Unsupervised learning has been extensively adopted to train deep neural networks (DNNs) for learning wireless resource allocation. Yet, the performance of DNNs is vulnerable to distribution shifts between training and test data, e.g., wireless channels. In this paper, we propose an offline unsupervised training method to enhance the out-of-distribution (OOD) generalizability of DNNs. Inspired by adversarial training (AT), the method trains DNNs using progressively identified adversarial examples out of the training distribution. To reflect OOD degradation of a DNN in the context of unsupervised learning, we reformulate the optimization problem of AT. The proposed method is evaluated by learning hybrid precoding. Simulation results showcase the enhanced OOD performance of multiple kinds of DNNs with approximately 5\(\sim\)20\% improvement across various channel distributions, even when the samples only from a single distribution (e.g., Rayleigh fading) are used for training.

The optimization of multi-user multi-input multi-output (MU-MIMO) precoders is a widely recognized challenging problem. In this paper, we develop a gradient-driven GNN design method for the learning of fully digital and hybrid precoding policies. The proposed GNNs leverage two kinds of knowledge, namely the gradient of signal-to-interference-plus-noise ratio (SINR) to the precoders and the permutation equivariant property of the precoding policy. To demonstrate the flexibility of the proposed method for accommodating different optimization objectives and different precoding policies, we first apply the proposed method to learn the fully digital precoding policies. We study two precoder optimization problems for spectral efficiency (SE) maximization and log-SE maximization to achieve proportional fairness. We then apply the proposed method to learn the hybrid precoding policy, where the gradients to analog and digital precoders are harnessed for the design of the GNN. Simulation results show the effectiveness of the proposed methods in achieving good performance with low inference complexity, particularly for large-scale systems, and demonstrate superior generalization to the numbers of both users and antennas compared to the baseline GNNs.

User scheduling and hybrid precoding in wideband multi-antenna systems have never been learned jointly due to the challenges arising from the massive user combinations on resource blocks (RBs) and the shared analog precoder among RBs. In this paper, we strive to jointly learn the scheduling and precoding policies with graph neural networks (GNNs), which have emerged as a powerful tool for optimizing resource allocation thanks to their potential in generalizing across problem scales. By reformulating the joint optimization problem into an equivalent functional optimization problem for the scheduling and precoding policies, we propose a GNN-based architecture consisting of two cascaded modules to learn the two policies. We discover a same-parameter same-decision (SPSD) property for wireless policies defined on sets, revealing that a GNN cannot well learn the optimal scheduling policy when users have similar channels. This motivates us to develop a sequence of GNNs to enhance the scheduler module. Furthermore, by analyzing the SPSD property, we find when linear aggregators in GNNs impede size generalization. Based on the observation, we devise a novel attention mechanism for information aggregation in the precoder module. Simulation results demonstrate that the proposed architecture achieves satisfactory spectral efficiency with short inference time and low training complexity, and is generalizable to the numbers of users, RBs, and antennas at the base station and users.

Incorporating mathematical properties of a wireless policy to be learned into the design of deep neural networks (DNNs) can reduce their hypothesis space, thereby improving learning efficiency. Multi-user precoding policies in multi-antenna systems possess a permutation equivariance property, which has been harnessed to design the parameter-sharing structure of the weight matrix of DNNs. In this paper, we study a stronger property than permutation equivariance, namely unitary equivariance, for precoder learning, which has the potential to further reduce the DNN hypothesis space. We first demonstrate that unitary equivariance cannot be exploited in the same manner as permutation equivariance, i.e., solely through parameter sharing in the weight matrix, which prevents the learning of the optimal precoder. Recognizing this limitation, we develop a novel non-linear processing function for DNN layers that satisfies unitary equivariance, based on which we construct a joint unitary and permutation equivariant DNN architecture. Simulation results show that the proposed DNN not only outperforms existing learning methods in learning performance and generalizability but also reduces training complexity.

Optimizing power allocation among access points (APs) is an effective way to improve the spectral efficiency of user-centric cell-free multi-antenna systems, where each user equipment (UE) associates with adjacent APs, leading to dynamic association relationship. The serving APs can adopt different beamforming strategies, say distributed beamforming (D-BF) and centralized beamforming (C-BF). Although D-BF requires lower fronthaul capacity than C-BF, it is unable to eliminate inter-AP interference. To reduce the high computational complexity of numerical algorithms for solving optimization problems, deep neural networks (DNNs) have been designed to learn the power allocation policies with D-BF, which however disregard the impact of dynamic association and are with high training complexity. Besides, there are no existing works to learn the power allocation policies with C-BF, where the power constraint of each AP needs to be satisfied. In this paper, we design graph neural networks (GNNs), including D-GNN and C-GNN, to learn the power allocation policies under D-BF and C-BF, respectively. For achieving high learning efficiency, these GNNs exploit the permutation properties of the optimal policies. The D-GNN is adaptive to dynamic association and the C-GNN can satisfy the per-AP power constraint. Simulation results show that the designed GNNs outperform the existing DNNs with low training complexity and short inference time, and are generalizable to the numbers of APs, antennas, and UEs.

Hybrid analog and digital beamforming (HBF) is an essential technique for terahertz (THz) communications to support high spectral efficiency with affordable cost. Optimizing hybrid beamforming with deep learning can improve system performance and enhance robustness to imperfect channels. However, pure data-driven neural networks suffer from high training complexity and weak interpretability, while the performance of existing model-based approaches (e.g., deep unfolding) is limited by the algorithm itself. In this paper, we propose a model-based neural network, namely HBF-NN, to optimize hybrid beamforming for multi-antenna multi-carrier THz systems, which consists of two jointly trained modules for optimizing analog and digital beamforming matrices, respectively. To simplify the function to be learned, we propose to optimize the analog beamforming in angle domain. To learn the digital beamforming efficiently, we conceive a graph neural network structure by harnessing the permutation property, recursive property, and the structure of a commonly-used algorithm of singular value decomposition. Simulation results show that the proposed HBF-NN achieves higher spectral efficiency than numerical algorithms, while requiring significantly fewer training samples, free parameters, and less training time than existing data-driven counterpart to achieve the same performance.

Learning-based methods have been developed for user scheduling and precoding in multi-antenna systems, among which most existing studies learned the two policies separately. In this paper, we strive to learn jointly optimized user scheduling and hybrid precoding policy with graph neural network (GNN), which has emerged as a powerful tool for optimizing resource allocation thanks to its potential in generalizability to problem scales. We find that the GNN for selecting users simultaneously does not perform well, due to a same-feature same-action phenomenon. To alleviate its adverse impact, we propose a sequential GNN (SGNN) architecture, which is a cascade of preprocessor, scheduler consisting of multiple sub-schedulers, and precoder modules. To assist SGNN in learning favorable scheduling policy, we add two model-based inputs into the preprocessor. To help reduce multiuser interference and allow generalizability to problem scales, we integrate attention mechanism into the GNN for precoding. Simulation results show that the joint scheduling and precoding policy learned by the proposed SGNN achieves higher sum-rate than separately optimized scheduling and precoding by numerical algorithms with much shorter running time, and is generalizable to the numbers of users and antennas.

Deep learning has been widely used for wireless optimization. In most existing studies, a deep neural network (DNN) is trained for a particular optimization problem and then tested on the same problem with samples drawn from the same distribution as the training data. Practical systems, however, typically involve multiple optimization problems with conflicting objective functions. In this paper, we study the learning for multi-objective optimization (MOO) by using beamforming in multi-user multi-antenna system as an illustrative example. We seek to learn a beamforming policy for two distinct optimization problems: power-constrained sum rate maximization and signal-to-interference-plus-noise ratio-constrained power minimization. Different from conventional MOO methods, which primarily find Pareto-optimal solutions to balance the conflicting objective functions, we resort to transfer learning (TL) and model-agnostic meta-learning (MAML) to learn an adaptive beamforming policy, allowing efficient fine-tuning of trained DNNs with limited samples for either of the two optimization problems. Simulation results demonstrate that both TL and MAML enable the trained DNNs to efficiently adapt to the optimization problems, and graph neural network is a promising network architecture for learning adaptive beamforming policies.

Graph neural networks (GNNs) have been used for optimizing resource allocation due to their potential in scalability and size generalizability. To facilitate their application to large- scale wireless systems, decentralized inference with GNNs has been investigated recently. Yet decentralized training of GNNs at wireless nodes, which can alleviate the computing load at central server and protect privacy of users, has never been studied. In this paper, we strive to train GNNs in mobile systems in a decentralized manner, by taking power control optimization for interference coordination as an example. We present a framework for decentralized training of GNNs at wireless nodes, and propose two algorithms to tackle the challenge of training GNNs over dynamic graph topology. Simulation results show that the power control policy learned by the GNN performs very close to decentralized numerical algorithms with lower signaling overhead for inference.

Viewpoint and position (VP)-adaptive mixed reality (XR) streaming requires uploading the trajectory of user behavior, thereby causing privacy leakage. Preserving privacy leads to the performance loss of VP prediction of users, subsequently degrading the quality of experience (QoE). This work is the first to improve the QoE-privacy tradeoff for XR streaming. We find the key difference of sample importance for privacy attack and VP prediction, based on which we propose a framework to improve the tradeoff. By designing the noisy entropy function and remapping function, it can achieve a better tradeoff than simply adding the noise to the actual trajectory. The performance of the framework is evaluated with the state-of-the-art VP predictors and practical XR streaming platforms. The results show that the loss of QoE can be mitigated by 55%$\sim$100% to achieve the same privacy level as simply adding noise. When the privacy level achieves the maximum, the QoE is degraded by 0$\sim$3%, compared to VP-adaptive streaming without any privacy-preserving.

Learning-based precoding has been shown able to be implemented in real-time, jointly optimized with channel acquisition, and robust to imperfect channels. Nonetheless, existing works rarely explain when and why a deep neural network (DNN) for learning precoding policy can perform well. In this paper, we strive to understand the learning performance by taking baseband precoding as an example, for which the optimal precoding matrices of several problems such as sum rate maximization have mathematical structure. Toward this goal, we design a graph neural network (GNN) with edge-update mechanism, whose inductive bias matches to the precoding policy, and analyze its connection to the commonly used convolutional neural networks (CNNs). Noticing that the learning performance can be decomposed into approximation and estimation errors, which depend on the smoothness of a policy and the inductive bias of a DNN, we analyze in which system settings the precoding policy is harder to be approximated by a DNN and how the inductive biases introduced by parameter sharing affect estimation errors. We proceed to derive the estimation error bounds of the DNNs. Simulations validate our analyses and verify the gain of GNN over CNNs in terms of reducing sample complexity.

Learning precoding policies with neural networks enables low complexity implementation, robustness to channel impairments, and joint optimization with channel acquisition. However, pure data-driven methods for learning precoding suffer from high complexity of training and poor generalizability to problem scales, while existing model-driven learning methods are either algorithm-specific or problem-specific. In this paper, we propose a model-based graph neural network (GNN) to learn precoding policies by harnessing their properties and relevant mathematical model. We first show that a vanilla GNN cannot learn zero-forcing precoding when the numbers of antennas and users are large, and is not generalizable to the numbers of users. Then, we conceive a new GNN structure by resorting to the iterative Taylor’s expansion of matrix pseudo-inverse, which can adapt to the interference strength among users. Simulation results show that the proposed GNN can well-learn different precoding policies (say spectral efficient and energy efficient precoding policies as well as coordinated beamforming) with low training complexity. Moreover, it can be generalized to the number of users, which is highly desirable in practice since the number of scheduled users may change in milliseconds.

Graph neural networks (GNNs) can improve the efficiency of learning wireless policies by leveraging their permutation properties and topology prior. While mismatched permutation property to a policy may degrade the learning performance and overlooked permutations incurs low sample efficiency, there is still lacking a systematical approach for modeling graph and designing structure of GNNs to harness all permutation properties. Moreover, the information of input feature may lose during updating hidden representations with GNNs, which leads to poor learning performance. In this paper, we propose a unified framework to learn permutable wireless policies with multidimensional GNNs, which update the hidden representations of hyper-edges to avoid the information loss. We provide a method to construct graph for a policy, over which a GNN with proper parameter sharing can exploit all possible permutations of the policy. We also investigate the permutability of wireless channels that affects the sample efficiency, and show how to trade off the training, inference, and design complexities of GNNs. To showcase how to design the GNNs within the framework, we consider precoding optimization in different systems. Simulation results validate the gain of the proposed GNNs over existing counterparts from exploiting the permutation prior and avoiding the information loss.

Deep learning has been introduced to optimize wireless policies such as precoding for enabling real-time implementation. Yet prevalent studies assume that training and test samples are drawn from the same distribution, which is not true in dynamic wireless environments. As a result, a well-trained deep neural network (DNN) may require retraining to adapt to new environments, incurring the overhead of data collection. The required training samples for adaptation can be reduced by introducing inductive biases into DNNs, which can be learned automatically by meta-learning or embedded in DNNs by designing graph neural networks (GNNs). Almost all previous works on meta-learning overlooked the prior-known permutation equivariance (PE) properties, which widely exist in wireless policies and can be harnessed to reduce the hypothesis space of a DNN. In this paper, we strive to answer the following question: which way of introducing inductive biases is more effective in reducing samples for retraining, GNNs or meta-learning? We take the sum-rate maximization precoding problem as an example to answer the question. Simulation results show that the GNNs are more efficient than meta-learning, and meta-learning for precoding cannot adapt to new scenarios where the number of users differs from the training scenario.

Virtual Reality (VR) applications offer an immersive user experience at the expense of privacy leakage caused by inevitably streaming various new types of user data. While some privacy-preserving approaches have been proposed for protecting one type of data, how to design and evaluate approaches for multiple types of user data are still open. On the other hand, preserving privacy will degrade the quality of experience of VR applications or say the utility of user data. How to achieve efficient utility-privacy tradeoff with multiple types of data is also open. Both call for a dataset that contains multiple types of user data and personal attributes of users as ground-truth values. In this paper, we collect a 6 degree-of-freedom VR dataset of 3D virtual worlds for the investigation of privacy-preserving approaches and utility-privacy tradeoff.

Multi-modal data collected from various sensors is instrumental in enhancing proactive handover management, beam directions and received powers prediction. However, what essential information to extract and how to effectively allocate wireless resources to transmit the information to a central processor (e.g., a base station (BS)) for decision making is a challenging task. In this work, we consider an uplink multi-user goal-oriented system, where images extracted from users’ depth cameras reflect blockage status between users and their serving BS, which are then used for future received power prediction. In the system, we employ a convolutional neural network to learn a joint semantic source and channel encoder such that essential information is extracted from images. Subsequently, we model the multi-user subcarrier communication system as a hypergraph and use hyper-edge graph neural networks to learn precoders at the user side and detector at the BS side. Simulation results demonstrate that by jointly training a deep neural network-based encoder, decoder, precoder and detector, the communication system can achieve lower prediction errors than traditional precoder and detector, especially in low signal-to-noise ratio scenarios. We also show a trade-off between prediction performance and the computational complexity.

Deep neural networks (DNNs) have been widely used for learning precoding policy to achieve good system performance with low computational complexity. However, existing DNNs for learning precoding cannot be generalized to the number of users. Consequently, the DNNs have to be retrained frequently, which incurs unaffordable computational cost for training, especially when the number of antennas is large. As a metric of time complexity, the number of floating-point operations (FLOPs) has been analyzed for inference in literature. Yet the time complexity of training DNNs for learning wireless policies has only been evaluated in terms of running time. To gain useful insight into developing deep learning methods with low-cost training, in this paper we derive the numbers of FLOPs of training three DNNs used for learning precoding, which are circular convolution neural network (CCNN), linear convolution neural network with fully-connected layers (F-LCNN), and fully-connected neural network (FNN). We then compare them with those of inference. Analytical results show that the ratio of the approximate numbers of FLOPs for training to those for inference depends on the numbers of epochs and training samples. Simulation results show that the time complexity of training scales with the number of antennas faster than that of inference, and the complexity of training the F-LCNN and FNN scales faster than that of training the CCNN.

Graph neural networks (GNNs) have been shown promising in optimizing power allocation and link scheduling with good size generalizability and low sample complexity, which are important for learning wireless policies under dynamic environments. This attributes to their matched permutation equivariance (PE) properties to the policies to be learned. Nonetheless, existing works have demonstrated that only satisfying the PE property cannot ensure a GNN for learning precoding policy to be generalizable to the unseen problem scales, say the number of users. Incorporating models with neural networks helps improve size generalizability, which however is only applicable to specific problems. In this paper, we strive to design a size generalizable GNN that does not depend on any mathematical model, such that the GNN can learn wireless policies including but not limited to baseband and hybrid precoding in multi-user multi-antenna systems. To this end, we first identify the key characteristics of the update equation of a GNN that affect its size generalization ability. Then, we design a size-generalizable GNN that is with these key characteristics and satisfies the PE property of a precoding policy in a recursive manner. Simulation results show that the proposed GNN can be well-generalized to the number of users for learning precoding policies.

Learning precoding with deep neural networks (DNNs) enables real-time implementation and robustness to imperfect channels. However, existing DNNs for learning precoding suffer from high training complexity and weak generalization ability to problem scales, which impedes their practical use in wireless systems with user scheduling. In this paper, we propose a graph neural network (GNN) to learn precoding policies efficiently by resorting to the model of Taylor’s expansion of matrix pseudo-inverse. The GNN can capture the importance of neighbored edges when aggregating their information, which is critical for improving the learning efficiency. We also interpret the role of the trainable parameters on learning the powers and directions of the precoding vectors. Simulation results show that the proposed model-driven GNN can well-learn spectral and energy efficient precoding policies with low training complexity, and is generalizable to the numbers of users.

Hybrid precoding can support high data rate with low cost for millimeter wave (mmWave) multi-antenna systems. To achieve near-optimal performance with low computing latency and enable end-to-end learning, deep learning has been introduced for optimizing hybrid precoding. Most research efforts focus on learning hybrid precoding under static channels. In mobile communications, however, the channel aging effect incurs severe performance degradation of multi-antenna systems. In this paper, we resort to a proactive optimization method to learn the analog and digital precoders for multiple users in time-varying mmWave channels with implicit channel prediction. We consider the practical frame structure used in prevalent cellular systems, and propose a method to learn the hybrid precoding polices for multiple downlink subframes in parallel. Simulation results demonstrate that the proposed method performs closely to the hybrid precoding that assuming perfect future channel information and outperforms existing methods.

This paper studies the joint scheduling and power allocation for uplink multiuser multi-input multi-output (MU-MIMO) orthogonal frequency division multiple access (OFDMA) systems. The objective is to minimize the number of occupied resource blocks (RBs) subject to per-user rate constraints. The problem is a mixed integer and non-convex programming problem. We first propose a hierarchical algorithm to find a solution, where in the outer layer the number of RBs are reduced in a greedy manner while in the inner layer the power allocation and scheduling of users are optimized to determine which RB should be unoccupied. The inner problem is non-convex high-complexity problem. To reduce the complexity, we further employ a deep neural network to learn the solution of the inner problem. Simulation results show that compared to two baseline methods, the proposed method can effectively reduce the occupied RBs with much lower complexity.

Reconfigurable intelligent surface (RIS) is capable of controlling environment smartly for improving the performance of wireless communications. To reduce the pilot overhead of estimating the high-dimensional channels in RIS-aided systems, deep neural networks have been introduced to learn the beam-forming policy with received pilot sequences in an end-to-end (E2E) manner. However, existing works either ignore or only consider part of the permutation equivariant (PE) properties of the E2E policy. As a result, the designed neural networks suffer from high sample complexity. In this paper, we analyze the PE property of an E2E active and passive beamforming policy in a RIS-aided multi-user multi-antenna system, and design a graph neural network (GNN) architecture with matched inductive bias to learn the policy. By taking sum rate maximization problem as an example, simulation results demonstrate the benefits of the proposed GNN in terms of reducing the sample complexity to achieve the expected sum rate.

This paper studies the uplink scheduling for multiinput multi-output orthogonal frequency division multiple access (MIMO-OFDMA) systems, aimed at minimizing the number of occupied resource blocks (RBs) subject to per-user rate constraints. This is a combinatorial optimization problem, whose global optimal solution is generally difficult to find due to the prohibitively large search space. To tackle the difficulty, we propose a deep learning based scheduling method, where two deep neural networks (DNNs), namely FeaNet and SchNet, are designed, respectively. FeaNet aims to judge the feasibility of the rate requirements of users given the maximal transmit power and RB resources, while at the same time learn an initial feasible scheduling decision if the scheduling problem is feasible. SchNet aims to learn the optimal scheduling decision by finding a path direction from the initial scheduling decision to the optimal decision. Simulation results demonstrate the performance gain of the proposed method over baseline scheduling methods.

Learning-based resource allocation can be implemented in real-time, but deep neural networks (DNNs) developed in other fields such as computer vision are with high training complexity and weak generalizability. Leveraging domain knowledge in communications is promising for learning wireless policies efficiently. In this paper, we propose a framework of integrating the Shannon formula with DNNs, and derive a data rate-based DNN (DRNN), for learning resource allocation by taking power allocation as an example. The DRNN is with an iterative structure with multiple update layers, each consisting of a pre-determined model function, an update network, and a dimension reduction network. To justify the iterative structure, we prove the existence of an iteration function that converges to the optimal policy for the update layer to learn. To justify the structure of each update layer, we provide the conditions for the iteration function to be a composite function of the model function. We further incorporate permutation equivariance properties into the DRNN. Simulation results show that the numbers of training samples and free parameters, and the training time to achieve a desired system performance can be reduced remarkably by harnessing the data rate model and PE prior.

