Secure ranging refers to the capability of upper-bounding the actual physical distance between two devices with reliability. This is essential in a variety of applications, including to unlock physical systems. In this work, we will look at secure ranging in the context of ultra-wideband impulse radio (UWB-IR) as specified in IEEE 802.15.4z (a.k.a. 4z). In particular, an encrypted waveform, i.e. the scrambled timestamp sequence (STS), is defined in the high rate pulse repetition frequency (HRP) mode of operation in 4z for secure ranging. This work demonstrates the security analysis of 4z HRP when implemented with an adequate receiver design and shows the STS waveform can enable secure ranging. We first review the STS receivers adopted in previous studies and analyze their security vulnerabilities. Then we present a reference STS receiver and prove that secure ranging can be achieved by employing the STS waveform in 4z HRP. The performance bounds of the reference secure STS receiver are also characterized. Numerical experiments corroborate the analyses and demonstrate the security of the reference STS receiver.

Hybrid beamforming has been widely considered in millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system, which can greatly reduce power consumption and hardware cost of data paths. Compared to the fully-connected hybrid beamforming architecture, the sub-connected architecture is more practical for its reduced complexity. However, optimal precoding design for the sub-connected architecture is not straightforward due to the specific block-diagonal structure of analog phase shifter network. Algorithms on fully-digital or fully-connected hybrid beamforming architecture cannot be directly applied to sub-connected architecture. Meanwhile, most existing precoding algorithms in such case can only solve the approximate problem, which results in significant performance loss. In this paper, we study the sum rate maximization problem in the sub-connected architecture. We first relax the objective function and derive a relaxed upper bound of the original problem. Then we propose an algorithm to solve the original problem with a local-optimal solution. Simulation results show that the proposed local-optimal algorithm outperforms the baseline algorithms with better sum rate and energy efficiency performance. Besides, the proposed algorithm also converges quickly and is robust.

Communication bottleneck and statistical heterogeneity are two critical challenges of federated learning (FL) over wireless networks. To tackle both challenges, in this paper we propose an over-the-air computation (AirComp) assisted hierarchical personalized FL (HPFL) framework, where a device-edge-cloud based three-tier network architecture is adopted to simultaneously learn a global model and multiple personalized local models. We analyze the convergence of the AirComp-assisted HPFL framework and formulate an optimization problem to minimize the transmission distortion, which is an essential component of the convergence upper bound. An efficient algorithm is subsequently developed to optimize the transceiver design by leveraging successive convex approximation and Lagrangian duality. We conduct extensive simulations to demonstrate that our developed algorithm achieves a near-optimal performance and a much greater test accuracy than the baseline algorithms.

Massive multiple-input multiple-output (MIMO) is a key technology in next generation wireless communication. However, the increasing number of radio frequency (RF) chains results in higher cost and power consumption. Given that hundreds or even thousands of transmit antennas are equipped at the base station (BS), low resolution digital-to-analog converters (DACs) are preferred to reduce the power consumption on both DACs and power amplifiers (PAs). Currently, there have been some studies about the application of low-resolution DACs for single-carrier systems. For multi-carrier systems, this problem hasn’t been fully investigated. This paper aims to design a 1-bit downlink precoding algorithm for massive multi-user MIMO orthogonal frequency division multiplexing (OFDM) systems. A nonlinear precoding algorithm is proposed, which can address the non-convex optimization problem with discrete output constraint and guarantee convergence. Meanwhile, the proposed algorithm factors in the different path-losses experienced by different users. Furthermore, some approximation schemes can be applied to bring down the computational complexity of the proposed algorithm further. Simulation results illustrate that our algorithm performs the best among other nonlinear precoding methods in OFDM systems.

Implementation cost and power consumption are two important considerations in large-scale multi-antenna systems where the number of individual radio-frequency (RF) chains may be significantly larger than before. In this work, we propose to deploy a single low-noise amplifier (LNA) on the uplink multiple-input-multiple-output (MIMO) receiver to cover all antennas. This architecture, although favorable from the perspective of cost and power consumption, introduces challenges in the LNA gain control and user transmit power control. We formulate an energy efficiency maximization problem under practical system constraints, and prove that it is a constrained quasiconcave optimization problem. An efficient algorithm, Bisection – Gradient Assisted Interior Point (B-GAIP), is proposed to solve this optimization problem. The optimality and complexity of B-GAIP are analyzed, and further corroborated via numerical simulations. In particular, the performance loss due to using a shared LNA as opposed to separate LNAs in each RF chain, when using B-GAIP to determine the LNA gain and user transmit power, is very small in both centralized and distributed MIMO systems.

In this paper, we propose to exploit the interconnection in the Internet of Vehicles (IoV) to realize efficient traffic network control, which is indispensable in building intelligent transportation systems (ITS). In addition to control the traffic signals as in conventional traffic network control schemes, we propose to control the detouring behavior of the connected vehicles as well, with an objective to further enhance the traffic efficiency. Specifically, we formulate the joint traffic signal and connected vehicle control problem as a reinforcement learning (RL) problem, the action and state spaces of which are specifically designed to take into account the connected vehicles. To characterize the detouring behavior of the connected vehicles while keeping the decision process simple, we introduce a new concept termed as detouring ratio, which is defined as the fraction of connected vehicles that detour. Moreover, we also design an effective rewarding mechanism that takes into account the impact of the detouring on the network traffic efficiency. By utilizing tools from deep RL, we put forward an efficient algorithm to jointly control the traffic signals and the connected vehicles. Numerical results demonstrate the validity of our proposed models and show that the proposed joint control algorithm can significantly enhance the network traffic efficiency in terms of the travel time and the waiting time.

