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This article studies the mean standard deviation (mean-std) Canadian traveller problem (CTP). Different from the canonical CTP, which aims at minimizing the traveller's expected travel time, while considering edge breakdown probabilities, we introduce the reliability version of CTP, which tries to find a routing policy with the minimal linear combination of the travel time's mean and standard deviation. With the recent development of internet-of-things (IoT) technology, the transportation network's edges' travel-time statistics, i.e., mean and standard deviation, as well as the traversal probabilities, are available to the end users. With those information, we propose a dual dynamic programming (DDP) method, which simultaneously estimates the first-order and the second-order moments of a given decision-list (DL) policy, and thereby makes improvements towards to the optimal one through the generalized policy iteration (GPI) scheme. We construct an open source benchmark environment to evaluate the performance of different mean-std CTP solutions, and show that the DDP method outperforms state of the arts in a range of transportation networks. [ABSTRACT FROM AUTHOR]

A variety of emergency events such as traffic accidents often happen in urban areas and the related emergency information needs to be urgently diffused to vehicles that may pass through the accident place to avoid escalation of the event. However, due to the dynamics of vehicles, the information diffusion links between vehicles and roadside units (RSUs) may be unstable, and thus the information diffusion based on fixed RSUs may lead to the low information propagation rate and the limited influence range. To address the above issues, we propose an emergency information diffusion strategy based on the Social Internet of Vehicle (SIoV), where vehicles can build inter-vehicle social relationships without human intervention and exchange emergency information through stable vehicle-to-vehicle (V2V) links. In detail, we design a $b$ -matching based stable link construction algorithm to build stable V2V links by considering the social characteristics and connection probability simultaneously, and then depict the vehicle link graph. Based on this graph, we formulate the information diffusion problem as an influence maximization problem. To solve this problem, we design an SIoV based emergency information influence maximization (SEIM) algorithm to maximize the influence range by selecting some influential seed vehicles and boosting some vehicles’ influence. The theoretical analysis and simulation results show that our algorithms have a lower dissemination delay and larger influence range by sacrificing part of the signaling overheads performance. [ABSTRACT FROM AUTHOR]

In this paper, we investigate non-orthogonal multiple access (NOMA) assisted secure computation offloading under the eavesdropping-attack, in which a malicious node overhears the edge-computing user's (EU’s) offloading transmission to the edge-computing server (ES), and NOMA is used for the EU's offloading and meanwhile for providing artificial jamming to the eavesdropper. Since multi-user simultaneous transmission over a same frequency channel can be enabled by NOMA, a wireless user (WU) can form a NOMA pair with the EU to provide cooperative jamming to the eavesdropper while also gaining an opportunity of sending its data. Focusing on the EU-WU pair with the fixed WU's energy-provisioning, we exploit the physical layer security to quantify the EU's offloading throughput with the help of WU's jamming. We then study the joint optimization of the EU's computation offloading and the EU-WU's NOMA transmission for minimizing the EU's total energy consumption subject to its latency-requirement in completing the computation-task. By utilizing the feature of analytical solution of the WU's transmission, we then investigate the WU's optimal energy-provisioning for the EU-WU pair, such that both the EU and WU can benefit from the cooperative jamming in a fairness manner. Specifically, we formulate the EU-WU's cooperation as a Nash bargaining game. By identifying the monotonic feature of Nash bargaining problem, we propose a polyblock approximation based algorithm for determining the WU's optimal energy-provisioning to achieve the win-win solution for the paired EU and WU. Finally, we investigate the scenario of multiple EUs and WUs, and aim at finding the stable pairing between the EUs and WUs, such that no individual EU (or WU) would like to change its partner. An efficient algorithm, which is based on the Gale-Shapley theory while exploiting the quantitative feature of EUs’ and WUs’ net-rewards, is proposed to achieve the stable EU-WU pairings. Numerical results are provided to validate our proposed algorithms and demonstrate the advantage of our proposed NOMA assisted computation offloading via cooperative jamming. [ABSTRACT FROM AUTHOR]