This paper optimizes predictive power allocation to minimize the average transmit power for video streaming subject to the constraint on stalling time, one of the most important factors affecting the experience of users requesting video-on-demand service. Different from the widely used first-predict-then-optimize strategy that regards the predicted channels as the real future channels, we integrate the prediction of large-scale channels into the optimization of power allocation, such that the quality of service constraint can be controlled. Due to the channel prediction errors, stalling is unavoidable and the stalling duration is random. This motivates us to consider an average stalling fraction constraint conditioned on the observed large-scale channel gains, which can be transformed into a conditional probabilistic constraint. The resultant optimization problem is difficult to solve since the probabilistic constraint lacks a closed-form expression. We resort to end-to-end deep learning to optimize the future powers from the past channels. In particular, we propose a method to learn the conditional probabilities in multiple steps with a single neural network. Simulation results show the advantages of the proposed method in reducing average power consumption and in ensuring the probabilistic constraint.

To support modern machine-type communications, a crucial task during the random access phase is device activity detection, which is to identify the active devices from a large number of potential devices based on the received signal at the access point. By utilizing the statistical properties of the channel, state-of-the-art covariance based methods have been demonstrated to achieve better activity detection performance than compressed sensing based methods. However, covariance based methods require to solve a high dimensional nonconvex optimization problem by updating the estimate of the activity status of each device sequentially. Since the number of updates is proportional to the device number, the computational complexity and delay make the iterative updates difficult for real-time implementation especially when the device number scales up. Inspired by the success of deep learning for real-time inference, this paper proposes a learning based method with a customized heterogeneous transformer architecture for device activity detection. By adopting an attention mechanism in the architecture design, the proposed method is able to extract features reflecting relevance among device pilots and received signal, permutation equivariant with respect to devices, and its training parameter number is independent of the device number. Simulation results demonstrate that the proposed method achieves better activity detection performance with much shorter computation time than state-of-the-art covariance approach, and generalizes well to different numbers of devices and BS-antennas, different pilot lengths, transmit powers, and cell radii.

Hybrid precoding in millimeter wave systems can support high spectral efficiency with affordable cost. With deep learning, fairly good solutions that are robust to imperfect chan-nels can be obtained with low complexity from the non-convex optimization problems. Yet previous works for hybrid precoding are with high cost for training neural networks because the mathe-matical properties of the optimization problems are not taken into account. In this paper, we show that hybrid precoding problems exhibit a multi-set permutation equivariance (PE) property and a phase invariance property. We propose a method to design a graph neural network (GNN) that can satisfy the PE property, and propose a processing method to harness the phase invariance property. Simulation results show that the proposed GNN is more efficient than the commonly used convolutional neural network, which requires much fewer trainable parameters and training samples to achieve the same sum-rate and achieves higher sum-rate with the same number of training samples.

This paper studies the resource allocation for the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) services at a single user, which sends the data of the two services in the uplink. The basic task is to determine how many and which resource blocks (RBs) should be allocated to each service with how much power, aimed at minimizing the total number of occupied RBs, subject to the quality of service (QoS) requirements of the two services. The formulated joint power and RB allocation problem is a mixed-integer programming problem. We propose an algorithm to find the global optimal solution with low complexity. Simulation results show that the proposed algorithm achieves the optimal performance with much lower complexity than exhaustive searching, and demonstrate the behaviors of the optimal power and RB allocation policies.

Optimizing power allocation in cellular systems with deep learning enables real-time coordination of inter-cell interference. When channels are time-varying, the deep neural networks (DNNs) need to be re-trained frequently and the training samples need to be re-collected in a timely manner. To achieve higher sum rate with fewer training samples and lower training cost, domain knowledge should be resorted for designing DNNs. In this paper, we propose a DNN structure where the formula of data rate is used to facilitate the learning of power allocation policy, called data-rate based DNN (DRNN). Since such a model-based deep learning method does not exclude the use of prior knowledge for reducing the hypothesis space of a DNN, we further exploit a permutation equivariance prior by introducing parameter sharing into the DNN structure. By integrating the model and prior into DNN, simulations show that either sum rate is improved for given number of training samples or training complexity is reduced to achieve an expected performance.

Proactive tile-based virtual reality (VR) video streaming can use the trace of FoV and eye movement to predict future requested tiles, then render and deliver the predicted tiles before playback. The quality of experience (QoE) depends on the combined effect of tile prediction and consumed resources. Recently, it has been found that with the field of view (FoV) and eye movement data collected for a user, one can infer the identity and preference of the user. Existing works investigate the privacy protection for eye movement, but never address how to protect the privacy in terms of FoV and how the privacy protection affects the QoE. In this paper, we strive to characterize and satisfy the FoV privacy requirement. We consider "trading resources for privacy". We first add camouflaged tile requests around the actual FoV and define spatial degree of privacy (SDoP) as a normalized number of camouflaged tile requests. By consuming more resources to ensure SDoP, the actual FoVs can be hidden. Then, we proceed to analyze the impacts of SDoP on the QoE by jointly optimizing the durations for prediction, computing, and transmission that maximizes the QoE given arbitrary predictor, configured resources, and SDoP. We find that a larger SDoP requires more resources but degrades the performance of tile prediction. Simulation with state-of-the-art predictors on a real dataset verifies the analysis and shows that a user requiring a larger SDoP can be served with better QoE.

Optimizing precoding with deep learning enables its real-time implementation. In addition to the learning perfor-mance such as sum rate, training complexity is also important since neural networks (NNs) have to be re-trained in time-varying channels. By leveraging the prior-known property for a policy to be learned, inductive biases can be introduced to the structure of NNs to balance the learning performance and training com-plexity. Most existing works use convolutional neural networks for learning precoding policy, without considering whether their inductive biases match the precoding task. In this paper, we first show that full-digital precoding policy exhibits permutation equivariance property and introduce graph NN (GNN) to learn the policy. We then analyze and show the connections between the structures and inductive biases of several NNs. Simulation results show that the inductive bias of the GNN is well-matched to the precoding policy, and hence achieves higher sum-rate with given number of training samples and needs lower training complexity to achieve the same sum-rate than other NNs.

A well-trained deep neural network (DNN) enables real-time resource allocation by learning the relationship between a policy and its impacting parameters. When wireless systems operate in dynamic environments, the DNN has to be re-trained frequently and hence training complexity should be low. A promising approach to deal with this issue is to construct DNNs with prior knowledge. In this paper, we show that the power allocation policy in multi-cell-multi-user systems exhibits a combination of permutation equivariance properties, which can be harnessed by graph neural networks (GNNs). In particular, we construct a heterogeneous graph and resort to heterogeneous GNN for learning the policy, whose outputs are only equivariant to some permutations of vertexes rather than arbitrary permutations as homogeneous GNNs. We prove that the properties of the functions learned by existing heterogeneous GNN for the formulated graph are inconsistent with the properties of the policy. To avoid the performance degradation by embedding wrong priors, we design a parameter sharing scheme for heterogeneous GNN such that the learned relationship satisfies the desired properties. Simulation results show that the sample and computational complexities for training the constructed GNN are much lower than existing DNNs to achieve the same sum rate.

This paper proposes a deep neural network (DNN) based method to solve the multicell power control problem that maximizes the sum rate subject to per-user rate constraints. The basic idea is to employ a two-DNN concatenating network structure, where the second DNN associated with a randomization processing is designed to guarantee the per-user rate constraints via supervised learning, given which the first DNN is trained to directly maximize the sum rate by unsupervised learning. Simulation results demonstrate that the proposed method can achieve better performance with low complexity compared to existing deep learning and numerical optimization methods.

Proactive tile-based virtual reality video streaming computes and delivers the predicted tiles to be requested before playback. All existing works overlook the important fact that computing and communication (CC) tasks for a segment may squeeze the time for the tasks for the next segment, which will cause less and less available time for the latter segments. In this paper, we jointly optimize the durations for CC tasks to maximize the completion rate of CC tasks under the task duration-squeezing-aware constraint. To ensure the latter segments remain enough time for the tasks, the CC tasks for a segment are not allowed to squeeze the time for computing and delivering the subsequent segment. We find the closed-form optimal solution, from which we find a minimum-resource-limited, an unconditional and a conditional resource-tradeoff regions, which are determined by the total time for proactive CC tasks and the playback duration of a segment. Owing to the duration-squeezing-prohibited constraints, the increase of the configured resources may not be always useful for improving the completion rate of CC tasks. Numerical results validate the impact of the duration-squeezing-prohibited constraints and illustrate the three regions.

Neural networks (NNs) have been applied to solve various problems in ultra-reliable and low-latency communications (URLLC). Facing the stringent quality of service requirement, the time for training and running NNs is not negligible, and how to ensure the reliability with learning-based solutions is challenging, especially in a dynamic environment. In this paper, we propose an online learning method, which fine-tunes the NNs trained without supervision to ensure the reliability of URLLC for mobile users. A joint power and bandwidth allocation problem, aiming to minimize the bandwidth required for satisfying the quality of service of each user, is considered as an example. A "learning-to-optimize" method with offline training is provided for comparison. Simulation results show that the proposed online learning method can achieve comparable system performance as the offline training method, where the time consumed for online training and inference is about 25% of the 1 ms latency bound for the considered setup. Besides, the online learning method adapts to the abrupt change of average packet arrival rate quickly and can ensure reliability by setting the required overall packet loss probability conservative slightly. By contrast, the offline training method yields much worse reliability when the arrival rate varies.

Optimizing power control in multi-cell cellular networks with deep learning enables such a non-convex problem to be implemented in real-time. When channels are time-varying, the deep neural networks (DNNs) need to be re-trained frequently, which calls for low training complexity. To reduce the number of training samples and the size of DNN required to achieve good performance, a promising approach is to embed the DNNs with a priori knowledge. Since cellular networks can be modelled as a graph, it is natural to employ graph neural networks (GNNs) for learning, which exhibit permutation invariance (PI) and equivalence (PE) properties. Unlike the homogeneous GNNs that have been used for wireless problems, whose outputs are invariant or equivalent to arbitrary permutations of vertexes, heterogeneous GNNs (HetGNNs), which are more appropriate to model cellular networks, are only invariant or equivalent to some permutations. If the PI or PE properties of the HetGNN do not match the property of the task to be learned, the performance degrades dramatically. In this paper, we show that the power control policy has a combination of different PI and PE properties, and existing HetGNN does not satisfy these properties. We then design a parameter sharing scheme for HetGNN such that the learned relationship satisfies the desired properties. Simulation results show that the sample complexity and the size of designed GNN for learning the optimal power control policy in multi-user multi-cell networks are much lower than the existing DNNs, when achieving the same sum rate loss from the numerically obtained solutions.

User scheduling is an efficient way to harvest the frequency and multiuser diversity gain for uplink Orthogonal Frequency Division Multiple Access (OFDMA) system. To solve the non-convex scheduling problem, existing numerical or searching based solutions face the difficulty of meeting the real-time requirement of fast scheduling. In this paper, a deep learning based method is proposed to solve the user scheduling problem, aimed at reducing the scheduling complexity for real-time implementation. The key challenge of learning the scheduling decisions lies in how to ensure that the learned decisions satisfy the coupled binary constraint. To tackle the difficulty, we design a deep neural network (DNN) to approximate the binary vector quantization operation. The DNN is then used as the activation function in the output layer of another DNN, where the latter is trained to directly maximize the performance utility via unsupervised learning. Simulation results demonstrate that the proposed method is able to largely reduce the complexity with marginal performance and fairness loss compared to the greedy searching method.

As one of the key communication scenarios in the fifth-generation and also the sixth-generation (6G) mobile communication networks, ultrareliable and low-latency communications (URLLCs) will be central for the development of various emerging mission-critical applications. State-of-the-art mobile communication systems do not fulfill the end-to-end delay and overall reliability requirements of URLLCs. In particular, a holistic framework that takes into account latency, reliability, availability, scalability, and decision-making under uncertainty is lacking. Driven by recent breakthroughs in deep neural networks, deep learning algorithms have been considered as promising ways of developing enabling technologies for URLLCs in future 6G networks. This tutorial illustrates how domain knowledge (models, analytical tools, and optimization frameworks) of communications and networking can be integrated into different kinds of deep learning algorithms for URLLCs. We first provide some background of URLLCs and review promising network architectures and deep learning frameworks for 6G. To better illustrate how to improve learning algorithms with domain knowledge, we revisit model-based analytical tools and cross-layer optimization frameworks for URLLCs. Following this, we examine the potential of applying supervised/unsupervised deep learning and deep reinforcement learning in URLLCs and summarize related open problems. Finally, we provide simulation and experimental results to validate the effectiveness of different learning algorithms and discuss future directions.

In this paper we study a data-supported caching policy design for wireless D2D caching networks, which is based on a dataset collected from a campus Wi-Fi network. After a well-designed preprocessing for the dataset, for the first time, we conduct a real dataset based performance evaluation for the caching policies designed based on the homogeneous Poisson Point Process (PPP) model and a clustered PPP model. We proceed to propose a novel approach for the design of the D2D caching policy. It directly models the number of D2D neighbours, instead of characterizing the locations of users as the PPP models. We show that the number of D2D neighbours can be well modeled by a discrete Gamma distribution. Given the model, we develop an iterative algorithm to optimize the D2D caching policy, and also provide a method to optimize the cache update time in order to balance the caching gain and overhead. Simulation results based on the dataset show that the proposed caching policy can achieve good performance with low cost of cache updating.

Proactive tile-based video streaming can avoid motion-to-photon latency of wireless virtual reality (VR) by computing and delivering the predicted tiles to be requested before playback. All existing works either focus on designing predictors or allocating computing and communications resources. Yet to avoid the latency, the successively executed prediction, communication, and computing tasks should be accomplished within a predetermined time. Moreover, the quality of experience (QoE) of proactive VR streaming depends on the worst performance of the three tasks. In this paper, we jointly optimize the duration of the observation window for predicting tiles and the durations for computing and transmitting the predicted tiles, aimed at balancing the performance for three tasks to maximize the QoE given arbitrary predictor and configured resources. We obtain the closed-form optimal solution by decomposing the formulated problem equivalently into two subproblems. With the optimized durations, we find a resource-limited region where the QoE increases rapidly with configured resources, and a prediction-limited region where the QoE can be improved more efficiently with a better predictor. Simulation results using three existing predictors and a real dataset validate the analysis and demonstrate the gain from the joint optimization over non-optimized counterparts.

In the paper we study a deep learning based method to solve the multicell downlink power control problem for sum rate maximization subject to per-user rate constraints and per-base station (BS) power constraints. The core difficulty of this problem is how to ensure that the learned power control results by the deep neural network (DNN) satisfy the per-user rate constraints. To tackle the difficulty, we propose to cascade a projection block after a traditional DNN, which projects the infeasible power control results onto the constraint set. The projection block is designed based on a geometrical interpretation of the constraints, which is of low complexity, meeting the real-time requirement of online applications. Explicit-form expression of the backpropagated gradient is derived for the proposed projection block, with which the DNN can be trained to directly maximize the sum rate via unsupervised learning. Simulation results demonstrate the advantages of the proposed method over existing deep learning and numerical optimization methods, and show the robustness of the proposed method to the model mismatch between training and testing datasets.

Predictive power allocation is conceived for energy-efficient video streaming over mobile networks using deep reinforcement learning. The goal is to minimize the accumulated energy consumption of each base station over a complete video streaming session under the constraint that avoids video playback interruptions. To handle the continuous state and action spaces, we resort to deep deterministic policy gradient (DDPG) algorithm for solving the formulated problem. In contrast to previous predictive power allocation policies that first predict future information with historical data and then optimize the power allocation based on the predicted information, the proposed policy operates in an on-line and end-to-end manner. By judiciously designing the action and state that only depend on slowly-varying average channel gains, we reduce the signaling overhead between the edge server and the base stations, and make it easier to learn a good policy. To further avoid playback interruption throughout the learning process and improve the convergence speed, we exploit the partially known model of the system dynamics by integrating the concepts of safety layer, post-decision state, and virtual experiences into the basic DDPG algorithm. Our simulation results show that the proposed policies converge to the optimal policy that is derived based on perfect large-scale channel prediction and outperform the first-predict-then-optimize policy in the presence of prediction errors. By harnessing the partially known model, the convergence speed can be dramatically improved. The code for reproducing the results of this article is available at https://github.com/fluidy/twc2020.

Deep neural networks (DNNs) have been employed for designing wireless networks in many aspects, such as transceiver optimization, resource allocation, and information prediction. Existing works either use fully-connected DNN or the DNNs with specific structures that are designed in other domains. In this paper, we show that a priori knowledge widely existed in wireless tasks is permutation invariance. For these tasks, we propose a DNN with special structure, where the weight matrices between layers of the DNN only consist of two smaller sub-matrices. By such way of parameter sharing, the number of model parameters reduces, giving rise to low sample and computational complexity for training a DNN. We take predictive resource allocation as an example to show how the designed DNN can be applied for learning an optimal policy with unsupervised learning. Simulations results validate our analysis and show dramatic gain of the proposed structure in terms of reducing training complexity.

Proactive tile-based video streaming can avoid motion-to-photon latency of wireless virtual reality (VR) by computing and delivering the predicted tiles in a segment to be requested before playback. All existing works either focus on tile prediction or on tile computing and delivering, overlooking the facts that these three tasks have to share the same duration and the quality of experience (QoE) depends on the worst performance of them. In this paper, we jointly optimize the duration of the observation window for prediction and the durations used for computing and communication to maximize the QoE of watching a VR video. We find the global optimal solution by decomposing the original problem equivalently into subproblems, with which we find prediction-limited or resource-limited region. Simulation results demonstrate the gain of the optimized durations by using two existing prediction methods with a real dataset.

User fairness is an important metric for cellular systems. It has been widely considered for wireless transmission when optimizing radio resource allocation but rarely considered for femto-caching. In this paper, we optimize caching and bandwidth allocation policies to improve long-term user fairness during content placement and content delivery by harnessing heterogeneous user preference. To this end, we maximize the minimal average data rate, where the average is taken over large-and small-scale channel gains as well as individual user requests. This gives rise to a complicated two-timescale optimization problem involving functional optimization. The objective function of the problem does not have closed-form expression due to unknown user preference and channel distributions, and the “variables” to be optimized include a function. To solve such a challenging problem, we first optimize bandwidth allocation policy given arbitrary caching policy, user locations and user requests, whose structure can be found. We next optimize the caching policy given the optimized bandwidth allocation policy. To handle the difficulty of unknown distributions, we resort to stochastic optimization. Simulation results show that optimizing caching policy exploiting user preference can support much higher minimal average rate than optimizing caching policy based on content popularity when user preferences are less similar. Besides, better user fairness can be achieved by optimizing caching policy than by optimizing bandwidth allocation.

File popularity prediction plays an important role in proactive edge caching. The widely-used methods for popularity prediction are based on centralized learning, which needs to collect the request information and even more personal information from users, incurring the privacy-disclosure risk. In this paper, we propose a method of predicting file popularity with federated learning to address the privacy issue, where the request data of each user for each file is only employed for local training at each user. To facilitate the popularity prediction at the base station (BS) without information disclosure as well as the supervised training of a neural network at each user, we let each user upload a weighted sum of its own preference and file popularity to the BS. The neural network is employed to predict the weighted sum at each user by training with the user's historical request records for each file. The local models and uploaded results of the users are aggregated at the BS. We show the convergence of the proposed popularity prediction method with a synthetic dataset. We use simulation results with a real dataset to show that the proposed method performs closely to the centralized learning based method in terms of caching performance.

Proactive caching at base stations (BSs) has been shown promising in offloading traffic, where most priori works consider full frequency reuse among cells and assume known file popularity. To facilitate proactive caching, recent works either adopt linear models or shallow neural networks to predict popularity, without explaining the rationale. In this paper, we consider cache-enabled multi-tier heterogenous networks. To show if full frequency reuse is superior, we consider underlay and overlay modes with random bandwidth allocation for interference management. After optimizing the caching policy and bandwidth allocation to maximize successful offloading probability, we show that the overlay mode outperforms the underlay mode. To confirm that complex non-linear prediction models are unnecessary for proactive caching when using the historical numbers of requests, we employ several linear and non-linear models to predict content popularity and user density, using MovieLens and Youku datasets. To interpret why the caching performance achieved by the optimized policies with the information predicted by the linear models is close to that using non-linear models, we prove that deterministic popularity with typical profiles can be predicted with linear models. Then, we show that most popular files are with these profiles in the real dataset.

Proactive edge caching, as a promising approach to accommodate the explosively increased mobile data demand of Video on demand (VoD) service, has received extensive attention. However, although targeted to VoD service, existing caching policies are mainly designed for file downloading service while the unique requirement of quality of experience (QoE) for VoD service has been seldom considered. Aimed at maximizing the weighted average QoE of VoD service, this paper optimizes the proactive edge caching polices including the cached fraction and encoding bit rate of every video. We consider a two-tier network, where the helpers equipped with caching resource are deployed in the coverage area of traditional base stations. We formulate the caching optimization problems and show their hidden convexity properties, then we find that the optimal caching policy is determined by the weighted popularity-to-duration ratio of videos. Based on the result, we develop a low-complexity algorithm to find the optimal caching policy. Simulation results demonstrate evident performance gain of the proposed policy over the existing policy for VoD service.

Predicting file popularity is an important task for proactive caching, which has been shown a promising way to support the explosive increase of data traffic. Both linear and non-linear predictors have been proposed in literature to predict file popularity, and shallow neural networks have been shown to achieve good performance for caching. In this paper, we strive to explain why simple models perform well in predicting dynamic popularity with the historical number of requests. We consider linear regression, deep neural networks, random forest, support vector regression, and a persistence model for predicting popularity. We employ MovieLens and Youku datasets for analyzing the time-varying pattern of popularity and for evaluating the caching performance. We show that the cache hit ratios achieved by the caching policy with predicted popularity using linear models are close to that using non-linear models. We interpret the observation by first proving that deterministic popularity with typical profiles can be predicted with linear models and then showing that majority of the popular files are with these profiles in the real dataset.

