmitting users, Lasso, which is a well-known practical compressed sensing algorithm, is applied for efficient user identification. The compressed sensing algorithm enables the AP to handle more users than the conventional random multiple access schemes do. We develop distributed scheduling methods for maximizing the system sum throughput, and we analyze the corresponding optimal throughput for three different cases of channel knowledge, i.e., the channel state information at the transmitter (CSIT), the channel state information at the receiver (CSIR), and the imperfect channel state information at the receiver (ImCSIR). We also derive the closed-form expressions of asymptotically optimal scheduling parameters and the corresponding maximum sum throughput for each CSI assumption. The results show the effects of system parameters on the sum throughput and provide useful insights on using compressed sensing for throughput maximization in random multiple access schemes.

We propose a novel cooperative user relaying scheme for a two-user multiple-antenna downlink cellular system where each user has to receive a certain required amount of information. A user who successfully receives its required amount of information is supposed to help the other user in receiving its required amount of information through cooperative user relaying. For the proposed cooperative user relaying scheme, we jointly design linear beamformers at the base station over three transmission phases to minimize the total transmission time required for both users to receive their respective required amounts of information, which turn out to be approximated equivalent to the maximization of the sum throughput. In addition, considering a practical hybrid automatic repeat request (HARQ) protocol with user relaying, we modify the proposed scheme to minimize the required number of retransmissions. Our numerical results show that the proposed cooperative user relaying scheme achieves substantial gains over conventional transmission without user relaying in terms of both the average sum throughput and the transmission failure probability.

This paper investigates device-to-device (D2D) communication nested in a cellular network, where a pair of D2D users directly exchanges their information using the uplink frequency band of the cellular network. When the D2D user treats the interference from the cellular user as noise, power control at the cellular user is optimal for maximizing the rate of the cellular user while controlling the interference to the D2D user. However, if the D2D user can perform successive interference cancelation (SIC), the cellular user needs to adjust both transmit power and rate to maximize its rate, because the decodability of the interfering signals at the D2D user depends not only on the signal power but also on the rate of the cellular user. To control the interference from the cellular user, we propose a joint transmit power and rate control scheme at the cellular user. Forcing the cellular user to transmit with a reduced data rate compared with the maximum possible rate, given its transmit power, the proposed joint power and rate control scheme efficiently enables SIC at the D2D user. To reduce the computational complexity, we also propose a near-optimal scheme that employs either power control or rate control depending on the channel conditions.

In distributed wireless storage systems, failed recovery probability depends on not only wireless channel conditions but also storage size of each distributed storage node. For efficient utilization of limited storage capacity, we asymptotically analyze the failed recovery probability of a distributed wireless storage system with a sum storage capacity constraint when signal-to-noise ratio goes to infinity, and find the optimal storage allocation strategy across distributed storage nodes in terms of the asymptotic failed recovery probability. It is also shown that when the number of storage nodes is sufficiently large the storage size required at each node is not so large for high exponential order of the failed recovery probability.

This paper proposes a user cooperation scheme to improve the achievable rate of a cell edge user in a two cell model. In the proposed user cooperation, one user helps decoding of the other user by either desired signal forwarding (SF) or interference forwarding (IF). We derive an achievable rate region of the proposed user cooperation and compare it with that of a conventional two-cell system without user cooperation. It is shown that the proposed user cooperation exploiting either SF or IF is beneficial in terms of achievable rate region compared to the conventional two-cell system.

What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.

We characterize the Pareto boundary of the achievable rate region of the two-user symmetric Gaussian interference channel when transmitters control their transmission power and receivers have successive interference cancellation capability. By identifying the shape of achievable rate region according to the strength of interference channels, we find combinations of decoding and power allocation strategies achieving the Pareto boundary. We also characterize the Pareto boundary of the achievable rate region when time sharing is additionally considered.

Recently, the use of artificial noise has attracted considerable attention in enhancing the physical-layer security of wireless systems. However, the interaction between artificially generated noise and the inherent network interference can significantly affect the level of secrecy. In this paper, we consider the additional secrecy enhancement with artificial noise and secrecy protected zone in the presence of eavesdroppers and interferers with unknown locations. Specifically, we derive the secrecy transmission rate and investigate the relationship between artificial noise and various system parameters like secrecy protected zone radius and intensity of interferers and eavesdroppers on the secrecy transmission rate. In interference-limited networks, we derive the optimal power allocation between the information-bearing signal and artificial noise to maximize the achievable secrecy transmission rate subject to connection and secrecy outage probabilities. Numerical results show that artificial noise is still beneficial in the presence of inherent network interference to improve secrecy transmission rate and provide rules of thumb to quantify when optimal power allocation is useful.

In this paper, we consider a downlink heterogeneous cellular network (HCN) where K tiers operate in a common spectrum and differ in terms of transmit power, target data rate, and base station (BS) density. We employ a repulsive cell activation (or planning) in the HCN by ensuring a minimum separation distance between interfering BSs in each tier. We consider a modified Matern hardcore process (MHP) for rendering a minimum separation distance between the BSs, which is realized by outweighing random BS distribution for closed and open access networks. Repulsive cell activation not only improves the coverage probability but also plays a role in balancing per-cell loads effectively according to varying user density. Assuming a finite BS capacity in terms of a limited number of per-cell users, we point out the importance of a relative BS density control between the tiers, and propose a tier-wise density and power control by introducing a load factor for configuring a HCN distributively while satisfying per-tier user throughput constraints given user density.

In this paper, we devise and comprehensively analyze an efficient transmission protocol for multiple half-duplex relay communication without a direct link but with inter-relay interference. The proposed protocol achieves high diversity gain with the help of Hybrid Automatic Retransmission reQuest (H-ARQ) even when multiplexing gain is quite high. As the maximum allowable number of retransmission rounds (L) increases, an additional time diversity gain is obtained in short-term static channels. Compared to pre-existing protocols which adopt H-ARQ in a similar manner, its diversity gain at high multiplexing gain is much improved. As L increases, the dominating region become wider. In addition, in long-term static channels, we show that more than two retransmissions (i.e., L > 2) are unnecessary for improvement of the diversity gain.