Traffic signal control for a large-scale traffic network is one challenging problem in intelligent transportation systems (ITS). High communication overheads are typically required to achieve the optimal control of the traffic signals in multiple road intersections. In this paper, in order to avoid these communication overheads among spatially distributed intersections, we propose to recover the global traffic state at each intersection in a real-time fashion by only utilizing the traffic state observed at the local intersection. Specifically, a generative adversarial network (GAN) based traffic information recovery method is presented for each intersection controller to recover the global traffic state. We also exploit a few statistics from other intersections during the training of the proposed GAN to improve the traffic state recovery accuracy. Comprehensive numerical results demonstrate the effectiveness of the proposed scheme in recovering the global traffic state.

High data throughput is desired in the wireless communication system design. Rate adaptation is an efficient way to update the data rate in the dynamic wireless environment. Conventional rate adaptation algorithms rely on the feedback of acknowledgment/negative acknowledgment (ACK/NACK) messages or signal to noise ratio (SNR). Existing rate adaptation algorithms can not achieve satisfactory transmission rates in time-varying environments. In this paper, we model the rate selection problem as a multi-armed bandit (MAB) problem and propose an online learning rate adaptation algorithm that learns the channel status from both RSSI and ACK/NACK signals. Compared with existing rate adaptation algorithms, the proposed algorithm can adapt to the time-varying channel better and achieve near-optimal transmission rate performance.

Fog computing is a promising solution to enable delay-sensitive applications in the Internet of Things (IoT). In this article, based on the simultaneous wireless information and power transfer (SWIPT) technology, we investigate the joint offloading of tasks and energy (JOTE) in fog-enabled IoT networks. Specifically, the task node is allowed to offload energy and tasks to multiple neighboring helper nodes in a time-division multiple access (TDMA) manner. When there are no task queues in the nodes, the offloading decision for each task is independent. We first find the offloading strategy to minimize the task execution delay as well as the energy consumption for a specific task and then, analyze the condition under which the JOTE is beneficial. We show that it becomes more and more desirable to offload both the tasks and the energy from the task node as the number of helper nodes gets large. When there are task queues in the nodes, the offloading decision for each task becomes temporally correlated. We then characterize the optimal strategies to offload the tasks and energy jointly over multiple time slots. An online offloading policy based on the Lyapunov optimization is then proposed to minimize the time average expected delay while stabilizing the system operation. Comprehensive numerical results corroborate our theoretical results and demonstrate the superior performance of the proposed JOTE algorithms.

In this article, we propose to extract the angle-delay-Doppler power spectrum (ADD-PS) from the acquired uplink (UL) channel states in massive multiple-input-multiple-output systems. Meanwhile, the corresponding power leakages due to finite angle and Doppler resolutions are analyzed in the case of noisy channel state information (CSI) observations at the base station (BS). It is demonstrated that the number of antennasMand the number of observed reference symbolsTare complementary in the sense that the amount of power leakages in the ADD-PS will diminish as long as the product ofMandTincreases. Potential applications of the extracted 3-D channel power spectrum in the industrial Internet of Things (IoT) are also discussed. In particular, the extracted 3-D ADD-PS can be utilized as a powerful fingerprint to facilitate localization in industrial IoT. Thanks to the additional Doppler dimension, it is further demonstrated that the extracted 3-D ADD-PS can also enable the recognition of user moving behavior. We also verify all our findings by carrying out extensive numerical experiments.

Fog computing (FC) has the potential to process computation-intensive tasks in Industrial Internet of Things (IIoT) systems. In parallel with the development of FC, non-orthogonal multiple access (NOMA) has been recognized as a promising technique to significantly improve the spectrum efficiency. In this paper, a NOMA-based FC framework for IIoT systems is considered, where multiple task nodes offload their tasks via NOMA to multiple nearby helper nodes for execution. We formulate a joint task scheduling and subcarrier allocation problem, with an objective to minimize the total cost in terms of the delay and energy consumption, while taking into account the practical communication and computation constraints. Note that the task scheduling includes task, computation resource, and power allocations. Since the task and subcarrier allocations involve binary variables, it is challenging to obtain an optimal solution for such a combinatorial problem. To this end, we solve the task scheduling and subcarrier allocation problem in an online learning fashion. During the online learning process, we propose an iterative algorithm to jointly optimize the subcarrier allocation and task scheduling in each time episode. Simulation results show that the proposed scheme can significantly reduce the sum cost compared to the baseline schemes.

Computation offloading is a promising technology in mobile edge computing (MEC) systems. In this paper, we take into account the system dynamics and the user mobility and formulate the mobile computation offloading as a stochastic optimal control problem. On the one hand, when the system information is fully known, we derive the optimal offloading policy. On the other hand, in the case of limited system information, we design a Q-learning algorithm which also gives optimal system performance yet with a slower converge rate. To speed up the convergence and deal with a more complex system, we further develop one more algorithm based on the deep-Q-network (DQN). Simulation results show our proposed DQN-based algorithm indeed converges at a much faster rate.

Fog computing (FC) has the potential to enable computation-intensive applications for the next generation wireless networks. In parallel with the development of FC, nonorthogonal multiple access (NOMA) has been recognized as a promising solution to improve the spectrum efficiency. In this paper, a NOMA-based FC system is considered, where multiple task nodes perform task scheduling via NOMA to a helper node, the helper node with abundant computation resource is required to compute the computation task from the task nodes. We formulate a joint task scheduling, computational resource allocation, and power allocation problem with an objective to minimize the sum cost (i.e., delay and energy consumptions for all task nodes) realizing energy-delay tradeoff. It is challenging to obtain an optimal policy for such a combinatorial optimization problem. To this end, we propose an online learning-based optimization framework to tackle this problem. Simulation results show that the proposed scheme significantly reduces the sum cost compared to the baselines.