Wireless systems are becoming more and more complicated. As a consequence, the expressions of objective function or constraints of many optimization problems are hard or even impossible to derive. In this paper, we propose a model-free framework to learn the mapping from environment parameters to the solutions of generic constrained optimization problems without the labels generated by numerically finding the optimal solution. We use neural networks respectively for parameterizing the policy to be optimized, the Lagrange multiplier function associated with instantaneous constraint, and approximating the unavailable objective function or constraints. We provide learning algorithms to train all the neural networks simultaneously. We reveal the connections of the proposed framework with reinforcement learning, which is a widely recognized tool for model-free problems. Numerical and simulation results demonstrate the efficiency of model-free learning by taking a well-known power control problem as an example.

Wireless virtual reality (VR) can provide unconstrained immersive experience, which however is resource-demanding to satisfy the unique user experience, i.e., motion-to-photon latency and degree of overlap (DoO). To improve the quality of experience with constrained resource, in this paper we propose tile-based proactive VR streaming, which selects, transmits and computes the tiles in a segment most likely requested in future before playback. To select the tiles to be delivered under limited communication and computing resources, it is necessary to learn the user behaviour in requesting tiles in an online manner. We formulate a joint communication and computing duration allocation and tile selection problem to maximize the average DoO for a VR video under the communication and computing resource constraints. To reduce computational complexity and implicitly predict the tile request information, we decouple the original problem into two subproblems, which are respectively solved via convex optimization and hierarchial online learning. Simulation results on a real dataset demonstrate evident gain of the proposed method over the first-predict-then-optimize scheme.

In this paper, we propose a policy to optimize predictive power allocation for video streaming over mobile networks with deep reinforcement learning. The objective is to minimize the average energy consumption for video transmission under the quality of service constraint that avoids video stalling. To handle the continuous state and action spaces, we resort to deep deterministic policy gradient to solve the formulated problem. In contrast to previous predictive resource policies for video streaming, the proposed policy operates in an on- line and end-to-end manner. By judiciously designing action and state, the policy can exploit future information without explicit prediction. Simulation results show that the proposed policy can converge closely to the optimal policy with perfect prediction of future large-scale channel gains and outperforms the prediction-based optimal policy when prediction errors exist.

In this paper, we study how to solve resource allocation problems in ultra-reliable and low- latency communications by unsupervised deep learning, which often yield functional optimization problems with quality-of-service (QoS) constraints. We take a joint power and bandwidth allocation problem as an example, which minimizes the total bandwidth required to guarantee the QoS of each user in terms of the delay bound and overall packet loss probability. The global optimal solution is found in a symmetric scenario. A neural network was introduced to find an approximated optimal solution in general scenarios, where the QoS is ensured by using the property that the optimal solution should satisfy as the ''supervision signal''. Simulation results show that the learning-based solution performs the same as the optimal solution in the symmetric scenario, and can save around 40% bandwidth with respect to the state-of-the-art policy.

Learning the optimized solution as a function of environmental parameters by deep neural networks (DNN) is effective in solving numerical optimization in real time for time-sensitive resource allocation in wireless systems. Existing works of learning to optimize train the DNN with labels, which are generated by solving the optimization problems. The learned solution are often inaccurate and hence cannot be employed to ensure the stringent quality of service. In this paper, we propose a framework to learn the latent function with unsupervised deep learning, where the property that the optimal solution should satisfy is used as the "supervision signal" implicitly. The framework is applicable to both variable and functional optimization problems with constraints, which are respectively formulated to optimize variables and functions of concern. We take a variable optimization problem in ultra-reliable and low-latency communications as an example, which demonstrates that the ultra-high reliability can be supported by the DNN without supervision labels.

The existing proactive caching policies are designed by assuming that all users request contents with identical activity level at uniformly distributed or known locations, among which most of the policies are optimized by assuming that user preference is identical to content popularity. However, these assumptions are not true based on the recent data analysis. In this paper, we investigate what happens without these assumptions. To this end, we establish a framework to optimize caching policy for base stations exploiting heterogeneous user preference, activity level, and spatial locality. We derive success probability and average rate of each user as utility function, respectively, and obtain the optimal caching policy maximizing a weighted sum of average utility (reflecting network performance) and minimal utility of users (reflecting user fairness). To investigate the intertwined impact of individual user request behavior on caching, we provide an algorithm to synthesize user preference from given content popularity and activity level with controlled preference similarity and validate the algorithm with the real datasets. Analysis and simulation results show that exploiting individual user behavior can improve both network performance and user fairness, and the gain increases with the skewness of spatial locality, and the heterogeneity of user preference and activity level.

In this paper, we establish a framework for enabling ultra-reliable and low-latency communications in the control and non-payload communications (CNPC) links of the unmanned aerial vehicle (UAV) communication systems. We first derive the available range of the CNPC links between UAVs and a ground control station. The available range is defined as the maximal horizontal communication distance within which the round-trip delay and the overall packet loss probability can be ensured with a required probability. To exploit the macro-diversity gain of the distributed multi-antenna systems (DAS) and the array gain of the centralized multi-antenna systems (CAS), we consider a modified DAS (M-DAS), where the ground control station is equipped with the distributed access points (APs), and each AP can have multiple antennas. We then show that the available range can be maximized by judiciously optimizing the altitude of UAVs, the duration of the uplink and downlink phases, and the antenna configuration. To solve the non-convex problem, we propose an algorithm that can converge to the optimal solution in DAS and CAS, and then extend it into more general M-DAS. The simulation and numerical results validate our analysis and show that the available range of M-DAS can be significantly larger than those of the DAS and CAS.

In this paper, we aim to find the global optimal resource allocation for ultra-reliable and low-latency communications (URLLC), where the blocklength of channel codes is short. The achievable rate in the short blocklength regime is neither convex nor concave in bandwidth and transmit power. Thus, a non-convex constraint is inevitable in optimizing resource allocation for URLLC. We first consider a general resource allocation problem with constraints on the transmission delay and decoding error probability, and prove that a global optimal solution can be found in a convex subset of the original feasible region. Then, we illustrate how to find the global optimal solution for an example problem, where the energy efficiency (EE) is maximized by optimizing antenna configuration, bandwidth allocation, and power control under the latency and reliability constraints. To improve the battery life of devices and EE of communication systems, both uplink and downlink resources are optimized. The simulation and numerical results validate the analysis and show that the circuit power is dominated by the total power consumption when the average inter-arrival time between packets is much larger than the required delay bound. Therefore, optimizing antenna configuration and bandwidth allocation without power control leads to minor EE loss.

Recommendation system is able to shape user demands, which can be used for boosting caching gain. In this paper, we jointly optimize content caching and recommendation at base stations to maximize the caching gain meanwhile not compromising the user preference. We first propose a model to capture the impact of recommendation on user demands, which is controlled by a user-specific psychological threshold. We then formulate a joint caching and recommendation problem maximizing the successful offloading probability, which is a mixed integer programming problem. We develop a hierarchical iterative algorithm to solve the problem when the threshold is known. Since the user threshold is unknown in practice, we proceed to propose an ε-greedy algorithm to find the solution by learning the threshold via interactions with users. Simulation results show that the proposed algorithms improve the successful offloading probability compared with prior works with/without recommendation. The ε-greedy algorithm learns the user threshold quickly, and achieves more than 1 - ε of the performance obtained by the algorithm with known threshold.

Predictive resource allocation can exploit residual resources in wireless networks to support high throughput, improve user experience, and enhance energy efficiency. Most priori works assume that fine-grained knowledge for user trajectory and/or traffic load is known, which is hard to predict in practice. In this paper, we investigate predictive resource allocation to achieve high throughput for mobile users requesting video-on-demand (VoD) services, which employs cell-level coarse grained information. In the start of a prediction window, we only need to predict the cells the users to be associated with, the sojourn time of each user in each cell, the loads of VoD traffic and realtime traffic at each base station (BS). These information is translated into two thresholds, which are introduced to help each BS to determine when and how much data to transmit. Two-threshold-based algorithms are provided. Simulation results show that the algorithms perform closely to the optimal predictive resource allocation with perfect fine-grained information in terms of supporting high request arrival rate and improving user experience, and one algorithm even outperforms the optimal method with prediction errors.

In this work, we study the benefit of retransmission to ultra-reliable and low-latency communications (URLLC). We consider a retransmission policy that employs frequency hopping to improve the retransmission success probability for the packets suffering deep fading. We investigate how to satisfy the quality-of-service (QoS) with retransmission by taking downlink transmission as an example. End-to-end (E2E) delay components, including transmission delay, queueing delay and backhaul latency, and packet loss components, including transmission error probability and E2E delay violation probability are considered. Resource allocation for the retransmission policy is optimized. Numerical results show that the requirement on transmission error probability can be significantly relaxed with retransmission, which does not compromise the E2E delay requirement owing to the short packets in URLLC. Simulation results indicate that more users can be supported with QoS guarantee compared to a counterpart system without retransmission, and the performance gain increases with the times of retransmissions.

Assigning radio resources in advance to nonrealtime (NRT) service in a proactive manner can exploit residual resource after serving realtime service to boost the performance of wireless networks. By predicting future average data rate of each mobile user requesting NRT service in a time window, either directly or indirectly, a plan for assigning future resources to each user can be made. Most existing works make the plan by solving optimization problems, which require high computational complexity when the number of users is large and the prediction window in long. In this paper, we design a deep neural network (DNN), which contains an autoencoder and a fully-connected neural network, to learn the resource allocation pattern in a prediction window. With the help of the DNN trained offline, the plan can be made with low complexity. To increase the generalizability to time-varying traffic load for both NRT and realtime services, we resort to selective sampling in active learning. Simulation results show that the proposed method performs closely to the optimal solution in supporting high throughput with given quality of service requirement.

The rapid urbanisation has transformed cities to the preferential human settlement and allowed cities to quietly witness all range of human activities. As the key enabler in the information and communications technology industry, cellular networks play a decisive role in delivering communication messages and entertainment content. In particular, cellular network operators respond to human initiated service requests by gradually deploying necessary infrastructure and calibrating transmission protocols. Hence, cellular network records encompass the interesting interaction between human‐initiated messages and network‐triggered responses. In this study, the authors collect the ‘big data’ in urban cellular networks and try to dig out the human and urban planning properties. Specifically, they focus on the statistical modelling of three representative scenarios like spatial deployment density of base stations, packet length or traffic volume of mobile services, as well as inter‐arrival time and dwell time of human mobility. Through extensive data mining, they validate the heavy‐tailed feature universally existing in these scenarios. Afterwards, they discuss the implications of this heavy‐tailed feature and talk about its fundamental contribution to intelligent resource adjustment, proactive content caching, and enhanced connection management in cellular networks. Finally, they highlight the applications of this feature towards smarter cellular networks and cities.

Prior works in a designing caching policy do not distinguish content popularity with user preference. In this paper, we illustrate the caching gain by exploiting individual user behavior in sending requests. After showing the connection between the two concepts, we provide a model for synthesizing user preference from content popularity. We then optimize the caching policy with the knowledge of user preference and activity level to maximize the offloading probability for cache-enabled device-to-device communications, and develop a low-complexity algorithm to find the solution. In order to learn user preference, we model the user request behavior resorting to probabilistic latent semantic analysis, and learn the model parameters by the expectation maximization algorithm. By analyzing a Movielens data set, we find that the user preferences are less similar, and the activity level and topic preference of each user change slowly over time. Based on this observation, we introduce a prior knowledge-based learning algorithm for user preference, which can shorten the learning time. Simulation results show a remarkable performance gain of the caching policy with user preference over existing policy with content popularity, both with realistic data set and synthetic data validated by the real data set.

In this paper, we strive to improve the throughput of heterogeneous cellular networks by exploiting the pre-cached files at user end to manage interference. We consider a transmission scheme, where network-coded multicast is employed to help cancel multi-user interference, and reconstructed interference cancellation (RIC) is used to help eliminate inter-cell interference. Because RIC is opportunistic, base station (BS) muting is used to coordinate the residual strong interference. Since user association affects residual interference and is coupled with BS muting while both are operated in a very different timescale from content caching, we jointly optimize user association and BS muting for a given caching policy to maximize the number of users simultaneously served by the transmission scheme. By transforming the formulated problem into a maximal independent set problem with constructed conflict graph, the global optimal solution is found with graph theory methods. By exploiting the topology feature of heterogeneous networks, we proceed to propose two low-complexity algorithms, respectively, implemented in a centralized and distributed manner, which are viable for large-scale networks. Simulation results show that the optimized transmission scheme achieves a remarkable performance gain over the existing schemes.

Ultra-reliable and low-latency communications (URLLC) have stringent requirements on quality-of-service and network availability. Due to path loss and shadowing, it is very challenging to guarantee the stringent requirements of URLLC with satisfactory communication range. In this paper, we first provide a quantitative definition of network availability in the short blocklength regime: the probability that the reliability and latency requirements can be satisfied when the blocklength of channel codes is short. Then, we establish a framework to maximize the available range, defined as the maximal communication distance subject to the network availability requirement, by exploiting multi-connectivity. The basic idea is using both device-to-device (D2D) and cellular links to transmit each packet. The practical setup with correlated shadowing between D2D and cellular links is considered. Besides, since processing delay for decoding packets cannot be ignored in URLLC, its impacts on the available range are studied. By comparing the available ranges of different transmission modes, we obtained some useful insights on how to choose transmission modes. Simulation and numerical results validate our analysis and show that multi-connectivity can improve the available ranges of D2D and cellular links remarkably.

Supporting ultra-reliable and low-latency communications (URLLC) is one of the major goals for the fifth-generation cellular networks. Since spectrum usage efficiency is always a concern, and large bandwidth is required for ensuring stringent quality-of-service (QoS), we minimize the total bandwidth under the QoS constraints of URLLC. We first propose a packet delivery mechanism for URLLC. To reduce the required bandwidth for ensuring queueing delay, we consider a statistical multiplexing queueing mode, where the packets to be sent to different devices are waiting in one queue at the base station, and broadcast mode is adopted in downlink transmission. In this way, downlink bandwidth is shared among packets of multiple devices. In uplink transmission, orthogonal subchannels are allocated to different devices to avoid strong interference. Then, we jointly optimize uplink and downlink bandwidth configuration and delay components to minimize the total bandwidth required to guarantee the overall packet loss and end-to-end delay, which includes uplink and downlink transmission delays, queueing delay, and backhaul delay. We propose a two-step method to find the optimal solution. Simulation and numerical results validate our analysis and show remarkable performance gain by jointly optimizing uplink and downlink configuration.

Hybrid architecture is recognized as complexity and cost effective for the implementation of massive multi-input multi-output (MIMO) systems, where analog precoder is used to form narrow beams toward users for data transmission with reduced number of radio frequency chains. However, besides transmitting the user-specific data, the system also needs to broadcast control signaling intended for all users simultaneously, which requires wide beams to ensure the coverage. In this paper, we study hybrid precoder design for downlink space-division multiaccess and orthogonal frequency-division multiplexing wideband massive MIMO systems, aimed at minimizing the total transmit power of the base station, subject to both the coverage constraint of signaling and data rate requirements of users. We first derive the coverage probability of massive MIMO systems with hybrid architecture under general spatially correlated channels, based on which an alternating optimization algorithm and a low-complexity algorithm with channel statistics based analog precoder are, respectively, proposed to optimize the hybrid precoder. Simulation results show the performance gain of the proposed hybrid precoder over the method that only considers data rate requirements or coverage constraints and also demonstrate the necessity of separating data and signaling planes at high frequencies.

In this paper, we propose a framework for cross-layer optimization to ensure ultra-high reliability and ultra-low latency in radio access networks, where both transmission delay and queueing delay are considered. With short transmission time, the blocklength of channel codes is finite, and the Shannon capacity cannot be used to characterize the maximal achievable rate with given transmission error probability. With randomly arrived packets, some packets may violate the queueing delay. Moreover, since the queueing delay is shorter than the channel coherence time in typical scenarios, the required transmit power to guarantee the queueing delay and transmission error probability will become unbounded even with spatial diversity. To ensure the required quality-of-service (QoS) with finite transmit power, a proactive packet dropping mechanism is introduced. Then, the overall packet loss probability includes transmission error probability, queueing delay violation probability, and packet dropping probability. We optimize the packet dropping policy, power allocation policy, and bandwidth allocation policy to minimize the transmit power under the QoS constraint. The optimal solution is obtained, which depends on both channel and queue state information. Simulation and numerical results validate our analysis, and show that setting the three packet loss probabilities as equal causes marginal power loss.

Ultra-reliable and low-latency communications (URLLC) is expected to be supported without compromising the resource usage efficiency. In this paper, we study how to maximize energy efficiency (EE) for URLLC under the stringent quality of service (QoS) requirement imposed on the end-to-end (E2E) delay and overall packet loss, where the E2E delay includes queueing delay and transmission delay, and the overall packet loss consists of queueing delay violation, transmission error with finite blocklength channel codes, and proactive packet dropping in deep fading. Transmit power, bandwidth and number of active antennas are jointly optimized to maximize the system EE under the QoS constraints. Since the achievable rate with finite blocklength channel codes is not jointly concave in radio resources, it is challenging to optimize resource allocation. By analyzing the properties of the optimization problem, the global optimal solution is obtained. Simulation and numerical results validate the analysis and show that the proposed policy can improve EE significantly compared with existing policy.

In this paper, we strive to improve the throughput of wireless networks by exploiting the pre-cached files at user end to manage interference. We employ network-coded multicast (NCM) to help cancel multi-user interference, and reconstructed interference cancellation (RIC) to help cancel inter-cell interference. Because inter-cell interference may not be thoroughly canceled by RIC, base station (BS) muting is used to coordinate the residual strong interference. We jointly optimize user association and BS muting to maximize the number of users simultaneously served by a system using NCM and RIC with any given caching policy. Owing to the complex relation among BS muting, user association, NCM, and RIC, the formulated problem is NP-hard. By transforming the problem into the well-known maximal independent set problem with constructed conflict graph, the global optimal solution can be found efficiently with graph theory methods. Simulation results show that the proposed transmission strategy achieves a remarkable performance gain.

Caching at base station (BS) has attracted significant research efforts for future wireless networks. Most existing works are based on the assumption of static content catalogue and stationary popularity distribution, which however is far away from the reality as recently reported in the literature. In this paper, we take the popularity dynamics into account, and study the caching policy at BS for a traffic model consisting of two categories of contents respectively with long and short lifespans. By modeling the two categories of contents with Independent Reference Model (IRM) and Shot Noise Model (SNM), we formulate a cache resource allocation problem to maximize the total cache hit ratio for both categories of contents, which gives rise to a hybrid proactive and reactive caching policy. We solve the problem numerically for general case and provide closed-form solutions for several special cases. Numerical and simulation results demonstrate remarkable performance gain of the proposed caching policy over non-hybrid caching polices.

Caching at wireless edge is a promising way to support the explosive growth of traffic load. In this paper, we jointly optimize the caching and bandwidth allocation policy in a cache-enabled heterogeneous network, where macro base stations (BSs) each with high capacity backhaul but not cache and helpers each with cache are random located. Probabilistic caching and random bandwidth allocation are considered. We derive the closed-form expression of successful offloading probability, which is defined as the probability that a user is associated with helpers with the date rate greater than a threshold. The joint optimal problem is not concave in general case. Nonetheless, when the density of helpers is large, the joint global optimum can be obtained, where the optimal bandwidth allocation needs onedimensional search and the optimal caching policy is with closedform expression. Numerical results show that the offloading gain can be significantly improved by the joint optimization.

Cache-enabled device-to-device (D2D) communications can boost network throughput. By pre-downloading contents to local caches of users, the content requested by a user can be transmitted via D2D links by other users in proximity. Prior works optimize the caching policy at users with the knowledge of content popularity, defined as the probability distribution that each file in a library is requested by all users. However, content popularity can not reflect the interest of each individual user and thus existing caching policy based on popularity may not fully capture the performance gain introduced by caching. In this paper, we optimize caching policy for cache-enabled D2D by learning user preference, which is defined as the conditional probability distribution of a user's request given that the user sends a request. We first formulate an optimization problem with given user preference to maximize the offloading probability, which is proved as NP-hard, and then provide a greedy algorithm to find the solution. In order to predict the preference of each individual user, we model the user request behavior by probabilistic latent semantic analysis (pLSA), and then apply expectation maximization (EM) algorithm to estimate the model parameters. Simulation results show that using pLSA can learn user preference quickly. Compared to existing caching policy exploiting content popularity, the offloading gain achieved by the proposed policy can be remarkably improved even with predicted user preference.

In this paper, we consider low-complexity design of multi-antenna uplink non-orthogonal multiple access (NOMA) system. There are 2N user equipments (UEs) distributed uniformly in a circle, where the N UEs nearer to the base station (BS) are called strong UEs and the other N farther UEs are called weak UEs. The BS decodes the strong UEs' signals by zero-forcing (ZF) detector first, and subtracts the strong UEs' signals. Then, the BS decodes the weak UEs' signals by ZF detector. For the proposed scheme we obtain the closed-form expressions of the average rate under three scenarios. In addition, a power allocation policy is proposed to reduce the interference between the UEs and maximize the average sum rate. Numerical results verify the analytical results and show that the average sum rate of the proposed NOMA scheme outperforms that of the orthogonal multiple access scheme employing time division multiple access.