In this paper, we propose one joint interference alignment and subchannel allocation (JIARA) interference management scheme, aiming at maximizing the number of satisfactory users with different degrees of freedom (DoF) demand in ultra-dense wireless networks, while the existing works only consider the same DoF demands. The original JIARA scheme is equivalent to the maximum $K$ -colorable subgraph (MKCS) problem in graph theory for a given $K$ -colors and the multicoloring demand at each vertex, hence NP-hard. Therefore, we propose one graph theory based low complexity sub-optimal JIARA scheme. Firstly, we propose one virtual allocation of subchannel based scheme to construct transformed the conflict graph, which is applicable for the different DoF demands and different size of the feasible IA group. Furthermore, we propose one greedy multi-coloring based algorithm to determine the maximum number of satisfactory users. Simulation results show that the proposed JIARA scheme outperforms the subchannel resource allocation only scheme. [ABSTRACT FROM AUTHOR]

With proliferation of fifth generation (5G) new radio (NR) technology, it is expected to meet the requirement of diverse traffic demands. We have designed a coordinated multi-point (CoMP) enhanced flexible multi-numerology (MN) for 5G-NR networks to improve the network performance in terms of throughput and latency. We have proposed a CoMP enhanced joint subcarrier and power allocation (CESP) scheme which aims at maximizing sum rate under the considerations of transmit power limitation and guaranteed quality-of-service (QoS) including throughput and latency restrictions. By employing difference of two concave functions (D.C.) approximation and abstract Lagrangian duality method, we theoretically transform the original non-convex nonlinear problem into a solvable maximization problem. Moreover, the convergence of our proposed CESP algorithm with D.C. approximation is analytically derived with proofs, and is further validated via numerical results. Simulation results have demonstrated that our proposed CESP algorithm outperforms the conventional non-CoMP and single-numerology mechanisms along with other existing benchmarks in terms of lower latency and higher throughput under the scenarios of center and edge users. [ABSTRACT FROM AUTHOR]

In this paper, we consider a distributed joint sensing and communication (DJSC) system in which each radar sensor as a JSC node is equipped with a sensing function and a communication function. Deploying multiple JSC nodes may require a large amount of bandwidth. Therefore, we investigate the bandwidth allocation problem for the DJSC system. In particular, we aim to optimize the bandwidth allocation to the sensing function and the communication function of the JSC nodes. To improve the allocation efficiency while benefiting the spatial diversity advantage of the DJSC systems, the objective is to maximize the sum of sensing performances, i.e., estimation rates, communication performances, i.e., communication data rates, and fairness of all the users. The optimization problem is non-convex and difficult to be solved. For this, we propose a fully polynomial time approximation algorithm, and we prove that the approximation algorithm can guarantee a near-optimal solution with an accuracy bound of $\epsilon$. Furthermore, we propose to use a heuristic algorithm with lower complexity. The simulation results show that both the proposed algorithms are able to achieve the solutions close to the optimum in a computationally efficient fashion. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) and backscatter communication are two emerging technologies for low-power communication. In this paper, we consider a NOMA backscatter system, where signals from two backscatter devices are multiplexed on a frequency resource block using NOMA in each time slot. Our objective is to maximize the average energy efficiency by optimizing backscatter device pairing, reflection coefficients of backscatter devices, and the transmit power of the reader. We formulate the average energy efficiency maximization problem subject to the minimum circuit power and the minimum data rate requirements of the backscatter devices, and the transmit power constraint of the reader. The formulated problem is nonconvex. To obtain a suboptimal solution for this problem, we use alternating optimization technique and decompose the problem into two subproblems. The subproblems are solved by using fractional programming, Dinkelbach’s algorithm, and successive convex approximation method. Simulation results show that our proposed algorithm converges quickly to a suboptimal solution. Our proposed algorithm outperforms several baseline algorithms, including the genetic algorithm, fixed device pairing scheme, conventional device pairing scheme, maximum transmit power allocation scheme, and random reflection coefficient selection scheme, in terms of the average energy efficiency. The optimality gap of our proposed algorithm is investigated by comparing with the optimal scheme, in which the optimal device pairing is obtained based on exhaustive search. [ABSTRACT FROM AUTHOR]