This paper investigates the price-based task offloading in fog-enabled networks. Specifically, we consider a fog-enabled network with one task node (TN) and multiple helper nodes (HNs) where the TN and each HN are assumed to be M/M/1 queuing systems. Considering that the tasks are not processed for free at the HNs, we address the optimal task offloading strategy for the TN and optimal pricing scheme for the HNs, respectively. In particular, assuming the equal quality of service (EQoS) at the HNs, a low complexity algorithm is proposed to obtain the optimal task offloading strategy for the TN when the pricing scheme for the HNs is given. Meanwhile, by taking into account the optimal task offloading strategy for the TN, we derive a necessary condition that the optimal pricing scheme for the HNs must satisfy. Furthermore, a low complexity algorithm is proposed to obtain one near optimal pricing scheme for the HNs. Numerical results demonstrate the advantage of the proposed algorithms.

Dense heterogeneous network (HetNet) has become one of the key architectural features in the 5G communication network. In this paper, we aim at solving the frequent handover (FHO) problem in dense HetNets. We first present an unified framework suitable for various optimization objectives in wireless communication networks. A novel bandit algorithm is then proposed to obtain better results compared with the existing works. The corresponding bounds for the regret and the number of handovers are rigorously proved accordingly. The results are further generalized by taking into account the imperfect feedbacks. Extensive performance comparisons with different bandit algorithms are provided in order to highlight the advantages of the proposed algorithm and its potential for pursuing a general solution.

The large-scale antenna array is one key technology for the next generation wireless communications. Both spectral-efficiency and energy-efficiency can be improved significantly with appropriate beamforming which focuses signals on the target receivers in a large-scale antenna system (LSAS). However, as the formed beams become narrower, inaccurate channel state information (CSI) can lead to severe system performance degradation. The transmission rate selection is also important to enable full system capacity. Therefore, it is worth investigating the joint rate and beamwidth adaptation for data transmission in the presence of CSI uncertainties. In this paper, the dynamic selection of the transmission rate and the beamwidth is first modeled as a stochastic multi-armed bandit (MAB) problem with constraints. We then propose an iterative algorithm based on the upper confidence bound to adapt the rate and beamwidth jointly to get high throughput while providing long-term QoS guarantees. Numerical results demonstrate the benefits of taking beamwidth adaptation into account and illustrate the superior performance of our proposed algorithm.

Fog computing is emerging as a promising paradigm to perform distributed, low-latency computation. Efficient computation peer offloading is critical to fully utilize the computational resources in fog networks. In this paper, we consider computation peer offloading problem in a fog network with time-varying stochastic time of arrival tasks and channel conditions. Such time-varying conditions are not available to all fog nodes. In order to minimize the latency of accomplishing arrival tasks, we propose an online algorithm based on combinatorial upper confidence bounds algorithm with two uncertain variables under the non-stationary bandit model. The proposed computation offloading policy is optimized based on historical feedback. The performance of the proposed scheme is validated through numerical simulations.

Fog computing is considered to be a promising solution to enable latency-critical applications in Internet of Things (IoT). In this paper, we consider a fog-enabled IoT network where the task node can offload both the tasks and the energy to the helper nodes by applying the simultaneous wireless information and power transfer (SWIPT) technology. The task node first offloads energy to the helper nodes and then offloads the tasks to the helper nodes according to a time division multiple access (TDMA) protocol and an optimized ordering. In order to jointly minimize the task execution delay and the energy consumption, we formulate a combinatorial optimization problem. A low-complexity algorithm is then proposed to solve it based on the decomposed sub-problems. Additionally, we characterize the sufficient condition under which the joint task and energy offloading becomes beneficial. We also derive the offloading probability when taking into account the random fading channels. We further show that it is always desirable to offload both the task and the energy from the task node when the number of helper nodes goes to infinity. Numerical results are provided to validate our proposed algorithm and theoretical results.

In a time-division duplex (TDD) massive multiple-input multiple-output (MIMO) system, the number of available orthogonal pilot sequences in each cell is limited. In this paper, by assuming Gauss-Markov fading channels, we consider a massive MIMO system where there are less available orthogonal pilots than the number of users. In order to optimize the long-term performance in estimating the uplink (UL) channels, the base station (BS) judiciously decides the allocation of the available pilots to different users in each training phase. The pilot allocation problem is a partially observable Markov decision process with an exploitation-exploration tradeoff that is difficult to analyze. We thus investigate the problem in the framework of restless multi-armed bandits (RMAB) problems and carry out an indexability analysis for the problem. Furthermore, we solve this problem by using the Whittle's index policy with a low complexity. Numerical results demonstrate the superiority of the Whittle's index policy.

In wireless sensor networks, sensor nodes have limited energy budget, in general. Energy efficiency is a critical issue which is directly related to the network lifetime. On the other hand, stringent latency requirements are enforced in some applications. To save energy, ordering transmissions is an effective approach in which sensor nodes transmit more informative data to the fusion center earlier. Ordering, however, does not perform well in latency-sensitive scenarios. In this paper, we propose a distributed method based on the framework of ordered transmissions. The proposed algorithm is illustrated in the discretized estimation problem with latency constraint. In our proposed method, each sensor node has specific time slots to transmit data, and can determine its transmission order independently. The proposed algorithm can greatly reduce latency without loss of estimation accuracy, while the increased number of transmissions is negligible. Simulation results validate its effectiveness.

In this paper, we consider a computation offloading problem in a fog-enabled Internet of Things (IoT) network consisting of helper fog nodes and task nodes with energy harvesting capabilities. Considering the unpredictable energy supply and the low conversion efficiency in the case of energy harvesting, offloading scheduling plays a critical role to maintain the quality of experience (QoE) for computation intensive applications. Our goal is to find a scheduling policy to minimize the task abandoning ratio subject to the constraints on the energy causality and the queue stability. We propose an asymptotically optimal algorithm to solve this stochastic programming problem. In particular, the task admission, the local execution, and the offloading decisions are jointly optimized. Theoretical analyses are further presented to establish the performance guarantees of our proposed algorithm. Meanwhile, numerical simulations corroborate the optimality of the proposed policy.