Ultra-reliable and low-latency communications (URLLC) are considered as one of the typical scenarios in the fifth generation of wireless communications, which not only have stringent quality-of-service (QoS) requirement on packet loss and end-to-end delay but also have high network availability requirement. In this work, we study available range for URLLC with different transmission modes, including cellular mode, device- to-device (D2D) mode, and a hybrid mode that consists of cellular links and D2D links. The available range of a certain link is defined as the maximal communication distance of the link, within which the QoS and availability can be satisfied. The available ranges of cellular and D2D links are maximized with different transmission modes. The analysis shows that there is a tradeoff between the available range of cellular links and that of D2D links. Numerical results show that compared with D2D mode and cellular mode, the hybrid mode can double the available ranges of cellular and D2D links. By increasing antennas at the base station, available range of D2D links can be extended.

Proactive caching in base station (BS) is a promising way to leverage the human-behavior related information to boost the system throughput and improve user experience with low cost. Yet existing caching policies are "blind" to users, i.e., the users are unaware about whether the files to be requested are locally cached at the BS around them, whose performance will degrade in mobile systems due to the randomness of user behavior. For example, when a user stays in a cell where the BS caches the file to be requested later, the user may not initiate the request. When the user sends the request, it may have entered another cell where the BS does not cache the requested file. In this paper, we introduce a simple idea of informing the users about what the BS has cached to make local caching efficient, which can be regarded as a form of recommendation. Since the probabilities that the users request the files are unknown, we resort to Q-learning to perceive the request probability and the statistics of random arrival and departure of mobile users. Then, a cache replacement policy is optimized. Simulation results demonstrate that the proposed policy with recommendation can provide higher long-term system reward than existing policies without recommendation.

Caching popular contents at the base stations is an effective way to address the challenging data traffic demand driven by video-on-demand (VoD) service. Initial delay is one of the most important factors affecting the quality of experience (QoE) of users for VoD service. Existing work has optimized the caching policy to reduce the average delay for file downloading service. However, considering the fact that the QoE for VoD service does not linearly decrease with the initial delay, the caching policy minimizing the average delay is not optimal for VoD service. In this paper we optimize the caching policy to control the initial delay, aimed at maximizing the average QoE of a user with random requests of videos from a file library. By properly approximating the non-smooth non-concave QoE function, we obtain efficient caching policies for VoD service, based on which the impact of file popularity on the caching policy is analyzed. Simulation results demonstrate the advantages of the proposed caching policy.

Recent work has demonstrated the gain of full-duplex (FD) network over half-duplex (HD) network in bidirectional sum throughput under the assumption of symmetric uplink-downlink traffic demands and perfect self-interference suppression (SIS). In this paper, we study the performance gain of FD network over HD network under asymmetric bidirectional traffic demands and non-ideal SIS. To this end, we investigate the traditional static time division duplex (TDD) transmission mode and the advanced dynamic TDD transmission mode to obtain the performance of HD network, and investigate the pure FD transmission mode and a flexible HD-FD hybrid transmission mode, namely, XD mode, to obtain the performance of FD network. We use the number of users supported by a network as performance metric, which is defined as the minimum of the weighted numbers of users supported in uplink and downlink given random traffic demands of users. To maximize the number of users, we optimize the bidirectional transmit power for pure FD mode, bidirectional time slot configuration for dynamic TDD mode, and both for XD mode. Numerical results show an evident gain of pure FD mode and XD mode over static TDD mode for different levels of traffic asymmetry, but the gain over dynamic TDD mode is marginal, which cannot justify the application of FD technology in cellular systems without advanced interference control mechanisms.

Massive multi-input multi-output (MIMO) can support high spectral efficiency with simple linear transceivers, and is expected to provide high energy efficiency (EE). In this paper, we analyze the scaling laws of EE with respect to the number of antennas M at each base station of downlink multi-cell massive MIMO systems under spatially correlated channel, where both transmit and circuit power consumptions, channel estimation errors, and pilot contamination (PC) are taken into account. We obtain the maximal EE for the systems with maximum-ratio transmission and zero-forcing beamforming for given numbers of antennas and users by optimizing the transmit power subject to the minimal data rate requirement and maximal transmit power constraint. The closed-form expressions of approximated EE-maximal transmit power and maximal EE, and their scaling laws with M are derived. Our analysis shows that the maximal EE scales with M in O(log2M/M) for the system without PC, and in O(1/M) for the system with PC. The EE-maximal transmit V power scales up with M in O(√(M/ln M)) until reaching the maximal transmit power for the system without PC, and in O(1) for the system with PC. The analytical results are validated by simulations under a more realistic 3D channel model.

In this paper, we investigate the optimal caching policy, respectively, maximizing the success probability and area spectral efficiency (ASE) in a cache-enabled heterogeneous network (HetNet), where a tier of multi-antenna macro base stations (MBSs) is overlaid with a tier of helpers with caches. Under the probabilistic caching framework, we resort to stochastic geometry theory to derive the success probability and ASE. After finding the optimal caching policies, we analyze the impact of critical system parameters and compare the ASE with traditional HetNet where the MBS tier is overlaid by a tier of pico BSs (PBSs) with limited-capacity backhaul. Analytical and numerical results show that the optimal caching probability is less skewed among helpers to maximize the success probability when the ratios of MBS-to-helper density, MBS-to-helper transmit power, user-to-helper density, or the rate requirement are small, but is more skewed to maximize the ASE in general. Compared with traditional HetNet, the helper density is much lower than the PBS density to achieve the same target ASE. The helper density can be reduced by increasing cache size. With given total cache size within an area, there exists an optimal helper node density that maximizes the ASE.

By caching files at users, content delivery traffic can be offloaded via device-to-device (D2D) links if a helper user is willing to transmit the cached file to the user who requests the file. In practice, the user device has limited battery capacity, and may terminate the D2D connection when its battery has little energy left. Thus, taking the battery consumption allowed by the helper users to support D2D into account introduces a reduction in the possible amount of offloading. In this paper, we investigate the relationship between offloading gain of the system and energy cost of each helper user. To this end, we introduce a user-centric protocol to control the energy cost for a helper user to transmit the file. Then, we optimize the proactive caching policy to maximize the offloading opportunity and the transmit power at each helper to maximize the offloading probability. Finally, we evaluate the overall amount of traffic offloaded to D2D links and the average energy consumption at each helper, with the optimized caching policy and transmit power. Simulations show that a significant amount of traffic can be offloaded even when the energy cost is kept low.

Caching at base stations (BSs) is a promising way to offload traffic and eliminate backhaul bottleneck in heterogeneous networks (HetNets). In this paper, we investigate the optimal content placement maximizing the successful offloading probability in a cache- enabled HetNet where a tier of multi-antenna macro BSs (MBSs) is overlaid with a tier of helpers with caches. Based on probabilistic caching framework, we resort to stochastic geometry theory to derive the closed-form successful offloading probability and formulate the caching probability optimization problem, which is not concave in general. In two extreme cases with high and low user-to-helper density ratios, we obtain the optimal caching probability and analyze the impacts of BS density and transmit power of the two tiers and the signal-to-interference-plus-noise ratio (SINR) threshold. In general case, we obtain the optimal caching probability that maximizes the lower bound of successful offloading probability and analyze the impact of user density. Simulation and numerical results show that when the ratios of MBS-to-helper density, MBS-to-helper transmit power and user-to- helper density, and the SINR threshold are large, the optimal caching policy tends to cache the most popular files everywhere.

To ensure the low end-to-end (E2E) delay for tactile internet, short frame structures will be used in 5G systems. As such, transmission errors with finite blocklength channel codes should be considered to guarantee the high reliability requirement. In this paper, we study cross-layer transmission optimization for tactile internet, where both queueing delay and transmission delay are accounted for in the E2E delay, and different packet loss/error probabilities are considered to characterize the reliability. We show that the required transmit power becomes unbounded when the allowed maximal queueing delay is shorter than the channel coherence time. To satisfy quality-of-service requirement with finite transmit power, we introduce a proactive packet dropping mechanism, and optimize a queue state information and channel state information dependent transmission policy. Since the resource and policy for transmission and the packet dropping policy are related to the packet error probability, queueing delay violation probability, and packet dropping probability, we optimize the three probabilities and obtain the policies related to these probabilities. We start from single-user scenario and then extend our framework to the multi-user scenario. Simulation results show that the optimized three probabilities are in the same order of magnitude. Therefore, we have to take into account all these factors when we design systems for tactile internet applications.

Caching popular contents at base stations (BSs) is a promising way to unleash the potential of cellular heterogeneous networks (HetNets), where backhaul has become a bottleneck. In this paper, we compare a cache-enabled HetNet where a tier of multi-antenna macro BSs is overlaid by a tier of helper nodes having caches but no backhaul with a conventional HetNet where the macro BSs tier is overlaid by a tier of pico BSs with limited-capacity backhaul. We resort stochastic geometry theory to derive the area spectral efficiencies (ASEs) of these two kinds of HetNets and obtain the closed-form expressions under a special case. We use numerical results to show that the helper density is only 1/4 of the pico BS density to achieve the same target ASE, and the helper density can be further reduced by increasing cache capacity. With given total cache capacity within an area, there exists an optimal helper node density that maximizes the ASE.

This paper studies the mitigation of cross-tier inter-cell interference (ICI) generated by a macro base station to a small-cell user equipment (SUE) in heterogeneous networks. A full-duplex (FD) based successive ICI cancellation (SIC) scheme, called fSICIC, is devised by applying FD technique at the small-cell base station (SBS). The basic idea of the fSICIC is to let the SBS send the desired signal and forward the overheard cross-tier ICI simultaneously to the SUE, where the forwarded ICI is controlled to enhance the ICI at the SUE to facilitate SIC. We first investigate the feasibility of the fSICIC, and then optimize the fSICIC to maximize the data rate of the SUE. Simulation results demonstrate the advantages of the fSICIC on mitigating cross-tier ICI, especially for strong ICI.

Pushing contents to users based on the predicted user preference cannot only improve user experience, but also boost network throughput by exploiting the excess resources. However, prediction is never perfect. Due to the uncertainties of predicting the user's request, the base station (BS) may waste resources on pushing unnecessary files before the arrival of user's request. In this paper, we investigate the impact of the uncertainty in predicting the content to be requested and the request arrival time on the average energy consumption of pushing. To this end, we first introduce a pushing policy with a priori known prediction uncertainty. Then, we derive the average energy consumption of pushing, and analyze the energy saving gain over traditional transmission method, where the BS serves a user right after the request arrives. Analytical and numerical results show that pushing with prediction uncertainty can save energy by optimizing the number of the pushing files, and the gain is remarkable for a user who has stronger preference among the predicted file list.

This paper studies the energy efficient optimization for full duplex (FD) assisted closed-loop downlink transmission, where a FD base station (BS) serves multiple half duplex (HD) users in a time division multiple access manner. We optimize the durations of uplink training and downlink transmission, aimed at maximizing the energy efficiency (EE) of the system. We first show that the EE-optimal transmission scheme must use one of the two strategies: transmitting in HD mode or transmitting downlink data over all time slots so that the BS operates in FD mode during the whole uplink training phase. We then derive an approximate average net data rate of the system considering the bidirectional interference between uplink and downlink users in FD mode. Based on the result, a closed-form expression of the EE is obtained. We prove that the EE in FD mode is quasi-concave with respect to the duration of uplink training, with which the optimal durations of uplink training and downlink transmission are obtained. Simulation results show the evident gain of the proposed scheme over existing FD and HD schemes.

Device-to-Device (D2D) communications can increase the throughput of cellular networks significantly, where the interference among D2D links should be properly managed. In this paper, we propose an opportunistic cooperative D2D transmission strategy by exploiting the caching capability at the users to deal with the interference among D2D links. To increase the cooperative opportunity and improve spatial reuse gain, we divide the D2D users into clusters and cache different popular files at the users within a cluster, and then find the optimal cluster size. To maximize the network throughput, we assign different frequency bands to cooperative and non- cooperative D2D links and optimize the bandwidth partition. Simulation results demonstrate that the proposed strategy can provide 500%~600% throughput gain over existing cache-enabled D2D communications when the popularity distribution is skewed, and can provide 40%~80% gain even when the popularity distribution is uniform.

Tactile internet requires ultra-low latency and ultrahigh reliability, which bring new challenges to the design of mobile systems. In this paper, we study how much resources are required to ensure the short end-to-end (E2E) delay and high reliability by taking a vehicle communication system as an example, where the E2E delay includes both queueing delay and transmission delay, and the reliability is captured by the packet loss and packet error caused by finite blocklength channel codes, queueing delay violation and packets dropping during deep channel fading periods. To this end, we optimize the bandwidth allocation among multiple users to minimize the average transmit power required to ensure the queueing delay and its violation probability of each packet, and analyze the maximal transmit power required to guarantee the E2E delay and the reliability. Simulation and numerical results validate our analysis and show the required maximal transmit power, bandwidth, and number of antennas to ensure the extremely stringent quality of service.

Pushing files to users based on predicting the personal interest of each user may provide higher throughput gain than broadcasting popular files to users based on their common interests. However, the energy consumed at base station for pushing files individually to each user is also higher than broadcast. In this paper, we propose an energy-saving transmission strategy for pre- downloading the files to each user by exploiting the excess resources in the network during off-peak time. Specifically, a power allocation and scheduling algorithm is designed aimed to minimize the extra energy consumed for pushing, where network and user level context information are exploited. Simulation results show that when the energy of both content placement and content delivery is taken into account, the proposed unicast strategy consumes less energy and achieves higher throughput than broadcasting when the files popularity is not uniform and the personal interest prediction is with less uncertainty.

Device-to-Device (D2D) communications can offload the traffic and boost the throughput of cellular networks. By caching files at users, content delivery traffic can be offloaded via D2D links, if a helper user are willing to send the cached file to the user who requests the file. Yet it is unclear how much energy needs to be consumed at a helper user to support the traffic offloading. In this paper, we strive to find the minimal energy consumption required at a helper user to maximize the amount of offloaded traffic. To this end, we introduce a user-centric proactive caching policy that can control the energy cost for a helper user to convey a file, and then optimize the caching policy to maximize the offloaded traffic. To reduce the energy during transmission, we optimize the transmit power to minimize the energy consumed by a helper to send a file. We analyze the relationship between traffic offloading and energy cost with the optimized caching policy and transmit power by numerical and simulation results, which demonstrate that a significant amount of traffic can be offloaded with affordable energy costs.

When the future achievable rate is perfectly known, predictive resource allocation can provide high performance gain over traditional resource allocation for the traffic without stringent delay requirement. However, future channel information is hard to obtain in wireless channels, especially the small-scale fading gains. In this paper, we analytically demonstrate that the future large-scale channel information can capture almost all the performance gain from knowing the future channel by taking an energy-saving resource allocation as an example. This result is important for practical systems, since large-scale channel gains can be easily estimated from the predicted trajectory of mobile users and radio map. Simulation results validate our analysis and illustrate the impact of the estimation errors of large-scale channel gains on energy saving.

After about a decade of intense research, spurred by both economic and operational considerations, and by environmental concerns, energy efficiency has now become a key pillar in the design of communication networks. With the advent of the fifth generation of wireless networks, with millions more base stations and billions of connected devices, the need for energy-efficient system design and operation will be even more compelling. This survey provides an overview of energy-efficient wireless communications, reviews seminal and recent contribution to the state-of-the-art, including the papers published in this special issue, and discusses the most relevant research challenges to be addressed in the future.

This paper addresses an energy-saving problem for the downlink of a cloud-assisted heterogeneous network (HetNet) using a time-division duplex (TDD) model, which aims to minimize the base stations (BSs) sum power consumption while meeting the rate requirement of each user equipment (UE). The basic idea of this work is to make use of the scalability of system configurations such that green resource management can be employed by flexibly switching off some unnecessary hardware components, especially for off-peak traffic scenarios. This motivates us to utilize a flexible BS power consumption formulation to jointly model its signal processing and circuit power, transmit power, and backhaul transmission power. Instead of using the integer variables [1,0] to control the “on/off” two status of a BS in most previous work, we employ the group sparsity of a transmit power vector to denote the activity of each frequency carrier (FC) such that the signal processing and circuit power can be scaled with the effective bandwidth, thereby leading to multiple sleep modes for a BS in multi-FC systems. Based on this BS power model and the group sparsity concept, a simplified resource allocation scheme for joint BS-UE association, FC assignment, downlink power allocation, and BS sleep modes determination is presented, which is based on the average channel statistics computed over the coherence time of the large scale fading (LSF). This semidynamic green resource management mechanism can be formulated as a NP-hard optimization problem. In order to make it tractable, the successive convex approximation (SCA)-based algorithm is applied to efficiently find a stationary solution using a cloud-based centralized optimization. Simulation results also verify the effectiveness of the proposed mechanism under the developed BS power consumption model.

Caching popular contents at base stations (BSs) can reduce the backhaul cost and improve the network throughput. Yet whether locally caching at the BSs can improve the energy efficiency (EE), a major goal for fifth generation cellular networks, remains unclear. Due to the entangled impact of various factors on EE such as interference level, backhaul capacity, BS density, power consumption parameters, BS sleeping, content popularity, and cache capacity, another important question is what are the key factors that contribute more to the EE gain from caching. In this paper, we attempt to explore the potential of EE of the cache-enabled wireless access networks and identify the key factors. By deriving closed-form expression of the approximated EE, we provide the condition when the EE can benefit from caching, find the optimal cache capacity that maximizes the network EE, and analyze the maximal EE gain brought by caching. We show that caching at the BSs can improve the network EE when power efficient cache hardware is used. When local caching has EE gain over not caching, caching more contents at the BSs may not provide higher EE. Numerical and simulation results show that the caching EE gain is large when the backhaul capacity is stringent, interference level is low, content popularity is skewed, and when caching at pico BSs instead of macro BSs.

Predictive resource allocation is an emerging approach to improve the performance of mobile systems as human behavior is reported predictable by leveraging big data analytics. Yet what information can be predicted by big data, what information need to be predicted for wireless access optimization, how to translate the information, and how to exploit the synthetic knowledge for allocating radio resources are not well understood and largely explored. In this paper, we are concerned with the latter two issues. In particular, we devise an energy-saving resource planning and allocation policy for multiple base stations (BSs) to serve mobile users with non-real-time (NRT) traffic by exploiting the user, network, and application levels of context information, where RT traffic may occupy partial resources of each BS. Inspired by the solution from an energy minimization problem with future instantaneous information, a low complexity multi-timescale predictive policy is proposed. Upon the arrival of each NRT user request, the resource planning is made with the user and network level context information, defined as the average channel gains of the NRT users and the statistics of residual bandwidth after serving RT traffic, with which the scheduling, power allocation, and BS sleeping can be accomplished after instantaneous channel information and residual network resource are available at each BS in each time slot. Simulation results show that the proposed policy can dramatically reduce the energy consumed by the BSs for serving the NRT traffic.

This paper strives to reduce pilot contamination, a bottleneck for massive multiple-input multiple-output (MIMO) systems, by exploiting channel sparsity. Considering that typical wideband massive MIMO channel is correlated in both space and frequency domains, we employ Karhunen-Loéve Transform (KLT) and Discrete Fourier Transform (DFT) to capture the hidden sparsity of the channel. KLT basis is optimal in extracting the uncorrelated information from channel, but requires channel statistical information. As a suboptimal alternative, DFT basis can be determined without channel statistics, which is more viable for practical use. By representing the channel with DFT basis, we find that the subspaces of the desired and interference channels are approximately orthogonal, even when the number of antennas is not so large. Inspired by this observation, we propose a pilot decontamination method, where a pilot assignment policy is designed to help identify the subspace of the desired channel, and a desired channel subspace aware least square channel estimator is derived to remove the pilot contamination. The proposed method does not need channel statistics and pilot co-ordination. By exploiting channel stationary, the method does not introduce extra training overhead. Simulation results demonstrate substantial sum rate gain of the proposed method over existing methods.

In this paper, we analyze the feedback overhead of channel direction information for downlink coherent base station (BS) cooperative transmission. The per-cell codebooks are considered, which are of practice importance. Instead of analyzing the required number of bits for feedback to keep a constant rate loss, we analyze the required overhead to ensure a target average signal-to-interference-plus-noise ratio (SINR) of each user. To this end, we formulate an optimization problem of bit allocation among the codebooks for local and cross channels that minimizes the total number of bits under the constraint of the average SINR, and find the explicit expression of the solution. We proceed to study the impact of various system parameters and channel features on the overall feedback overhead. Analytical and simulation results reveal that the overhead scales linearly with the overall number of transmit antennas, but decreases with the grow of the cell-edge signal-to-noise ratio. Moreover, the overhead can be significantly reduced by exploiting the diverse performance requirements of the users and the heterogeneous channel features through allocating the number of bits for feedback among multiple users and multiple per-cell links. This provides useful insight for the feedback strategy design of BS cooperation systems.

It is well known that the average transmit power can be traded off by average delay. This paper strives to study the relation between the maximal energy efficiency (EE) and the delay bound with a given violation probability for wireless systems serving random arrivals. We show that if the minimal average transmit and circuit powers consumed at a base station linearly increase with the required average service rate, i.e., the power-rate relation is linear, then a non-tradeoff region will appear in the EE-delay relation. By taking multi-input-multi-output system as an example, we show that the power-rate relation will be linear if transmit power and bandwidth are jointly allocated, and bandwidth constraint is inactive to support a required delay bound. The impacts of bandwidth constraint on the power-rate and EE-delay relations are then analyzed. To study fundamental EE-delay relation, aqueue length-dependent two-state policy is optimized. By further considering a compound Poisson arrival process in large number of transmit antennas asymptotics, we find the boundary of the tradeoff and non-tradeoff regions,and provide a lower bound of the Pareto optimal EE-delay relation in the tradeoff region, all with closed-form expressions. Our results show that the non-tradeoff region increases with the maximal bandwidth and the number of transmit antennas.

