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In this paper, an intelligent reflecting surface (IRS) is deployed to assist the terahertz (THz) communications. The sum-rate of user equipments (UEs) is maximized while guaranteeing the rate requirement of each UE. A block coordinate searching (BCS) algorithm is proposed to jointly optimize the IRS’s coordinates, phase shifts, THz sub-bands allocation and power control. Specifically, the relaxation with penalties based (RPB) algorithm is developed to obtain feasible coordinates of the IRS and guarantee the monotonicity of objective value. In addition, the IRS phase shifts are formulated as closed-form expressions with introduced pricing factors. Simulation results show that the proposed scheme can significantly enhance system performance. [ABSTRACT FROM AUTHOR]

In this paper, we consider a computation offloading problem in a three-tier network consisting of multiple mobile users (MUs), multiple edge clouds, and a central cloud. On this basis, we formulate a system Energy Time Cost ($ETC$) minimization problem by jointly optimizing the resource allocation, mobile-edge matching decision, and offloading decision. Due to the difficulty of directly solving the formulated problem, we decompose it and propose two offloading algorithms, namely Gale-Shapley based Minimum/Sequential Offloading Algorithm (GS-MOA/GS-SOA), to optimize offloading decisions by minimizing the system $ETC$ , in which the mobile-edge matching strategies based on the proposed Gale-Shapley based Mobile-Edge mAtching aLgorithm (GS-MEAL) and resource allocations are optimized iteratively. The simulations show the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Deep neural networks (DNNs) have been widely applied for classification tasks in wireless communications such as link scheduling, user association and base station (BS) muting. When training a DNN using datasets with highly-skewed class distribution where most data belong to a few majority classes, learning performance for minority classes will degrade since the imbalanced data forces the training process to be biased towards the majority classes. In many wireless problems, multiple decisions for classification are made jointly, say deciding which BSs in a network should be muted to avoid interference. In this paper, we employ DNN to learn an optimal predictive interference coordination policy, and strive to avoid biased learning in such a multi-label multi-class classification problem. The major contribution is proposing a training method to encourage fairness among classes by minimizing the maximal cost of decisions among classes, which is converted into a problem to optimize the weighting factors on the training cost of each class. We provide a gradient descent algorithm to optimize the weighting factors and the model parameters of the DNN by alternative updates. Simulation results show that the training method can improve the learning performance for the minority class and can achieve higher network utility than existing training methods. [ABSTRACT FROM AUTHOR]

This paper studies the information-energy ($\mathrm{I}$ - $\mathrm{E}$) region for simultaneous wireless information and power transfer (SWIPT) networks in mobility scenarios, where a moving transmitter simultaneously transmits information and power to a fixed receiver, and the receiver adopts power splitting (PS) receiving architecture. In order to characterize the tradeoff between the received information and harvested energy, the $\mathrm{I}$ - $\mathrm{E}$ region is defined, and corresponding optimization problems are formulated to explore the system $\mathrm{I}$ - $\mathrm{E}$ regions by jointly optimizing the transmit power at the transmitter and the PS ratio at the receiver with three popular energy harvesting (EH) models, i.e., traditional linear EH model, the logistic nonlinear EH model and the piecewise nonlinear EH model. Particularly, to efficiently solve the nonconvex optimization problem with the logistic nonlinear EH model, a successive convex approximate (SCA)-based algorithm is proposed, which is able to characterize the lower bound of the $\mathrm{I}$ - $\mathrm{E}$ with low complexity. To solve the optimization problems with the linear and piecewise EH models, as they are convex, some closed and semi-closed solutions are derived by using Lagrange dual method and KKT conditions. Numerical results show that compared with the $\mathrm{I}$ - $\mathrm{E}$ regions under the linear and piecewise EH models, that under the logistic nonlinear EH model is smaller due to the limitations of practical EH circuit features. Moreover, with a fixed moving speed, when the transmit power is relatively large, the logistic nonlinear EH model can be replaced with the piecewise one due to the relatively small gap between their achieved $\mathrm{I}$ - $\mathrm{E}$ regions. Additionally, for a fixed moving track length, the higher moving speed yields the smaller $\mathrm{I}$ - $\mathrm{E}$ region with all three EH models. [ABSTRACT FROM AUTHOR]