In this paper, we study the achievable degree-of-freedom (DoF) of an (n, K)-user interference network where n transmitter-receiver pairs are randomly distributed but only K transmitter-receiver pairs are allowed to communicate (n ≫ K). We propose a distributed user scheduling method to achieve the maximum DoF (i.e., K), which sequentially adds a transmitter-receiver pair causing/receiving interference to/from the previously selected transmitter-receiver pairs below a certain threshold level. It is proven that the maximum K DoF is achievable if the total number of communication pairs n scales ω(SNRK(K-1)) where SNR denotes the received signal-to-noise ratio. In addition, the total amount of the required feedback for the worst case and the feedback overhead per user are investigated in interference limited environments.

In this paper, we consider a two-tier heterogeneous cellular network (HCN) where macrocells and distributed low power cells, namely daughtercells, are operated in a common spectrum. Due to the ad-hoc nature of daughtercell BS deployments such as pico and femto cells, the mutual interference varies and obviously the coverage probability behaves differently in terms of transmit powers and densities of macrocells and daughtercells. In this paper, we employ repulsive cell activation in the interfering daughtercell network and see the impact of a minimum separation distance between the daughtercell BSs in terms of coverage under open access and power efficiency. The control of the minimum separation distance plays a role in balancing cell load effectively according to changing user density and is justified for the coexistence of low power daughtercells. The optimal minimum separation distance in terms of user density and target per-tier user throughput requirements is found by a numerical search based on a simple bisection method. Numerical results show the benefit of cell repulsion in terms of increased user density support and less area power consumption.

We propose new ergodic interference alignment techniques forK-user interference channels with delayed feedback. Two delayed feedback scenarios are considered - delayed channel information at transmitter (CIT) and delayed output feedback. It is proved that the proposed techniques achieve total 2K/(K+2) DoF which is higher than that by the retrospective interference alignment for the delayed feedback scenarios.

We investigate achievable degrees-of-freedom (DoF) of an (N, K)-user interference channel where only K user (transmitter-receiver) pairs among N user pairs are allowed to simultaneously communicate in a dense network (N ≫ K). Each node is assumed to have M antennas and to be randomly located. We propose a distributed scheduling protocol to achieve the maximum DoF (i.e., MK), which sequentially and opportunistically selects a user pair causing/receiving interference lower than a pre-determined threshold to/from already selected user pairs in each step. It is proven that the proposed protocol achieves the maximum DoF, MK, in the (N, K)-user interference channel with less stringent network size N, compared with the conventional centralized protocol which has been known as the best. With zero-forcing detector at receiver, we prove that it is sufficient that the network size N scales at least as ω(SNR(M2)K(K-1))to achieve the maximum number of DoF MK, where SNR denotes the received signal-to-noise ratio. We also investigate the required feedback overheads of the proposed protocol and show that it is quite small when the network is strongly interference-limited because only a small number of users are required to transmit their signaling. Our numerical results show that our proposed scheme controls interference more effectively than the centralized protocol.

This paper studies a new symmetric two-user Gaussian interferencechannel (IC) in heterogeneous networks where twotransmitter-receiver pairs are underlaid with one primarytransmitter-receiver pair. When underlay transmitters must restricttheir transmission to guarantee target degrees of freedom (DoF) of aprimary receiver, the achievable DoF of the underlay receivers inheterogeneous networks is investigated. Either transmit power ortransmission time is regulated to secure the target DoF of a primaryreceiver and their effects on the achievable DoF are analyzed for thesimple Han-Kobayashi scheme. Our analytical results show that theachievable DoF of the underlay two-user IC is substantiallydifferent from that of a conventional two-user IC due to the imposedrestriction on the underlay users. It is also shown that jointdecoding capability at the primary receiver using common codebookrelaxes the restriction imposed on the underlay users and henceimproves the achievable DoF of the underlay receivers.

This paper presents a spectrally efficient protocol for half-duplex multi-relay systems in block fading channels where a direct source-destination link is unavailable. The proposed protocol adaptively selects either successive interference cancelation (SIC) or joint decoding according to the causal decoding status of each relay. We also adopt dynamic refreshing that restarts the protocol whenever it is advantageous to do so, even if the relay decoding set (the set of relays that are able to decode the message) is not empty. The achievable diversity-multiplexing tradeoff (DMT) of the proposed protocol with m-antenna nodes is analyzed via a Markov chain whose states are related to the cardinality of a decoding set. This protocol strictly improves the DMT of the existing DF half-duplex relay-selection protocols without decoding delay, and in the low multiplexing gain region is able to meet the DMT upper bound. The main contributions of the paper are the state-dependent decoding strategies in DF multi-relay systems and also the dynamic refresh for the flushing of residual interferences in the system, concepts that may find usefulness beyond the gains in the high-SNR regime.

In this paper, we propose opportunistic interference alignment (OIA) for three-transmitter multiple-input multiple-output interference channels. In the proposed OIA, each transmitter has its own user group and selects a single user who has the most aligned interference signals. The user dimensions provided by multiple users are exploited to align interfering signals. Contrary to conventional IA, perfect channel state information of all channel links is not required at the transmitter, and each user just feeds back one scalar value to indicate how well the interfering channels are aligned. We prove that each transmitter can achieve the same degrees of freedom (DoF) as the interference free case via user selection in our system model that the number of receive antennas is twice of the number of transmit antennas. Using the geometric interpretation, we find the required user scaling to obtain an arbitrary non-zero DoF. Two OIA schemes are proposed and compared with various user selection schemes in terms of achievable rate/DoF and complexity.

