Large-scale antenna arrays in millimeter wave (mmWave) systems are the cornerstone for next-generation Internet of Things (IoT) infrastructure. However, wideband mmWave systems suffer from frequency-dependent channel responses, known as beam squint. With beam squint effect, the far-field over-the-air (OTA) signal may exhibit frequency selective fading, rendering ineffective digital predistortion (DPD) design based on an OTA feedback structure. Therefore, existing mmWave DPD feedback architectures need to be examined carefully. In this article, we analyze the impact of beam squint on DPD and propose a proper DPD feedback architecture. To the best of our knowledge, this is the first work to address DPD in wideband mmWave systems with beam squint. Our results indicate that, with beam squint, the nonlinearity observed at the far-field OTA side differs from that at the power amplifier (PA) output side. We demonstrate that the far-field OTA received signal is no longer suitable as the feedback signal for DPD estimation. Moreover, we propose an effective DPD scheme for mmWave systems with beam squint. In this scheme, analog beamforming coefficients are fixed for DPD identification. Compared to existing solutions, hardware complexity of the proposed DPD scheme is much reduced. Numerical results validate the effectiveness of the proposed DPD scheme.

We investigate fast data aggregation via over-the-air computation (AirComp) over wireless networks. In this scenario, an access point (AP) with multiple antennas aims to recover the arithmetic mean of sensory data from multiple wireless devices. To minimize estimation distortion, we formulate a mean-squared-error (MSE) minimization problem that considers joint optimization of transmit scalars at wireless devices, denoising factor, and receive beamforming vector at the AP. We derive closed-form expressions for the transmit scalars and denoising factor, resulting in a non-convex quadratic constrained quadratic programming (QCQP) problem concerning the receive beamforming vector. To tackle the computational complexity of the beamforming design, particularly relevant in massive multiple-input multiple-output (MIMO) AirComp systems, we explore the optimal structure of receive beamforming using successive convex approximation (SCA) and Lagrange duality. By leveraging the proposed optimal beamforming structure, we develop two efficient algorithms based on SCA and semi-definite relaxation (SDR). These algorithms enable fast wireless aggregation with low computational complexity and yield almost identical mean square error (MSE) performance compared to baseline algorithms. Simulation results validate the effectiveness of our proposed methods.

Norovirus, primarily transmitted via fomite route, poses a significant threat to global public health and the economy. Airports, as critical transportation hubs connecting people from around the world, has high potential risk of norovirus transmission due to large number of public surfaces. A total of 21.3 hours of video episodes were recorded across nine functional areas at the airport, capturing 25,925 touches. A surface transmission model based on a Markov chain was developed. Using the beta-Poisson dose-response model, the infection risk of norovirus and the effectiveness of various interventions in different airports' areas were quantified. Without any preventive measures, restaurants at airports exhibited the highest risk of norovirus transmission, with an infection probability of 8.8×10-3% (95% CI, 1.5×10-3% -2.1×10-2%). This means approximately 4.6 (95% CI, 0.8-10.9) out of 51,494 passengers who entered the restaurants would be infected by an infected passenger. Comparing with no surface disinfection, disinfecting public surfaces every 2 hours can reduce the risk of norovirus infection per visit to the airport by 83.2%. In contrast, comparing with no hand washing, handwashing every 2 hours can reduce the infection risk per visit to the airport by only 2.0%, making public surface disinfection significantly more effective than handwashing. If the mask-wearing rate increases from 0% to 50%, the infection risk of norovirus would be decreased by 48.0% (95% CI, 43.5-52.3%). Furthermore, using antimicrobial copper/copper-nickel alloy coatings for most public surfaces could reduce the infection risk by 15.9%-99.2%.