The multi-UAV network is promising to extend conventional networks by providing broader coverage, and better reliability. Nevertheless, the broadcast nature of wireless signals, and the broader coverage expose multi-UAV communications to the threats of passive eavesdroppers. Recent studies mainly focus on securing a single legitimate link, or communications between a UAV, and multiple ground users in one/two-UAV-aided networks, while the physical layer secrecy analysis for hierarchical multi-UAV networks is underexplored. In this paper, we investigate a general two-tier UAV network consisting of multiple UAV transmitters (UTs), and multiple UAV receivers (URs) in the presence of multiple UAV eavesdroppers (UEs). To protect all legitimate UT-UR links against UEs at the physical layer, we design a two-stage framework consisting of a UT-UR association stage, and a cooperative transmission stage. Specifically, we formulate the secure transmission problem into a many-to-one matching game followed by an overlapping coalition formation (OCF) game, taking into account the limited capabilities, and the throughput requirements of URs, as well as the transmission power constraints of UTs. A matching algorithm, and an OCF algorithm are proposed to solve these two sequential games whose convergences, and stabilities are guaranteed theoretically. Simulation results show the superiority of our algorithms, and the effectiveness of our two-stage game framework in the terms of secrecy performance. [ABSTRACT FROM AUTHOR]

Next-generation wireless communications are expected to provide reliable high-rate connectivity. The recent concept of fog-enabled architecture comes to rescue by invoking a substantial amount of data storage close to end-user devices for meeting these challenges. Thus, caching popular contents at heterogeneous devices, e.g., fog nodes (FNs) or fog access points (FAPs), constitutes a promising technique of reducing both the traffic and the energy consumption of the backhaul links. Therefore, in this paper, we propose an energy-efficient caching and node association algorithm for cache-aided fog networks. First, we solve the problem of energy-efficient content caching and delivery in the FNs/FAPs in conjunction with a fixed node association strategy, where the FNs communicate either with other FNs by node-to-node (N2N) communications or with the FAPs in their proximity. In both caching scenarios, we investigate the relationship between the caching probability of the file and the energy-efficient content delivery by formulating the associated energy efficiency (EE) optimization problem under caching memory constraints. Then, we derive a joint modulation mode allocation strategy and caching policy for each content caching node and conceive a joint node association and caching algorithm for maximizing the EE. Finally, we quantify both the overall EE and throughput for demonstrating that the proposed caching and transmission strategy achieves significant performance improvements. [ABSTRACT FROM AUTHOR]

Caching in wireless device-to-device (D2D) networks can bring contents closer to users and reduce backhaul traffic. However, the major obstacle to realize cache-enabled D2D communications is the selfish nature of users. In this paper, we propose an incentive mechanism for pricing the contributions of D2D transmitters in cache-enabled D2D-underlaid cellular networks modeled by stochastic geometry. A Stackelberg game is employed to resolve the conflict of interests between the operator and D2D transmitters. The main objective of this game is to reach the Stackelberg equilibrium, in which both the operator and D2D transmitters achieve the maximum profit by designing the optimal incentive price and the optimal caching strategy. We show the existence of the equilibrium and propose a low-complexity algorithm to obtain the near-optimal caching strategy and incentive price. Numerical results verify the existence of the SE and the advantages of the proposed scheme in terms of the utilities for the operator and D2D transmitters over existing caching schemes with fixed incentive. [ABSTRACT FROM AUTHOR]

Terahertz (THz) band communication is envisioned as a key technology to satisfy the increasing demand for ultrabroadband wireless systems, thanks to its ultrabroad bandwidth. Tailored for the unique properties of pulse-based communications in the THz band, two timing acquisition algorithms are proposed and analyzed thoroughly in this paper. First, a low-sampling-rate (LSR) synchronization algorithm is proposed, by extending the theory of sampling signals with finite rate of innovation in the communication context and exploiting the properties of the annihilating filter. The simulation results show that the timing accuracy at an order of ten picoseconds is achievable. In particular, the LSR algorithm has high performance with uniform sampling at $1/20$ of the Nyquist rate when the signal-to-noise ratio (SNR) is high (i.e., greater than 18 dB). Complementary to this, a maximum likelihood (ML) approach for timing acquisition is developed, which searches for the timing offsets by adopting a two-step acquisition procedure based on the ML criterion. The simulation results show that the ML-based algorithm is well suitable in the low SNR case with a half-reduced search space. For further evaluation, the error performance and the resulting bit-error-rate sensitivity to the timing errors in the LSR and the ML algorithms are both analytically and numerically studied. This paper provides very different and promising angles to efficiently and reliably solve the timing acquisition problem for pulse-based THz band wireless systems. [ABSTRACT FROM PUBLISHER]

To efficiently trade off system sum rate and link fairness, power allocation in wireless spectrum-sharing networks often concentrates upon proportional fairness. The corresponding problem has been proved to be convex for a single tone but NP-hard for more than two tones. However, in the two-tone case, the complexity of the problem for achieving proportional fairness has not been addressed yet. In this paper, we prove that the issue of proportional-fairness optimization for the two-tone situation is NP-hard by reducing the problem of finding the maximum independent set in an undirected graph to it. Moreover, a computationally efficient algorithm is proposed to provide an efficient suboptimal solution. Simulation results are presented to illustrate the effectiveness of our proposal. [ABSTRACT FROM PUBLISHER]