Deploying ultra dense networks (UDNs) can boost the spectral efficiency (SE) and energy efficiency (EE) of cellular networks, where the density of base stations (BSs) may exceed the density of users. For UDNs, inter-cell interference is a limiting factor on improving both the SE and EE, and complicated interference coordination is highly undesirable. In this paper, we investigate energy and spectral efficient frequency reuse factors in randomly deployed cells, where BS sleeping is allowed for the cells without active users. Toward this goal, we find the frequency reuse factor that maximizes the SE or EE upper bound of the network with given ratio of BS density to user density, and quantify the SE and EE gains of universal frequency reuse over partial frequency reuse in UDNs. Our analytical results show that the universal frequency reuse is SE-optimal for the networks with arbitrary BS/user density ratios, but is EE-optimal only when the ratio exceeds a threshold, which highly depends on the entire bandwidth of the network and the number of antennas at each BS. Both the normalized SE and EE gains in UDNs increase with the BS/user density ratio. The simulation results are provided to validate our analysis.

Deploying ultra dense networks (UDNs) is a major trend in the evolution of cellular networks, where the number of base stations (BSs) may exceed the number of users. In this paper, we investigate energy and spectral efficient frequency reuse strategies in hexagonally deployed cells, where BS sleeping is allowed for a cell without active users. Toward this goal, we derive the closed form expression of the energy efficiency (EE)and spectral efficiency (SE), and find the optimal frequency reuse strategies that maximize the EE and SE, respectively. Our analyses show that full frequency reuse is SE-optimal for cellular networks with all ratios of BS to user density, but is EE-optimal only when the BS-user density ratio exceeds a threshold (i.e. in UDNs), while using orthogonal frequency band among adjacent cells is EE-optimal when the ratio of density is less than the threshold. The normalized EE gain of full frequency reuse in UDNs increases with the number of BSs. Simulation results validate our analysis and illustrate the EE gain with typical BS-user density ratios.

The throughput of small cell networks (SCNs) is limited by intercell interference. Except numerous techniques of interference coordination, simply caching several popular files at each user provides an alternative approach to reduce interference by pre-offloading. This becomes possible nowadays due to the facts that the cost of memory devices is reducing quickly and content delivery is gradually dominating the traffic load. In this paper, we quantify the performance gain introduced by precaching at users. We derive the average throughput for non-coordinated downlink SCN with precaching, and compare with the SCN without caching and with existing prefetching policy. We use simulation to validate the analytical results and further evaluate the average download time of different policies. Our results show that the caching gain is remarkable when the traffic load is high and the popularity distribution is far from uniform.

Improving energy efficiency of wireless systems by exploiting the context information has received attention recently as the smart phone market keeps expanding. In this paper, we devise energy-saving resource allocation policy for multiple base stations serving non-real-time traffic by exploiting three levels of context information, where the background traffic is assumed to occupy partial resources. Based on the solution from a total energy minimization problem with perfect future information, a context-aware BS sleeping, scheduling and power allocation policy is proposed by estimating the required future information with three levels of context information. Simulation results show that our policy provides significant gains over those without exploiting any context information. Moreover, it is seen that different levels of context information play different roles in saving energy and reducing outage in transmission.

Pilot contamination leads to a dramatic performance loss in massive multiple-input multiple-output (MIMO) systems. In this paper, we propose an advanced- fractional frequency reuse scheme for massive MIMO systems to tackle the pilot contamination problem. We optimize the size of a cell-center region (CCR) to maximize the average throughput of the system without compromising the data rate of cell-edge users. After simplifying the problem with a lower bound of average throughput, we obtain a low complexity suboptimal solution. By analyzing this solution, we show the relationship between the size of the CCR and the number of antennas at each base station. Simulations results validate our analysis and demonstrate that the proposed scheme outperforms existing method employed in the unity frequency reuse system.

This paper studies the suppression of cross-tier intercell interference (ICI) generated by a macro base station (MBS) to pico user equipments (PUEs) in heterogeneous networks (HetNets). Different from existing ICI avoidance schemes such as enhanced ICI cancellation (eICIC) and coordinated beamforming, which generally operate at the MBS, we propose a full duplex (FD)-assisted ICI cancellation (fICIC) scheme, which can operate at each pico BS (PBS) individually and is transparent to the MBS. The basic idea of the fICIC is to apply FD technique at the PBS such that the PBS can send the desired signals and forward the listened cross-tier ICI simultaneously to PUEs. We first consider the narrowband single-user case, where the MBS serves a single macro UE and each PBS serves a single PUE. We obtain the closed-form solution of the optimal fICIC scheme, and analyze its asymptotical performance in ICI-dominated scenario. We then investigate the general narrowband multiuser case, where both MBS and PBSs serve multiple UEs. We devise a low-complexity algorithm to optimize the fICIC aimed at maximizing the downlink sum rate of the PUEs subject to user fairness constraint. Finally, the generalization of the fICIC to wideband systems is investigated. Simulations validate the analytical results and demonstrate the advantages of the fICIC on mitigating cross-tier ICI.

This paper optimizes resource allocation that maximizes the energy efficiency (EE) of wireless systems with statistical quality of service (QoS) requirement, where a delay bound and its violation probability need to be guaranteed. To avoid wasting energy when serving random sources over wireless channels, we convert the QoS exponent, a key parameter to characterize statistical QoS guarantee under the framework of effective bandwidth and effective capacity, into multi-state QoS exponents dependent on the queue length. To illustrate how to optimize resource allocation, we consider multi-input-multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. A general method to optimize the queue length based bandwidth and power allocation (QRA) policy is proposed, which maximizes the EE under the statistical QoS constraint. A closed-form optimal QRA policy is derived for massive MIMO-OFDM system with infinite antennas serving the first order autoregressive source. The EE limit obtained from infinite delay bound and the achieved EEs of different policies under finite delay bounds are analyzed. Simulation and numerical results show that the EE achieved by the QRA policy approaches the EE limit when the delay bound is large, and is much higher than those achieved by existing policies considering statistical QoS provision when the delay bound is stringent.

With the increase of cell density and explosive growth of data traffic, user centric is becoming one of the design principles of next-generation cellular networks. One meaning of user centric lies in that no matter where a user is located, its demand in quality of service (QoS) will be guaranteed in high probability. One approach to achieve such an ambitious goal is allowing each user to select several preferred base stations to transmit cooperatively. In this paper, we propose a user-centric downlink cooperative transmission scheme with orthogonal beamforming based limited feedback, where the cooperative clusters of multiple users may overlap and per-cell codebooks are considered. To assist the central unit (CU) for scheduling users with guaranteed QoS and performing adaptive transmission, a method for each user to estimate its signal-to-noise-and-interference ratio is derived. Targeting to ensure the required QoS of multiple users, we propose a method to select the cooperative cluster at each user and provide a method to schedule users based on their service priorities and channel conditions at the CU, where the clusters are selected semidynamically. Simulation results show that the proposed scheme significantly increases the percentage of users with satisfactory QoS demands.

Energy efficiency (EE) has become an important design goal for mobile communication systems. Since the EE of a system is evaluated during a period of time, a transmission strategy designed from maximizing instantaneous EE (INEE) may not achieve the maximal EE of the system. To exploit the accumulative nature of the energy, accumulative EE (ACEE) can be used as the objective function, which is the ratio of the accumulated amount of data transmitted to the accumulated energy consumed until the time for optimization. In this paper, we study when ACEE is beneficial to EE-oriented optimization. By taking a single user multi-antenna multi-subcarrier system serving two classes of traffic as an example, we formulate three problems to optimize rate allocation among subcarriers and time slots respectively maximizing the INEE, ACEE and EE upper-bound, which can be easily extended to multi-user systems. We proceed to analyze the behavior and performance of the corresponding solutions. Analytical and simulation results show that using ACEE yields a more energy efficient design for the systems serving best effort traffic with less transmit antennas, serving less users simultaneously or at low signal to noise ratio under time-varying channels. However, the conclusions for real-time traffic are different.

In this paper, we study the degrees of freedom (DoF) of partially-connected symmetrically-configured multi-input-multi-output interference broadcast channel (MIMO-IBC) and the impact of partial connectivity on the DoF. We investigate the information theoretic maximal DoF and maximal DoF achieved by linear interference alignment (IA) for the symmetrically-connected symmetrically-configured MIMO-IBC, and prove that the DoF are achievable by asymptotic IA and linear IA for the asymmetrically-connected symmetrically-configured network when the maximal number of interfering cells seen at each BS and each user (denoted as the degrees of connectivity) is bounded. We find that for the symmetrically-connected symmetrically-configured MIMO-IBC, the maximal achievable DoF are independent of the number of total cells but depend on the degrees of connectivity. When the degrees of connectivity decrease, the maximal DoF achieved by linear IA are close to the information theoretic maximal DoF.

This paper studies the degrees of freedom (DoF) region of the 2 × 2 × 2 interference network, which is comprised of two sources, two relays and two destinations, each with arbitrary number of antennas. We prove that with linear transceivers, the cut-set outer bound can be achieved without any symbol extensions, except for one specific system setup, which has one-DoF gap to the cut-set bound. We show that to achieve the outer-bound, the transceivers include interference avoidance, cancelation, neutralization and alignment, depending on the antenna configuration.

In this paper, we study the degrees of freedom (DoF) of the asymmetric multi-input-multi-output interference broadcast channel (MIMO-IBC). By introducing a notion of connection pattern chain, we generalize the genie chain proposed in [10] to derive and prove the necessary condition of IA feasibility for the asymmetric MIMO-IBC, which is denoted as irreducible condition. It is necessary for both linear interference alignment (IA) and asymptotic IA feasibility in MIMO-IBC with arbitrary configurations. In a special class of the asymmetric two-cell MIMO-IBC, the irreducible condition is proved to be the sufficient and necessary condition for asymptotic IA feasibility, while the combination of proper condition and irreducible condition is proved to the sufficient and necessary condition for linear IA feasibility. From these conditions, we derive the information theoretic maximal DoF and the maximal DoF achieved by linear IA. The derived maximal DoF per user for the asymmetric two-cell MIMO-IBC are also the upper-bounds of DoF per user for the asymmetric G-cell MIMO-IBC.

Energy efficiency (EE) has become an important design goal for wireless systems. Since the EE of a system is evaluated in a duration where the channel may vary, designing a transmission strategy to maximize instantaneous EE may lead to a loss in achievable EE of the system. To exploit the accumulative information of throughput and energy, accumulative EE (ACEE) can be used as the objective function, which is the ratio of the accumulated throughput divided by the overall energy consumed over the past time slots. By analyzing the solutions of three EE maximal problems to optimize rate allocation among multiple subcarriers in multiple time slots, we show when and why the ACEE can achieve high system EE. Simulation results verify our theoretical analysis. Our analysis show that the ACEE is beneficial for the systems with low circuit power consumption under time-varying channels when either the data rate requirement or signal to noise ratio is low, and either the number of antennas or subcarriers is small.

Coordinated multi-point (CoMP) transmission can significantly improve the spectral efficiency of cellular networks. To reduce the training overhead and the complexity for implementing CoMP, an effective way is to divide base stations (BSs) into cooperative clusters. However, with disjointed clusters, the cluster-edge users suffer from inter-cluster interference. In this paper, a scheme is designed to select a set of BSs to serve each user with CoMP coordinated beamforming, where the clusters of different users may overlap. To reduce the signaling overhead, the average net throughput of the network is maximized considering the training overhead. The proposed scheme depends on large-scale channel gains and can be operated in a semi-dynamic manner. A low complexity algorithm is proposed to form the clusters, which achieves similar performance to the optimal solution with exhaustive searching. Simulation results show the proposed algorithm outperforms the existing CoMP joint transmission with dynamic clustering and the Non-CoMP system.

With the increase of cell density and explosive growth of data traffic, the design principle of next generation cellular networks is evolving towards user-centric. No matter where a user is located, its high demand in quality of service will always be guaranteed. To achieve such an ambitious goal, each user can select preferred base stations to transmit cooperatively. In this paper, we study user-centric downlink cooperative transmission with orthogonal beamforming based limited feedback, where the central unit (CU) selects multiple users with orthogonal beamformers quantized by per-cell codebooks. To assist the CU for user scheduling and adaptive transmission, a method for users to estimate its signal to noise and interference ratio is derived. To ensure the required rate of each user, a method for the user to select cooperative set is proposed. Simulation results show that the proposed scheme outperforms existing schemes both in data rate per-user and in throughput.

In heterogeneous cellular networks, cross-tier inter-cell interference is often in mixed interference scenario where the macro-cell causes strong interference to pico-cells while pico-cells only cause weak interference to the macro-cell. In this scenario, the popular interference coordination schemes using power control or time/frequency division multiplexing are far from achieving the spectrum efficiency potential. Exploiting the difference of transmit powers and channel gains in heterogeneous networks, the transmission schemes based on interference cancelation have great opportunity to gain more throughput benefit. In this paper, we propose a coordinated precoding and proactive interference cancelation scheme, where the precoding design is based on the known decoding order and is to maximize the sum-rate of two interfering users. Since the optimization problem is non-convex, we find a local maximum by constructing a concave lower bound of the objective function and iteratively tightening it. Simulation results show that the network throughput is remarkably improved in mixed interference scenarios relative to orthogonal division scheme and coordinated multipoint transmission scheme with zero forcing precoding.

This paper aims to explore the potential of a high spectrum efficiency (SE) technology, coordinated multi-point (CoMP) transmission, for improving energy efficiency (EE) of downlink cellular networks. To this end, a traffic-aware mechanism, named cooperative base station (BS) doze, is introduced and optimized. The key idea is to allow BS idling by exploiting the delay tolerance of some users as well as the short-term spatio-temporal traffic fluctuations in the network, and to increase the opportunity of the idling by using CoMP transmission. The cooperative BS doze strategy involves BS time-slot doze pattern, and multicell user scheduling and cooperative precoding with different amount of data sharing, which are jointly optimized in a unified framework. To ensure various performance requirements of multiple users including delay tolerance and data rate, we maximize the network EE under different time-average rate constraints for different users, where the consumptions on transmit power, circuitry power and backhauling power are taken into account. We then propose a hierarchical iterative algorithm to solve the optimization problem. Simulations under practical power consumption parameters demonstrate that cooperative BS doze can provide substantial EE gain and support high data rate services with high achievable SE.

In time division duplex coordinated multi-point transmission with joint processing (CoMP-JP) systems, the uplink-downlink channels are no longer reciprocal. Due to the difficulty of antenna calibration among the coordinated base stations (BSs), practical downlink channels differ from uplink channels by multiplicative ambiguity factors, which lead to severe performance degradation. In this paper, we propose an inter-BS antenna calibration strategy to facilitate downlink CoMP-JP transmission, whose basic idea is to estimate the ambiguity factors. To establish the observation equations for estimation, an extra uplink training frame except for the regular uplink and downlink frames is introduced, and existing signalling framework of limited feedback can also be reused. To improve the estimation performance, we can either select multiple users or employ multiple frames of one user to assist the calibration. After establishing the observation equations respectively with uplink training or limited feedback, the weighted least square criterion is used for estimation. We proceed to analyze and compare the mean square errors of the estimators, and provide a principle to select the users for assisting calibration. Simulation results show that the channel reciprocity is largely retrieved by the proposed antenna calibration strategy, which provides substantial throughput gain over the CoMP-JP systems without inter-BS calibration.

In this paper, we strive to improve the energy efficiency (EE) of downlink base station (BS) cooperative transmission systems by switching off some BSs and antennas and allocating power to the users according to the required data rate and channel of each user. To show the potential of EE gain, we first propose an optimal dynamic switching off scheme using instantaneous channel information that maximizes the EE under per-user data rate and per-BS power constraints. To provide a feasible scheme for practical systems, we proceed to propose a semi-dynamic scheme where the BS-antenna operation pattern selection is based on average channel gains. Low complexity iterative algorithms are provided to find solutions for the dynamic and semi-dynamic strategies. Simulation results with typical circuit power consumption show that the semi-dynamic scheme performs close to the dynamic scheme and both schemes provide a substantial EE gain over the scheme with all antennas at all BSs active, when the data rate requirement is low. By comparing the overall power consumption for the systems of two extreme cases in deploying the active antennas, we find that the scheme only switching off BSs is more cost-effective in terms of energy than the scheme switching off antennas when the data rate requirement is low and the number of active antennas far exceeds that of the users.

In this paper, we study the maximal achievable degrees of freedom (DoF) for G-cell, K-user M×N symmetric multi-input-multi-output (MIMO) interference broadcast channel (MIMO-IBC) with constant coefficients by linear interference alignment (IA). We start by analyzing the sufficient and necessary conditions of the IA feasibility, which are associated with generalized Fibonacci sequences. Except for the well-known proper condition, we find another condition necessary to ensure a kind of irreducible interference to be eliminated, denoted as irreducible condition. We proceed to find the minimal number of antennas at each base station and each user to support a required number of interference-free data streams, from which we obtain the maximal achievable DoF. We find that the feasibility conditions in terms of the required antenna resources can be divided into two regions according the ratio of ρ = M/N. If ρ falls into the region that characterized by the proper condition, the achievable DoF per user with spatial extension is (M + N)/(GK + 1). If ρ lies in the region that characterized by the irreducible condition, the DoF is a piecewise linear function of M or N alternately.

Shutdown of low traffic load base stations (BSs) is recognized as a promising approach to increase energy-efficiency (EE) and reduce total power consumption, especially for small cell networks (SCN). In this paper, we study BS closing strategies for downlink multi-antenna multi-carrier SCN supporting best effort traffics. We formulate the optimization problem of long-term BS closing, BS-user association and subcarrier allocation to maximize EE or minimize total power consumption under the constraints of average sum rate and rate proportion. We obtain an optimal solution for maximizing EE and a suboptimal solution for minimizing total power consumption. Simulation results show that the solutions provide substantial gain both in saving power consumption and increasing EE, and minimizing the total power consumption will not lead to the maximal EE.

In this paper, we study the feasibility of interference neutralization (IN) in partially connected relay-aided multi-input-multi-output (MIMO) interference broadcast channel (IBC), where each relay only communicates with users in the same cell. Due to partial connectivity, there is no need to exchange channel information among the relays in different cells. We first present the necessary and sufficient condition for IN feasibility using linear transceiver. We then provide the minimum relay configuration required to support the maximal number of data streams without interference. Finally, the achievable degrees-of-freedomregion is obtained.

Energy-efficiency (EE) has been considered as an important goal for designing future high throughput cellular networks. In this paper, we strive to improve the EE of downlink coherent cooperative transmission systems by adaptively selecting the transmitting mode, i.e., by switching off some antennas and base stations (BSs) according to the required data rate of each user and then by allocating powers to the users. To show the potential gain in EE, we first propose an optimal dynamic mode selection scheme to select the most energy-efficient active antenna and BS pattern based on the instantaneous channels, where per-user date constraint and the per-BS-power constraint are taken into account. To provide a feasible scheme for practical systems, we proceed to propose an optimal semi-dynamic mode selection scheme where the antenna and BS switching is based on the average channel gains. Simulation results with typical circuit power consumptions show that the semi-dynamic scheme performs closely to the dynamic scheme when the cell-edge signal to noise ratio is high, and both mode selection schemes provide substantial EE gain over the traditional scheme with all antennas at all BSs active.

With rapid growing of wireless data service, heterogeneous network (HetNet) has evolved as an spectrum and energy efficient cellular network architecture. However, the inter-cell interference (ICI) between the macro-cell and small cells is complicated. Due to the transmit power difference between various nodes and the randomness of user locations, the network may work in strong, mixed or weak interference scenarios. To eliminate the severe impact of ICI in HetNet, we propose a Han-Kobayashi (H-K) coding based coordination scheme to address different interference scenarios systematically. For a pair of macro-user and pico-user, each subcarrier may work in different interference scenario due to the frequency-selective fading. With the known achievable rates of H-K coding, we study an optimized power allocation method to coordinate the transmit powers of two users on each subcarrier. The throughput optimization problem of multi-carrier interference network is generally non-convex, we propose an iterative convex approximation (ICA) method to maximize a concave lower bound of the throughput and iteratively approach its optimum. Simulation results show the superiority of the proposed optimization method, and demonstrate that the proposed ICI coordination scheme can provide substantial throughput gain over the orthogonal transmission schemes.

Coordinated beamforming has been optimized to maximize the sum rate under the transmit power constraints, or to minimize the transmit power under the data rate constraints. In this paper, we study coordinated beamforming to maximize the energy efficiency (EE) of multi-cell multi-antenna systems meanwhile ensuring the individual data rate requirement of each user. To find a solution of the non-convex optimization problem for the precoding design, we construct a convex subset of the original constraint set and a quasi-concave lower bound of the EE. Then, we propose an iterative algorithm to maximize the lower bound of the EE within the convex subset. We evaluate the EE of the proposed algorithm through simulations under different data rate requirements, user locations, and cell-edge signal-to-noise ratios. The results demonstrate that the proposed precoder is much more energy-efficient than the transmit power minimization precoder when the circuit power consumption dominates, and always outperforms two interference-free transmission schemes with the optimized transmit power toward maximizing the EE.

With the increasing number of mobile terminals, the capacity of cellular network can hardly satisfy the user requirement in the future. In next generation cellular systems, heterogeneous network is proposed to improve the spatial spectrum efficiency. In this paper, we study a joint coding scheme to increase the achievable sum-rate of multi-carrier heterogeneous systems, where we introduce transmission cooperation among subcarriers in the amplitude space to assist interference elimination at the receiver. Specifically, we propose to transmit information through cross links on some of the subcarriers where the users suffer from the strong interference, and use this information at the receiver to cancel the interference experienced on other subcarriers. We develop the direct-link transmission strategy using layered lattice code, and design subcarrier allocation algorithm between direct-link and cross-link transmissions. Simulation results show that the proposed joint coding scheme achieves higher sum rate than separated coding schemes in strong interference channels.