Phase noise can cause significant performance degradation in multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, especially for high-order data transmission. To mitigate the effect of phase noise on data transmission, pilot-based and blind-based algorithms are widely adopted in the existing works, which suffer from spectral efficiency degradation or formidable computational cost due to the large-scale and time-dependent properties of phase noise. In this paper, we propose an efficient multi-level maximum a posteriori (MMAP)-based blind data detection algorithm to address the phase noise compensation in MIMO-ODFM systems. The proposed algorithm, exploiting the spectral low-dimensional property of phase noise and the approximate message passing (AMP) rule, achieves a near optimal detection performance. The exploitation of low-pass characteristics of phase noise spectrum significantly reduces the computational complexity, and the adoption of AMP principle ensures a linear complexity of the algorithm with respect to the number of antennas and subcarriers. Thus, a good complexity-accuracy trade-off is obtained. Besides, the proposed algorithm is applicable to the scenarios of commonly shared oscillators and independent oscillators. The numerical experiments show that the proposed pilot-free data detection algorithm can achieve superior data transmission performance for channels with strong phase noise at a low complexity.

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.

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.

Federated learning (FL) leverages the private data and computing power of multiple clients to collaboratively train a global model. Many existing FL algorithms over wireless networks adopting synchronous model aggregation suffer from the straggler issue, due to the heterogeneity of local computing power and channel conditions. To address this issue, we in this paper advocate an asynchronous FL framework with adaptive client selection for training latency minimization, taking into account the client availability and long-term fairness. We consider a practical scenario, where the channel conditions and the locally available computing power are not known in prior. This makes the client selection problem challenging, as the training latency consists of the uplink/downlink transmission time and the local training time. To this end, we tackle the asynchronous client selection problem in an online manner by converting the latency minimization problem into a multi-armed bandit problem, and leverage the upper confidence bound policy and virtual queue technique in Lyapunov optimization to solve the problem. We theoretically show that the proposed algorithm achieves sub-linear regret performance, ensures long-term fairness, and guarantees training convergence. Results show that the proposed algorithm can reduce the training time by up to 50% when compared to the baseline algorithms.

The Unmanned aerial vehicle (UAV) assisted Internet of Things (IoT) has attracted substantial attention as it is capable of collecting scattered data to meet the stringent demands of emerging IoT applications. Dispatching UAV to collect data from IoT devices (IoTDs) can significantly improve data freshness, which can be measured by Age of Information (AoI). On the other hand, the quantity of IoTDs increases and existing UAV navigation algorithms for dozens of IoTDs can not be applied to massive IoTDs scenarios directly. In this paper, we investigate the AoI minimization problem in massive IoTDs scenarios. Considering unknown traffic patterns of IoTDs, we reformulate the AoI minimization problem as a Markov decision process (MDP). Then we propose a twin delayed deep deterministic policy gradient (TD3) based UAV navigation algorithm to minimize the average AoI of data collected from IoTDs. Simulation results demonstrate that the proposed algorithm can significantly reduce the average AoI in massive IoTDs scenarios when compared with baseline algorithms.

In the 5th generation (5G) massive machine-type communication (mMTC), random access is the limiting factor of performance because there may be massive user equipments (UEs) up to 1 million unit competing for the access opportunities. Existing random access schemes in the 4th generation (4G) or 5G systems are not able to handle such a large amount of concurrent random access requests. This article proposes a random access scheme based on directional beams, which offers a new spatial degree of freedom to improve the random access performance in 5G mMTC. In this scheme, a cell is divided into Beam Zones in the space domain. UEs in different Beam Zones choose a certain physical random access channel (PRACH) resource with a different probability. When preamble collision occurs, uplink radio resource is allocated to the Beam Zone which has low collision probability. We provide theoretical performance analysis of the proposed scheme using different performance metrics. Comparisons are carried out between the proposed and several existing random access schemes. The proposed scheme can be easily deployed without any modification to the 5G framework. Existing random access optimizing algorithms can also be applied to the proposed scheme.