In this paper, we study the distributed precoding problem for simultaneous wireless information and power transfer (SWIPT) in decode-and-forward (DF)-based multiple-input–multiple-output (MIMO) relay networks. The system model considered here consists of a source, a relay, an information decoding (ID) receiver, and an energy harvesting (EH) receiver, all mounted with multiple antennas. Since full channel state information (CSI) is usually unavailable, a practical scenario, where only local CSI is required, is considered in this paper. Such a practical scenario naturally constitutes a noncooperative game, where the source and the relay can be regarded as two rational game players. With properly designed utilities, it can be further shown that the existence and uniqueness of the pure-strategy Nash equilibrium (NE) of the proposed game can both be guaranteed under some mild conditions. Therefore, a distributed iterative precoding algorithm can be developed based on the best-response dynamic to obtain the unique NE solution for the proposed game. Moreover, a proximal-point-based regularization approach is also pursued to ensure the convergence of the proposed algorithm without requiring special restrictions on the channel ranks. Numerical simulations are also provided to demonstrate the proposed algorithm. Results show that our algorithm can converge quickly to a satisfactory solution with guaranteed convergence. [ABSTRACT FROM PUBLISHER]

Unmanned aerial vehicles (UAVs) are anticipated to be widely deployed in future wireless communications, due to their advantages of high mobility and easy deployment. However, the broadcast nature of air-to-ground line-of-sight wireless channels brings a new challenge to the information security of UAV-ground communication. This paper tackles such a challenge in the physical layer by exploiting the mobility of UAV via its trajectory design. We consider a UAV-ground communication system with multiple potential eavesdroppers on the ground, where the information on the locations of the eavesdroppers is imperfect. We formulate an optimization problem, which maximizes the average worst case secrecy rate of the system by jointly designing the robust trajectory and transmit power of the UAV over a given flight duration. The nonconvexity of the optimization problem and the imperfect location information of the eavesdroppers make the problem difficult to be solved optimally. We propose an iterative suboptimal algorithm to solve this problem efficiently by applying the block coordinate descent method, $\mathcal {S}$ -procedure, and successive convex optimization method. Simulation results show that the proposed algorithm can improve the average worst case secrecy rate significantly, as compared to two other benchmark algorithms without robust design. [ABSTRACT FROM AUTHOR]

With the rapid emergence of applications in Mobile Opportunistic Networks (MONs), understanding and characterizing their properties becomes extremely important. A fundamental model for MONs is time-varying graph, which currently remains poorly understood since many well-recognized properties of static graphs have no obvious counterpart in dynamic ones. In MONs, the dynamical links change opportunistically and usually the system energy is very limited, which results in unique and unknown properties about the network connectivity and reachability. In this paper, taking the communication energy into account, we introduce the concept of Energy-aware Temporal Reachability Graphs (ETRG), which characterizes the connectivity of MONs with the consideration of communication consumed energy, and consequently reveals the communication capabilities of MONs with the given of real-world system parameters of data size, tolerable delay, and energy budget. We come up with efficient algorithm to calculate ETRG from the corresponding time-varying graphs. By applying ETRG to several mobile networks recorded by real-life human and vehicular mobility traces, we characterize their network connectivity properties in terms of average reachability, communication asymmetry, and stability. Moreover, utilizing ETRG that places upper bounds of the communications capabilities, we reveal the fundamental relations and tradeoffs among large-scale variability of the system metrics of energy budget, tolerable delay, and data size on the system performance. [ABSTRACT FROM AUTHOR]

Recent advances in loop interference cancelation techniques enable full-duplex relaying (FDR) systems, which transmit and receive simultaneously in the same band with high spectrum efficiency. Meanwhile, wireless virtualization has attracted a lot of attention from both academia and industry because it can provide more flexibility, diversity, and other benefits in the process of wireless network design, construction, operation, and management. In this paper, we introduce the idea of wireless virtualization into FDR cellular networks. Then, the problem of energy-aware virtual resource management is formulated as a three-stage Stackelberg game. The subgame perfect equilibrium for each stage is analyzed. In addition, the interplays of the three-stage game are discussed, and an iterative algorithm is proposed to obtain the Stackelberg equilibrium solution. Simulation results are presented to show the effectiveness of the proposed scheme. [ABSTRACT FROM PUBLISHER]