Coordinated multi-point transmission (CoMP) is a promising strategy to provide high spectral efficiency for cellular systems. To facilitate multicell precoding, downlink channel is estimated via uplink training in time division duplexing systems by exploiting channel reciprocity. Virtual subcarriers in practical orthogonal frequency division multiplexing (OFDM) systems degrade the channel estimation performance severely when discrete Fourier transform (DFT) based channel estimator is applied. Minimum mean square error (MMSE) channel estimator is able to provide superior performance, but at the cost of high complexity and more a priori information. In this paper, we propose a low complexity channel estimator for CoMP multi-antenna OFDM systems. We employ series expansion to approximate the matrix inversion in MMSE estimator as matrix multiplications. By exploiting the feature of frequency domain training sequences in prevalent systems, we show that the proposed estimator can be implemented by DFT. To reduce the required channel statistical information, we use the average channel gains instead of channel correlation matrix, which leads to minor performance loss in CoMP systems. Simulation results show that the proposed channel estimator performs closely to the MMSE estimator.

When the circuit power consumption and the overhead for channel estimation are taken into account, the system designed for maximizing the spectrum efficiency (SE) does not necessarily yield high energy efficiency (EE). In this paper, we strive to optimize the uplink training length towards maximizing the EE of closed-loop multi-antenna systems under the constraint of the SE. The upper bounds of the system EE and the net downlink SE with channel estimation errors are derived, based on which the optimization problem is proved as convex, and the impacts of signal-to-noise ratio (SNR) and circuit power consumption on the optimal training length are analyzed. Analytical and simulation results show that in general the EE-oriented optimization leads to a longer training length than the SE-oriented optimization, and it will reduce to the SE-oriented optimization at high SNR and very low SNR regime, or with very high circuit power consumption.

Due to the difficulty of self antenna calibration among different base stations (BSs), the uplink-downlink channels are no longer reciprocal in time division duplex coordinated multi-point transmission with joint processing (CoMP-JP) systems. To retrieve the channel reciprocity, we propose an inter-BS antenna calibration method. Specifically, we estimate the inter-BS ambiguity factors with weighted least square criterion by using the uplink and downlink channel estimates and the received signal of a calibration sounding signal of multiple supporting users or multiple frames. We then analyze the mean square error of the proposed estimator, and compare the performance of the estimators with multiple supporters and with multiple frames. Simulation results show that the channel reciprocity is largely retrieved by the proposed antenna calibration method, which provides substantial throughput gain over the CoMP-JP systems without calibration.

In this paper, we investigate an energy-efficient coordinated multiple point (CoMP) transmission strategy for downlink heterogeneous cellular networks. We combine CoMP joint processing (CoMP-JP) and coordinated beamforming (CoMP-CB), two special cases of CoMP, in a time division manner to improve both energy efficiency (EE) and spectral efficiency (SE). We formulate the problem as minimizing the total transmit power consumed by both the macro- and pico-base stations (BSs) under the constraints on the data rate requirements from the macro- and pico-users, and on the maximum transmit powers of the macro- and pico-BSs. Both the transmit time and the transmit powers allocated to the CoMP-JP and CoMP-CB transmissions are optimized. Simulation results show that the hybrid CoMP-JP and CoMP-CB strategy provides a larger capacity region than the CoMP-JP-only or CoMP-CB-only transmission. The time proportion of the CoMP-JP in the hybrid strategy decreases with the data rate requirement of the macro-user and increases with the maximum transmit power of the pico-BS and the average channel gain from the macro-BS to the macro-user. Increasing the transmit power of the pico-BS can improve the EE in the high SE region of the macro-user.

We study low-signalling overhead scheduling for downlink coordinated multi-point (CoMP) transmission with multi-antenna base stations (BSs) and single-antenna users. By exploiting the asymmetric channel feature, i.e., the path-loss differences towards different BSs, we derive a metric to judge orthogonality among users only using their average channel gains, based on which we propose a semi-orthogonal scheduler that can be applied in a two-stage transmission strategy. Simulation results demonstrate that the proposed scheduler performs close to the semi-orthogonal scheduler with full channel information, especially when each BS is with more antennas and the cell-edge region is large. Compared with other overhead reduction strategies, the proposed scheduler requires much less training overhead to achieve the same cell-average data rate.

This paper studies transmission strategies for downlink time division duplex coordinated multi-point (CoMP) systems with non-ideal uplink-downlink channel reciprocity, where several multi-antenna base stations (BSs) jointly serve multiple single-antenna users. Due to the inherent antenna calibration errors among different BSs, the uplink-downlink channels are no longer reciprocal such that the channel information at the BSs is with multiplicative noises. To mitigate the performance degradation caused by the imperfect channels, we employ a weighted sum rate estimate as the objective function for robust precoder design. To show the impact of data sharing on the performance of CoMP under the non-ideal channel reciprocity, we provide a unified framework for designing precoder for CoMP systems with different amount of data sharing. The optimal parametric linear precoder structure that maximizes the weighted sum rate estimate under per-BS power constraints is characterized, based on which a closed-form robust signal-to-leakage-plus-noise ratio (SLNR) precoder is proposed to exploit the statistics of the calibration errors. Simulation results show that the proposed precoder in conjunction with user scheduling provides substantial performance gain over Non-CoMP transmission and the CoMP transmission with non-robust precoders.

The potential of coordinated multi-point (CoMP) transmission in providing high spectral efficiency for cellular systems largely depends on the channel quality at the cooperated base stations. In this paper, we investigate whether channel prediction is useful for downlink CoMP systems with backhaul latency in time-varying channels, where both the centralized and decentralized CoMP joint processing (CoMP-JP) as well as the CoMP coordinated beamforming (CoMP-CB) are considered. Toward this goal, we resort to large system analysis with large number of transmit antennas to derive closed form expressions of the average per-user rate of the CoMP systems, when predicted or estimated channels are employed for downlink precoding. By comparing with Non-CoMP systems, we find that channel prediction provides much higher performance gain over channel estimation for CoMP systems, depending on the strategy of the cooperation and the user location. As a result, CoMP systems can perform fairly well for mobile users even under the large backhaul latency, if channel prediction will be used instead of channel estimation. Simulation results are provided to validate our analysis.

In this paper, we study energy efficient orthogonal frequency division multiplexing (OFDM) relay system, where two source nodes communicate with each other via a half-duplex amplify-and-forward relay node. We aim to maximize the energy efficiency (EE), where both the transmit and circuit power consumptions are taken into consideration. To this end, we optimize the active number of subcarriers and the number of bits to each subcarrier at the two source nodes. The optimal solution turns out to be a bidirectional water-filling bit allocation to minimize the overall transmit power, and a subcarrier reduction algorithm to balance the transmit and circuit power consumptions. When the data amounts at the two source nodes are unequal, the bidirectional water-filling leads to a hybrid one- and two-way relaying strategy, which employs one-way relaying on some subcarriers and two-way relaying on some of the other subcarriers. Simulation results demonstrate that the hybrid one- and two-way relaying with subcarrier reduction, which is optimized towards EE, is more energy efficient than that without subcarrier reduction, which only minimizes the transmit power.

Heterogeneous network is a new paradigm in next generation cellular systems, which is promised to significantly improve the spatial spectrum efficiency through overlapped coverage. This however calls for efficient interference management techniques. In this paper, we propose an amplitude-space sharing strategy among the macro-cell user and pico-cell users, where different users occupy different levels in the signal amplitude-space. By optimizing the space sharing scheme, different layers of signal and interference are separable at each receiver and the network sum-rate can be maximized. We start from the single pico-cell scenario, where we employ Han-Kobayashi coding and derive the optimal transmit powers allocated to the private and common information of the users. With a unified framework, we derive the achievable sum-rates for various interference scenarios ranging from very strong to very weak cases. We then illustrate how the amplitude-space sharing strategy can be applied to the multiple pico-cell scenarios by developing a simple transmission scheme. Simulation results show the superiority of the proposed scheme over other interference management schemes.

In this paper, we investigate adaptive configuration of spatial and frequency resources to maximize energy efficiency (EE) and reveal the relationship between the EE and the spectral efficiency (SE) in downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We formulate the problem as minimizing the total power consumed at the base station under constraints on the ergodic capacities from multiple users, the total number of subcarriers, and the number of radio frequency (RF) chains. A three-step searching algorithm is developed to solve this problem. We then analyze the impact of spatial-frequency resources, overall SE requirement and user fairness on the SE-EE relationship. Analytical and simulation results show that increasing frequency resource is more efficient than increasing spatial resource to improve the SE-EE relationship as a whole. The EE increases with the SE when the frequency resource is not constrained to the maximum value, otherwise a tradeoff between the SE and the EE exists. Sacrificing the fairness among users in terms of ergodic capacities can enhance the SE-EE relationship. In general, the adaptive configuration of spatial and frequency resources outperforms the adaptive configuration of only spatial or frequency resource.

In this paper, a channel direction information (CDI) feedback strategy is proposed to assist coordinated multi-point beamforming (CoMP-CB) between a macro base station (MBS) and multiple pico BSs (PBSs) in heterogeneous networks, where the numbers of bits for feedback are adaptive to the average channel gains of multiple users. To maximize the average sum rate achieved by the users with given feedback resource, we optimize the overall number of feedback bits for each user and the codebook sizes for quantizing the per-cell CDIs of each user. To provide feasible feedback scheme for practice use, a low complexity two-step solution is developed from deriving the lower bounds of the per-user average rate. In the first step, we optimize the number of overall feedback bits for each user, which provides a tradeoff between the accuracy of CDI quantization and the reliability of feedback transmission. In the second step, we optimize the bit allocation for quantizing the per-cell CDIs, which accommodates the difference in transmit powers and antennas of the MBS and PBSs and in the channels of macro and pico cells. Two adaptive feedback schemes are developed from two lower bounds. By characterizing the degree of freedom (DoF) achieved by each user, we show that the systems using the proposed adaptive feedback schemes are no longer interference-limited. The maximum per-user DoFs achieved by the proposed schemes depend on the per-antenna feedback resource constraint for each user. Simulation results show significant performance gain of the proposed scheme over NonCoMP and CoMP-CB with fixed codebook size.

Coordinated multi-point transmission (CoMP) joint processing (CoMP-JP) is a promising technique for supporting high spectral efficiency in future cellular systems. However, this technique requires high-capacity backhaul links for connecting multiple base stations (BSs), which leads to high deployment costs. CoMP coordinated beamforming (CoMP-CB) needs less backhaul capacity, but with lower maximal multiplexing gain. In this paper, we study downlink CoMP strategies with limited-capacity backhaul. We start by investigating the optimal interference-free transmission strategy under backhaul rate constraints, where only a part of the data is shared among the BSs. We then study soft and hard switching between the CoMP-JP and CoMP-CB modes. Finally, a closed-form semi-dynamic distributed hard switching scheme is proposed, which depends on the local large-scale channel gains of the users. Simulation results show that both the soft and hard mode switching transmission perform closely to the optimal strategy and outperform the single-mode CoMP transmission especially when the backhaul capacity is very limited.

In this paper, we design energy efficient relay transmission strategy in a system where two source nodes transmit to each other assisted by an amplify-and-forward relay node. We first compare the energy efficiencies (EEs) between one-way relay transmission (OWRT) and two-way relay transmission (TWRT), which shows that when the bidirectional data amounts are equal, TWRT performs better, otherwise, OWRT may offer higher EE. To achieve the maximal EE in various settings, we propose hybrid relay transmission (HRT) that transmits partial messages with OWRT and the remaining messages with TWRT, and jointly optimize the data amounts and transmission time allocated to the OWRT and TWRT parts. Simulation results show the superiority of the HRT over both OWRT and TWRT.

This paper studies the degrees of freedom (DoF) of relay-aided interference broadcast channels and the relay resource to achieve them. We show that, with the aid of half-duplex relays, a G-cell system can achieve GMBS/2 DoF, where MBSis the number of transmit antennas in each cell. By studying the interference-free constraints, we obtain a lower bound of the relay resource that ensures interference-free transmission. Instead of directly solving the complicated multivariate problem with cubic interference-free constraints, we propose to relax the problem to linear equations by randomly initiating certain variables, and derive an achievable bound on relay resource. Numerical results show that the relay-aided interference broadcast channels require less relay resource than relay-aided interference channels and transmission protocol has large impact on the required resources.

In this paper, we compare the energy efficiencies (EEs) of direct transmission (DT), one-way relay transmission (OWRT) and two-way relay transmission (TWRT) in a system where two sourcie nodes transmit to each other and may be assisted by an amplify-and-forward relay node. We first find the maximum EEs of DT, OWRT and TWRT by jointly optimizing the transmission time and the transmit powers at each node. Then we compare the maximum EEs of the three strategies, and analyze the impact of circuit powers and bidirectional data amounts. Analytical and simulation results show that relaying is not always more energy efficient than DT. The EE of TWRT is higher than those of DT and OWRT when the bidirectional data amounts are equal. Otherwise, the advantage of TWRT diminishes, while either DT or OWRT may provide higher EE.

In this paper, we investigate adaptive configuration of spatial and frequency resources to maximize energy efficiency (EE) and reveal the relationship between the EE and the spectral efficiency (SE) in downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We formulate the problem as minimizing the total power consumed at the base station under constraints on the average data rates from multiple users, the total number of subcarriers, and the number of radio frequency (RF) chains. We develop a two-step searching algorithm to solve this problem, which first finds the near-optimal numbers of subcarriers for multiple users based on Karush-Kuhn-Tucker (KKT) conditions and then optimize the number of active RF chains. Simulation results demonstrate that increasing frequency resource improves both the SE and the EE, and is more efficient than increasing spatial resource. Consequently, there exists tradeoff between the SE and the EE only when the frequency resource is limited. In general, the adaptive configuration of spatial and frequency resources outperforms the adaptive configuration of only spatial resource and that of only frequency resource.

Coordinated multi-point (CoMP) transmission through joint processing (JP) and coordinated beamforming (CB) is promising to provide high spectral efficiency by avoiding in-tercell interference in downlink heterogeneous cellular networks. Pseudo-inverse based zero-forcing beamforming (P-ZFBF) is a popular low-complexity multi-user precoder that can achieve maximal sum rate under sum power constraint. The P-ZFBF under pure CoMP-JP transmission mode is able to achieve high multiplexing gain and array gain, which however leads to very inefficient usage of transmit power in heterogeneous networks. The P-ZFBF under pure CoMP-CB mode can fully use transmit power but achieves low multiplexing and array gains. This paper investigates hybrid cooperative transmission strategies to exploit the advantages of CoMP-JP and CoMP-CB. Both the optimal and closed-form suboptimal hybrid strategies are proposed. Simulation results demonstrate their evident performance gain over the pure CoMP-JP and CoMP-CB transmission modes.

In this paper, we investigate the configuration of frequency resources from the perspective of maximizing the energy efficiency (EE) of downlink multi-cell multi-carrier multi-antenna systems. We first formulate an optimization problem of subcarrier assignment to minimize the total power consumption at the base stations under the constraints of spectral efficiency (SE) requirements from multiple users. Then we find its closed-form solution by analyzing different cases. Analytical and simulation results show that when the SE requirement is low, using non-overlapped frequency resources is more energy efficient than using overlapped frequency resources and the EE increases with the SE. To support high SE, more spatial resources should be configured but a trade-off between SE and EE appears. Serving cell-center users will provide higher EE, while when serving the cell-edge users maximizing the EE will lead to a minor loss of the SE.

The performance of coordinated multi-point (CoMP) transmission systems will be severely degraded by channel quantization error when each user employs limited number of bits to quantize and feed back its channel information. This is especially true for multi-user multi-antenna CoMP with joint processing (CoMP-JP), where multi-user interference becomes dominant for high power region. As an alternative strategy, orthogonal beamforming (ORBF) based limited feedback strategy has the potential to improve the performance due to its robustness to the quantization error. In this paper, we consider limited feedback downlink CoMP-JP systems based on per-cell codebooks, which are scalable for large systems and compatible to existing systems. We propose an ORBF limited feedback strategy for CoMP-JP, where the coordinated base stations (BSs) schedule multiple users with mutually orthogonal codewords for their corresponding per-cell channels with the same BS. To facilitate spatial scheduling, we derive a method to estimate the signal to noise and interference ratio (SINR) at each user that will be fed back to the BSs. It is demonstrated by simulations that the proposed feedback scheme provides substantial performance gain over the channel quantization based limited feedback CoMP-JP and CoMP coordinate beamforming (CoMP-CB), as well as the ORBF based Non-CoMP systems, especially when the number of coordinated BSs is small and the number of antennas at each BS is large.

In this paper, we design energy efficient relay transmission strategy for an orthogonal frequency division multiplexing (OFDM) system, where two source nodes exchange information with each other via an amplify-and-forward relay node. To maximize the energy efficiency (EE), which is defined as the number of transmitted bits per unit of energy, we jointly optimize the bidirectional bit allocation, which leads to a hybrid one- and two-way relay scheme. In particular, we propose a bit allocation algorithm to balance the transmit and circuit power consumptions of the hybrid relay in order to minimize the overall power consumption. Simulation results show that the proposed algorithm achieves much higher EE than the approach that only minimizes the transmit power, and the hybrid relay is more energy efficient than the pure one- and two-way relay.

This paper studies robust transmission strategies for downlink time division duplex coordinated multi-point (CoMP) systems with non-ideal uplink-downlink channel reciprocity due to imperfect antenna calibration. By exploiting the statistics of antenna calibration errors, we first characterize the optimal parametric precoder structure that maximizes the weighted sum rate, based on which a closed-form robust signal-to-leakage-plus-noise ratio (RSLNR) precoder with properly selected parameters is then proposed. Simulation results show that the proposed precoder together with user scheduling provides near-optimal performance and data sharing among coordinated BSs may become detrimental depending on the employed precoders and the accuracy of antenna calibration.

In this paper, we develop linear interference alignment (IA) approach for multi-input-multi-output interference broadcast channel (MIMO-IBC). Since multiple data streams from each base station (BS) to multiple mobile stations (MSs) experience identical channel, it is hard to ensure the rank constraint to the intended MSs in the desired cell meanwhile ensure the interference aligned at the unintended MSs in other cells. Considering the difficulty in aligning interference at the receiver in MIMO-IBC, we design a transceiver to align and eliminate the interference at the transmitter. Specifically, we first design receive vectors of all MSs to align the inter-cell interference at the BS side, and then design the precoder of each BS to eliminate all the intra- and inter-cell interference. The proposed approach can be applied for general MIMO-IBC and has closed-from solutions for some antenna configurations. Simulation results validate our analysis and show that the proposed IA transceiver can achieve the maximal degrees of freedom of MIMO-IBC and attain a good trade-off between the maximal number of data streams and signal-to-noise ration gain.

The quality of channel state information (CSI) has large impact on the performance of coordinated multi-point (CoMP) systems. In this paper we study the impact of channel prediction on the performance of coherent CoMP transmission in time-varying channels with large backhaul latency. We resort to large system analysis to derive an explicit expression of the average user rate of the CoMP system when predicted channels are employed for downlink precoding. By comparing with the Non-CoMP systems, we find that channel prediction brings much higher performance gain to CoMP systems with respect to channel estimation. As a result, a CoMP system can perform well for mobile users even under large backhaul latency, if channel prediction will be used instead of channel estimation. Simulation results are provided to validate our analysis.

Coordinated multi-point transmission (CoMP) has been widely recognized as a promising technique for improving spectral efficiency in future cellular systems. However, it may require high-capacity backhaul links, which can increase expense. In this paper, we study the cooperative transmission method with limited-capacity backhaul. Downlink CoMP transmission requires the exchange of information between base stations, depending on the type of CoMP schemes. Either channel state information (CSI) information of all users (for coordinated beamforming), or both data and CSI (for joint processing) need to be shared among all cooperative base stations. The latter method exhibits better performance, but requires high capacity backhaul links. For this reason, we propose in this paper a transmission mode selection method under capacity constrained backhaul. We start by analyzing the performance of two kinds of CoMP transmission modes: joint processing and coordinated beamforming with various types of user scheduling. Then we propose a closed-form decision rule, which depends on the strongest average channel gains of the users co-scheduled in the same time-frequency resource. Simulation results show that the proposed transmission mode selection outperforms the single-mode CoMP transmission.

In this paper, we design interference free transceivers for multi-user two-way relay systems, where a multi-antenna base station (BS) simultaneously exchanges information with multiple single-antenna users via a multi-antenna amplify-and-forward relay station (RS). To offer a performance benchmark and provide useful insight into the transceiver structure, we employ alternating optimization to find optimal transceivers at the BS and RS that maximizes the bidirectional sum rate. We then propose a low complexity scheme, where the BS transceiver is the zero-forcing precoder and detector, and the RS transceiver is designed to balance the uplink and downlink sum rates. Simulation results demonstrate that the proposed scheme is superior to the existing zero forcing and signal alignment schemes, and the performance gap between the proposed scheme and the alternating optimization is minor.