In this paper, we consider a multiple-input multiple-output broadcast channel with limited feedback where all users share the feedback rates. Firstly, we find the optimal feedback rate sharing strategy using zero-forcing transmission scheme at the transmitter and random vector quantization at each user. We mathematically prove that equal sharing of sum feedback size among all users is the optimal strategy in the low signal-to-noise ratio (SNR) region, while allocating whole feedback size to a single user is the optimal strategy in the high SNR region. For the mid-SNR region, we propose a simple numerical method to find the optimal feedback rate sharing strategy based on our analysis and show that the equal allocation of sum feedback rate to a partial number of users is the optimal strategy. It is also shown that the proposed simple numerical method can be applicable to finding the optimal feedback rate sharing strategy when different path losses of the users are taken into account. We show that our proposed feedback rate sharing scheme can be extended to the system with stream control and is still useful for the systems with other techniques such as regularized zero-forcing and spherical cap codebook.

This paper investigates how multiuser dimensions can effectively be exploited for target degrees of freedom (DoF) in interfering broadcast channels (IBC) consisting of K-transmitters and their user groups. First, each transmitter is assumed to have a single antenna and serve a single user in its user group where each user has receive antennas less than K. In this case, a K-transmitter single-input multiple-output (SIMO) interference channel (IC) is constituted after user selection. Without help of multiuser diversity, K-1 interfering signals cannot be perfectly removed at each user since the number of receive antennas is smaller than or equal to the number of interferers. Only with proper user selection, non-zero DoF per transmitter is achievable as the number of users increases. Through geometric interpretation of interfering channels, we show that the multiuser dimensions have to be used first for reducing the DoF loss caused by the interfering signals, and then have to be used for increasing the DoF gain from its own signal. The sufficient number of users for the target DoF is derived. We also discuss how the optimal strategy of exploiting multiuser diversity can be realized by practical user selection schemes. Finally, the single transmit antenna case is extended to the multiple-input multiple-output (MIMO) IBC where each transmitter with multiple antennas serves multiple users.

Cognitive radio (CR) systems are typically classified into two types. In overlay CR, a secondary user opportunistically accesses primary user's spectrum when it is unused, while a secondary user in underlay CR accesses the spectrum at any time as long as the interference power received at a primary user is below a certain level. This paper investigates a hybrid CR system where a secondary user probabilistically changes its spectrum access mode for secondary user's throughput maximization while guaranteeing primary user's target throughput. Basically, the hybrid CR works like overlay CR. The secondary user constantly monitors activity of the primary user and transmits data with maximum transmit power if transmission of the primary user is not detected. However, the only difference from overlay CR is that even when transmission of the primary user is detected, the secondary users are able to access the spectrum with regulated low transmit power with switching rate c. We show that there is a trade-off of increasing c due to recursive interactions between primary and secondary users, and optimizing c is crucial to balance the gain and loss of secondary user's underlay spectrum access. The proposed hybrid CR is shown to achieve higher throughput and be more robust to detection errors than conventional CR systems.

This paper proposes a new strategy combining interference alignment (IA) and rate splitting for a three user interference channel with a cognitive transmitter. Although a half of available dimensions are sacrificed to align interference signals from other users in conventional IA, the proposed scheme recycles the sacrificed dimensions with the help of a cognitive transmitter. The achievable rate region and generalized degrees of freedom are derived. Compared with conventional IA, the proposed scheme improves achievable rate and generalized degrees of freedom.

In this paper, we propose two opportunistic jammer selection schemes for secure communications aided by jammers using the concept of interference alignment. Because jamming signals are interference to both a legitimate receiver and a eavesdropper, the legitimate receiver selects two jammers whose jamming signals are the most aligned in a small dimensional subspace. The alignment is measured by either interference-to-noise ratio (INR) or chordal distance for the opportunistic jammer selection. We find the achievable secure degrees of freedom (DoF) by the proposed jammer selection schemes when the number of jammers goes to infinity and find the computational complexity of each scheme.

This paper analyzes the capacity of a three-user interference channel with a cognitive transmitter which non-causally knows the messages of the other two transmitters. Especially, We derive the capacity region when all interference channel gains are strong and show that the capacity is achieved by superposition coding. From the capacity results for the specific channel conditions, we also analyze the capacity region for three-user Gaussian interference channel with a cognitive transmitter.

We investigate the cooperative diversity gain in underlay cognitive radio (CR) systems where secondary users access the spectrum licensed to primary users under an interference power constraint at primary receivers. Our analysis shows that the cooperative diversity gain has different characteristics from that in non-CR systems since the transmit power of a secondary transmitter is limited by the interference power constraint. If a fixed interference power constraint is imposed, the cooperative diversity gain is lost regardless of the number of relays even if peak transmit power grows to infinity. We analyze the critical value of peak transmit power beyond which increasing peak transmit power does not help in improving the outage probability. However, if a proportional interference power constraint is imposed, a full diversity gain is shown to be attained even without instantaneous interference channel information at the secondary transmitters.

A novel user relaying scheme based on bit concatenation is proposed for multiple-input multiple-output (MIMO) broadcast channels. In a broadcasting phase, a part of information for a destination user is concatenated to data for a relaying user and transmitted to the relaying user. The destination user receives the part of information via the relaying user in a relaying phase in addition to the data in the broadcasting phase. An achievable rate region for the proposed scheme is derived and an algorithm to characterize the boundary of the rate region is proposed. Our analysis shows that the proposed user relaying in a MIMO BC expands the achievable rate region for the zero-forcing dirty paper coding (ZF-DPC) in the MIMO BC without relaying. It is also shown that the proposed user relaying scheme always outperforms a superposition coding (SC) based user relaying scheme. Optimal and suboptimal user pair scheduling algorithms are investigated for a two-user MIMO BC and the proposed suboptimal user pair scheduling scheme is shown to approach the optimal scheduling when the relaying channel gain is large.