This paper considers a heavy loaded full-duplex (FD) small cell consisting of one FD base station and multiple half-duplex users where the number of subchannels is less than the number of uplink and downlink users. With the objective of maximizing the total sum-rate, a joint power allocation, and user pairing problem is formulated. To solve this problem, a novel decomposition method is proposed, which decouples the power allocation and user pairing problem into two subproblems. It is proven that the optimal solution of subproblems is the optimal solution of the original problem. Then, optimal algorithms are developed for the two subproblems. Numerical results show that the proposed algorithm achieves better performance compared with the baselines of the greedy algorithm and random pairing algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we consider a cellular/infostation integrated network that supports on-demand data service delivery over a wireless link to users in a high-speed train. For the requests with different lifetimes and prices that they are willing to pay, we develop the optimal packet schedule that aims at earning the maximum revenue. To this end, we formulate the problem as an integer linear program (ILP). The problem cannot be solved efficiently by existing ILP solvers with guaranteed polynomial-time complexity in the worst case. By exploring the special structure of the formulated ILP, we propose a novel Checker algorithm and prove that this algorithm is guaranteed to find the offline optimal schedule in polynomial time. Based on the relevant insights, we further develop a class of online Checker algorithms that require only causal knowledge of service demands and wireless channel capacities. It is established that these online algorithms have a worst-case competitive ratio of 1/2, i.e., total revenue earned by them is at least half as much as the revenue earned by an offline optimal schedule that knows the complete a priori knowledge of future requests and channel conditions. Simulation results are provided to demonstrate the merit of the proposed algorithms. [ABSTRACT FROM PUBLISHER]

We consider the uplink transmission in multiuser wireless systems with multiple single-antenna transmitting users and a multiantenna receiver. We address two problems in this paper. The first problem is user admission, i.e., given a large number of users, how to admit the maximum number of users that can simultaneously satisfy quality of service (QoS) and power constraints. The second problem is how to distribute the data among the users when they are allowed to share data before transmission. The aim is to minimize the total data exchange cost. Such a problem is called user clustering. We formulate those problems into sparsity-maximization problems, which are NP-hard. Inspired by compressive sensing techniques, we propose a common framework to tackle those problems by first applying the \ell1 -norm relaxation and then solving them with convex optimization methods. Simulations show that the proposed algorithms achieve excellent performance. For user admission, the numbers of admitted users by the proposed algorithms are close to the optimum numbers of admitted users obtained by exhaustive search (ES) . For user clustering, the total data exchange cost is reduced by more than 10% after only a few iterations. When the QoS requirement is low, the user data exchange can be avoided using the proposed method, which achieves the optimum result obtained by ES. [ABSTRACT FROM PUBLISHER]

In this paper, we study energy-efficient power allocation in distributed antenna systems (DASs). Unlike previous works that assumed perfect channel-state information (CSI) at the transmitter, we investigate a more practical case, where only the slowly varying large-scale CSI is known, to reduce the system overhead for CSI acquisition. Under a generalized DAS model, the power allocation problem for energy-efficiency (EE) maximization is formulated as a nonconvex fractional programming problem. Using the random matrix theory, the nonlinear fractional programming theory, and the saddle-point theory, we propose an iterative algorithm that achieves the global optimal solution. We also show that deploying the antennas in a distributed manner may enlarge the region where the optimal EE and the optimal spectral efficiency (SE) can be simultaneously achieved. Accordingly, we suggest the DAS-like way to densify the future 5G and beyond cellular networks to achieve EE and SE simultaneously. [ABSTRACT FROM PUBLISHER]

In emergency communications, guaranteeing ultrareliable and low-latency communication is challenging yet crucial to save human lives and to coordinate the operations of first responders. To address this problem, we introduce a general approach for channel selection in mission-critical communications, i.e., choose channels with the best quality timely and accurately via channel probing. Since the channel conditions are dynamic and initially unknown to wireless users, choosing channels with the best conditions is nontrivial. Thus, we adopt online learning methods to let users probe channels and predict the channel conditions by a restricted time interval of observation. We formulate this problem as an emerging branch of the classic multiarmed bandit (MAB) problem, namely thepure-exploration banditproblem, to achieve a tradeoff between sampling time/resource budget and the channel selection accuracy (i.e., the probability of selecting optimal channels). The goal of the learning process is to choose the “optimal subset” of channels after a limited time period of channel probing. We propose and evaluate one learning policy for the single-user case and three learning policies for the distributed multiuser cases. We take communication costs and interference costs into account, and analyze the tradeoff between these costs and the accuracy of channel selection. Extensive simulations are conducted and the results show that the proposed algorithms can achieve considerably higher channel selection accuracy than previous exploration bandit approaches and classic MAB methods. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the cross-layer resource optimization for wireless relay networks with dynamic node-selfishness information. The degree of intrinsic selfishness (DeIS) and the degree of extrinsic selfishness (DeES) of the relay node (RN) are defined as the effects of its own residual energy resource and the incentive in an incentive mechanism, respectively. A model of two-timescale node-selfishness dynamics is constructed to describe the time-varying characteristics of the RN's DeIS and DeES. Based on the two-timescale node-selfishness dynamics of the RNs, a two-timescale resource-optimization dynamical algorithm (TRODA) is employed to control the transmit powers of both RNs and end users (EU), as well as the flow rates of the data packets injected by all EUs. Meanwhile, the stability and the tracking error of the TRODA under the two-timescale node-selfishness dynamics are analyzed by Lyapunov theory. Our simulation results demonstrate that the two-timescale optimization algorithm is stable and has trivial tracking error. [ABSTRACT FROM AUTHOR]