Emerging virtual reality (VR) applications require high data rate transmission and low end-to-end latency, which has become one of the main challenges for future wireless networks. Unmanned aerial vehicle (UAV) mounted base stations and computing facilities can be used to provide better wireless connectivity and computing services to edge VR users to meet their computing needs and reduce the end-to-end latency. We propose a novel UAV assisted mobile edge computing (MEC) network to enable high-quality mobile 360-degree video VR applications by leveraging UAVs to provide the required communication and computing needs. Then, we formulate the joint UAV placement, MEC and radio resource allocation, and 360-degree video content layer assignment (UAV-MV) problem, which aims to select the allocation of computing and communications resources and the location of the UAVs such that the delivered quality of experience (QoE) is maximized across the mobile VR users, given various system constraints. We show that the problem is NP-hard, and decompose it into three lower-complexity subproblems that we solve sequentially. We design an approximation algorithm with performance guarantees that solves the UAV-MV problem based on the solutions to the three subproblems. Our simulation results show that the average QoE enabled by the proposed algorithm is 15% and 90% greater relative to two competitive reference methods. [ABSTRACT FROM AUTHOR]

Multi-carrier faster-than-Nyquist (MFTN) signaling constitutes a promising spectrally efficient non-orthogonal physical layer waveform. In this correspondence, we propose a pair of low-complexity generalized approximate message passing (GAMP)-based frequency-domain equalization (FDE) algorithms for MFTN systems operating in multipath channels. To mitigate the ill-condition of the resultant equivalent channel matrix, we construct block circulant interference matrices by inserting a few cyclic postfixes, followed by truncating the duration of the inherent two-dimensional interferences. Based on the decomposition of the block circulant matrices, we develop a novel frequency-domain received signal model using the two-dimensional fast Fourier transform for mitigating the colored noise imposed by the non-orthogonal matched filter. Moreover, we derive a GAMP-based FDE algorithm and its refined version, where the latter relies on approximations for circumventing the emergence of the ill-conditioned matrices. Our simulation results demonstrate that, for a fixed spectral efficiency, MFTN signaling can significantly improve the bit error rate (BER) performance by jointly optimizing the time- and frequency-domain packing factors. Compared to its Nyquist-signaling counterpart, our proposed MFTN systems employing the refined GAMP equalizer can achieve about 39% higher transmission rates at a negligible BER performance degradation. [ABSTRACT FROM AUTHOR]

In this paper, two widely used spectrum sensing algorithms, sphericity test and energy detection are considered. The statistics of sphericity test and energy detector with estimated noise power are weighted and fused to propose a mixture detector. The proposed mixture detector based on the fusion test statistics can simultaneously utilize the power information and correlation feature of the received signals. Specifically, the theoretical optimal weight is derived by maximizing the deflection coefficient of the mixture detector. Besides, the analytical expressions for the decision threshold, false alarm probability and detection probability of this algorithm are derived by moment-matching Beta approximation. Numerical examples are provided to illustrate the validity of theoretical analysis. And simulations show that the proposed mixture detector with optimal weight can improve the spectrum sensing performance compared with existing algorithms. [ABSTRACT FROM AUTHOR]

Worst-case robust approximation was proven to be efficient in alleviating the non-line-of-sight (NLOS) influence for source positioning. However, the existing time-difference-of-arrival (TDOA)-based worst-case solutions still have two issues: 1) Inaccurate objective transformations are introduced in some algorithms, which reduce the accuracy; 2) A method with higher accuracy is computationally intensive. This study proposes an accurate and simplified worst-case approximation method to tackle the troubles. Precisely, we first prove that the nonconvex worst-case objective is piecewise monotone to the NLOS bias. We further use monotonicity to derive an accurate and convex expression of the worst-case objective. Then, we propose simplified transformations to redefine the worst-case approximation problem with fewer constraints. Besides, we prove the effectiveness of the simplified transformations. Simulations and experiments demonstrate that the proposed method with moderate computation exhibits better performance than the state-of-the-art worst-case approximation algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the tradeoff between the profits gained by mobile virtual network operators (MVNOs) in an orthogonal frequency division multiple access (OFDMA)-based virtualized wireless network (VWN). In this respect, MVNOs rent the network resources from a mobile network operator (MNO) to create virtual resources based on allocated rates and the cost due to allocated transmit powers in two different strategies: resource-based isolation strategy and rate-based isolation strategy. In resource-based isolation strategy, it is assumed that the whole bandwidth in each base station is divided equally between MVNOs whereas in rate-based isolation strategy, the whole bandwidth in each base station is allocated dynamically between MVNOs. We then formulate a multi-objective optimization problem (MOOP) to show the tradeoff between the maximum profit of each MVNO. To solve the formulated MOOP, we adopt the weighted Tchebycheff approach, which can provide the complete Pareto optimal region for nonconvex problems. Thus, by developing such formulation, we will be able to find the best profit gained by MVNOs. We also employ monotonic optimization to obtain the global optimal solutions of the non-convex problems by applying polyblock outer approximation algorithm. Our numerical results show the importance of the aforementioned tradeoffs and confirm that the proposed geometric programming (GP) based algorithm achieves excellent performance as compared to the monotonic method. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new beamforming design for intelligent reflecting surface (IRS) assisted multi-user systems where a base station with multiple antennas support several users with an aid of an IRS. We jointly optimize the sum rate and the user fairness by adopting a single objective function. To tackle the non-convex problem, we present an iterative algorithm based on the successive convex approximation method. Simulation results demonstrate that the proposed scheme outperforms other conventional IRS based schemes in terms of both the sum-rate and user fairness. [ABSTRACT FROM AUTHOR]