In cognitive radio (CR) systems, the Gaussian approximation (GA) has been widely used in determining the optimal threshold for spectrum sensing owing to its analytical simplicity because the GA has been known to be accurate when the number of samples is large. However, this paper shows that if the received signals are weak, the determined sensing threshold by the GA causes significant performance degradation of CR systems even when the number of received samples is large. We analytically quantify the degradation in terms of throughput efficiency and show that the threshold for spectrum sensing should be determined by an exact distribution which is derived in this paper if the received signals are weak.

In this paper, we investigate the impact of relay cooperation for maintaining coverage area against aggregate interference from incumbent underlaid interferers. We employ a guard zone for uplink so that non-urgent interferers are inhibited and urgent ones are admitted with a controlled access probability as long as the primary receiver can tolerate. Numerical results show the outage probability of the primary relay network with the guard zone and that a desired quality-of-service (QoS) determines the access probability of urgent interferers depending on interfering node density.

In this paper, we show that opportunistic interference alignment (OIA) by receiver selection achieves a total K degrees of freedom (DoF) in a K-user 1 × 3 single-input multiple-output (SIMO) interference channel (IC) when the number of receive antennas is three (NR=3) at each receiver. The concept of OIA is geometrically interpreted on the complex unit sphere when the number of interferers is larger than NR, i.e., K ≥ 4. We prove that OIA achieves DoF of one if the number of receivers associated with each transmitter increases with PIK-3where PIis the average power of each interferer.

A novel user relaying scheme based on bit concatenation is proposed for a two-user multiple input multiple output(MIMO) broadcast channel (BC). In broadcasting phase, a part of information for a destination user is concatenated to data for a relaying user and transmitted to a relaying user. The destination user receives the part of information via the relaying user in relaying phase in addition to the data in broadcasting phase. Achievable rate region of the proposed scheme is derived and an algorithm to characterize the boundary of the rate region is proposed. Our analysis and simulations show that the proposed user relaying in a MIMO BC expands the achievable rate region of a zero-forcing dirty paper coding (ZF-DPC) in a MIMO BC without relaying.

Most studies on hybrid automatic repeat request (HARQ) with respect to rate adaptation have been focused on throughput maximization. However, for real-time traffic, resource minimization is a more efficient rate adaptation method in OFDMA-based systems. Moreover, most previous work have dealt with either slow or fast fading channel although practical wireless channels typically exhibit a time-correlated property. In this paper, we analyze the outage probability and average resource usage of HARQ with rate adaptation in a time-correlated channel model. Then, we propose a rate adaptation scheme to minimize the average resource usage and evaluate the performance of the proposed scheme under delay and packet loss constraints of real-time traffic. Our analytical and numerical results show that the proposed rate adaptation scheme is more efficient than the conventional schemes.

In this paper, we propose a practical interference alignment technique based on an opportunistic user selection in a three-user MIMO interference channel where no information is allowed to be shared among transmitters. In the proposed scheme, the user whose interference signals from other transmitters are most aligned with each other is selected at each transmitter. The proposed opportunistic interference alignment (OIA) is shown to achieve notable sum rate compared to conventional opportunistic user selection schemes. In addition, the proposed OIA significantly reduces not only the amount of feedback information but also computational complexity.

Most analytical studies on hybrid automatic repeat request (HARQ) have been carried out in either slow fading or fast fading assumptions. However, since practical wireless channels typically exhibit a time-correlated property, we need to take into account the effects of time correlation in the design of HARQ. In this paper, we analyze the outage probability and delay-limited throughput of HARQ with rate adaptation in a time-correlated channel model. Then, we propose a rate adaptation scheme to maximize the delay-limited throughput and evaluate the performance of the proposed scheme under a delay constraint. Our analytical and numerical results show that the proposed rate adaptation scheme outperforms the conventional rate adaptation schemes. The proposed rate adaptation scheme is further simplified by using a Gaussian approximation on the effective channel power gain. The rate adaptation based on the proposed Gaussian approximation reduces the complexity and achieves a throughput gain comparable to that with the exact channel distributions.

This paper proposes a linear precoding technique for multiple-input multiple-output (MIMO) decode-and-forward (DF) based relay communications. The proposed precoder is constructed by linearly combining two independently designed precoders that maximize data rates at relay and destination, respectively. Contrary to conventional precoding in DF relay communications, the proposed precoding balances direct and relay links and maximize the achievable rate of the overall system. We also propose a distributed precoder design method using limited information. The proposed algorithms are shown to significantly reduce computation complexity of precoder design. The numerical results show that the proposed precoders with full information and limited information achieve significantly higher data rates than conventional precoding neglecting a direct link and comparable data rate to the optimal precoding even when the coefficients for linear combining are restricted to real numbers.

In this paper, we propose a practical interference alignment technique based on an opportunistic user selection in a three-user 2 ×2 MIMO interference channel where no information is allowed to be shared among transmitters. In the proposed scheme, the user whose interference signals from other transmitters are most aligned with each other is selected at each transmitter. We also investigate optimal and suboptimal beamforming at the selected receiver using extra degrees of freedom by multiple receive antennas. The proposed opportunistic interference aligned user selection (OIAUS) schemes combined with various postprocessing vectors are shown to achieve notable capacity compared to conventional opportunistic user selection schemes while they significantly reduce not only the amount of feedback information but also computational complexity.

In relay communications, the relay is in cooperation without doubt at a price of coordination signaling such as synchronization and associated channel state information (CSI) acquisition. Distributed relay selection based on average channel gain information available at the relays is preferred from the perspective of the cost of coordination signaling only if the link reliability can be maintained. In this paper, we propose a simple distributed scheme for relay selection such that a set of relays that satisfies QoS constraints is only activated and one of them is selected randomly from an opportunistic set of QoS relays. The proposed scheme avoids unnecessary power consumption caused by non-beneficial relay cooperation and performs comparable to the centralized best relay selection while it minimizes signaling overheads.