In the 5th generation (5G) unlicensed band communication, listen-before-talk (LBT) is a key mechanism to allow fair coexistence with other radio access technologies (RATs). With LBT, the user device (UE) needs to transmit a reservation signal (RS) between LBT success and the next slot boundary to avoid competing transmission from other RATs. This scheme, on the other hand, also blocks other UEs from accessing to the base station (BS). The uplink multiple access ability is prominently degraded due to LBT. In this article, an improved LBT scheme is proposed for uplink transmission in the 5G unlicensed band. The RS is enhanced by carrying an indicator of a successful LBT. Other UEs of the same RAT check for enhanced RS in addition to the conventional energy detection. Once the enhanced RS is detected, UE stops LBT, mutes until the next slot boundary, and transmits data in the next slot. We provide theoretical performance analysis of the proposed scheme. Comparisons are carried out between the proposed scheme and other existing schemes. The proposed scheme has better performance than other schemes. In a typical uplink transmission scenario, the proposed scheme improves the average number of UEs with successful LBT by 92% over the existing scheme. In addition, the proposed scheme can be easily deployed complying with the current LBT framework of the 5G unlicensed band.

With the expected explosive use of the Internet of Everything in sixth generation (6G), the cybertwin network is able to convert user information to digital assets and provide extensive services. However, protecting and enhancing privacy of the processed and transmitted data in cybertwin-driven 6G is still in its infancy. Federated learning (FL) is a nascent distributed machine learning paradigm that is able to facilitate privacy protection in cybertwin networks. In a cybertwin network, imbalanced data distribution of the clients can increase the bias of the global model and sacrifice the performance of the FL model. Prior research work dealing with imbalanced data requires extra data information exchanged between clients and the server, which increases the risk of privacy leakage. To avoid privacy leakage, we design an estimation algorithm to determine the distribution of local data collected at the clients without the awareness of specific raw data. We consider two scenarios in FL: 1) the server could receive the individual trained model for each selected device and 2) the server could receive the aggregated model from the selected clients. We formulate two device selection problems to improve the training performance of the aforementioned scenarios. We develop two online learning algorithms to tackle the selection problems for both individual model uploading and aggregated model uploading. The proposed algorithms are conducted on the server, thereby avoiding privacy leakage and extra computation at the clients. We validate the effectiveness of the proposed client selection algorithms with sufficient experiments in cybertwin-driven 6G networks.

For wireless caching networks, the scheme design for content delivery is non-trivial in the face of the following tradeoff. On one hand, to optimize overall throughput, users can associate their nearby APs with great channel capacities; however, this may lead to unstable queue backlogs on APs and prolong request delays. On the other hand, to ensure queue stability, some users may have to associate APs with inferior channel states, which would incur throughput loss. Moreover, for such systems, how to conduct predictive scheduling to reduce delays and the fundamental limits of its benefits remain unexplored. In this paper, we formulate the problem of online user-AP association and resource allocation for content delivery with predictive scheduling under a fixed content placement as a stochastic network optimization problem. By exploiting its unique structure, we transform the problem into a series of modular maximization sub-problems with matroid constraints. Then we devisePUARA, a Predictive User-AP Association and Resource Allocation scheme which achieves a provably near-optimal throughput with queue stability. Our theoretical analysis and simulation results show that PUARA can not only perform a tunable control between throughput maximization and queue stability, but also incur a notable delay reduction with predicted information.

Federated learning (FL) is emerging as a promising paradigm for achieving distributed machine learning while protecting users' privacy. The accuracy and convergence speed of the global model benefit from involving as many clients as possible during the model training. On the other hand, the scarcity of wireless spectrum restricts the number of clients involved at each round. In this paper, we aim to maximize the number of participating clients in each round with fixed wireless bandwidth. Instead of assuming that the prior information about wireless channel state is available, we consider a more practical scenario under the absence of prior information. We first reformulate the client selection problem with limited bandwidth as a combinatorial multi-armed bandit (CMAB) problem and then propose an online learning algorithm with elegant bandwidth allocation based on the framework of combinatorial upper confidence bound. The proposed algorithm can make full use of the scarce bandwidth to increase the number of involved clients in each round and minimize the training latency for a given training accuracy. Numerical results validate the efficiency of the proposed algorithm.