This paper investigates rate splitting multiple access (RSMA) for multigroup multicast beamforming in a cache-enabled cloud radio access network (C $^2$ -RAN). To maximize the minimum weighted rate among all users and ensure the base station (BS) transmit power and backhaul capacity constraints, we jointly optimize the dynamic BS clustering, message splitting, and beamforming vectors. More specifically, users requesting the same content are collected to a multicast group and served by a cluster of BSs. Each BS has limited storage space and connects to the central processor (CP) via a backhaul link. However, the resulting optimization problem is discrete and nonconvex and has a non-smooth objective function. To alleviate these problems, we construct two surrogates to approximate the objective function locally. These surrogates are strictly lower bounds and concave. Also, we build an upper-bound convex surrogate function to handle the backhaul cost constraint and derive a minorization-maximization (MM)-based iterative algorithm. Besides, we adopt a quadratic transform method to recast the achievable rate and binary BS selection problems to a more tractable form to reduce time cost. We transform the resulting problem into a two-tier alternating optimization problem; the outer-tier problem is handled with closed-form expressions while the inner-tier one is solved by a convex framework. We thus develop a quadratic transform-based alternating (QTA) algorithm. Extensive simulation results confirm the superiority of our proposed transmit strategy and algorithms. [ABSTRACT FROM AUTHOR]

Millimeter wave (mmWave) communication systems using adaptive-resolution analog-to-digital converters (RADCs) have recently drawn considerable interests from the research community as benefit of their high energy efficiency and low implementation cost. In this paper, we focus on the mmWave uplink using RADCs and investigate the joint user scheduling and resource allocation problem. Specifically, we seek to maximize the system throughput of the scheduled users by jointly optimizing their transmit power level and hybrid combiners as well as the number of quantization bits, subject to practical constraints. By relying on fractional programming (FP) techniques, we first covert this problem into a form amenable to optimization and exploit the specific structures in its solutions with the aid of the so-called Ky Fan $n$ -norm.Then, the resultant optimization problem is solved using a penalty block successive concave approximation (P-BSCA) algorithm. Our numerical results reveal that the proposed algorithm substantially enhances the throughput of the scheduled users compared to the state-of-the-art benchmark schemes and provides more flexible and efficient resource allocation control. [ABSTRACT FROM AUTHOR]

The explosive computation demands in the Internet of Things (IoT) have triggered the research interests on unmanned aerial vehicle (UAV) assisted mobile-edge computing (MEC) systems even though there are still many challenges, such as computing delay requirement, multi-UAV cooperation, and resource management. This letter focuses on the computing delay issue in MEC systems assisted by multiple UAVs with the goal of task completion time minimization. In particular, both the partial offloading and binary offloading modes are considered by jointly optimizing time slot size, terminal devices scheduling, computation resource allocation, and UAVs’ trajectories. Particularly, an non-LoS channel model is adopted for UAV-ground communication. To handle the formulated problems, we develop alternating optimization algorithms by invoking the successive convex approximation method, Karush-Kuhn-Tucker conditions and penalized method. Numerical results show that the completion time is significantly decreased by the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the resource allocation scheme for orthogonal frequency division multiple access (OFDMA) full-duplex (FD) distributed antenna systems (DASs), in which both remote access units (RAUs) and users having the full-duplex capability. We aim to maximize the system spectral efficiency through jointly optimizing subcarrier assignment and power allocation, where users’ uplink/downlink quality of service requirements, per-user uplink/downlink transmit power constraints, and subcarrier reuse are considered. The user-centric virtual cell approach is adopted to reduce the computational and signaling overhead. Based on the successive convex approximation method and penalty factor, we propose an efficient overall optimization algorithm to tackle the non-convex optimization problem. Simulation results show that the proposed algorithm can achieve performance close to the exhaustive search algorithm, comparable to the hierarchical decomposition algorithm using the optimal subcarrier allocation scheme, and better than other benchmark algorithms. In addition, the proposed algorithm can guarantee fairness among users at a low cost of system spectral efficiency. [ABSTRACT FROM AUTHOR]

