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.

This paper studies the uplink and downlink power allocation for interactive augmented reality (AR) services, where the live video captured by an AR device is uploaded to the network edge, and then the augmented video is subsequently downloaded. By modeling the AR transmission process as a tandem queuing system, we derive an upper bound for the probabilistic quality of service (QoS) requirement concerning end-to-end latency and reliability. The resource allocation under the QoS requirement results in a functional optimization problem. To address it, we design a deep neural network to learn the power allocation policy, leveraging the optimal power allocation structure to enhance learning performance. Simulation results demonstrate that the proposed method effectively reduces transmit power while meeting the QoS requirement.

Recent studies have demonstrated the effectiveness of graph neural networks in precoder learning by exploiting the permutation equivariance property of precoding policies. In this paper, we propose a novel precoder learning method for weighted sum rate maximization (WSRM). Our method leverages two key properties: both the unitary and permutation equivariance properties between precoders and channels, and the permutation equivariance property between precoders and user priority factors. Simulation results demonstrate that the proposed method significantly outperforms the baseline learning methods in terms of both learning and generalization performance while maintaining low training and inference complexity.

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.

Integrated sensing and communication (ISAC) has emerged as a promising technology for the sixth-generation (6G) mobile communication system. This paper investigates the optimization of power allocation for an orthogonal frequency division multiplexing (OFDM)-based ISAC system, aimed at minimizing the symbol error rate (SER) for communication and the Cramér-Rao lower bounds (CRLBs) for joint delay and Doppler estimation. We first reveal the conflicting requirements for power allocation by communication and sensing objectives by finding the optimal power allocation for each individual objective. Then, we resort to deep learning to learn the power allocation that minimizes the weighted sum of the three objective functions. Simulation results validate the theoretical analysis and demonstrate the gain of the proposed scheme in reducing SER and CRLBs over baseline schemes.

Integrated sensing and communication holds immense potential in the sixth generation (6 G) mobile communication system. Hand gesture recognition, in particular, represents a critical application. While research on single-user gesture recognition has been extensive, the impact of unpredictable moving interferers remains unexplored, leading to significant inter-user interference. This paper proposes a disturbance-avoidant wireless gesture recognition scheme based on the fifth generation (5G) New Radio (NR) cellular signal. By capturing the user’s location as a channel feature in the spatial-delay domain, we can not only detect gestures but also minimize interference through spatial-delay domain processings. To validate the effectiveness of this approach, a prototype system is developed, and gesture recognition tests using NR signals have been conducted. The results demonstrate that the proposed scheme can effectively mitigate interference and achieve remarkable recognition accuracies, even under highly disruptive conditions.

Wireless sensing through opportunistic signals offers distinct advantages in diverse fields. Compared with 4G long term evolution (LTE), 5G new radio (NR) signal possesses larger bandwidth and more antenna ports, thus is a better candidate for wireless sensing. However, the commercial NR signals have not been widely exploited due to the absence of off-the-shelf modem for channel state information (CSI) extraction and intricate high-layer signaling interaction procedures to obtain the reference signal parameters. For this purpose, we present a channel feature based blind detection method that identifies complete CSI reference signal (CSI-RS) parameters solely from physical layer, which enables opportunistic exploitation of commercial NR signal and full-bandwidth multi-port CSI acquisition. The simulation results demonstrate the superiority of the proposed CSI-RS blind detection method in frequency-selective channel. Furthermore, a prototype system is built to showcase the enhanced sensing ability of commercial NR signal.

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.

Employing graph neural networks (GNNs) for learning the multiuser multi-input multi-output precoder has gained significant attention recently. By modeling the precoder optimization problem in a graph format, GNN can effectively capture the representation of the precoder by leveraging the information aggregated and propagated across the graph. In this paper, we strive to design the information aggregation mechanism of GNN. By analyzing the behavior of the numerical gradient descent algorithm for precoder optimization, we identify the relevant information and the appropriate form for aggregation, enabling us to develop new update equations for GNNs. Simulation results demonstrate the advantages of the proposed GNNs in learning and generalization performance.

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.

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.

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.

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.

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.

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.

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.

Existing wireless mobile networks configure the uplink (UL) and downlink (DL) resources based on the orthogonal duplex principle, which has significantly evolved from the static frequency/time division duplex (FDD/TDD) to the semi-dynamic TDD in 4G and the fully-dynamic TDD in 5G. Fueled by the successful realizations of full duplex (FD) transmission, non-orthogonal duplex (NOD) emerges as an attractive candidate technique for future networks to improve the bidirectional throughput. In this paper, we investigate the performance limit of the NOD scheme, which imports the FD mode on the basis of fully-dynamic TDD. We first focus on a single-cell scenario by assuming no cooperation among cells, and strive to characterize the bidirectional capacity region by finding the capacity-optimal transmission mode scheduling policy. We obtain the optimal policies for the systems with and without modulation and coding scheme (MCS), respectively, where the policies are based on the instantaneous channel gains and the distribution of channels. We proceed to develop a scheduling policy that only relies on the instantaneous channel and queue information, and extend it to the multicell multiuser scenario with coordinated scheduling. Numerical and simulation results demonstrate the great potential of the NOD scheme in increasing bidirectional capacity and reducing the queuing delay.