Location estimation and tracking for mobile stations have attracted a significant amount of attention in recent years. Different types of signal sources are considered available to provide measurement inputs for location estimation and tracking in heterogeneous wireless networks. Various techniques have been studied and combined for location tracking, e.g., the least square methods for location estimation associated with the Kalman filters for location tracking. In this paper, the hybrid unified Kalman tracking (HUKT) technique is proposed to provide an integrated algorithm for precise location tracking based on both time of arrival (TOA) and time difference of arrival (TDOA) measurements. A new variable is incorporated as an additional state within the Kalman filtering formulation to consider the nonlinear behavior in the measurement update process. The relationship between this new variable and the desired location estimate is applied in the state update process of the Kalman filter. Three different designs of hybrid factor are proposed to adaptively adjust the weighting value between the TOA and TDOA measurements. Moreover, similar concepts are also utilized in the design of unified Kalman tracking schemes for pure TOA and TDOA measurement inputs in this paper. Compared with existing schemes, numerical results illustrate that the proposed HUKT algorithm can achieve enhanced accuracy for mobile location tracking, particularly under environments with an insufficient number of measurements in one of the signal paths. [ABSTRACT FROM PUBLISHER]

In this paper, we dynamically select the transmission rate and design wireless network coding to improve the quality of services, such as delay for time-critical applications. In a network coded system, with a low transmission rate and, hence, a longer transmission range, more packets may be encoded, which increases the coding opportunity. However, a low transmission rate may incur extra transmission delay, which is intolerable for time-critical applications. We design a novel joint rate selection and wireless network coding (RSNC) scheme with a delay constraint to maximize the total benefit (where we can define the benefit based on the priority or importance of a packet for example) of the packets that are successfully received at the destinations without missing their deadlines. We prove that the proposed problem is NP-hard and propose a novel graph model to mathematically formulate the problem. For the general case, we propose a transmission metric and design an efficient algorithm to determine the transmission rate and coding strategy for each transmission. For a special case when all delay constraints are the same, we study pairwise coding and present a polynomial-time pairwise coding algorithm that achieves an approximation ratio of \1 -(\1/e) to the optimal pairwise coding solution, where $e$ is the base of the natural logarithm. Finally, simulation results demonstrate the superiority of the proposed RSNC scheme. [ABSTRACT FROM PUBLISHER]

The high-speed railway wireless communication network architecture is gradually transforming from a traditional GSM-Railway (GSM-R) cellular network to a 5G heterogeneous ultra-dense communication network. How to effectively deal with the interference while obtaining the capacity gain has become an inevitable problem. This article proposes a power allocation interference alignment algorithm based on game equilibrium with the goal of joint optimization about throughput and energy efficiency for imperfect channels in high-speed railway communication. Firstly, the imperfect Channel State Information including delay and fading is calculated. Then, in order to improve system throughput and energy efficiency, we establish a game model and prove the existence of Nash equilibrium in the model. At the same time, a power allocation iterative algorithm based on Recurrent Neural Network is proposed. Finally, combined with the interference alignment algorithm based on a maximal signal-to-noise ratio, the optimal power matrix is calculated by iteration to achieve interference management optimization. The simulation results prove that our algorithm has superior performance in improving the system throughput, energy efficiency and transmission reliability in high-speed railway wireless communication with imperfect channels. [ABSTRACT FROM AUTHOR]

Most slot assignment algorithms that are proposed for spatial time-division multiple-access (STDMA) mesh networks use a simplified transmission model that does not consider the time-varying fading characteristic of a wireless channel. In addition, these algorithms are usually link based, and scheduling in each slot is fixed and done for links. When the scheduled link is in deep fade or has no traffic, the slot that is given to that link will be wasted. In this paper, we present a node-based slot assignment scheme in which scheduling in each slot is done for nodes and not links. Because a node has multiple links, it can exploit multiuser diversity, i.e., different links have different traffic and fading conditions, to achieve much better performance. [ABSTRACT FROM PUBLISHER]

Cooperative communication (CC) offers an efficient and low-cost way to achieve spatial diversity by forming a virtual antenna array among single-antenna nodes that cooperatively share their antennas. It has been well recognized that the selection of relay nodes plays a critical role in the performance of CC. Most existing relay selection strategies focus on optimizing the outage probability or energy consumption. To fill in the vacancy of research on throughput improvement via CC, we study the relay selection problem with the objective of optimizing the throughput in this paper. For unicast, it is a P problem, and an optimal relay selection algorithm is provided with a correctness proof. For broadcast, we show the challenge of relay selection by proving it nonprobabilistic hard (NP-hard). A greedy heuristic algorithm is proposed to effectively choose a set of relay nodes that maximize the broadcast throughput. Simulation results show that the proposed algorithms can achieve high throughput under various network settings. [ABSTRACT FROM PUBLISHER]