Energy Harvesting Wireless Body Area Networks (EH-WBANs) where sensor nodes can harvest energy from their ambient environment are of great potential for low-power medical monitoring systems. The availability of energy in EH-WBANs, however, is challenged by uncertainty of harvested energy sources and limited charging efficiency, and thus the energy saving is still of great importance. To improve energy efficiency, this paper studies the sleep scheduling problem in EH-WBANs, which is of fundamental importance for network lifetime and connectivity while being not systematically addressed. Technically, this problem is proved to be NP-Complete, and thus needs non-trivial efforts to deal with. To that end, we present a series of approximation algorithms with proven performance from perspective of constructing minimum dominating set (DS), where energy saving and energy harvesting techniques are combined to prolong the network lifetime while guaranteeing real-time requirement of EH-WBANs. Specifically, we first propose a centralized algorithm that can construct DS with minimum size and discover the maximum number of DSs. We then design two distributed algorithms independent of prior global knowledge to improve network scalability, namely EEU and Improved EEU respectively. Theoretical analysis and extensive simulations are conducted to confirm the superiority of the proposed sleep scheduling algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the detection problem for a massive machine-type communication (mMTC) system that has correlated user activities. Two deep learning assisted algorithms are proposed to exploit the user activity correlation to facilitate channel estimation and user identification. Due to the dependency among user activities, conventional element-wise minimum mean square error (MMSE) denoiser used in the orthogonal approximate message passing (OAMP) algorithm cannot achieve satisfying performance during the two-step iterative process. Therefore, we propose a deep learning modified OAMP (DL-mOAMP) algorithm, which iteratively modifies the user activity ratio via exploiting the user activity correlation in the MMSE denoiser based on the estimated sequence during each OAMP iteration. Moreover, given a specific false alarm probability, a constant threshold employed in the conventional user identification is not optimal in the presence of user activity correlation. Thus, we propose a neural network framework that is dedicated to the user identification (DL-mOAMP-UI algorithm), which minimizes the missed detection probability under a pre-determined false alarm probability. Numerical results show that the proposed DL-mOAMP algorithm provides a substantial mean squared error performance gain compared to the conventional OAMP algorithm and the DL-mOAMP-UI algorithm can further improve the user identification accuracy of an mMTC system. [ABSTRACT FROM AUTHOR]

In this paper, we propose a user selection algorithm suitable for digital and hybrid maximum ratio transmission (MRT) precoding to alleviate inter-user interference. For digital MRT, the minimum inter-user interference problem is reduced to the notorious NP-hard called max k-cut. We first relax the max k-cut problem into a semi-definite programming (SDP), which can be solved by the interior point method. Then, we propose a randomized rounding-based greedy user selection algorithm (RRGUSA) to get the approximate solution of the max k-cut from the SDP solution. For hybrid MRT, the minimum inter-user interference problem is more complicated. We first force the interference minimization problem of hybrid MRT to be a quasi-max k-cut problem according to the law of large numbers. Then, RRGUSA is still used to obtain an approximate solution. Simulation results show that, for digital MRT, the proposed algorithm obtains a near-optimal solution, and for hybrid MRT, the proposed algorithm brings better performance by deploying more antennas. [ABSTRACT FROM AUTHOR]