Fog computing is a promising architectural to alleviate increasingly intensive transmission over the network. In addition to the data transmission capability, a fog node (FN) also has spare resources of data storage and computing. In this paper, we study the computation offloading scenario that takes advantage of fog architecture and utilizes the FN resources. A social welfare maximization problem is formulated to distribute the data among FNs based on the trade-off between the considered computational cost and communication cost. A distributed adaptation algorithm is developed based on a Jacobi-Proximal alternating direction method of multipliers (ADMM) algorithm. The computational burden for solving the optimization problem is fully distributed to FNs, software defined network (SDN) controller, where local variables of FNs are updated in parallel. Performance of the proposed algorithm is validated with simulation results.

Task offloading is a promising technology to exploit the available computational resources in spatially distributed fog nodes efficiently in the era of fog computing. In this paper, we look for an online task offloading strategy to minimize the long-term cost, which factors in the latency, the energy consumption, and the switching cost. To this end, we formulate a stochastic programming problem and the expectations of the system parameters are allowed to change abruptly at unknown time instants. Meanwhile, we consider the fact that the queried nodes can only feed back the processing results after finishing the tasks. Then we put forth an effective bandit learning algorithm, i.e., the BLOT, to solve this challenging stochastic programming under the non-stationary bandit model. We also demonstrate that our proposed BLOT algorithm is asymptotically optimal in a non-stationary fog-enabled network. Numerical experiments further verify the superb performance of BLOT.

In-band relay nodes (RNs) can be utilized to enhance the coverage of heterogeneous networks (HetNets) in a cost effective way. However, the in-band RNs also consume the limited spectrum resources. Appropriate spectrum resource management/cooperation is necessary to ensure the balanced resource usages between the macro base stations (BSs) and the RNs. In this paper, we study the sparse spectrum reuse strategy in a HetNet with in-band RNs to maximize the overall proportional fairness metric. Although limiting the number of active reuse patterns will degrade the performance and render the resulting problem non-convex, we first show that there must exist one solution achieving the optimum when the upper bound on the number of active reuse patterns is not less than the total number of mobile stations (MSs) and RNs. We also put forth one active pattern identification scheme based on the re-weighted l1-norm algorithm to deal with the non-convex problem and refine the set of active reuse patterns in a soft manner. Furthermore, in order to offload the heavy computation burden from the central server, one distributed resource allocation algorithm based on the alternating direction method of multipliers (ADMM) algorithm is developed. Numerical simulations demonstrate the superiority and effectiveness of our proposed algorithm.

Massive multiple-input multiple-output (MIMO) can increase the spectral efficiency. Besides, it can provide accurate localization. In some applications, e.g. autonomous driving, user behavior such as velocity and moving direction needs to be detected in addition to the position. In this paper, we propose Massive MIMO based Detection and Ranging (MIDAR) to offer a solution for joint localization and behavior recognition based on the power spectrum in multiple domains. An angle-delay-Doppler power spectrum (ADD-PS) is extracted from a mass of channel state information (CSI) as the fingerprint of a particular position with a certain behavior. By matching this fingerprint to a big data set of pre-collected reference points, we can obtain improved localization performance and detect the user behavior at the same time. In order to take full advantage of the geometrical structure of the multi-dimensional ADD-PS, algorithms in tensor analysis are considered and a chordal distance based kernel (CDBK) method is exploited for the large-scale fingerprint matching. Numerical results corroborate the feasibility of MIDAR for joint localization and behavior recognition and demonstrate that the CDBK approach outperforms conventional matching schemes for massive MIMO based localization.

Task offloading is a promising technology to exploit the benefits of fog computing. An effective task offloading strategy is needed to utilize the computational resources efficiently. In this paper, we endeavor to seek an online task offloading strategy to minimize the long-term latency. In particular, we formulate a stochastic programming problem, where the expectations of the system parameters change abruptly at unknown time instants. Meanwhile, we consider the fact that the queried nodes can only feed back the processing results after finishing the tasks. We then put forward an effective algorithm to solve this challenging stochastic programming under the non-stationary bandit model. We further prove that our proposed algorithm is asymptotically optimal in a non-stationary fog-enabled network. Numerical simulations are carried out to corroborate our designs.

In wireless sensor networks (WSN s), energy is always precious for sensor nodes. To save energy, censoring is introduced to cut the total number of transmission by only transmitting informative data. This algorithm, however, ignores the energy consumption during the delivery of parameters, which can be significant comparing to the saved power. In this paper, we consider the adaptive censoring from the energy perspective. A distributed censoring algorithm with energy constraint is developed that allows sensor nodes to make autonomous decisions on whether to transmit the incoming data. We show that with the proposed algorithm, the overall energy consumption of the WSN s is reduced, while the performance loss in terms of the estimation error is negligible. Simulation results validate its effectiveness.

In time-division duplexing (TDD) systems, massive multiple-input multiple-output (MIMO) relies on the channel reciprocity to obtain the downlink (DL) channel state information (CSI) with the acquired uplink (UL) CSI at the base station (BS). However, the mismatches in the radio frequency (RF) analog circuits at different antennas at the BS break the end-to-end UL and DL channel reciprocity. To restore the channel reciprocity, it is necessary to calibrate all the antennas at the BS. This paper addresses the interconnection strategy for the internal self-calibration at the BS where different antennas are interconnected via hardware transmission lines. Specifically, the paper reveals the optimality of the star interconnection and the daisy chain interconnection respectively. From the results, we see the star interconnection is the optimal interconnection strategy when the B S are given the same number of measurements. On the other hand, the daisy chain interconnection outperforms the star interconnection when the same amount of time resources are consumed. Numerical results corroborate our theoretical analyses.