In this paper, power allocation between pilots and data symbols is investigated to maximize energy efficiency (EE) for downlink orthogonal frequency division multiple access (OFDMA) networks. We first derive an EE function considering channel estimation error, which depends on large-scale channel gains of multiple users, allocated power to pilots and data symbols, and circuit power consumption. Then an optimization problem is formulated to maximize the EE under overall transmit power constraint. Exploiting the quasiconcavity property of the EE function, we propose an alternating optimization method in the low transmit power region and reformulate a joint quasiconcave problem in the high transmit power region. Analysis and simulation results show that the power ratio for pilots decreases with the circuit power. When the circuit power is small, the optimal overall transmit power increases with the circuit power. Otherwise, the optimal transmit power does not depend on it. Transmitting more data symbols to the users with higher channel gains improves the EE but at a cost of sacrificing the fairness among multiple users. Simulation results also demonstrate that compared with spectral efficiency (SE)-oriented design, the EE-oriented design can improve the EE performance significantly with a relatively small SE loss.

Coordinated multi-point (CoMP) transmission is a promising technique for improving spectral efficiency of full frequency reuse cellular systems. However, its performance will be severely degraded if the feedback strategies are not carefully designed for providing channel information through limited uplink resources to the coherently cooperated base stations. In this paper, we study per-cell codebook based limited feedback scheme, which is highly desirable for CoMP systems due to its scalability and flexibility. Specifically, we investigate the issues related to phase ambiguity (PA), which comes from the per-cell codebook structure. We first analyze how many bits are required for the PA feedback to ensure an allowed performance loss. We then propose an adaptive bit allocation algorithm to divide a given amount of feedback bits between the per-cell codebooks for single-cell channel direction quantization and a codebook for PA quantization, aiming at minimizing the average global channel direction quantization error. Finally we provide a scaling law of the number of feedback bits per-user when the bit allocation algorithm is used. Simulation results show that the proposed bit allocation algorithm significantly improves the throughput of coherent CoMP systems either with equal number or different number of antennas at each base station.

Two-way relaying can significantly improve the spectrum efficiency of bi-directional information transmission between two nodes with the help of half-duplex relays. However, in the general case of asymmetric channel gains, the achievable rates of both directions are restricted by the link with the worse channel quality. In this paper, we propose a new three-phase cooperative transmission protocol, which exploits the direct link and the time resource of a two-way relay system more efficiently. We derive the rate region outer bound of this protocol, and propose a practical transmission scheme based on doubly nested lattice codes, which can achieve this outer bound within 0.5 bit. Numerical results show the superiority of this protocol over two other classical protocols in asymmetric channels.

Fractional frequency reuse (FFR) is an efficient way to mitigate inter-cell interference (ICI) in multi-cell orthogonal frequency division multiple access (OFDMA) networks. In this paper, we investigate the throughput and the optimal threshold for the FFR scheme. The average cell throughputs are derived for both round robin (RR) and maximum SINR (MSINR) scheduling strategies when users are uniformly distributed in the cell region. It is shown from the analysis and simulation results that the throughput increases and the optimal distance threshold decreases with the number of users for both scheduling strategies. The optimal distance threshold approaches the minimum distance that users can be away from the base station when the number of users goes to infinity. The optimal distance threshold increases with the frequency reuse factor of the cell-edge region when the MSINR scheduling is used. The impact of the RR scheduling strategy on the optimal threshold of the FFR scheme is negligible. Simulation also demonstrates that the FFR scheme with the optimal threshold significantly outperforms that with the existing fixed threshold.

Two-way relay transmission (TWRT) is more spectral efficient than direct transmission (DT) and one-way relay transmission (OWRT). In this paper, we try to find out if TWRT is also more energy efficient in practice. To this end, we first derive and compare the energy efficiency of DT, OWRT and TWRT systems, when either the receiver processing power consumption is taken into account or not. We then compare the maximal energy efficiency achieved by TWRT and OWRT, as well as the corresponding spectral efficiency. Both analytical and simulation results show that TWRT is not always more energy efficient than DT and OWRT, depending on the channel condition, processing power consumption and the bidirectional date rate requirements. Generally speaking, in high spectral efficiency and large path loss attenuation region, the TWRT provides high energy efficiency.

In this paper, power allocation between pilots and data symbols is investigated aiming at maximizing energy efficiency(EE) for downlink orthogonal frequency division multiple access (OFDMA) networks. We first derive an EE function when the channel estimation error is considered, which depends on the large-scale channel gains of multiple users, the allocated power to pilots and data symbols, and the circuit power consumption. Then an optimization problem is formulated to maximize the EE under overall transmit power constraint. The relationship between the power for pilots and data symbols is analyzed based on Karush-Kuhn-Tucker (KKT) conditions and the impacts of channel gains on both power allocation and the EE are studied. Exploiting the quasiconcavity property of the EE function, a bisection searching algorithm is developed to find the optimal power allocation. Simulation results demonstrate the performance gain of the proposed optimal power allocation scheme in terms of the EE and the required overall transmit power.

In this paper, we propose a cognitive interference management approach, called fractional frequency donation, to alleviate co-channel interference among selfish femtocells. In such networks, our approach allows each femtocell to access all available bands but requires good femtocells with high throughput to "donate" some bands to poor ones. When the donors and the corresponding donated bands are properly selected, both good performance on average- and 5% edge-throughputs can be achieved. Simulation results show that in femtocell networks, the proposed fractional frequency donation approach is more suitable than the conventional fractional frequency reuse ones.

This paper studies downlink multicell cooperative transmission with imperfect CSI sharing led by backhaul latency, assuming full data sharing amongst coordinated base stations (BSs). Different from the traditional centralized cooperative transmission systems where multicell precoder is designed at a central unit, a so-called BS-processing system is considered, which enables a decentralized design of multicell precoder at each BS. We show that the resulting precoder design problem falls within the framework of team decision theory, based on which a decentralized multicell precoder is proposed, aimed at maximizing the weighted sum rate. We evaluate the performance of our precoder through simulations.

Coherent base station (BS) cooperative transmission provides high spectrum efficiency for cellular systems when channel state information (CSI) is perfectly known at the BSs. For frequency division duplexing systems, the CSI is fed back with limited number of bits, which linearly increases with the number of cooperating BSs. In this paper, we study the impact of the quantized CSI on cooperative transmission when zero-forcing beamforming is used. By deriving an optimal bit allocation among local and cross channels according to the locations of users, we show the minimal feedback bits required by cooperative transmission for providing performance gain over non-cooperative transmission.

In this paper, we study a multi-user multi-antenna two-way relay system, where a multi-antenna base station (BS) exchanges uplink and downlink signals with multiple users via a multi-antenna amplify-and-forward relay station (RS). We jointly design the BS and RS transceivers, aiming to maximize the system bidirectional sum rate under inter-user interference free constraint. Since the optimization problem is non-convex, we employ alternating optimization algorithm to design the transmit and receive weighting matrices at the BS and RS. Simulation results show that the proposed solution offers higher bidirectional sum rate than existing schemes.

Fractional frequency reuse (FFR) is an efficient method to suppress inter-cell interference (ICI) in multi-cell OFDMA networks. In this paper, the optimal threshold is designed for FFR schemes to identify cell central and edge users. We first derive throughput of the FFR scheme considering small-scale fading channels and different scheduling strategies. Then we conclude from the analysis and simulation results that the throughput of maximum normalized SINR (MNSINR) scheduling is larger than that of round robin scheduling, and the optimal thresholds for both scheduling strategies decrease with the increase of cell user number. Simulation results also show that the performance of FFR scheme with the optimal threshold outperforms that with the conventional predefined threshold.

Switching underutilized base stations (BSs) to sleep mode is recognized as a promising approach to reduce energy consumption of cellular networks, but it may increase the transmit power of remaining active BSs to guarantee service coverage. Coordinated multi-point (CoMP) can effectively reduce transmit power of BSs through BS cooperation but requiring extra power consumption due to extra signal processing and backhaul traffic. In this paper, we investigate the energy efficiency of BS sleeping combined with CoMP. A joint power and subcarrier allocation algorithm is proposed to minimize the overall network power consumption with minimum data rate constraints, which can be implemented distributedly across multiple clusters. Simulation results show that BS sleeping combined with CoMP can improve network energy efficiency for high data rate users compared with Non-CoMP systems without BS sleeping.

Base station cooperative transmission is an effective strategy to mitigate inter-cell interference. Centralized multi-cell transmission provides considerable performance gains but is impractical in large cellular systems, due to its prohibitive complexity and large amount of overhead. Dividing cells into small clusters enables practical channel acquisition and coordination within each cluster but still suffers from out-of-cluster interference. In this paper, we propose a dynamic cooperative framework for large cellular systems, which divides cells into groups such that neighboring cells belong to different groups. Based on the cell-grouping, a distributed scheduling strategy is proposed which can effectively coordinate the interference between cell-groups. With limited signalling among BSs and lower complexity, the cell-grouping based distributed scheduling and beamforming shows performance advantages over the fixed clustering based centralized scheduling and beamforming.

Coherent multi-cell cooperative transmission, also referred to as coordinated multi-point transmission (CoMP), is a promising way to provide high spectral efficiency for universal frequency reuse cellular systems. To report the required channel information to the transmitter in frequency division duplexing systems, limited feedback techniques are often applied. Considering that the large scale fading gains of channels from multiple base stations (BSs) to one mobile station are different and the number of cooperative BSs may be dynamic, it is not flexible nor compatible to employ a large codebook for directly quantizing the CoMP channel. In this paper, per-cell codebook for separately quantizing local and cross channels are studied. We first optimize the bit allocation among per-cell codebooks, aiming at minimizing the average quantization error of the aggregated CoMP channel. A closed-form codebook size allocation method is proposed, which only depends on the large scale fading gains of per-cell channels. Considering that the optimal per-cell codeword selection for CoMP channel is of high complexity, we propose a serial codeword selection method, whose complexity is quite low but the performance approaches that of the optimal codeword selection. Simulation results validate our analysis and demonstrate an evident performance gain of our methods.

To exploit the full benefit of base station (BS) cooperative transmission, also known as coordinated multi-point (CoMP) transmission, large amount of feedback is required to gather the channel information. In this paper, we analyze the impact of channel asymmetry, which is inherent in CoMP systems, on downlink coherent BS cooperative transmission using zero-forcing beamforming with limited feedback. Per-cell quantization of multicell channels is considered, which quantizes the local channel and cross channels separately and is more feasible in practice. We analyze the per-user rate of limited feedback multi-user CoMP systems, and provide approximate expressions for both the inter-cell and intra-cell residual multi-user interference introduced by the quantization errors. When the desired user is at the cell center, the former is weak, but the latter is strong and depends not only on the signal to noise ratio but also on the location of its co-scheduled users. Simulation results validate our theoretical analysis.

Per-cell codebook based limited feedback is desirable for coordinated multi-point (CoMP) transmission system due to its flexibility and scalability. In this paper, we study if and when the quantization performance of CoMP channel direction information will benefit from the phase ambiguity (PA) feedback. To this end, we analyze the average quantization performance of the feedback strategies with or without PA feedback, respectively. By deriving the approximated bounds, we show that when the number of coordinated base stations is large and the number of antennas at each base station is small, the PA feedback will introduce evident performance gain especially for cell-edge users. Simulation results validate our analysis.

In a two-way relay system where two nodes exchange information via a half-duplex relay, the achievable data rates of both directions are limited by the weaker link. When the two-way channel is asymmetric due to relay positioning or channel fading, the sum rate of the two-way relay system will suffer from severe degradation. In this paper, we propose a novel three time-slot cooperative transmission strategy in order to transmit at the maximum rate of each link, and therefore, to improve the sum rate of the two-way relay system. We obtain capacity region for the new strategy and derive an achievable rate region. Time slot allocation and power allocation at the relay are optimized to achieve the maximum sum rate. Simulation results show that the proposed strategy significantly outperforms existing transmission strategies under asymmetric channel conditions both in AWGN and Rayleigh fading channels.

In this paper, both configuration of active radio frequency (RF) chains and resource allocation are investigated for improving energy efficiency of downlink multiple-input-multiple-output (MIMO) orthogonal frequency division multiple access (OFDMA) systems. We first formulate an optimization problem to minimize the total power consumed at the base station with the maximum transmit power constraint and ergodic capacity constraints from multiple users. Then a two-step suboptimal algorithm is proposed. Specifically, the continuous variable optimization problem is first solved, and then a discretization algorithm is presented to obtain the number of active RF chains and the number of subcarriers allocated to each user. Simulation results demonstrate that the proposed algorithm can provide significant power-saving gain over the all-on RF chain scheme and the adaptive subcarrier allocation helps to save more power.

This paper proposes a novel codebook for multiuser multiple-input multiple-output systems under spatially correlated channels. Existing codebooks designed for correlated channels either require accurate channel statistics which is not favorable for practical systems, or only perform well in highly correlated channels. In this paper, we first analyze the per-user rate loss led by using DFT codebook, then propose a two-level codebook, named weighted DFT codebook. It consists of a DFT-based codebook and a Grassmannian linear packing codebook. The proposed scheme does not need accurate channel statistics and can adapt to both correlated and uncorrelated scenarios. Simulation results show significant performance gain of the proposed codebook over the existing codebooks in various correlated channels.

Per-cell codebook based limited feedback strategy is desirable for coordinated multi-point (CoMP) transmission system due to its scalability. In this paper, we study phase ambiguity quantization for centralized CoMP system with per-cell codebook based global channel quantization. We first analyze the performance degradation led by the phase ambiguity when codeword is selected for each cell separately, and show that the loss can be effectively recovered by feeding back a few bits in each cell for the phase ambiguity information. We then analyze the impact of the inherent asymmetric feature of CoMP channel due to heterogeneous path loss on the performance of per-cell feedback scheme, and show that the quantization error of phase ambiguity will lead to significant performance degradation of the global channel quantization only for cell edge users. Simulation results are provided that validate our analysis.

Cooperative base station transmission, also known as coordinated multi-point (CoMP)transmission, is a promising technique to improve spectrum efficiency of cellular networks. however, coherent CoMP joint transmission needs huge training overhead which will reduce the effective data transmission rate. In this paper, a downlink transmission mode selection method is proposed, aiming at improving cell-edge throughput without reducing cell-center user rate. To reduce the adverse impact of the overhead, we select the following users for CoMP transmission: those can obtain significant performance gain from CoMP and those can increase the multiplexing gain. A threshold for large scale SINR is defined to find cell-edge users. An adaptive orthogonal threshold is designed for selecting partner users for the cell-edge users. Impact of the two thresholds are analyzed and performance of the proposed method are evaluated through simulations, which shows evident performance gain over both Non-CoMP and Full-CoMP systems.

Base stations (BSs) cooperative transmission provides high spectrum efficiency for cellular networks, where the performance gain of each user depends on its location. When training overhead is considered, however, a user jointly served by multiple BSs may even achieve lower net rate than with non-cooperative transmission. In this paper, we consider semi-dynamic mode selection for users. We first derive the average rate of each user when it is served under cooperative or non-cooperative transmission. We then propose a closed-form mode selection method where only large-scale fading gains of a user are used. Simulation results validate our analysis and demonstrate a performance gain of the proposed method over both full cooperative and non-cooperative transmission.

Cooperative base station transmission, which is also called coordinated multi-point (CoMP) transmission, can significantly enhance spectrum efficiency of future cellular systems. To gain the promised benefits of coherent CoMP transmission, limited feedback is necessary for conveying channel state information in frequency division duplex systems. In this paper, we address per-cell codebook based quantization for CoMP channels, which is flexible and scalable in practice. We first analyze the structure of channel direction information (CDI) of CoMP channel and its connection with per-cell channels, then provide an approximate method to re-construct the CoMP CDI. Exploiting the degrees of freedom provided by using the per-cell codebook, we present and compare several ways of generating global codebook and selecting per-cell codewords for CoMP channel quantization. Simulation results are given that validate our analysis.

Two-way relay (TWR) transmission provides high spectral efficiency when one-pair of two users exchange information via a single relay. However, in multiuser relay systems where multi-pair of users exchange messages, if the relay does not have sufficient degrees of freedom, the gain of TWR communication may vanish. Fortunately, signal alignment (SA) signaling can recover the spectral efficiency of TWR transmission in multiuser scenarios, which is originally proposed for multiple-input-multiple-output (MIMO) systems. In this paper we investigate signal alignment for multicarrier code division multiple access (MC-CDMA) TWR systems. Due to the difference in channel characteristics and degrees of freedom, the existing SA signalings designed for MIMO TWR systems do not always perform well in MC-CDMA TWR systems. By exploiting the unique features of both TWR systems and MC-CDMA channels, we propose a spectral-efficient SA signaling for MC-CDMA TWR systems, where each pair of users employ a maximal ratio transmitter of its counterpart to align their signals at the relay. We then analyze the spectral efficiency of the designed SA signaling, compare it with non-SA (NSA) signaling, and optimize the power allocation among the relay and users. It is shown from asymptotic analysis and simulation results that the proposed SA signaling can support more users and achieve higher spectral efficiency than NSA signaling.

In this paper we present a method to alleviate the performance degradation led by non-ideal channel reciprocity in TDD downlink base station (BS) cooperative transmission systems, which comes from imperfect antenna calibration among BSs. By exploiting the statistics of the ambiguity factors between uplink and downlink channels, a robust multiuser precoder is proposed aimed at maximizing the lower bound of the average signal-to-leakage-plus-noise ratio (SLNR). The precoder is able to adaptively control the cooperation level among the coordinated BSs according to the antenna calibration accuracy among BSs. Simulation results demonstrate an evident performance gain of the proposed robust precoder over the non-robust precoder.

In this paper, we design transceivers in a multi-user multi-antenna two-way relay system, where a single multi-antenna base station exchanges information with multiple users via a single multi-antenna relay station. We consider the half-duplex amplify-and-forward relay protocol. We aim to maximize the bidirectional sum rate under the constraint of no interference among different users. Suboptimal solutions to the problem that respectively maximizing the uplink and downlink rates are derived. We then introduce a threshold to balance the uplink and downlink rates so as to maximize the bidirectional sum rate. Simulation results show that the proposed scheme achieves considerably higher bidirectional sum rate than existing schemes.

In this paper, we consider the decentralized estimation over non-orthogonal multiple-access fading channels (MACs) with zero-mean channel coefficients in wireless sensor networks (WSNs). Using the type-based multiple-access (TBMA) with bandwidth extension transmission scheme, we introduce a low-complexity two-stage estimator and analyze its performance. It shows that the performance can be improved by allocating bandwidth among types, but the optimal bandwidth allocation scheme requires a priori information of the parameter being observed. We then introduce a broadcasting feedback method for the sensors to obtain the required information. This feedback scheme is more efficient and feasible than the channel state information (CSI) feedback over orthogonal MACs. Besides the estimation accuracy, we also evaluate the bandwidth efficiency of the proposed methods by introducing a metric as the bandwidth normalized performance gain in simulations. It shows that the proposed feedback and bandwidth allocation schemes outperform the TBMA with CSI feedback in non-orthogonal MACs and the optimal transmission scheme in orthogonal MACs at typical operating signal-to-noise ratio levels of the WSNs.

Type-based multiple-access (TBMA) is a bandwidth efficient transmission scheme for decentralized estimation in wireless sensor networks (WSNs) over non-orthogonal multiple-access channels, whereas its performance degrades severely in zero-mean fading channels. In this paper, we improve its performance in the fading channels by using more resources for transmitting each type, which is named as TBMA with bandwidth extension (TBMA-BE). This transmission scheme needs no feedback of channel state information as existing solutions, thus it is feasible for large scale WSNs. We then develop an approximate maximum likelihood (ML) estimator, and derive the Cramér-Rao lower bound to reveal how the performance and the bandwidth efficiency are related. It is shown from the simulations that the TBMA-BE transmission scheme with the approximate ML estimator performs fairly well in non-orthogonal multiple-access channels with Rayleigh fading, and is even superior to the orthogonal multiple-access protocol at low communication signal to noise ratio (SNR). It also shows that the TBMA-BE is bandwidth efficient for the typical scenarios of WSNs where the communication SNR is low, since the total bandwidth consumption is proportional to the SNR.

In this paper, we investigate channel allocation for cooperative relays in cognitive radio networks. Different from conventional cooperative relay channels, cognitive radio relay channels are actually a combination of three kinds of channels: direct, dual-hop, and relay channels, which belong to different spectrum bands and provide parallel end-to-end transmission. In order to maximize the achievable end-to-end throughput, we propose two channel allocation approaches with different complexities to assign all the channels cooperatively. Numerical results illustrate the performance improvement in different number of available channels. In particular, it has about 40% improvement in throughput when the average SNR is 15 dB and eight available channels are used.

Space-time focusing transmission in impulse-radio ultra-wideband (IR-UWB) systems resorts to the large number of resolvable paths to reduce the inter-pulse interference as well as the multiuser interference, and to simplify the receiver design. In this paper, we study the spatial user capacity of IR-UWB systems with space-time focusing transmission where the users are randomly distributed. We will derive the power distribution of the aggregate interference and investigate the collision probability between the desired focusing peak signal and interference signals. The closed-form expressions of the upper and lower bound of the outage probability and the spatial user capacity are obtained. Analysis results reveal the connections between the spatial user capacity and various system and channel parameters such as antenna gain, frame length, path loss factor, and multipath delay spread, which provide design guidelines for IR-UWB networks.

In a cognitive network where both primary and secondary systems are Orthogonal Frequency Division Multiplexing modulated, carrier frequency offset between the two systems is inevitable and may cause harmful interference. In this paper, we jointly optimize the transceiver for the secondary system considering the frequency offset between secondary transmitter (ST) and primary receiver (PR). We first derive unified interference constraints with different information of inference channels known at ST, and formulate the transceiver design problem based on minimum mean square criterion as a convex optimization problem. To reveal the structure of secondary transceiver, we derive closed-form pre- processors in two special cases. It is shown from the simulations that with the increase of frequency offset, the performance of primary system degrades evidently when its bandwidth is smaller than that of secondary system, whereas the performance of secondary system using the proposed transceiver improves due to frequency diversity.