Usually, vehicles' stop-and-go driving will consume more fuel and emit more \CO2 than constant speed driving. To reduce vehicles' \CO2 emissions, vehicles' travel should be smoothed by reducing the stop-and-go times. In this paper, a three-tier structure is proposed to realize dynamic traffic light control for smoothing vehicles' travel. In tier-1, an electronic toll collection (ETC) system is employed for collecting road traffic flow data and calculating the recommended speed. In tier-2, radio antennas are installed near the traffic lights. Road traffic flow information can be obtained by wireless communication between the antennas and ETC devices. In tier-3, a branch-and-bound-based real-time traffic light control algorithm is designed to smooth vehicles' travels. After smoothing vehicles' travels, more vehicles can pass intersections with less waiting time and fewer short-time stops; therefore, the vehicles' \CO2 emissions can be reduced. Simulation results indicate that the proposed scheme performs much better than the adaptive fuzzy traffic light control method: The average waiting time, short-time stop times, and \CO2 emissions are greatly reduced, and the nonstop passing rate is greatly improved. [ABSTRACT FROM PUBLISHER]

Proportional fair scheduling has attracted much attention and has been well studied in the single-cell network scenario, where only one base station (BS) serves many users. However, for a network where one user may obtain services from multiple BSs geographically located apart, not much work has been done. In this paper, we investigate how to achieve proportional fair allocation of users' throughput in a wireless network with multiple BSs and multiple users. A low-complexity algorithm is presented. We prove that the developed algorithm is approximately optimal in the sense of maximizing a global proportional fairness utility. Furthermore, two different implementation strategies to achieve the global proportional fair scheduling are proposed: one for networks where inter-BS communications are allowed and the other for networks where only the transmissions between BSs and users are possible. The later implementation makes the algorithm operate in a totally distributed manner. Various simulations confirm that the new global fair scheduling scheme outperforms the single-cell proportional fair scheme in terms of the average user throughput, user throughput oscillation, and fairness. Low-complexity and distributed implementation achieves almost the same performance as the centralized counterpart. [ABSTRACT FROM PUBLISHER]

In this paper, we study joint performance optimization on network lifetime and video distortion for an energy-constrained wireless video sensor network (WVSN). To seek an appropriate tradeoff between maximum network lifetime and minimum video distortion, a framework for joint source/channel rate adaptation is proposed, where the video encoding rate, link rate, and power consumption are jointly considered, formulating a weighted convex optimization problem. In the context of lossy wireless channels, an efficient error control scheme that couples network coding and multipath routing is explored. Moreover, an integrated power consumption model, including power dissipation on video compression, error control, and data communication, is specifically developed for the video sensor node. By primal decomposition, the original problem is decomposed into a two-level optimization procedure, with the high-level procedure for source adaptation (source rate optimization) and the low-level procedure for channel adaptation (network resource allocation). Finally, a fully decentralized iterative algorithm is developed to resolve the target optimization problem. Extensive simulation results evaluate the convergence performance of the proposed algorithm and demonstrate the best tradeoff performance. [ABSTRACT FROM PUBLISHER]

Interference alignment (IA) is a transmission technique for exploiting all available degrees of freedom in the frequency- or time-selective interference channel with an arbitrary number of users. Most prior work on IA, however, neglects interference from other nodes in the network that are not participating in the alignment operation. This paper proposes three generalizations of IA for the multiple-antenna interference channel with multiple users that account for colored noise, which models uncoordinated interference. First, a minimum interference-plus-noise leakage (INL) algorithm is presented and shown to be equivalent to previous subspace methods when noise is spatially white or negligible. This algorithm results in orthonormal precoders that are desirable for practical implementation with limited feedback. A joint minimum mean square error design that jointly optimizes the transmit precoders and receive spatial filters is then proposed, whereas previous designs neglect the receive spatial filter. Finally, a maximum signal-to-interference-plus-noise ratio (SINR) algorithm is developed and proven to converge, unlike previous maximum SINR algorithms. The sum throughput of these algorithms is simulated in the context of a network with uncoordinated cochannel interferers that are not participating in the alignment protocol. It is found that a network with cochannel interference can benefit from employing precoders that are designed to consider that interference, but in extreme cases, such as when only one receiver has a large amount of interference, ignoring that the cochannel interference is advantageous. [ABSTRACT FROM PUBLISHER]