Selected mapping (SLM) is an efficient peak-to-average-power-ratio (PAPR) reduction algorithm for orthogonal frequency division multiplexing (OFDM) systems. Conventional SLM requires extra resources for side information transmission. If the side information is transmitted implicitly, significantly high computation overhead is imposed to the receiver at the user equipment (UE) side. In the orthogonal frequency division multiple access (OFDMA) system, the SLM can not be directly applied since the UE does not have access to the signal of other UEs. In this paper, we propose a novel SLM algorithm for the OFDMA system that requires no side information transmission. With the proposed SLM algorithm, each UE can receive its own data without the knowledge of other UEs. The SLM demapping at the UE side is much simplified. Besides, detection of the implicit side information with the proposed algorithm is more robust than existing SLM algorithms. Numerical results validate the theoretical performance of the proposed algorithm.

The intelligent reflecting surface (IRS) technology has been recently proposed as a promising solution to improve the coverage of communication systems. The performance of the IRS-aided communication systems, on the other hand, highly depends on the controllability and consistency of the IRS elements. Previous works mainly discussed the IRS system performance with analog mismatches in the single-user scenario. In this paper, we study the performance of the IRS-aided downlink multi-user multiple-input single-output (MU-MISO) system in the presence of analog mismatches, where the inter-user interference (IUI) may greatly degrade the system performance. The upper bound of general system performance expression is derived. Closed-form expressions can be obtained for some special cases. We show that the system performance is significantly degraded when analog mismatches exist, since the IUI shows up and can not be completely removed.

Comparing to cloud computing, fog computing performs computation and services at the edge of networks, thus relieving the computation burden of the data center and reducing the task latency of end devices. Computation latency is a crucial performance metric in fog computing, especially for real-time applications. In this article, we study a peer computation offloading problem for a fog network with unknown dynamics. In this scenario, each fog node (FN) can offload its computation tasks to neighboring FNs in a time slot manner. The offloading latency, however, could not be fed back to the task dispatcher instantaneously due to the uncertainty of the processing time in peer FNs. Besides, peer competition occurs when different FNs offload tasks to one FN at the same time. To tackle the above difficulties, we model the computation offloading problem as a sequential FN selection problem with delayed information feedback. Using the adversarial multiarm bandit framework, we construct an online learning policy to deal with delayed information feedback. Different contention resolution approaches are considered to resolve peer competition. Performance analysis shows that the regret of the proposed algorithm, or the performance loss with suboptimal FN selections, achieves a sublinear order, suggesting an optimal FN selection policy. Besides, we prove that the proposed strategy can result in a Nash equilibrium (NE) with all FNs playing the same policy. Simulation results validate the effectiveness of the proposed policy.

Fog computing provides computation and services to the edge of networks to support real-time applications. The latency performance is a crucial metric in fog computing. In this article, we consider a computation offloading problem in a fog network with unknown dynamics. In this network, mobile users can offload their computational tasks to neighborhood fog nodes (FNs) in each time slot. The queue of arrival tasks at each FN follows a Markov model with unknown statistics. In order to provide a satisfactory quality of experience, the network latency needs to be minimized. In this article, we construct an offloading policy with interleaved exploration and exploitation epochs to solve the sequential FN selection problem. An upper bound of regret is derived to show the effectiveness of the proposed method. The proposed policy is optimal in the sense that it achieves a regret with sublinear order. In addition, the proposed policy can be applied to both single-user setting and multiuser setting. Simulation results show that when compared with the existing offloading algorithms, the proposed algorithm can reduce the average latency by 7%–47% in the single-user setting, and 91% in the multiuser setting.

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.

Fog computing brings computation and services to the edge of networks enabling real time applications. In order to provide satisfactory quality of experience, the latency of fog networks needs to be minimized. In this paper, we consider a peer computation offloading problem for a fog network with unknown dynamics. Peer competition occurs when different fog nodes offload tasks to the same peer FN. In this paper, the computation offloading problem is modeled as a sequential FN selection problem with delayed feedback. We construct an online learning policy based on the adversary multi-arm bandit framework to deal with peer competition and delayed feedback. Simulation results validate the effectiveness of the proposed policy.