This paper analyzes the multiuser diversity gain in a cognitive radio (CR) system where secondary transmitters opportunistically utilize the spectrum licensed to primary users only when it is not occupied by the primary users. To protect the primary users from the interference caused by the missed detection of primary transmissions in the secondary network, minimum average throughput of the primary network is guaranteed by transmit power control at the secondary transmitters. The traffic dynamics of a primary network are also considered in our analysis. We derive the average achievable capacity of the secondary network and analyze its asymptotic behaviors to characterize the multiuser diversity gains in the CR system.

In this paper, we propose an opportunistic relaying with multiple decode-and-forward (DF) relay nodes based on interference cancelation (IC). The relay that succeeds decoding data in the former phase performs IC to decode another data in the current phase without suffering from interference caused by the selected best relay that forwards the data of the former phase. It is shown that the proposed scheme compensates for the loss of spectral efficiency caused by half duplexing relay nodes when the number of relays is large or SNR is high. In a whole systemic view, the proposed system asymptotically works as a single full-duplex transceiver. Our analytical and numerical results confirm that the proposed scheme outperforms the opportunistic single selection relaying with DF in terms of outage capacity when the number of relays is large or SNR is high.

This paper proposes a hybrid cognitive radio (CR) system where underlay and overlay CR approaches are combined. Occasional switches from an overlay CR mode to an underlay CR mode enable to maximize the average throughput of a secondary (unlicensed) network and stable transmission of a secondary user. By controlling switching from an overlay CR mode to an underlay CR mode in a probabilistic sense, the throughput of a secondary user is maximized while the target departure rate of a primary (licensed) user is retained. Since a primary user in a hybrid approach is likely to suffer from additional interference, optimal transmit power of a secondary user in an overlay mode is derived for given switching rate. Our analysis and numerical results show that the proposed hybrid CR system takes benefits in maximizing throughput and maintaining stability of a queue.

This paper proposes a novel partial decode-and-forward (DF) relaying strategy with multiple antennas. In the first phase, the source broadcasts data streams consisting of non-forwarding and forwarding data streams. In the second phase, a relay node forwards only forwarding data streams to a destination node, and a destination node decodes both non-forwarding and forwarding data streams by successive interference cancellation (SIC). We provide an analytical framework of achievable rate and design a linearly combined precoding matrix for rate maximization. Our results show that the proposed partial DF relaying with a linearly combined precoding matrix achieves substantially higher rate than a conventional DF relaying scheme.

A simple linear preceding scheme to cancel interference is proposed for downlink multiuser cellular relay networks where the available information is asymmetric between the base station and the relay node. The interference signal from the relay is eliminated by linear pre-pocessing at the base station without any cooperation between the base station and the relay. In the information asymmetric environment, it is shown that the proposed scheme outperforms a time division multiple access (TDMA) scheme and approaches an ideal scheme based on dirty paper coding (DPC) in terms of capacity. An mathematical framework for the analysis of the proposed technique is also presented.

In this letter we propose per antenna superposition coding (PASC) by which partial information can be relayed instead of full information, to exploit the higher capacity of source-relay-destination link. Here, the PASC is designed across antennas, producing basic layer and superposed layer for each data stream per antenna. It is shown that an overall data rate of partial information relaying with PASC can be increased beyond that offered by full information relaying by virtue of fast forwarding of partial information over relatively better link.

This paper proposes an adaptive multi-node incremental relaying technique in cooperative communications with amplify-and-forward (AF) relays. In order to reduce the excessive burden of MRC with all diversity paths at the destination node, the destination node decides if it combines signals over the first N(<; K) time slots/frames or over all of the K times slots, where K is the number of relay nodes. Our analytical and simulation results show that the proposed adaptive multi-node incremental relaying outperforms the conventional MRC in terms of outage probability in AF based cooperative communications since the proposed scheme effectively reduces the spectral efficiency loss. Our asymptotic analysis also shows that the proposed adaptive multi-node incremental relaying achieves full diversity order K + 1.

This paper proposes a novel generalized selection combining (GSC)/maximal ratio combining(MRC) switching technique based on error detection in cooperative communications with amplify-and-forward (AF) relays. In order to reduce the excessive burden of MRC with all diversity paths at the destination node, the destination node decides if it performs GSC with order N(< K) or MRC with order K + 1 based on the error detection, where K is the number of relay nodes. Our analytical and simulation results show that the proposed GSC/MRC switching outperforms the conventional MRC in terms of outage probability in AF based cooperative communications since the proposed scheme effectively reduces the spectral efficiency loss with the help of error detection codes.

In this paper, we propose a spectral efficiency enhancement algorithm through simultaneous accesses to heterogeneous networks, where a mobile station (MS) can simultaneously access different non-interfering networks with different radio access technologies (RATs). This simultaneous access (i.e., RAT diversity) is determined in terms of the distributed optimal solution, which implies that MSs can decide whether they connect with multiple RATs or not. To derive the optimal solution, we first show the advantages of multiaccess (MA) by an example in heterogeneous networks, and analyze the spectral efficiency of MA in multi-RAT system. Through our analysis and simulations, it is shown that the proposed algorithm is able to achieve the increase of spectral efficiency compare with vertical handoff (VHO) method, which selects only one network having the best channel quality. As a result, the proposed MA algorithm can be considered as a viable solution to get more spectral efficiency in heterogeneous environments.

We propose a decode-and-forward (DF) based cooperative phase steering scheme and analyze its outage probability. The cooperative phase steering scheme is to make the received signals from multiple relay nodes co-phased at a destination node by pre-adjusting the phase differences.With a reasonable amount of feedback information from a destination node, the cooperative phase steering scheme circumvents the drawbacks of conventional cooperative diversity techniques such as maximal ratio combining (MRC) reception, maximal ratio transmission (MRT), and opportunistic relay selection schemes. Our analytical and simulation results show that the cooperative phase steering scheme outperforms the opportunistic relay selection scheme and approaches the MRT scheme known as a theoretically optimal cooperative diversity technique. It is also shown that cooperative phase steering has sufficient robustness to phase incoherence.