For the fast access with high reliability, a single grant allows a transmitter to transmit multiple redundant packets with new redundancy information in the time domain without waiting for feedback from a receiver. However, entire redundant packets shall be retransmitted in response to a negative acknowledgment of those redundant packets, which increases the service latency. To reduce a long HARQ RTT due to a number of redundant packets, we propose packet-by-packet parallel processing that achieves lower latency by alleviating the long HARQ RTT of repeated transmission. We also provide a model and analysis for repeated transmission functionality, in terms of the wireless transmission latency with packet-by-packet processing. Numerical analysis and simulation in various network environments show that the proposed algorithm achieves lower latency without trading reliability off. [ABSTRACT FROM AUTHOR]

Device-free identification (DFI) is a promising technique, which could recognize human identity using his/her unique influence on surrounding wireless signals in a device-free and contact-free manner. It could maintain the privacy of a user and enable smart applications to provide customized service for a specific user. Despite its advantages over other person's identification systems, one fundamental problem to solve is that the accuracy of the DFI system is a little bit low due to the extremely noisy wireless measurements. The goal of this work is to explore and exploit a method to extract intrinsic features from the noisy channel state information (CSI) so as to realize high-performance DFI. To this end, we propose a novel empirical-mode-decomposition-based general DFI framework, which decomposes raw noisy CSI measurements into intrinsic mode functions (IMF) and extracts intrinsic features from the IMF components accordingly. Under the proposed framework, we also develop two DFI systems based on the respiration and gait biometric features. Extensive experiments carried out on commodity WiFi reveal that the developed systems could identify a person with an accuracy of over 90% from a group of ten persons. [ABSTRACT FROM AUTHOR]

A zero-forcing (ZF)-based transmission method is proposed for a wireless powered communication network (WPCN) with multiuser multi-input multi-output (MU-MIMO) channels. We formulate the max–min user rate optimization problem based on ZF-based uplink transmission, under the transmit power constraints determined by the downlink power transfer, and derive a Newton-like iterative algorithm to find the optimal solution of the proposed problem. Using the proposed ZF-based method, we design a downlink energy beamformer, and optimize the uplink power allocation vector, precoder, and combiner in terms of the max–min user rate for a MU-MIMO WPCN system. Numerical simulations show that the proposed method provides at least 3 dB signal-to-noise ratio gain compared to the conventional transmission schemes for WPCN. Moreover, through complexity analysis and simulations, it is shown that the proposed method can reduce at least 92% of the computation load in terms of runtime with moderate achievable rate loss, compared to the optimal method obtained by the interior-point method. [ABSTRACT FROM AUTHOR]

Many emerging applications, such as security surveillance and smart healthcare, have incubated the device-free localization (DFL) technique, which could estimate the location of a target without equipping any device. However, current DFL system needs labor-intensive training to learn the influence of the target on surrounding wireless signals, so as to provide model parameters or radio maps for the location estimation algorithm. To address this issue, we develop a novel robust training-free DFL system, named DeFi, which could directly estimate the target location by refining the angle-of-arrival (AOA) of the target reflection path based on WiFi channel state information. We analyze the characteristics of the static paths and the motion paths, eliminate the static paths using a background elimination algorithm, and separate out the target reflection path from the motion paths according to the AOA and equivalent time-of-flight measurements. With the AOA of the target reflection path as observation information, we robustly estimate the target location based on a particle filter algorithm. We implement the proposed DeFi system on commodity off-the-shelf WiFi devices and validate its performance in two indoor scenarios. Experimental results show that DeFi achieves a median error of less than 0.6 m. [ABSTRACT FROM AUTHOR]

Efficient distributed resource allocation and users to cells assignment over a heterogeneous integrated wireless environment is addressed, where multiple-access technologies coexist and cooperate toward achieving network-wide quality-of-service (QoS)-aware utility-based performance optimization. The integrated network is viewed as a global optimizer under appropriate intracell resource allocation constraints. Multiaccess diversity is addressed by setting a user-centric service-aware goal, thus maximizing users' QoS-aware service performance satisfaction, as expressed through properly assigned utility functions. The integrated network utility maximization problem is treated through a two-level dual-decomposition methodology, leading to a low-complexity distributed iterative subgradient algorithm for attaining a network-wide near-optimal operation point. Extensive numerical results verify the analytical claims, demonstrate the applicability of the proposed framework, and illustrate that significant performance gains can be achieved over popular existing resource allocation schemes. [ABSTRACT FROM PUBLISHER]