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.

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.

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 distributed wireless sensor networks (WSNs), the energy restriction of sensor nodes is always the bottleneck and limits the network lifetime. Emerging energy harvesting techniques can be potential solutions. The energy obtained from the renewable energy source, on the other hand, is not stable. In this paper, we consider the coefficients estimation problem in a WSN with renewable energy source. The limited and unstable power supply prevents sensor nodes from transmitting all collected data to the fusion center. We develop an optimal data censoring strategy for the estimation problem. Firstly, the behavior of the incoming renewable energy source is learned from historical data through Long Short Term Memory (LSTM) networks. Then the amount of available energy for the next period is predicted. The optimal data censoring strategy is developed and updated based on the available energy. We show that the proposed algorithm can fully enjoy the benefit of renewable energy with extended network lifetime as well as satisfactory estimation performance.

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.

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.

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.

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.

Fog computing is expected to cope with the long latency and heavy link burden existing in cloud- based networks. Computing tasks of the terminal node can be offloaded to nearby fog nodes thus achieving much lower processing delay than that of cloud-based networks. Existing researches for energy consumption in fog computing networks mainly focus on the total energy consumed by processing a task. However, fair offloading among multiple fog nodes while maintaining a low task delay is of great significance especially for the battery-powered fog nodes. This paper proposes an analytical framework of the fair task offloading for fog computing networks. Task delay and the corresponding energy consumption are formulated. Then, a fairness scheduling metric is constructed for each fog node. A two-step Fair Task Offloading (FTO) scheme is proposed finally, which selects offloading fog nodes according to the fairness metric and then offloads tasks to the selected nodes based on a rule that minimizes the task delay. Numerical simulations and comparisons indicate the satisfactory performance of the proposed task offloading scheme for maintaining a relatively high fairness index for energy consumption and low task delay in the fog computing networks.

Fog computing has recently emerged as a promising technique in content delivery wireless networks to alleviate the heavy bursty traffic burdens on backhaul connections. In order to improve the overall system performance, in terms of network throughput, service delay and fairness, it is very crucial and challenging to jointly optimize node assignments at control tier and resource allocation at access tier under dynamic user requirements and wireless network conditions. To solve this problem, in this paper, a fog-enabled multitier network architecture is proposed to model a typical content delivery wireless network with heterogeneous node capabilities in computing, communication, and storage. Further, based on Lyapunov optimization techniques, a new online low-complexity algorithm, namely fogenabled multitier operations scheduling (FEMOS), is developed to decompose the original complicated problem into two operations across different tiers. Rigorous performance analysis derives the tradeoff relationship between average network throughput and service delay, i.e., [O(1/V), O(V)] with a control parameter V, under FEMOS algorithm in dynamic wireless networks. For different network sizes and traffic loads, extensive simulation results show that FEMOS is a fair and efficient algorithm for all user terminals and, more importantly, it can offer much better performance, in terms of network throughput, service delay, and queue backlog, than traditional node assignment and resource allocation algorithms.

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.

Caching is a promising technique to alleviate the capacity bottleneck of content-centric wireless networks (CCWNs). Most existing work of wireless caching networks focuses on the content placement policy, while some other interesting problems including dynamic user-AP association and predictive scheduling were rarely investigated. In this paper, we make the first attempt to investigate the benefit of such new degrees of freedom in wireless caching networks. Based on predictive service model, we formulate an average network throughput maximization problem with request queue stability constraints. We design the predictive user-AP association and resource allocation (P-UARA) algorithm, which is an online algorithm with theoretically guaranteed performance and does not require any statistical information of the system dynamics. Given the NP-hard user-AP association and bandwidth allocation optimization problem in P-UARA, we reformulate it and show the equivalence to the problem of modular function maximization subject to two matroid constraints. We propose an efficient greedy algorithm that guarantees a low bound 1/2 of the optimal value. Simulation results validate the theoretical analysis of our proposed algorithm and demonstrate the benefit of dynamic user-AP association and the predictive scheduling.

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.

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.