We investigate the effects of multi-user diversity in a spectrum sharing system where secondary users restrictively utilize a spectrum licensed to primary users only if interference perceived at primary users is regulated below a predetermined level. This interference regulation affects the characteristics of multiuser diversity gains previously known in non-spectrum sharing systems. Our numerical and analytical results show that the multiuser diversity gain in a spectrum sharing system increases differently according to conditions given by the transmit power of secondary users, P, and a predetermined interference temperature, Q - if P is sufficiently larger than Q, the multiuser diversity gain in terms of capacity scales like log2(W (Ns)) similarly to a previously known scaling law in the non-spectrum sharing systems, where W(middot) and Nsdenote a Lambert W function and the number of secondary transmitters, respectively. However, the scaling law of multiuser diversity gain becomes log2(Ns) as P becomes sufficiently larger such that P Gt QNs.

This paper investigates the capacity and energy efficiency of spectrum sharing systems with opportunistic user selection where a secondary network utilizes spectrum bands licensed to a primary network under interference regulation. In spectrum sharing systems, secondary users consume a fraction of their resources in sensing the channels to the primary users to comply with the interference constraints. Although more resources for sensing improve reliability and performance, the throughput loss due to time overhead and energy loss due to power overhead should be properly incorporated in performance evaluation. In this context, we define and derive a new metric-average capacity normalized by the total energy consumption-reflecting time and power overhead for spectrum sensing. Based on the developed framework, the optimal normalizedcapacity is investigated. We also propose a simple and practical suboptimal best-n scheme motivated by the infeasibility and high computational complexity of the optimal strategy, where n denotes the number of sensing secondary users. Our analytical and simulation results show that the proposed best-1 scheme is an energy-efficient technique with near optimality in terms of the capacity normalized by the energy consumption.

We analyze the capacity of an opportunistic scheduling system in a spectrum sharing environment where multiple secondary users can share a frequency spectrum with multiple primary users as long as secondary users do not cause interference power exceeding a given threshold to the primary users. We consider three different power control schemes of secondary users: fixed transmit power, adaptive transmit power, and infinite transmit power schemes. Our numerical and simulation results show that the capacity of the adaptive transmit power scheme is similar to that of the fixed transmit power scheme in the low transmit power region, while the capacity of the adaptive transmit power scheme is close to that of the infinite transmit power scheme in the high transmit power region and is saturated beyond a certain point.

In this paper, we studied the method that improves the downlink performance of cellular relay system in multiple-cell environment by using cognitive radio. When relay stations in the network have spectrum agility, they can establish downlink channels which are orthogonal to downlink of base station. In this way, interference between base station and relay station can be reduced and spectral resource can be utilized more efficiently as well. To investigate the performance gain with spectrum agility, we quantify channel usage of the incumbent systems by i.i.d ON/OFF model and evaluate the normalized system capacity of spectrum-agile cellular relay network. The result of this paper shows that the spectrum-agile cellular relay network relieves other-cell interference and thus the system capacity is improved.

This letter proposes an approximate waterfill power loading scheme using limited feedback in orthogonal frequency division multiplexing (OFDM) systems. The proposed technique achieves nearly the capacity of optimal waterfill power loading, while significantly reducing feedback by using order information for the subcarrier channel gains. Furthermore, the proposed power loading technique can circumvent the practical shortcomings of previous limited feedback power loading techniques by simply exploiting order mapping and interpolation. The advantages are particularly visible at low SNR or for many subcarriers, both of which will be very common in emerging wireless broadband OFDM standards.

The capacity and robustness of cellular MIMO systems is very sensitive to other-cell interference which will in practice necessitate network level interference reduction strategies. As an alternative to traditional static frequency reuse patterns, this paper investigates intercell scheduling among neighboring base stations. We show analytically that cooperatively scheduled transmission, which is well within the capability of present systems, can achieve an expanded multiuser diversity gain in terms of ergodic capacity as well as almost the same amount of interference reduction as conventional frequency reuse. This capacity gain over conventional frequency reuse isO(Mtsquare-root of log Ns) for dirty paper coding andO(min (Mr, Mt) square-root of logNs) for time division, where Nsis the number of cooperating base stations employing opportunistic scheduling in anMtxMrMIMO system. From a theoretical standpoint, an interesting aspect of this analysis comes from an altered view of multiuser diversity in the context of a multi-cell system. Previously, multiuser diversity capacity gain has been known to grow as O(log logK), from selecting the maximum ofKexponentially-distributed powers. Because multicell considerations such as the positions of the users, lognormal shadowing, and pathless affect the multiuser diversity gain, we find instead that the gain isO(square-root of 2logicK), from selecting the maximum of a compound Iognormal-exponential distribution. Finding the maximum of such a distribution is an additional contribution of the paper.

This paper compares the system performance of transmit diversity schemes for use in interference-limited cellular systems. We focus on evaluating the performance of Alamouti space time block codes (STBC) and cyclic delay diversity (CDD) codes, which are being actively considered for adoption within the 3 GPP-long-term-evolution (LTE) and 802.16e WiMAX standards. While the performance of transmit diversity schemes has been extensively evaluated in noise-limited scenarios, their performance in co-channel interference (CCI) limited cellular systems has received limited attention so far. As cellular systems adopt frequency reuse of one, co-channel interference from other cells becomes a major limitation to system performance. We present system simulation results comparing the performance of transmit diversity schemes using matched filter reception for a multi-carrier OFDM-based cellular system. In particular, the performance of STBC and CDD schemes is compared with respect to the evolving third generation 3 GPP-LTE standard. Our comparison highlights the spatial as well as the multi-user diversity aspects of these schemes and considers a variety of scenarios that will require their use. Our results show that CDD can significantly improve the system spectral efficiency and cell-edge performance over STBC, when used in combination with frequency-dependent scheduling. For open loop transmission without scheduling, the relative diversity gains of STBC and CDD at link layer depend on the interference scenario considered, while the system performance of these schemes is very similar.