This paper addresses joint power control and link scheduling problem in Spatial-reuse Time Division Multiple Access (STDMA) networks with consecutive transmission constraint, where multiple links should be scheduled in a number of consecutive time slots. By multi-link management with power control and scheduling, the network can provide lower transmission latency or support more users. In this paper, a new heuristic solution is proposed. Different from previous works, our method is based on the lagrangian dual theory and can be applied to more general scenarios. Moreover, it can be efficiently implemented in a polynomial time with the help of binary searching. Simulation results show that the method can reduce the transmission latency significantly.

In this contribution we investigate the spectral-efficiency of a two-hop cooperative network using multicarrier code-division multiple-access (MC-CDMA) transmission scheme. The two-hop network constitutes K source users transmitting signals to K destinations with the aid of N relays. Our focus is on the relay optimization, when assuming that the N relays cooperate or do not cooperate with each other. Specifically, in this contribution the egocentric-altruistic (E-A) optimization is introduced, which constitutes an E-optimization motivating to suppress the multiuser interference (MUI) of the source-relay channels and an A-optimization aiming to pre-mitigate the potential MUI of the relay-destination channels. Both the minimum mean-square error (MMSE) and zero-forcing (ZF) optimization criteria are considered. Furthermore, the spectral-efficiency performance of the two-hop MC-CDMA systems using the proposed E-A relay optimization is investigated by simulations, when assuming communications over frequency-selective fading channels.

This letter aims to design a low-complexity subcarrier-power allocation scheme to improve the communication reliability of various types of frequency-division multiple-access (FDMA) systems. Both uplink and downlink are considered. Specifically, a low-complexity worst subcarrier avoiding (WSA) subcarrier-allocation scheme is proposed, in order to avoid assigning users the subcarriers experiencing severe fading. After the subcarrier-allocation, channel-inversion assisted powerallocation is employed to assign the subcarriers the corresponding power. Our studies and simulation results show that the achievable error performance of the FDMA systems employing the proposed subcarrier-power allocation algorithm is independent of the multiplexing method. The proposed algorithm outperforms the existing subcarrier-power allocation algorithms that have a similar complexity as the proposed one.

When both primary and secondary systems are orthogonal frequency division multiplexing modulated and are non-cooperative, carrier frequency offset between the systems is inevitable to cause harmful interference. In this paper, we jointly optimize secondary transceivers assuming that the frequency offset between the secondary transmitter (ST) and the primary receiver (PR) and different channel information from the ST to the PR are known at the ST. We first derive unified interference constraints and obtain the secondary transceivers minimizing the mean square error through convex optimization techniques. We then derive closed-form transceivers for several special cases to reveal the impact of the frequency offset on the secondary transceivers. We show that when there is no frequency offset between the ST and the PR, the optimal processing at the ST is power allocation. Otherwise, both power allocation and precoding are necessary. The impact of the frequency offset on the performance of both systems increases as the interference constraints become tighter and the bandwidth of the primary system becomes smaller. When the proposed transceivers are used, the performance of the secondary system is robust to the frequency offset and the performance of the primary system degrades little due to the remanent frequency offset.

In this paper, we address the problem of user scheduling in downlink coordinated multi-point transmission (CoMP) systems, where multiple users are selected and then served with zero forcing beamformer simultaneously by several cooperative base stations (BSs). To reduce the enormous overhead led by obtaining full channel state information at the transmitter, a low-feedback user scheduling method called channel norm-based user scheduler (NUS), is proposed by exploiting the asymmetric channel feature of CoMP systems. Simulation results show that the channel norm provides sufficient information for user scheduling when each BS has one antenna, where the performance gap between the NUS and the greedy user selection (GUS) is negligible with respect to both the cell average throughput and the cell edge throughput. When each BS has multiple antennas, NUS is inferior to GUS, but still significantly outperforms the uncoordinated systems.

In this paper, we design the near optimal transmission codebook for decentralized estimation in wireless sensor networks with uniform quantizations and digital communications over orthogonal Rayleigh fading channels. We start from deriving the maximum likelihood estimator (MLE) for arbitrary transmission codebook with known and unknown channel state information (CSI) at the fusion center (FC). Because the MLE is not a convex problem in the system we considered, it is not trivial to obtain an analytical expression of either its mean square error or Cramer-Rao lower bound (CRLB). By analyzing the likelihood function, we convert the optimal transmission codebook design minimizing the CRLB to a two-stage optimization problem, where the problem in each stages is convex. It is shown that the estimation accuracy with our designed codebooks and MLE is superior to that with available transmission schemes. The proposed codebook suggests that the sensors should use orthogonal codes to transmit different observations for unknown CSI, while the optimal codebook for known CSI is not orthogonal.

Most of existing works on spectrum sensing detect primary transmitters while the purpose of spectrum sensing is to avoid interfering with primary receivers (PRs). Therefore, it is more important to detect PRs. In this paper, we propose a proactive spectrum sensing method that detects whether a PR is within the coverage or the interference range of a CR transmitter by exploiting the close-loop power control policy in primary systems. With the proposed scheme, the CR user may still access the spectrum band even though a primary signal is present as long as its transmission does not interfere with the PR. Simulation results show the advantages of the proposed method.

This contribution considers the joint transmitter-receiver optimization in generalized multicarrier code-division multiplexing (GMCCDM) systems. The joint transmitter-receiver optimization consists of an one-tap post-frequency-domain equalizer (post-FDE) and an one-tap pre-FDE. The one-tap post-FDE is optimized under the criterion of minimum mean-square error (MMSE), while the one-tap pre-FDE is optimized in order to achieve either the highest possible throughput or best possible error performance. Furthermore, an optimum spreading scheme is proposed for the GMC-CDM system, which is capable of achieving certain signal-to-noise ratio (SNR) gain in addition to the promised close-loop diversity gain. In this paper the performance of the GMC-CDM systems is investigated, when communicating over frequency-selective Rayleigh fading channels. It can be shown that our proposed pre-FDE and post-FDE algorithms are highly efficient, which are low-complexity and capable of enhancing significantly the error or throughput performance of the GMCCDM systems.

In this paper, we propose a relay-assisted transmission scheme in cognitive radio (CR) to exploit spatial spectrum holes, which are generated by relay techniques. The proposed scheme enables CR users to coexist with primary users at the same time in the same geographic area and spectrum band. Compared to conventional schemes, a higher spectrum efficiency is achieved by our method. We further analyze the successful communication probability and present numerical results to show advantages of our method.

Spectrum sensing finds spectrum opportunities for cognitive radio (CR) and enables CR users to work without harmful interference to primary users. Most of existing contributions on spectrum sensing detect whether a primary signal is present or absent. Since the ultimate goal of spectrum sensing is to avoid interfering with primary receivers (PRs), it is more efficient to detect PRs directly. In this paper, we propose a proactive spectrum sensing scheme to detect whether a PR is within the coverage or the interference range of a CR transmitter by exploiting the close-loop power control that has been widely used in wireless systems. With the proposed scheme, the CR user may still be able to access the licensed spectrum band even though a primary signal is detected as long as its transmission does not interfere with the PR. As a result, more spectrum opportunities can be exploited compared to conventional spectrum sensing methods.

Spectrum hole (SH) is defined as a spectrum band that can be utilized by unlicensed users, which is a basic resource for cognitive radio (CR) systems. Most of existing contributions detect SHs by sensing whether a primary signal is present or absent and then try to access them so that the CR and primary users use the spectrum band either at different time slots or in different geographic regions. In this paper, we propose a novel scheme with relays or directional relays for CR users to exploit new spectrum opportunity, called spatial SH. It can provide higher spectrum efficiency by coexistence of primary and CR users at the same region, time, and spectrum band. In particular, when the spectrum opportunity of a direct link from a CR transmitter to a CR receiver does not appear, our scheme may still establish the communication through indirect links, i.e., other CR users act as relay stations to assist the communication by using other spatial domains. Furthermore, we analyze the successful communication probabilities of CR users and demonstrate that the spectrum efficiency can be considerably improved by our scheme.

Many successive scheduling schemes associated with linear transmit beamforming have been proposed which can asymptotically achieve the sum rate of dirty paper coding (DPC) in Ltd. channels. In this paper, the performance of the successive scheduling in spatially correlated channels is studied. Then we propose a low complexity alternating user scheduling (AUS), which can be applied to both zero-forcing beamforming (ZFBF) and regularized ZFBF (R-ZFBF). The new scheduling algorithm can achieve a comparable sum rate with that of the exhaustive searching in both i.i.d. and spatially correlated channels with the same order of complexity as the successive scheduling.

In this paper, we propose a cognitive radio (CR) transmission scheme, which enables secondary users to coexist with primary users by exploiting spatial spectrum holes (SSHs) through directional antennas or antenna arrays with beamforming. To ensure reliable CR links and avoid interference to primary users, some CR users may act as relays . We investigate successful communication probability of CR users when this scheme is applied. We further demonstrate that the spectrum efficiency can be greatly improved by multiplexing CR links with directional transmission.

In this paper, we consider the power and admission control for ultra-wideband (UWB) cognitive radio networks, which are important to provide secondary users guaranteed quality of service while not causing severe interference to primary users. A power control algorithm to maximize the minimal data rate of all admitted secondary users is developed by solving a quasi-convex optimization problem. An admission control algorithm is also proposed to reject secondary users, which induce infeasible power control or low sum data rate, by a new criterion - the channel gain ratio. The channel gains used in the proposed algorithms are estimated by exploiting the ranging capability of UWB radios. The estimation error due to unpredicted channel shadowing is coped with the interference hole and the signal-to-interference-noise ratio margin. Finally, the performance of the presented power and admission control strategy is examined through simulations.

Design of equalizers and spread-spectrum modes or code allocation schemes is crucial for achieving good performance multi-code multiple access systems. In this paper, a symbol- level frequency domain equalizer and spread-spectrum mode are jointly designed to maximize the diversity gain with low complexity. Since the proposed transceiver scheme is independent of the orthogonal codes, it can be applied to direct sequence code division multiple access (DS-CDMA), multi-carrier CDMA (MC-CDMA) and orthogonal frequency division multiple access (OFDMA) systems. Simulation results are provided which validate the theoretical analysis.

Efficiently reducing complexity and capturing energy are two critical issues for implementing equalizers in a high-rate ultra-wideband communication link with severe intersymbol interference. In this letter, we propose a low-complexity equalization algorithm using iterative interference cancellation. Its complexity is comparable with the Rake receiver, but its performance can approach that of the matched filter bound. The proposed algorithm can accommodate various signaling schemes, including pulse amplitude/position modulation and M-ary bi-orthogonal keying.

Optimal and suboptimal estimators in wireless sensor networks with Rayleigh fading channels are studied in this paper. A maximum likelihood estimator is introduced to combat both the observation noise and communication errors led by channel fading. A feasible suboptimal estimator is presented to reduce the computational complexity. Compared with the traditional methods with different transmission coding schemes, it is shown by simulations that the proposed estimators can reduce the mean square error of the estimation especially for low communication SNR.

In this paper, we investigate the performance of four closed-loop multiple-input multiple-output (MIMO) schemes with channel state information (CSI) feedback delay and channel estimation error. These schemes are conventional maximal ratio transmission scheme (C-MRT), improved MRT (I-MRT), conventional transmit antenna selection with space-time block code scheme (C-TAS/STBQ) and improved TAS/STBC (I-TAS/STBC). Exact bit error rate (BER) expressions of C-MRT and C-TAS/STBC and approximate BER expressions of I-MRT and I-TAS/STBC for two specific scenarios are derived for binary phase shift keying (BPSK) modulation. The performance of the schemes with CSI feedback delay and channel estimation error are validated and compared with each other by numerical and simulation results.

The major challenges confronted in the timing acquisition for ultra-wideband (UWB) communication systems are enormous search space, extremely low signal power and the heavy multiple access interference (MAI) environments. Since the acquisition methods with the conventional preamble structure suffer severe performance degradation in the presence of interference, the authors present a novel preamble structure in this paper where the preambles for different users have unequal symbol durations. The proposed structure associated with any acquisition algorithm can improve the accuracy of timing estimation and shorten the acquisition time significantly for multi-user systems, which is demonstrated by both theoretical analyses and simulation results.

In ultra-wideband (UWB) systems, the ultra-wide bandwidth and abundant multipath components can provide great spreading gain and diversity gain, but will also induce complicated interference suppression algorithm because of the requirements of very high sampling rate and large number of filter taps. A reduced-order multi-user detector (R-MUD) is proposed in this paper which can significantly reduce the sampling rate thus the computational burden by down-sampling the received multipath signal at appropriate locations, where the energy of the desired user can be effectively captured and the interferences from other users can be dramatically suppressed. The superior performance of the R-MUD is justified by computer simulations.

Ultra-wideband (UWB) radio is an emerging wireless physical layer technology which is considered the candidate for high data rate transmission or medium/low data rate asynchronous code-division multiple-access (CDMA) ad hoc networks thanks to its huge process gain (PG). On the other hand, UWB systems suffer long acquisition time (AT) led by the large bandwidth which induces a significant overhead. Hence, it is very important to understand the impact of both the huge PG and the long AT on network performance for designing efficient UWB networks. In this paper, we study the throughput performance of a peer-to-peer asynchronous CDMA network supported by a full duplex UWB physical layer taking into account above two distinct UWB features. Numerical results show that fast acquisition, multi-packet reception and network cooperation are necessary to improve the throughput of UWB asynchronous CDMA ad hoc networks.

In this paper, three new decentralized estimators in which observation noise and channel fading are taken into account are introduced to improve the estimation accuracy of the wireless sensor networks. With known channel state information and the local statistics of observation noise, a minimum error probability detector and two MMSE estimators are designed to estimate the quantized bits of the parameter to be observed, then the final estimation of the parameter is reconstructed by the estimated value according to the given quantization rule. It is shown by simulations that the decentralized estimation methods can provide superior performance in low average received SNR.

In this paper, the performance of RAKE receiver on channel shortening in ultra-wideband (UWB) systems is studied by analyzing the impact of inter-symbol interference (ISI) on the bit error rate (BER) error floor in dense multipath channel environments. A closed form expression for BER with Nakagami channel model is developed. The equivalent channel length after RAKE receiver is derived by comparing the different error floors of UWB systems with all RAKE (ARAKE) or selective RAKE (SRAKE) with one tap receiver. Numerical results are shown to demonstrate the partial equalization ability of RAKE receiver. For high data rate or large delay spread, we find that the length of equivalent channel after ARAKE receiver approximately equals the length of multipath channel divided by the spreading gain, which will provide guideline for designing high data rate UWB receivers.

A multi-symbol detection (MSD) method based on the approximation of ML decoding for detecting the signal modulated by PPM in unknown UWB channels is investigated. The explicit expression of the achievable rates of PPM with MSD is derived, from which we study the relationship between the achievable rates, SNR and channel bandwidth. The numerical results show that multi-symbol detection significantly improves the achievable rates over conventional non-coherent detection. When channel bandwidth tends to infinity, as long as symbol number N keeps up with the resolvable multipath number L, the achievable rates tend to AWGN capacity.

In this paper, a multicarrier time division multiple access (MC-TDMA) technique is presented for downlink broadband wireless communications. It combats multipath interferences by OFDM modulations and distinguishes multiple users through different chip-level time slots. In order to gain insights on its performance and complexity, the relationship between this MC-TDMA scheme and a special MC-CDMA technique with maximum multipath diversity gain is emphatically investigated, which reveals that the former can be viewed as an equivalent but simplified implementation approach of the latter and furthermore, the former offers more flexibility in the system design and reconfiguration than the latter.

In this paper, a new MAC protocol supporting cooperative diversity named as CD-Maca is presented. By making use of the information carried by the handshaking frames of the Maca protocol, which has been employed in IEEE 802.11 standard, the CD-Maca protocol allows terminals in the network to transmit data frames cooperatively, resulting in more reliable retransmission and better performance. We model the CD-Maca protocol with Markov chain and derive the throughout of the protocols. It's shown by numerical results and simulations that the performance of the CD-Maca protocol depends on the packet error ratio (PER) of the handshaking frames, the PER of the cooperative transmission and the network topologies.

Exploiting the abundant freedom of UWB signals can increase the Euclidean distance among codewords which will lead to an enhancement of the communication quality. In this paper, a coded modulation UWB system combining PPM, PAM and convolutional coding is proposed, which can significantly increase the power efficiency without degrading the data rate. The performance of this coded modulation system primarily depends on the free Euclidean distance and the number of corresponding error bits induced by the error paths. The code generator with maximal minimum free distance and minimum error bits is found to optimize the system. In order to avoid the difficulty of channel estimation and accurate time synchronization in rich multipath environments, a joint multiple symbol detector and decoder for the coded modulation system is presented which is able to approximate the performance of uncoded PPM system in AWGN channel under viable computational burden. Simulation results obtained from Nakagami multipath channel agree with our analysis.

In this paper, a new architecture of the redundant residue number system (RRNS) based quasi-chaotic coding is proposed for the secure telecommunication systems and networks. In the proposed architecture, a number of modulo operations required by the existing designs are replaced by binary coding operations to simplify the design. Also, a moduli selection method and a residue-to-binary converter with error-correction capability are proposed to further improve the efficiency of the design specifically for FPGA implementation. The proposed architecture is implemented and tested using Matlab and Xilinx FPGA hardware. The results show that compared to the existing design, the proposed design requires only 80% of the hardware resources while maintaining the same speed. The power consumption is also reduced by 25%.

The auxiliary vector filter (AVF) and the multistage Weiner filter (MSWF) are two important categories of reduced-rank filters and have been widely applied to the adaptive signal processing domain. The relationship between AVF and MSWF has also drawn a lot of attention. It has been indicated that AVF is equivalent to MSWF due to the identical reduced-rank subspace in the literature. However, except for the same subspace, the structures and the corresponding parameters of AVF are considerably different to that of MSWF. In order to gain further insights on the equivalence between AVF and MSWF, a computation scheme of parameters and a nested structure are presented for AVF in this paper. According to the identical nested structure, it can be proven that AVF has the same parameters as MSWF, which are calculated in different ways. As a consequence, it can be claimed that AVF and MSWF are two alternative computation schemes for the same reduced-rank filter.

The iterative algorithm for minimum variance distortionless response filter based on auxiliary-vector (IMVDR-AV) is generalized in this paper from a different perspective. By extending the optimization criteria on filter design, it is generalized to an iterative algorithm of linear constrained minimum power filter (ILCMP-AV). Starting from this algorithm, we present an iterative algorithm of LCMP filter based on local optimization criterion (ILCMP-LOC) which converges rapidly by further generalizing the conditional optimization criterion on weighting coefficient computation. For any positive definite input autocorrelation matrix and any linear constraint, the ILCMP-AV algorithm recursively generates a sequence of auxiliary vectors by maximizing the magnitude cross correlation under some constraint conditions and its corresponding weighting coefficients by minimizing the filter output variance. Theoretical analysis illustrates that the combination of the updated filters and the weighted auxiliary vectors forms a sequence of filters that converges to the LCMP solution.

A new demodulation algorithm is proposed for orthogonal frequency division multiplexing (OFDM) systems without guard time (GT) to mitigate inter-symbol interference (ISI) and inter-carrier interference (ICI) introduced by multipath channels. The proposed algorithm is analogous to the multiuser detection (MUD) technique, successive interference cancellation based on decision feedback (SIC-DF), for synchronous DS-CDMA systems, hence it is named SIC-DF multicarrier detection (MCD) algorithm. Theoretical analysis and simulation results demonstrate that SIC-DF MCD can efficiently combat ISI and ICI for OFDM systems without GT by using joint multi-carrier time domain equalization instead of one-tap frequency domain equalization at the cost of reasonable complexity increase, and moreover, with SIC-DF MCD algorithm, an OFDM system without GT can offer lower uncoded bit error rate than the conventional OFDM system with GT.

An improved blind adaptive multiuser detector based on subspace estimation is presented for code division multiple access (CDMA) systems. Several adaptive multiuser detectors based on subspace estimation have been proposed, which can offer substantial performance gains over previous adaptive detectors. However their performance always suffers from the subspace rank mismatch, low SNR and insufficient data. The main reason is that the estimated signal subspace may lose a lot of component of the desired signal when the detectors are adaptively implemented under these conditions. Motivated by this, an improved detector making use of the spreading sequence of the desired user to protect the desired signal from loss is proposed with little complexity increase. It can be more robust under the mentioned conditions. Numerical results are provided to support our claims.

An improved signal subspace estimation method is proposed and applied to blind multiuser detection. Firstly, a rough signal subspace is estimated from the received signal by direct eigendecomposition or subspace tracking. Then, the estimated signal subspace is modified with the desired user's signature waveform that is generally known. The new multiuser detector based on the improved signal subspace can offer substantial performance improvement over recently proposed multiuser detectors based on signal subspace estimation with a little attendant increase in computational complexity. Moreover, the estimated dimension of the signal subspace can be significantly reduced with little performance reduction when there are some interference signals weaker than the desired signal. Numerical simulation results are provided to support our claims.

Previously, several detectors based on auxiliary vector (AV) filters were presented to suppress multi-access interference (MAI) in direct-sequence/code-division-multiple-access (DS-CDMA) systems. Knowing the signature waveform of the desired user, these AV-based detectors are a class of promising candidates for MAI suppression due to their superior performance and low computational complexity. However, it is hard to obtain the desired user's signature waveform, which is the convolution of the user's spreading sequence and the channel impulse response, unless the channel vector is known or unless training signals are sent. In this letter, based on both of the merits of AV detectors and RAKE receivers, an AV-based RAKE-like receiver is proposed for DS-CDMA communication systems in multipath channels. Simulation results indicate that the performance of the new receiver is very close to that of the AV detectors, which know the exact signature waveform of the desired user.