Efficient mobility management is an important problem in modern wireless networks with heterogeneous cell sizes and increased nodes densities. We show that optimization- based mobility protocols cannot achieve long-term optimal performance, particularly in a time-varying environment for ultra-dense networks. To address the complex system dynamics, especially the possible change of statistics due to user movement and environment changes, we propose piece-wise stationary online-learning algorithms to track the activities of small base stations and solve frequent handover (FHO) problems. The BASD/BASSW algorithms are proved to achieve sublinear regret performance in finite time horizon and a linear, non-trivial rigorous bound for infinite time horizon. We study the robustness of the BASD/BASSW algorithms under missing feedback. Simulations show that proposed algorithms can outperform 3GPP protocols with the best threshold, and tend to be more robust than 3GPP to various dynamics which are common in practical ultra- dense wireless networks.

In time-division duplexing (TDD) systems, massive multiple-input multiple-output (MIMO) relies on the channel reciprocity to obtain the downlink (DL) channel state information (CSI) with the acquired uplink (UL) CSI at the base station (BS). However, the mismatches in the radio frequency (RF) analog circuits among different antennas at the BS break the end-to-end UL and DL channel reciprocity. To avoid the severe performance degradation with massive MIMO, it is necessary to calibrate all the M antennas at the BS to restore the end-to-end UL/DL channel reciprocity. In this paper, we examine the internal self-calibration scheme where different BS antennas are interconnected via hardware transmission lines. First, we study the resulting calibration performance for an arbitrary interconnection strategy. Next, we obtain closed-form Cramer-Rao lower bound (CRLB) expressions for each interconnection strategy at the BS with only (M-1) transmission lines. Basing on the derived results, we further prove that the star interconnection strategy is optimal for internal self-calibration due to its lowest CRLB. Numerical simulation results corroborate our theoretical analyses and results.

A homogeneous fog network is defined as a group of peer nodes with sharable computing and storage resources, as well as spare spectrum for node-to-node/device-to-device communications and task scheduling. It promotes more intelligent applications and services in different Internet of Things (IoT) scenarios, thanks to effective collaborations among neighboring fog nodes via cognitive spectrum access techniques. In this paper, a comprehensive analytical model that considers circuit, computation, offloading energy consumptions is developed for accurately evaluating the overall energy efficiency (EE) in homogeneous fog networks. With this model, the tradeoff relationship between performance gains and energy costs in collaborative task offloading is investigated, thus enabling us to formulate the EE optimization problem for future intelligent IoT applications with practical constraints in available computing resources at helper nodes and unused spectrum in neighboring environments. Based on rigorous mathematical analysis, a maximal energy-efficient task scheduling (MEETS) algorithm is proposed to derive the optimal scheduling decision for a task node and multiple neighboring helper nodes under feasible modulation schemes and time allocations. Extensive simulation results demonstrate the tradeoff relationship between EE and task scheduling performance in homogeneous fog networks. Compared with traditional task scheduling strategies, the proposed MEETS algorithm can achieve much better EE performance under different network parameters and service conditions.

Vehicular ad hoc networks, wireless sensor networks, Internet of Things, and mobile device-to-device communications can be modeled as different homogeneous fog networks, wherein similar terminals/things/devices/nodes are sharing their computation, communication, and storage resources in the neighborhood for achieving better system performance through effective collaborations. It is very desirable, but quite challenging, to simultaneously reduce service delay and energy consumption in such networks for delay-sensitive and energyconstraint applications, e.g., virtual reality and online 3-D gaming on mobile devices. In this paper, a cross-layer analytical framework is developed to formulate and study the balance between service delay and energy consumption. An effective control parameter V is derived to characterize their tradeoff relationship during dynamic task scheduling processes in fog networks. Combining this analysis with Lyapunov optimization techniques, a novel delay energy balanced tasking scheduling (DEBTS) algorithm is proposed to minimize the overall energy consumption while reducing average service delay and delay jitter. It is proved that DEBTS can achieve the theoretical [O(1/V), O(V)] tradeoff between these two performance metrics. Further, extensive simulation results show that DEBTS can offer much better delay-energy performance in task scheduling challenges. Specifically, for a typical V value of 4 × 104, DEBTS can save 26% and 29% more energy, and at the same time, reduce average service delay by 29% and 32%, than traditional random scheduling and least busy scheduling algorithms, respectively.

In the era of big data, adaptive censoring (AC) provides us a natural option of trimming data by only keeping the statistical informative data. However, the data chosen by AC may arrive in clusters, which do not relieve the computational resource requirement as expected. In this letter, we exploit queuing theory to model a single sink node with abundant sensor nodes. By adding a buffer to censored distributed wireless sensor networks (WSNs), the uncensored data can be modeled as a queue. With the buffer, the new algorithm entails simple, closed-form updates, and has no loss in terms of estimation accuracy comparing to the original AC method. The proposed model can further reduce the communication cost of distributed WSNs. The proposed model is illustrated in a linear regression setting. Numerical results validate the effectiveness of the proposed model in dealing with data congestion problem.