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.

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.

The high training cost of the massive multiple-input multiple-output (MIMO) systems motivates the use of hybrid digital/analog (HDA) beamforming structures. This paper considers the joint design of analog beamformers when both link ends of a millimeter (mm)-wave massive MIMO system are equipped with such HDA structures. We aim to maximize the multi-user MIMO net average throughput of the downlink in a frequency division duplex system. To achieve this, we develop an optimization framework, namely, user-centric virtual sectorization (UCVS), to explore the tradeoff of training overhead, beamforming gain, and spatial multiplexing gain. In the UCVS, both the channel-statistics-based analog beamforming design and a non-orthogonal downlink training scheme are investigated to reduce the necessary cost of instantaneous channel acquisition. By maximizing an approximate net average throughput, we devise efficient algorithms to realize the suboptimal UCVS. With generic mm-wave channel models, we demonstrate by simulations that our proposed scheme outperforms the state-of-the-art methods in various typical scenarios of mm-wave communications.

Considering the design of two-stage beamformers for the downlink of multi-user massive multiple-input multiple-output systems in frequency division duplexing mode, this paper investigates the case where both the link ends are equipped with hybrid digital/analog beamforming structures. A virtual sectorization is realized by channel-statistics-based user grouping and analog beamforming, where the user equipment only needs to feedback its intra-group effective channel, and the overall cost of channel state information (CSI) acquisition is significantly reduced. Under the Kronecker channel model assumption, we first show that the strongest eigenbeams of the receive correlation matrix form the optimal analog combiner to maximize the intra-group signal to inter-group interference plus noise ratio. Then, with the partial knowledge of instantaneous CSI, we jointly optimize the digital precoder and combiner by maximizing a lower bound of the conditional average net sum rate. Simulations over the propagation channels obtained from geometric-based stochastic models, ray tracing results, and measured outdoor channels, demonstrate that our proposed beamforming strategy outperforms the state-of-the-art methods.

Considering the joint design of analog beamformers when both link ends of a millimeter (mm)-wave massive multiple input multiple-output (MIMO) system are equipped with hybrid digital/analog (HDA) structures, we aim to maximize the multi-user (MU) MIMO net average throughput of the downlink in a Frequency Division Duplex (FDD) system. To achieve this, we develop an optimization framework, namely user-centric virtual sectorization (UCVS), which explores the tradeoff of training overhead, beamforming gain, and spatial multiplexing gain. In the UCVS, both the channel-statistics based analog beamforming design and a non-orthogonal downlink training scheme are investigated to reduce the necessary cost of instantaneous channel acquisition. By maximizing an approximate net average throughput, we devise efficient algorithms to realize the suboptimal UCVS. With generic mm-wave channel models, we demonstrate by simulations that our proposed scheme outperforms state-of the-art methods in various scenarios typical for mm-wave communications.

In this paper, we consider the design of analogue beamformers for the downlink of multi-user massive multiple-input-multiple-output (MIMO) systems. We specifically investigate systems where both link ends are equipped with hybrid digital/analog (HDA) beamforming structures, where the analog beamformers are adapted based on second order channel statistics, reducing the training overhead as well as hardware effort. We present a framework for the optimization of such beamformers operating in mm-wave channels, exploiting the directional characteristics and sparse nature of such channels. We develop an approximate upper bound of the ergodic sum capacity, based on which efficient beamforming algorithms are devised. Simulation results show significant performance improvements of the proposed algorithms compared to state-of-the-art algorithms.

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 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 consider the design of analog beamformers for the downlink of multi-user massive multiple-input-multiple-output (MIMO) systems. We specifically investigate systems, where both link ends are equipped with hybrid digital/analog beamforming structures, where the analog beamformers are adapted based on second order channel statistics, reducing the training overhead as well as hardware effort. We present a framework for the optimization of such beamformers operating in mm-wave channels, exploiting the directional characteristics and sparse nature of such channels. We develop an approximate upper bound of the ergodic sum capacity, based on which efficient beamforming algorithms are devised. Simulation results show significant performance improvements of the proposed algorithms compared with the state-of-the-art algorithms.

In this paper, we consider the design of two-stage beamformers for the downlink of multi-user frequency-division-duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. We consider the case that both link ends are equipped with hybrid analog/digital (HDA) beamforming structures. With analog beamforming and user grouping based on the second-order channel statistics, the user equipment (UE) only needs to feed back its intra-group effective channel. We first show that the strongest eigenbeams of the receive correlation matrix form the optimal analog combiner under the Kronecker channel model assumption. Then, with limited instantaneous channel state information, we jointly optimize the digital precoder and combiner for conditional average net sum-rate maximization by maximizing its lower bound. To initialize our algorithm efficiently, we present a digital precoder design to maximize the conditional average signal-to-leakage-plus-noise ratio (SLNR). Simulation results show significant performance improvements compared to state-of-the-art algorithms.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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 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.