Dual-band and multi-band power amplifiers (PAs) becomes popular as they can effectively reduce the equipment cost and operation cost. On the other hand, the nonlinear interference among signals in different bands needs special attention. Previous modeling results [1] suffer performance degradation when the intermodulation terms among bands fall into bands of the signals. In this paper, we start from the passband Volterra model and derive a general baseband Volterra model considering all possible intermodulation terms. Existing model in [1] is a special case of the proposed baseband Volterra model. Simulation results show that the proposed dual-band Volterra DPD model can provide additional 2 dB performance improvement comparing to the existing dual-band Volterra DPD model.

The emerging market of Internet of things has created great demand for low‐cost, low‐power wireless technologies. Existing IEEE 802.15.4 standard is designed for low‐rate wireless personal area networks (LR‐WPANs). However, current standard does not fully utilise the benefit of the code redundancy. In this study, the authors propose new coding schemes for LR‐WPANs with improved coding gain. They first propose a block code based on extended Bose–Chaudhuri–Hocquenghem (BCH) code that increases the minimum Hamming distance compared with the existing code used in LR‐WPANs. The computational complexity of the encoder and decoder remains about the same. In addition, by applying the extended BCH code directly to LR‐WPANs, the data rate of the system can be increased without sacrificing coding performance. They further propose a tail‐biting convolutional (TBC) code with optimum generator polynomials for LR‐WPANs. The proposed TBC code enjoys significant performance improvement while preserving the effective code rate as well as a low decoding complexity. Simulation results validate the effectiveness of the proposed coding schemes.

Analog network coding (ANC) is effective in improving spectrum efficiency. To coordinate ANC among multiple nodes without relying on complicated scheduling algorithm and network optimization, a new random access MAC protocol, called ANC-ERA, is developed to dynamically form ANC-cooperation groups in an ad hoc network. ANC-ERA includes several key mechanisms to maintain high performance in medium access. First, network allocation vectors (NAV) of control frames are properly set to avoid over-blocking of channel access. Second, a channel occupation frame (COF) is added to protect vulnerable periods during the formation of ANC cooperation. Third, an ACK diversity mechanism is designed to reduce potentially high ACK loss probability in ANC-based wireless networks. Since forming an ANC cooperation relies on bi-directional traffic between the initiator and the cooperator, the throughput gain from ANC drops dramatically if bi-directional traffic is not available. To avoid this issue, the fourth key mechanism, called flow compensation, is designed to form different types of ANC cooperation among neighboring nodes of the initiator and the cooperator. Both theoretical analysis and simulations are conducted to evaluate ANC-ERA. Performance results show that ANC-ERA works effectively in ad hoc networks and significantly outperforms existing random access MAC protocols.

Traffic flow modeling is important to the performance analysis/evaluation of services provided by Vehicular Adhoc Networks (VANET), and is also a useful guidance to the deployment of VANET. Different from prior work based on empirical data collected decades ago, in this work, we collect a large amount of empirical traffic flow data from five typical overhead road segments during two different time periods recently in Shanghai. Statistical results in a short time scale (i.e., within half an hour) show that the lane-level traffic volumes/vehicles' velocities in the monitored road segments followed truncated Gaussian distribution (with a match rate of roughly 90%) better than Poisson distribution (with a match rate of roughly 80%) which is normally assumed in the literature. Traffic flow characteristic in a long time scale (i.e., in a day or a week) is also presented, which shows that the traffic density in the night and daytime are very different, however, the traffic flow density in the daytime stays high. With the obtained traffic flow characteristics, we discuss the possibility of the coexistence of WAVE and WLAN in 5.9G band according to the Federal Communications Commission's intention, and point out that for areas in the vicinity of a overhead road inside Outer Ring road in Shanghai, it is not practical for WLAN devices to operate on 5.9G WAVE band.