Pilot contamination in the uplink (UL) puts asymptotic limits on the downlink (DL) spectral efficiency in time-division duplex (TDD) massive multiple-input multiple-output (MIMO) systems relying on TDD channel reciprocity. Pilot contamination can be induced by the UL pilot reuse across different neighboring cells. In this paper, we show that receiver front-end impairments also contribute to pilot contamination. In particular, we consider a TDD multi-user (MU) massive MIMO orthogonal frequency-division multiplexing (OFDM) system, where either time-division multiplexed (TDM) pilots or frequency-division multiplexed (FDM) pilots are utilized for UL channel sounding. We endeavor to characterize the impacts of the residual timing offsets (TOs) and the carrier frequency offsets (CFOs) on the DL performance of the massive MIMO-OFDM system. Closed-form expressions of the asymptotic DL MU spectral efficiencies are derived in the presence of both TOs and CFOs under different scenarios. Our analytical results reveal how the residual TOs and CFOs destroy the orthogonality among the UL training sequences from different users and give rise to pilot contamination. Specifically, we show that the DL spectral efficiencies become bounded even when the number of antennas goes toward infinity. Furthermore, to alleviate the impacts of TOs and CFOs, we propose different pilot decontamination methods based on our analyses for both the TDM pilots and the FDM pilots. Numerical simulation results corroborate our analyses and designs.

Device-to-device (D2D) communication has become one important part of next-generation mobile networks particularly due to the booming of proximity-based services, e.g. the ProSe standardized in LTE. However, D2D communications may create strong interference to nearby users that are sharing the same spectrum. Thus, interference management in a D2D-enabled cooperative network is critical. In this paper, the time reuse problem and its distributed solution in a D2D-enabled cooperative network are investigated. Even though the total number of possible time reuse patterns increases exponentially with the quantity of the users, the authors first show that the optimal network performance can be achieved by only activating a limited number of time reuse patterns. The effects of the incentive mechanism on the reuse pattern selection are also investigated. Meanwhile, the set of active reuse patterns are determined efficiently with the Frank-Wolfe method. For a specific set of active time reuse patterns, the authors further show that the optimal resource allocation problem can be formulated as one consensus-building problem. Based on the alternating-direction method of multipliers, one low-complexity algorithm is proposed to determine the optimal resource allocations in a distributed fashion. Numerical simulations are carried out to corroborate our designs.

Judiciously setting the base station transmit power that matches its deployment environment is a key problem in ultra dense networks and heterogeneous in-building cellular deployments. A unique characteristic of this problem is the tradeoff between sufficient indoor coverage and limited outdoor leakage, which has to be met without explicit knowledge of the environment. In this paper, we address the small base station (SBS) transmit power assignment problem based on stochastic bandit theory. We explicitly consider power switching penalties to discourage frequent changes of the transmit power, which causes varying coverage and uneven user experience. Unlike existing solutions that rely on RF surveys in the target area, we take advantage of the user behavior with simple coverage feedback in the network. In addition, the proposed power assignment algorithms follow the Bayesian principle to utilize the available prior knowledge and correlation structure from the self configuration phase. Simulations mimicking practical deployments are performed for both single and multiple SBS scenarios, and the resulting power settings are compared to the state-of-the-art solutions. Significant performance gains of the proposed algorithms are observed.

As more than 2.5 quintillion bytes of data are generated every day, the era of big data is undoubtedly upon us. Running analysis on extensive datasets is a challenge. Fortunately, a significant percentage of the data accrued can be omitted while maintaining a certain quality of statistical inference in many cases. Censoring provides us a natural option for data reduction. However, the data chosen by censoring occur non-uniformly, which may not relieve the computational resource requirement. In this paper, we propose a dynamic, queuing method to smooth out the data processing without sacrificing the convergence performance of censoring. The proposed method entails simple, closed-form updates, and has no loss in terms of accuracy comparing to the original adaptive censoring method. Simulation results validate its effectiveness.

In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, the downlink (DL) and uplink (UL) channels are not reciprocal anymore. However, some long-term parameters, e.g. the time delays and angles of arrival (AoAs) of the channel paths, still enjoy reciprocity. In this paper, through efficiently exploiting the aforementioned limited reciprocity, we address the DL channel state information (CSI) feedback in a practical wideband massive MIMO system operating in the FDD mode. In particular, the base station (BS) can transmit the FFT-based pilots with carefully-selected phase shifts to align the DL paths. Then the user can rely on the so-called time-domain aggregate channel (TAC) to derive the feedback of reduced dimensionality per the instructions from the serving BS. We further demonstrate that the BS can recover the DL CSIs with the scalable feedback from the users. Numerical simulation results corroborate our designs.

In a time-division duplexing (TDD) system, the uplink (UL) and downlink (DL) channel reciprocity gets impaired due to the mismatches of the antenna radio frequency (RF) electronics. Meanwhile, in massive multiple-input multiple-output (MIMO), as the antenna elements are placed close to each other, the mutual coupling effects among the adjacent antennas also become pronounced. Different coupling matrices for transmission and reception also hurt the end-to-end UL/DL channel reciprocity. Aiming to restore the end-to-end channel reciprocity with a small amount of over-the-air (OTA) resources, we propose to estimate the gain mismatches of the RF electronics and the mutual coupling mismatches separately. Specifically, we rely on the pilot exchanges among the BS antennas and the symmetry of the antenna array to recover the transmit and receive gains of all the RF components. Unlike prior calibration works, our schemes can recover the off-diagonal elements in the calibration matrix as well. Our designs are also corroborated with numerical simulations.

To meet ever-growing mobile data traffic, network spatial densification with various low-power nodes in addition to the conventional high-power macro base stations, also known as heterogeneous network (HetNet), is regarded as one key enabling solution. Due to the unplanned nature, HetNets are very irregular and severe interference can happen without judicious designs of the user association rules. Most prior works assumed best-effort traffic and sought the optimal association to maximize metrics, such as the sum of log-rates. In the case of bursty traffic with quality-of-service (QoS) requirements, existing association schemes cannot fully release the traffic offloading capabilities of HetNets. In this paper, we model the downlink traffic to individual mobile station explicitly. Aiming to minimize the network-wide packet delay, we investigate the optimal user association scheme and the corresponding resource allocation algorithm. We then propose the QoS-aware user association (QoSA) strategies of different flavors enjoying low complexity and guaranteed convergence. Furthermore, the proposed QoSA algorithms are readily implemented in distributed manners. Extensive network simulations are carried out to corroborate our designs.