Expectation Propagation (EP)-based Multiple-Input Multiple-Output (MIMO) detector achieves exceptional performance in high-dimensional systems with high-order modulations and flexible antenna configurations. However, based on our studies, the EP MIMO detector cannot achieve superior performance due to the empirical parameter selection, including initial variance and damping factors. According to the influence of the moment matching and parameter selection on the performance of the EP MIMO detector, we propose a modified EP MIMO detector (MEPD). To obtain the initial variance and damping factors which lead to better performance, we adopt a deep learning scheme, the iterative process of MEPD is unfolded to establish MEPNet for parameters training. The simulation results show that MEPD with off-line trained parameters outperforms the original one in various MIMO scenarios. Besides, the proposed MEPD with deep learning parameters selection is more robust than EPD in practical scenarios. [ABSTRACT FROM AUTHOR]

Orthogonal time frequency space (OTFS) modulation has attracted substantial attention recently due to its great potential of providing reliable communications in high-mobility scenarios. In this article, we propose a novel hybrid signal detection algorithm for OTFS modulation. Based on the system model, we first derive the near-optimal symbol-wise maximum a posteriori (MAP) detection algorithm for OTFS modulation. Then, in order to reduce the detection complexity, we propose a partitioning rule that separates the related received symbols into two subsets for detecting each transmitted symbol, according to the corresponding path gains. According to the partitioning rule, we design the hybrid detection algorithm to exploit the power discrepancy of each subset, where the MAP detection is applied to the subset with larger channel gains, while the parallel interference cancellation (PIC) detection is applied to the subset with smaller channel gains. We also provide the error performance analysis of the proposed hybrid detection algorithm. Simulation results show that the proposed hybrid detection algorithm can not only approach the performance of the near-optimal symbol-wise MAP algorithms, but also offer a substantial performance gain compared with existing algorithms. [ABSTRACT FROM AUTHOR]

Massive multiple-input-multiple-output (M-MIMO) features a capability for spatial multiplexing of large number of users. This number becomes even more extreme in extra-large (XL-MIMO), a variant of M-MIMO where the antenna array is of very large size. Yet, the problem of signal processing complexity in M-MIMO is further exacerbated by the XL size of the array. The basic processing problem boils down to a sparse system of linear equations that can be addressed by the randomized Kaczmarz (RK) algorithm. This algorithm has recently been applied to devise low-complexity M-MIMO receivers; however, it is limited by the fact that certain configurations of the linear equations may significantly deteriorate the performance of the RK algorithm. In this article, we embrace the interest in accelerated RK algorithms and introduce three new RK-based low-complexity receiver designs. In our experiments, our methods are not only able to overcome the previous scheme, but they are more robust against inter-user interference (IUI) and sparse channel matrices arising in the XL-MIMO regime. In addition, we show that the RK-based schemes use a mechanism similar to that used by successive interference cancellation (SIC) receivers to approximate the regularized zero-forcing (RZF) scheme. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is leveraged to reduce the latency for the computation-intensive and latency-critical tasks offloaded from wireless devices and Internet of Things Devices (IoTDs). Unmanned aerial vehicles (UAVs) have attracted much attention from both academia and industry attributed to high mobility, high flexibility, and high maneuverability of UAVs. In this article, a novel UAV-assisted MEC architecture is proposed to provision services to IoTDs, where a UAV provides both communication and computing services or works as a relay node. We then formulate the joint computation offloading, spectrum resource allocation, computation resource allocation, and UAV placement (Joint-CAP) problem in the UAV-MEC network to minimize the operation cost of provisioning IoTDs. Since the Joint-CAP problem is a mixed integer non-linear programming problem and NP-hard, we decompose it into two sub-problems and solve the sub-problems sequentially. Then, we propose a (1 + ε)-approximation algorithm, named AA-CAP, to solve the Joint-CAP problem, and the performance of the AA-CAP algorithm is demonstrated to be superior to the baseline algorithms via simulations. [ABSTRACT FROM AUTHOR]

The large-scale multiple-input-multiple-output (MIMO), also known as massive MIMO, is one of the key techniques for the fifth-generation (5 G) mobile communications. Due to large-scale antenna systems equipped at the basestations, the user-basestation distance in massive MIMO systems may be within the so-called Rayleigh distance. This would cause challenges in developing algorithms for user localization, because both near-field (NF) and far-field (FF) sources (users) may coexist. To solve this problem, most of existing algorithms are based on a simplified source-sensor spatial model, where the sensor-magnitude is assumed to be equal and the spatial phase is approximated by the Taylor polynomial. In contrast, a new algorithm based on the exact spatial geometry is developed, where no model simplification is made. The new algorithm is termed as MIxed Localization using the Exact model (MILE) in that it sets up a unified (non-approximation) model framework to the problem under consideration, and solves this problem in a mathematically quite simple manner. In fact, the MILE has the following three important advantages: (1) it is not restricted to exploit equally spaced arrays, (2) it can accommodate any arbitrary propagation loss, and (3) it does not suffer the model mismatch caused performance loss. All these advantages are not offered by current state-of-the-art techniques. The matlab codes for replication of the results in this study are available at: https://github.com/jinhesjtu/MILE.git. [ABSTRACT FROM AUTHOR]