Recently, visible light communication (VLC) has attracted much attention as a possible candidate technology to meet the ever growing demand in wireless data. However, current low-cost white LED has limited modulation bandwidth, which limits the throughput of the VLC. Optical MIMO can provide spatial diversity and thus achieve high data rate. Traditional multiple-input multiple-output (MIMO) techniques used in wireless communications cannot be directly applied to VLC. This paper studies the precoder and equalizer design of optical wireless MIMO system for VLC. First, we propose a MIMO VLC system, which can effectively support the flickering/dimming control and other VLC-specific requirements. Second, besides the transceiver design with perfect channel state information, we also take into account channel uncertainties for joint optimization in the MIMO VLC system. Numerical results show that the proposed MIMO solution for VLC is robust to combat the influence caused by the channel estimation imperfection. By taking into account the channel estimation errors, the proposed joint optimization method demonstrates the bit error rate (BER) improvements in the scenario of imperfect channel estimation.

With the developing of low-power wireless techniques such as the micro-sensors, analog and digital electronic technology and radio frequency technology, different kinds of wireless sensor networks are widely used in the field of military, civil, medical and so on. However, in practical applications, the low-power consumption and long-distance have not been well achieved in the wireless sensor networks. In this paper, a low-power and long-distance system based on wireless sensor networks is proposed. The system model consists of two parts: the hardware includes server node, client nodes and ARM boards. The software mainly handle the analysis and construction of the communication frame as well as data analysis and processing and then the data information is processed to report to the server. We can reduce the energy consumption and increase the transmission distance by the combined application of hardware and software. The system can be used in different occasions and does have a grand application prospect.

In digital predistortion (DPD) implementation, the computational complexity of coefficients estimation of the DPDmodel is a key performance metric. Conventional coefficients estimation algorithms, such as least squares (LS), recursive least squares (RLS), and least mean squares (LMS) cannot achieve a fast convergence with little computation. In this paper, we propose an RLS algorithm with reduced complexity by introducing orthonormal polynomial basis functions. The proposed algorithmis as simple as LMS algorithmyet as efficient as RLS algorithm. Simulation results validate our analysis.

In many predistorter designs, polynomial model was used to describe the nonlinearity of power amplifiers (PAs). However, numerical instability can occur during model coefficients estimation. Orthogonal polynomial models presented in [1] [2] can help to alleviate the numerical instability problem in fixed-point implementation. In this paper, we propose a new orthonormal polynomial model to further improve the numerical stability of the polynomial model. Both simulation and experiment results show that the orthonormal polynomial model can achieve more spectrum regrowth suppression than the conventional polynomial model as well as the orthogonal polynomial model with the same finite word length.

Tail-biting codes are efficient coding techniques to eliminate the rate loss in conventional known-tail convolutional codes at a cost of increased complexity in decoders. In addition, tail-biting trellis representation of block codes makes the trellis-based maximum likelihood (ML) decoder desirable for implementation. Circular Viterbi algorithm (CVA) is introduced to decode the tail-biting codes for its decoding efficiency. However, its decoding process suffers from circular traps, which degrade the decoding efficiency. In this paper, we propose an efficient checking rule for the detection of circular traps. Based on this rule, a novel maximum likelihood (ML) decoding algorithm for tail-biting codes is presented. On tail-biting trellis, computational complexity and memory consumption of this decoder are significantly reduced comparing to other available ML decoders, such as the two-phase ML decoder. To further reduce the decoding complexity, we propose a new near-optimal decoding algorithm based on a simplified trap detection strategy. The performance of the above algorithms is validated with simulation.

Delay mismatch between the input and output signals of power amplifiers (PAs) may lead to an erroneous assumption of memory effects. In adaptive digital predistortion (DPD) system, the delay mismatch affects the accuracy of coefficients estimation and degrades performance of the DPD system. In this paper, we reveal the impact of fractional delay mismatch, and analyze the relationship between delay mismatch and memory effects. The fractional delay compensation helps to reduce or eliminate the delay mismatch. Benefits of fractional delay compensation are provided through numerical analysis and experimental results.

