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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 the task of tile prediction or on the tasks of computing and communications, overlooking the facts that these successively executed tasks have to share the same duration to avoid the latency and 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 to maximize the QoE given arbitrary predictor and configured resources. We obtain the global optimal solution with closed-form expression by decomposing the formulated problem equivalently into two subproblems. With the optimized durations, we find a resource-limited region where the QoE can be improved effectively by configuring more resources, and a prediction-limited region where the QoE can be improved with a better predictor. Simulation results using three existing tile predictors with a real dataset demonstrate the gain of the joint optimization over the non-optimized counterparts., Comment: 32 pages, one column, 7 figures, submitted to IEEE for possible publication

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.

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.

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 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 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., Accepted by IEEE Transactions on Communications

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 cancelation (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.

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.

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 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 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.

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.

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.

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.

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.

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.

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, 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.

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 $\mathcal {O}({\log _{2}M}/{M})$ for the system without PC, and in $\mathcal {O}({1}/{M})$ for the system with PC. The EE-maximal transmit power scales up with $M$ in $\mathcal {O}(\sqrt {M/{\ln M}})$ until reaching the maximal transmit power for the system without PC, and in $\mathcal {O}(1)$ for the system with PC. The analytical results are validated by simulations under a more realistic 3D channel model.