As the number and variety of wireless devices sharing spectrum increases, it becomes increasingly important to characterize the sum interference that is produced by a large number of interferers. We show that, in the case of several interferers, the assumption of independent shadowing paths is very inaccurate and must be replaced by an appropriate correlation model. We choose one such model, which has desirable mathematical and physical properties, is tunable, and is particularly well suited for simulation, although our approach can also be used with other correlation models. In addition, we allow a very versatile channel and system model. The simulation cost of such systems quickly grows for large numbers of interferers due to the time and memory constraints of the Monte Carlo simulation algorithm using the classic matrix factorization (e.g., Cholesky) approach. We show how an alternative simulation approach using shadowing fields can significantly reduce the order of the computational cost. In addition, we show how judicious random sample reuse and extrapolation based on a numerical analysis of moments can be used to further simplify the simulation. Through the combination of these three approaches, using a mixture of simulation, numerical, and analytical techniques, we can obtain accurate approximations of the distribution of the total interference power while reducing computational time by factors of more than 1000. We can also make some mathematical statements about the problem, which may be useful for further developments. We argue that our model is complex enough to accommodate a good degree of realism and that our approach is a viable alternative to the pure analysis of such a complex and versatile problem. [ABSTRACT FROM PUBLISHER]

Wireless access points (APs) divide a plane into small areas where their coverage ranges overlap. A mobile device can be located within a particular small overlapped area based on the unique set of APs covering the device. We formally define an Optimal Loc-deployment problem for both coverage and area localization. Our objective is to deploy a minimum number of APs that provide full communication coverage while achieving the ability to locate a mobile device within a certain area no larger than a given accuracy parameter. We obtain a formula that precisely determines the optimal solution for more than half of the accuracy values. For the rest of the accuracy values, we propose an algorithm that will return an approximation whose difference to the optimal solution is less than \epsilon for any \epsilon > 0. Finally, we conduct extensive numerical evaluation and real experiments to validate our proposed solutions. [ABSTRACT FROM PUBLISHER]

We derive closed-form expressions for the secondorder statistics of the power gain (as a function of frequency) of wideband microwave indoor channels. We obtain our results within a framework that is general enough to be compatible with several popular channel models, such as the Saleh–Valenzuela (SV) channel model and those proposed by the IEEE 802.15.3a and IEEE 802.15.4a task groups. As in all these models, our channel description is based upon clusters and rays with (possibly mixed) Poisson arrivals and random amplitudes. Our results consist of closed-form expressions for the second-order statistics of the channel power frequency response, where statistical averages involve expectations over ray amplitudes and arrival times. These expressions reveal that the autocovariance of the spectral power gain between any two frequencies decreases and tends to zero as the difference between these frequencies tends to infinity if and only if the cluster arrival rate goes to infinity. They also show that the variance-to-squared-mean ratio of the narrow-band power gain exhibits exactly the same behavior with respect to the center frequency. We then use these results to obtain closed-form expressions for the variance and the second-order moment of the aggregate channel power gain over any given interval of frequencies. This allows us to express the channel spectral diversity as a function of model parameters and bandwidth. In addition, we illustrate how these equations allow one to devise automatic cluster identification algorithms which, from empirical estimates of the second-order spectral statistics of the channel power gain, can confirm or deny the existence of clusters in a given scenario. [ABSTRACT FROM PUBLISHER]

We present a blind recursive algorithm for tracking rapidly time-varying wireless channels in precoded multiple-input–multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. Subspace-based tracking is normally considered for slowly time-varying channels only. Due to the frequency correlation of the wireless channels, the proposed scheme can collect data not only from the time but from the frequency domain as well to speed up the update of the required second-order statistics. After each such update, the subspace information is recomputed using the orthogonal iteration, and then, a new channel estimate is obtained. We also investigate choices of precoder in terms of the tradeoff between the symbol recovery capability and the channel estimation performance and demonstrate the convergence properties of our approach. The proposed algorithm is evaluated in a Third-Generation Partnership Project (3GPP) Spatial Channel Model suburban macro scenario, in which a mobile station is allowed to move in any direction with a speed up to 100 km/h, corresponding to a maximum Doppler shift of about 230 Hz in this case. Numerical experiments show that the normalized mean square error of the channel estimates converges to a level of -30 dB within less than five OFDM symbols when the signal-to-noise ratio (SNR) (per symbol) is \geq 20 dB. [ABSTRACT FROM PUBLISHER]

A novel representation for a two-hop decentralized wireless relay network (DWRN) is proposed, where the relays operate in a completely distributive fashion. The modeling paradigm applies an analogous approach to the description method for double-directional multipath propagation channels and takes into account finite system spatial resolution and extended relay listening/transmitting time. Specifically, the double- and single-directional information azimuth spectra (IAS) are formulated to provide a compact representation of information flows in the DWRN. The proposed analytical framework is then studied from a geometry-based statistical modeling perspective. Finally, we look into the problem of relay network tomography (RNT), which solves an inverse problem to infer the internal structure of an unknown relay network by using the double-directional IAS recorded at multiple measuring nodes exterior to the relay region. Numerical examples are presented to demonstrate the efficacy of the suggested channel description method and the RNT algorithm. [ABSTRACT FROM PUBLISHER]