Pilot contamination in the uplink (UL) can severely hurt the channel acquisition performance at the base station (BS) in a massive multiple-input multiple-output (MIMO) system. To mitigate the interference, it is critical to explore all the allowed avenues to provide more orthogonal resources for the users to transmit non-interfering UL pilots. In this paper, with a realistic massive MIMO orthogonal frequency-division multiplexing (OFDM) system model, allowing the channels to be both frequency-selective and time-selective, we propose to mitigate the UL pilot contamination through aligning the channel power in multiple domains, i.e., the time, angular, and Doppler domains. First, we show how to align the power-delay profiles of different users so that the pilots sent within one common reference OFDM symbol are orthogonal. Second, we demonstrate our proposed time-varying pilots can asymptotically mitigate the pilot contamination by aligning the Doppler power spectra (DPSs) judiciously even when the UL channels vary with time. Furthermore, in the case of massive MIMO, as the angles of arrival of the UL paths are acquired by the BS, we show the pilot contamination can be reduced or eliminated by jointly aligning the DPSs and the angular power spectra. Extensive computer simulations convince us the proposed Power Aligning principle can serve as the baseline design philosophy for the UL pilots in massive MIMO.

In this paper, we look into the issue of intra-cell uplink (UL) pilot orthogonalization and schemes for mitigating the inter-cell pilot contamination with a realistic massive multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) system model. First, we show how to align the power-delay profiles (PDP) of different users served by one BS so that the pilots sent within one common OFDM symbol are orthogonal. From the derived aligning rule, we see much more users can be sounded in the same OFDM symbol as their channels are sparse in time. Second, in the case of massive MIMO, we show how PDP alignment can help to alleviate the pilot contamination due to inter-cell interference. We demonstrate that, by utilizing the fact that different paths in time are associated with different angles of arrival (AoA), the pilot contamination can be significantly reduced through aligning the PDPs of the users served by different BSs appropriately. Computer simulations further convince us PDP aligning can serve as the new baseline design philosophy for the UL pilots in massive MIMO.

To meet the ever-growing mobile data traffic, network spatial densification with various low-power nodes in addition to the conventional high-power macro base stations (BS), a.k.a. heterogeneous network (HetNet), is regarded as one key enabling solution. Due to the unplanned nature, HetNets are very irregular and severe interference can happen without judicious designs of the user association rules. Conventional maximum downlink (DL) signal-to-interference-plus-noise ratio (SINR) association rule, on the other hand, can not fully release the traffic offloading capabilities of HetNets. Most prior work assumed best-effort (BE) traffic and sought the optimal association to maximize metrics such as the sum of log-rate. In this paper, we model the DL quality-of-service (QoS) flow to each mobile station (MS) explicitly. Aiming to minimize the network-wide packet delay, we investigate the optimal user association scheme and the corresponding resource allocation algorithm. Our proposed traffic-aware user association strategy enjoys low complexity and fast convergence, which is also corroborated with simulations.

Single-carrier frequency-division multiplexing (SC-FDM) has been adopted as the uplink (UL) waveform in the 3GPP LTE standards due to its various desirable features. One key benefit of SC-FDM is its low peak-to-average-power ratio (PAPR), which is highly desirable in the UL to lower down the cost of the power amplifier (PA) in the user equipment (UE). In order to preserve the low PAPR, current LTE specification only allows one SC-FDM symbol to contain either all data or all pilots, but not both. This is insufficient and inflexible for scenarios like high Doppler. In this paper, we introduce one novel scheme to flexibly multiplex data and pilots within a single SC-FDM symbol while not distorting the single-carrier waveform. In the case of high Doppler, without increasing the pilot overhead, we can apply this scheme to multiplex pilots onto the data symbols to allow coherent channel estimation all the time. Numerical simulations demonstrate significant performance improvements over current LTE designs.

Pilot contamination in the uplink (UL) can severely degrade the channel estimation quality at the base station in a massive multi-input multi-output (MIMO) system. Thus, it is critical to explore all possible avenues to enable more orthogonal resources for the users to transmit noninterfering UL pilots. In conventional designs, pilot orthogonality typically assumes constant channel gains over time, which limits the amount of orthogonal resources in the case of time-selective channels. To circumvent this constraint, in this paper, we show how to enable orthogonal multiplexing of pilots in the case of Doppler fading by aligning the power spectrum densities (PSDs) of different users. From the derived PSD aligning rules, we can see multiple users can be sounded simultaneously without creating/suffering pilot contamination even when these users are experiencing time-varying channels. Furthermore, we provide analytical formulas characterizing the channel estimation mean square error performance. Computer simulations further confirm us the PSD alignment can serve as one important decontamination mechanism for the UL pilots in massive MIMO.

Massive multiple-input multiple-output (MIMO) in time-division duplexing (TDD) systems relies on the channel reciprocity to obtain the downlink (DL) channel state information (CSI) with the uplink (UL) CSI. But in practice, the transmit branch is composed of different radio frequency (RF) circuits from the receive branch, which breaks the end-to-end DL/UL channel reciprocity. Antenna array calibration is thus necessary to restore this reciprocity. However, in the context of massive MIMO, most existing results are obtained assuming ideal calibration. In this paper, we first analyze the signal-to-interference-plus-noise ratio (SINR) performance and the system capacity in the case of nonideal antenna array calibration and spatial correlation among transmit antennas. Then, we derive the calibration requirements for different beamforming schemes to limit the performance degradation within ℓdB. Furthermore, we verify the accuracy of our analytical results with extensive numerical simulations. This work can be utilized to guide the designs of TDD massive MIMO communication systems.