We propose two relay selection in cooperative relay communications. In a fixed scheme, M multiple relays that have strong signal strength are selected out of K relays and forward their received data from a source node to a destination node. As an alternative approach, a threshold-based adaptive relay selection scheme is also proposed to minimize the number of forwarding relays while satisfying a given outage requirement because if the number of forwarding relays increases, then the number of interfering sources also increases. The minimum number of relays that can prevent an outage event are selected to forward data to a destination. The performance of both schemes are evaluated through numerical analysis and Monte-Carlo simulations in terms of end-to-end outage probability and the number of forwarding relays. The result presents a bound that the fixed and adaptive relay selection schemes can achieve information-theoretically. Furthermore, the outage performance of the adaptive relay selection scheme is identical to that of the fixed relay selection scheme with M = K, while the number of forwarding relays is much less than that of the fixed relay selection scheme with M = K.

This paper investigates the effects of co-channel interference on spatial diversity techniques. By analyzing distributions of post-processing signal to noise plus interference ratio (SINR) after matched filtering, we capture the performance characteristics of spatial diversity techniques in an interference limited environment. Using intuition from the theoretical analysis of a single carrier system, the performance of spatial diversity techniques in an OFDMA system with co-channel interference is also characterized. Our analytical and simulation results show that space time block code (STBC) schemes are more sensitive to co-channel interference than other spatial diversity techniques so their performance gain in a noise-limited environment can be lost in an interference-limited environment.

This paper investigates the interaction between multiuser diversity and spatial diversity in an interference-limited environment based on post-receiver-processing signal-to-interference-plus-noise ratio (SINR) distributions. If opportunistic scheduling is employed, spatial diversity effects limit the achievable multiuser diversity gain. This paper quantifies the interaction by using order statistic theory and shows a spatial diversity technique with a larger SINR variance can be more effective under opportunistic scheduling. Through analysis and simulations, the authors show that the cyclic delay diversity technique gets the most benefit from the opportunistic scheduling among likely spatial diversity techniques and outperforms space time block coding (STBC), even though STBC is generally considered the most effective transmit diversity technique in a noise-limited environment.

In this paper, a novel transmission technique for the multiple-input multiple-output (MIMO) broadcast channel is proposed that allows simultaneous transmission to multiple users with limited feedback from each user. During a training phase, the base station modulates a training sequence on multiple sets of randomly chosen orthogonal beamforming vectors. Each user sends the index of the best beamforming vector and the corresponding signal-to-interference-plus-noise ratio for that set of orthogonal vectors back to the base station. The base station opportunistically determines the users and corresponding orthogonal vectors that maximize the sum capacity. Based on the capacity expressions, the optimal amount of training to maximize the sum capacity is derived as a function of the system parameters. The main advantage of the proposed system is that it provides throughput gains for the MIMO broadcast channel with a small feedback overhead, and provides these gains even with a small number of active users. Numerical simulations show that a 20% gain in sum capacity is achieved (for a small number of users) over conventional opportunistic space division multiple access, and a 100% gain (for a large number of users) over conventional opportunistic beamforming when the number of transmit antennas is four.

Distributed antenna systems (DAS) have been widely implemented in state-of-the art cellular communication systems to cover dead spots. Recent academic studies have shown that in addition to coverage improvements, DAS can also have potential advantages such as reduced power and increased system capacity in a single cell environment. This paper analytically quantifies downlink capacity of multicell DAS for two different transmission strategies: selection diversity (where just one or two of the distributed antennas are used) and blanket transmission (where all antennas in the cell broadcast data). Simple repeaters are a special case of our analysis. A generalized information theoretic analysis is provided to illuminate the fundamental limits of such systems in the cellular context. The results show that DAS reduces other-cell interference in a multicell environment and hence significantly improves capacity (by about 2x), with particularly large improvements for users near cell boundaries. Less obviously, from a communication theory standpoint, it is shown that selection diversity is preferable to blanket transmission in terms of achievable ergodic capacity. For blanket transmission, we show that the optimal transmission strategy is just phase steering due to the per antenna module power constraints in DAS

Even though spatial multiplexing provides a significant spectral efficiency advantage in a single point-to-point noise- limited link, recent studies have shown that this advantage can be lost in cellular MIMO systems unless extra diversity is provided. Spread spectrum is a likely candidate for the extra diversity because spread spectrum can simultaneously provide frequency diversity and robustness to interference. This paper investigates the effectiveness of spatial multiplexing in the forward link of cellular MIMO-CDMA systems with linear receivers. Through the development of new closed-form results on outage probability and capacity for MIMO-CDMA, we show that even MIMO-CDMA loses the spatial multiplexing gain in a cellular context. These results indicate that a practical cellular MIMO system, which will be interference-limited and have a low-complexity receiver, requires new study on techniques to efficiently reduce the impact of the other-cell interference. The developed analytical framework can be used for evaluating other techniques.

Direct Sequence Code Division Multiple Access (DS-CDMA) receivers typically use Maximal Ratio Combining (MRC) to favorably combine the energies of distinct multipath components from diversity branches. In previous research, compromising assumptions have been made to simplify the analysis, including a Gaussian approximation for interference or constant equal cross correlations. However, these assumptions corrupt the analysis especially in certain operating conditions such as a relatively small number of users, which is particularly relevant for modern CDMA systems that carry data. The contribution of this paper is to provide a general framework for accurately analyzing the performance of the diversity receiver in CDMA systems without these compromising approximations. From the analytical and numerical results, it is: shown that the developed framework provides higher accuracy than previous approaches.