Traffic flow models are essential to performance analysis/evaluation for applications/services provided by Intelligent Transportation Systems (ITS)/Vehicular Ad-hoc networks (VANET). They are also useful guidance for the deployment of ITS/VANET. Many macroscopic traffic flow models have been proposed in the past few decades on the basis of empirical data collected in the US, Canada, Turkey and etc. However these models may not be accurate for traffic flows in cities in China due to the differences in population, transportation infrastructure, and driving culture. In this paper, we collected a large amount of empirical traffic flow data in Shanghai overhead road during three different time periods. Statistical results showed that the lane-level traffic volumes in Shanghai followed Gaussian distribution rather than Poisson distribution which was normally assumed in literature. Regarding lane-level vehicles' velocities, they matched well with Gaussian distribution. The empirical probability mass functions (PMF) for both the traffic volumes and vehicles' velocities were presented. In addition, how these models would impact performance analysis in VANETs was discussed.

Tail-biting convolutional codes (TBCC) provide an efficient method to eliminate the rate loss caused by the known-tail encoding. To simplify the decoder design, circular Viterbi algorithm (CVA) has been proposed by recording and repeating the received block of (soft) symbols beyond the block boundary and continuing Viterbi decoding. However, CVA does not converge in the presence of circular trap. A checking rule is proposed for detecting the circular trap in existing CVA. Based on this rule, an efficient CVA-based decoding algorithm is obtained for tail-biting codes, which exhibits near-optimal performance for both short and long tail-biting codes. This new scheme provides faster convergence speed than the conventional CVA without increasing in complexity and storage space.

Orthogonal frequency division multiplexing (OFDM) transmission systems generally have low power efficiency, due to the large peak-to-average power ratio (PAR) of the OFDM signal. Selected mapping (SLM) is a promising technique to reduce the PAR for OFDM. A drawback of SLM is its high computational requirement, which hinders its practical implementation. In this paper, we propose a dynamic, two-buffer structure to reduce the computational requirement without sacrificing the PAR reduction capability. Performance analysis and simulations of the proposed technique are also carried out.

Many components used in communication systems are nonlinear and have a peak power or peak amplitude constraint. Nonlinearity generates distortions and thus signal-to-noise-and-distortion ratio (SNDR) is an appropriate performance measure. In this paper, we are interested in finding the nonlinear mapping that maximizes the SNDR subject to the peak amplitude constraint. The answer is a soft limiter with gain calculated based on the noise power and the probability density function of the input amplitude. We also investigate a bounding relationship between the SNDR and capacity of the nonlinear channel. The results of this paper can be applied for efficient transmission of high peak-to-average power ratio signals or for optimal linearization of nonlinear devices.

In this paper, we propose a baseband nonlinear transformation technique to improve the overall communication system performance, under the peak power constraint. A closed-form expression is derived for the signal-to-noise-and-distortion ratio (SNDR) of certain nonlinear transformations. A strategy for SNR-adaptive optimum clipping is proposed. For orthogonal frequency division multiplexing (OFDM), we show that the optimal clipping ratio leads to an SNDR improvement of 5-7 dB and accompanying decrease in symbol-error-rate. By applying an iterative symbol detection and clipping noise mitigation algorithm at the receiver, we demonstrate that clipping in OFDM can lead to large performance gains.

The polynomial model is commonly used in predistorter design. However, the conventional polynomial model exhibits numerical instabilities when high-order terms are included. We introduce a novel set of orthogonal polynomial basis functions for predistorter modeling. Theoretically, the conventional and the orthogonal polynomial models are "equivalent", and thus should have the same performance. In practice, however, the two approaches can perform quite differently in the presence of quantization noise and with finite precision processing. Simulation results show that the orthogonal polynomials can alleviate the numerical instability problem associated with the conventional polynomials and generally yield better predistortion linearization performance.

A metasearch engine supports unified access to multiple component search engines. To build a very large-scale metasearch engine that can access up to hundreds of thousands of component search engines, one major challenge is to incorporate large numbers of autonomous search engines in a highly effective manner. To solve this problem, we propose automatic search engine discovery, automatic search engine connection, and automatic search engine result extraction techniques. Experiments indicate that these techniques are highly effective and efficient.