In opportunistic mobile crowdsensing, the objective of service requesters is to have as many of their sensing tasks completed as possible within their budget constraints, whereas that of participants (workers) is to collect the highest monetary reward possible on their trajectories. However, these objectives can conflict and may result in unhappy service requesters or workers if the matching between them is not handled carefully. In this paper, we study the problem of finding task assignments that fulfill both coverage-aware preferences of service requesters and profit-based preferences of workers in a budget-constrained, opportunistic mobile crowdsensing system. Since this is a matching problem with bilateral preferences, we aim to find a matching in which everyone is satisfied with their assignment based on their preference profile. We first propose a polynomial-time approximation algorithm for general settings, and then show that a slightly modified version of this algorithm has a constant approximation ratio when the rewards offered to workers by service requesters are proportional to the coverage capability of workers for corresponding tasks. Through extensive simulations, we evaluate the performance of our algorithms in different settings, and show that they mostly provide substantially better task assignments in terms of user happiness and coverage quality while having a few orders of magnitude lower running times compared to the benchmark algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the secrecy energy efficiency (SEE) for a two-tier heterogeneous cellular network having a macro cell and multiple small cells, where the macro cell consists of a macro base station (MBS), a macro user and an eavesdropper and meanwhile each small cell comprises of a small base station (SBS), a small user and an eavesdropper. We consider the use of artificial noise (AN) signals emitted at MBS and SBSs at the expense of transmit power to defend against eavesdroppers. For the sake of obtaining a better balance between the secrecy rate (SR) and energy efficiency of heterogeneous cellular networks, we propose an SEE maximization (SEEM) scheme in small cells through jointly optimizing the beamformers of confidential signals and AN at MBS and SBSs, called joint beamforming based SEEM (JBF-SEEM) while guaranteeing the SR requirements and the power constraints for MBS and each SBS. However, the proposed JBF-SEEM problem is a complicated optimization problem because of the non-convex fractional form. Thus, we firstly transform the original objective function in fractional form into a subtractive one through applying the Dinkelbach's method, and then the non-convex subtractive form is approximated into a convex optimization problem by combining the penalty function and the difference of two-convex functions approach. Furthermore, we propose a two-tier iterative JBF-SEEM algorithm to obtain the near-optimal beamforming solution. Numerical results show that the JBF-SEEM algorithm is convergent while achieving a better SEE performance than conventional approaches. [ABSTRACT FROM AUTHOR]

Several gaps and errors in “Precoding and Artificial Noise Design for Cognitive MIMOME Wiretap Channels” by Fang et al. are identified and corrected. While accommodating these corrections, a rigours proof is given that the successive convex approximation algorithm of Fang et al. for secrecy rate maximization (SRM) does generate an increasing and bounded sequence of true secrecy rates and hence converges. It is further shown that its convergence point is a KKT point of the original SRM problem and, if the original problem is convex, this convergence point is globally-optimal, which is not necessarily the case in general. An interlacing property of the sequences of the true and approximate secrecy rates is established. [ABSTRACT FROM AUTHOR]

This paper considers the problem of high-speed maneuvering target detection, which involves the processing of complex range migration (RM) and Doppler frequency migration (DFM) within the coherent processing interval (CPI). By exploiting the coupling relationship between the range frequency and the Doppler frequency, a detection algorithm is proposed for high-speed maneuvering targets. After the range compression, approximate 2-D range-frequency and Doppler-frequency expressions are derived according to the principle of stationary phase (POSP) and series inversion extension. The mathematical range-Doppler frequency relationships are then exploited to search the target's first-order motion parameters and Doppler ambiguity numbers within a predetermined parameter range, and formulate the corresponding new data from the original 2-D data matrix. Finally, Fourier transform and constant false-alarm rate (CFAR) detector are further applied to detect the targets. Simulation results show that the proposed algorithm has low complexity requirements and is suitable for low signal-to-noise ratio (SNR) scenarios. Moreover, it is not sensitive to the motion order of targets. [ABSTRACT FROM AUTHOR]