In the massive multiple-input multiple-output (MIMO) era, there are hundreds of antenna elements at one single base station (BS). It becomes critical to take full advantage of time-division duplexing (TDD) channel reciprocity to learn the downlink (DL) channel state information (CSI) from the uplink (UL) channel measurements at the BS. However, due to different radio frequency branches for transmitting and receiving, antenna calibration at the BS is necessary to effect this reciprocity between the end-to-end DL and UL channels. In massive MIMO, this procedure becomes extremely challenging since all the existing antenna calibration approaches do not scale well with the size of the antenna array. In this paper, we rely on compressive sensing (CS) theory to develop an efficient and robust way to calibrate the massive antenna array at the BS, which only requires CSI feedback in the order of O(log N) instead of O(N) as in those conventional calibration schemes. Extensive simulations demonstrate our approach can maintain the whole large scale antenna system in a "calibrated'' state with only a small amount of feedback overhead. The proposed novel scheme thus allows us to make full use of the channel reciprocity in TDD massive MIMO systems.

Differential, transmitted reference (TR) and energy detection (ED) based ultra-wideband impulse radios (UWBIR) can collect the rich multipath energy offered by UWB channels with a low-complexity receiver. However, they perform satisfactorily only when the channel induced inter-pulse interference (IPI) is negligible. This can be achieved by appending a guard interval with duration greater than or equal to the channel's delay spread to each frame an operation limiting the maximum achievable data rate. As a remedy, this Letter advocates block transmissions in conjunction with orthogonal spreading sequences to remove the introduced IPI. The resultant scheme requires no channel knowledge besides timing offset and incurs slightly more complexity than non-block alternatives, while it increases the data rate at no cost in error performance. Given a fixed data rate of 25 Mbps, the novel block scheme exhibits about 1.8 dB gain relative to its non-block counterpart in single-user simulated tests.

In this paper, we study the achievable rates of practical ultra-wideband (UWB) systems using pulse position modulation (PPM) and transmitted-reference (TR) transceivers. TR obviates the need for complex channel estimation, which is particularly challenging in the context of UWB. Based on an upper bound we derive for the error probability with random coding, we establish that for SNR values of practical interest, PPM-UWB with TR can achieve rates on the order of C(∞) = P/N0(nats/second), where C(∞) denotes the capacity of an AWGN channel in the UWB regime for average received power P and noise power spectrum density N0.

In this letter, we study the achievable rates of practical ultra-wideband (UWB) systems using pulse position modulation (PPM) and transmitted-reference (TR) transceivers. TR obviates the need for complex channel estimation, which is particularly challenging in the context of UWB communications. Based on an upper bound we derive for the error probability with random coding, we establish that for signal-to-noise ratio values of practical interest, PPM-UWB with TR can achieve rates on the order of C(/spl infin/)=P/N/sub 0/ (nats/s), where C(/spl infin/) denotes the capacity of an additive white Gaussian noise channel in the UWB regime for average received power P and noise power spectrum density N/sub 0/.

Synchronization is a performance-critical factor in most communication systems: from classical narrowband and emerging (ultra) wideband (UWB) point-to-point links to cooperative or ad hoc networking, where access must deal with multi-user interference (MUI) and possibly severe intersymbol interference (ISI). For universal applicability to all these scenarios, we develop a blind synchronization and demodulation scheme which relies on intermittent transmission of nonzero mean symbols. These enable MUI- and ISI-resilient timing acquisition via energy detection and low-complexity demodulation by matching to a synchronized aggregate template (SAT). The resultant SAT receiver offers distinct advantages over RAKE, has low-complexity and lends itself naturally to decision-directed enhancements. Its blind operation nicely fits the requirements of multi-user ad hoc access and its ability to handle ISI is particularly attractive for UWB communications. Analytical performance evaluation and simulations testing our novel scheme in UWB settings confirm its high potential for deployment

A low-complexity, high performance demodulation algorithm suitable for ultra-wideband impulse radio (UWB-IR) is developed. Capitalizing on the recently proposed acquisition scheme based on dirty templates (TDT), the novel demodulator can cope with unknown timing offsets, unknown time-hopping spreading codes, and unknown multipath channels. Both data-aided (DA) and non-data-aided (NDA) TDT schemes are considered. Performance of the resultant TDT-based demodulator is evaluated and shown to be robust to timing estimation errors. Comparisons are provided with the UWB-RAKE receiver when timing errors are absent. It is asserted that the TDT-based demodulator outperforms the RAKE with limited number of fingers in the medium to high signal-to-noise-ratio (SNR) range. Analytical results are corroborated by simulations.

Synchronization is a factor critically affecting performance of ultra-wideband (UWB) communication systems. We develop a blind synchronization and demodulation scheme which relies on intermittent transmission of nonzero mean symbols. These enable multi-user interference (MUI)- and inter-symbol interference (ISI)-resilient timing acquisition via energy detection and low-complexity demodulation by matching to a synchronized aggregate template (SAT). It turns out that the resultant SAT receiver offers distinct advantages over the widely-deployed RAKE receiver. Its blind operation nicely fits the requirements of multi-user ad hoc access and its ability to handle ISI and MUI is attractive for UWB communications. Analytical performance evaluation and simulations testing our novel scheme confirm its high potential for deployment.

As low power, low cost and longevity of transceivers are major requirements in wireless sensor networks, optimizing their design under energy constraints is of paramount importance. To this end, we develop quantizers under strict energy constraints to effect optimal reconstruction at the fusion center. Propagation, modulation, as well as transmitter and receiver structures are jointly accounted for using a binary symmetric channel model. We first optimize quantization for reconstructing a single sensor's measurement. Optimal number of quantization levels and optimal energy allocation across bits are derived. We then consider multiple sensors collaborating to estimate a deterministic parameter in noise. Similarly, optimum energy allocation and optimum number of quantization bits are derived analytically and tested with simulated examples.