In this paper, a novel transmission technique for the multiple-input multiple-output (MIMO) broadcast channel is proposed that allows simultaneous transmission to multiple users under a limited feedback requirement. During a training phase, the base station modulates a training sequence on multiple sets of randomly generated orthogonal beamforming vectors. Then, based on the users' feedback, the base station opportunistically selects the users and corresponding orthogonal vectors that maximize the sum capacity. From theoretical analysis, the optimal amount of training to maximize the sum capacity is derived as a function of the system parameters. The main advantage of the proposed system is that it provides throughput gains for the MIMO broadcast channel with a small feedback overhead, and provides these gains even with a small number of active users. Numerical simulations show that a 20% gain in sum capacity is achieved (for a small number of users) over conventional opportunistic space division multiple access, and a 100% gain (for a large number of users) over conventional opportunistic beamforming.

As an alternative to traditional static frequency reuse patterns, this paper investigates cooperatively scheduling among neighboring base stations in a cellular multiple antenna system, where each cell adopts dirty paper coding. It is shown that cooperatively scheduled transmission can achieve almost the same amount of interference reduction as conventional frequency reuse and achieve an extra capacity gain. We analytically quantify the capacity gain of cooperatively scheduled transmission over conventional frequency reuse in an Mttimes Mrdirty paper coded MIMO system. The theoretical analysis of this paper also provides an altered view of multiuser diversity in the context of a multi-cell system. Because the positions of the users are important in a multi-cell system, we find that the gain is O(radiclog K), from selecting the maximum of a compound lognormal-exponential distribution, whereas multiuser diversity capacity gain has been previously known to grow as O(log log K), from selecting the maximum of K exponentially-distributed powers

Improving downlink CDMA capacity has been an area of intensive research for the past decade, especially as the downlink has become the capacity limiting link. Multi-antenna technologies are an obvious candidate for increasing the downlink capacity, but successfully decoding spatially multiplexed signals is very challenging in an interference-limited environment, such as that of CDMA cellular systems. In this paper, a simple and novel MIMO-CDMA system design is developed, in which users are assigned to a transmit antenna either without regard to channel knowledge (static) or based on antenna selection feedback bits (dynamic). These proposed antenna partitioning techniques have a minimal increase in complexity and require only small changes to existing CDMA standards. The outage probability and capacity of the proposed systems are derived and it is shown that they outperform conventional CDMA systems regardless of the number of antennas or antenna partitioning technique.

In this paper, the effectiveness of spatial multiplexing in the forward link of cellular CDMA systems with linear (i.e. low complexity) receivers is investigated. General MIMO systems without spreading are a special case of our analysis when the spreading gain is unity. Through the development of new closed-form results on outage probability and capacity for MIMO-CDMA, we show that cellular MIMO outage capacity is severely degraded by the enhancement of other-cell interference by the linear spatial receiver, and that a large number of transmit and receive antennas is required to simply break even with a SISO system. The results indicate that for practical cellular MIMO systems, which will be interference-limited and have low complexity receivers, future research is required on feasible methods for reducing the impact of other-cell interference. The framework presented in this paper can be used for future analysis of multicell MIMO systems.

In this letter, the performance for the delay diversity receiver is analyzed in asynchronous code division multiple access (CDMA) channels. The outage probability and the bit error probability of the delay diversity receiver are accurately derived and compared with those of the conventional diversity receiver. From the analytical and numerical results, it is confirmed that the delay diversity receiver achieves a remarkable diversity gain with reasonable cost and complexity in asynchronous CDMA channels. Specifically, for roughly the same hardware complexity, the delay diversity receiver achieves nearly twice the diversity order of the conventional receiver.

The paper provides a general framework for accurately analyzing the bit error probability of the maximal ratio combining (MRC) diversity receiver in CDMA systems. In previous research on MRC receivers for direct sequence code division multiple access (DS-CDMA), compromising assumptions have been made to simplify the analysis, including a Gaussian approximation for interference or constant equal cross correlations. These assumptions can produce inaccurate results, especially when the number of users is small or when the diversity order is high, as will increasingly be the case in high-rate CDMA systems. We derive a novel closed-form bit error probability expression without these assumptions and verify through simulations that this new framework is highly accurate.

In this paper, the performance of the delay diversity receiver is analyzed in asynchronous CDMA channels. The outage probability and the bit error probability of the delay diversity receiver are accurately derived and compared with those of the conventional diversity receiver. From the analytical and numerical results, it is confirmed that the delay diversity receiver achieves a remarkable diversity gain with reasonable cost and complexity in asynchronous CDMA channels. Specifically, for roughly the same hardware complexity, the delay diversity receiver achieves nearly twice the diversity order of the conventional receiver.

A new base station receiver is proposed and analyzed for a code-division multiple-access (CDMA) cellular system. The proposed receiver can achieve remarkable diversity gain by increasing diversity order with reasonable cost and complexity. From the numerical results, it is confirmed that the proposed receiver structure can be a practical solution for enhancing reverse-link capacity and improving performance in CDMA cellular system operations. The result in the letter can find its applications to legacy IS-95/cdma2000 1x base stations with simple modifications.

In mobile communication systems, there are areas where signal coverage is not adequately accomplished by base stations. These areas may be underground parking zones, underground stores, tunnels, and insides of buildings, etc. In these areas, repeaters are broadly used as an economical solution. However, the capacity is decreased as the number of repeaters increases because the power of the noise that flows into a base station in the reverse link is proportional to the number of repeaters. The reduction of capacity in the reverse link is no longer negligible in the case that several tens or hundreds repeaters are connected to a base station. We propose automatic on-off switching (AOS) repeater that is switched off automatically when there is no active user within its coverage. With the AOS repeater, we can reduce the unnecessary noise level enhancement when there is no active user within the repeater's coverage. The reverse link capacity of a DS/CDMA system with AOS repeater is analyzed mathematically and compared with that without AOS. From the numerical results, noticeable improvement with the proposed AOS repeaters is shown.