In the finite blocklength scenario, which is suitable for practical applications, a method of maximizing the average effective secrecy rate (AESR) is proposed for a UAV-enabled secure communication by optimizing the UAV's trajectory and transmit power subject to the UAV's mobility constraints and transmit power constraints. To address the formulated non-convex optimization problem, it is first decomposed into two non-convex subproblems. Then the two subproblems are converted respectively into two convex subproblems via the first-order approximation. Finally, an alternating iteration algorithm is developed by solving the two subproblems iteratively based on successive convex approximation (SCA) technique. Numerical results show that our proposed scheme can achieve a 6% to 13% improvement on the AESR performance compared with the benchmark schemes. [ABSTRACT FROM AUTHOR]

In this paper, we propose transmission point selection algorithms to provide lower service time between an application server to end users by selecting the transmission point used by a newly identified metric, i.e., service latency. In particular, the proposed ordered service latency-based transmission point selection algorithm is beneficial in a sense that it does not increase average service time but decrease the maximum service time although it brings additional computational complexity slightly. We also provide the performance bounds with proven approximation ratios and verify that the proposed algorithm achieves lower maximum service time without degrading the average service time as well as waiting time through simulations in practical environments. [ABSTRACT FROM AUTHOR]

This article investigates the cache-enabling unmanned aerial vehicle (UAV) cellular networks with massive access capability supported by non-orthogonal multiple access (NOMA). The delivery of a large volume of multimedia contents for ground users is assisted by a mobile UAV base station, which caches some popular contents for wireless backhaul link traffic offloading. In cache-enabling UAV NOMA networks, the caching placement of content caching phase and radio resource allocation of content delivery phase are crucial for network performance. To cope with the dynamic UAV locations and content requests in practical scenarios, we formulate the long-term caching placement and resource allocation optimization problem for content delivery delay minimization as a Markov decision process (MDP). The UAV acts as an agent to take actions for caching placement and resource allocation, which includes the user scheduling of content requests and the power allocation of NOMA users. In order to tackle the MDP, we propose a Q-learning based caching placement and resource allocation algorithm, where the UAV learns and selects action with soft ε-greedy strategy to search for the optimal match between actions and states. Since the action-state table size of Q-learning grows with the number of states in the dynamic networks, we propose a function approximation based algorithm with combination of stochastic gradient descent and deep neural networks, which is suitable for large-scale networks. Finally, the numerical results show that the proposed algorithms provide considerable performance compared to benchmark algorithms, and obtain a trade-off between network performance and calculation complexity. [ABSTRACT FROM AUTHOR]

Voronoi diagrams specify the coverage regions of base stations in cellular radio systems. Classical Voronoi diagrams are made up of regions which are convex polygons, but this is the case only for an unrealistic single-slope pathloss model. We provide a new algorithm which computes arbitrarily accurate boundaries of coverage regions under a very general pathloss model, which requires only that the radio signals decrease monotonically with distance and asymptotically as some power of the distance. The new algorithm is applicable to general cellular environments with multi-slope pathloss conditions, including those in vehicular-to-everything (V2X) communications. [ABSTRACT FROM AUTHOR]

Recent years have witnessed increasingly more uses of Unmanned Aerial Vehicle (UAV) swarms for rapidly providing wireless coverage to ground users. Each UAV is constrained in its energy storage and wireless coverage, and it consumes most energy on flying to the top of the target area, thus leaving limited leftover energy for hovering at its deployed position and keeping wireless coverage. The literature largely overlooks the sustainability issues of deploying UAV swarm and the UAV-to-UAV cooperation, and we aim to maximize the minimum leftover energy storage among all the UAVs after their deployment. Our new energy-saving deployment problem captures that each UAV's wireless coverage is adjustable by its service altitude, and also takes the no-fly-zone (NFZ) constraint into account. We show that a UAV with larger initial energy storage in the UAV swarm should be deployed further away from the UAV station, and we propose an optimal energy-saving deployment algorithm by jointly balancing heterogeneous UAVs’ flying distances on the ground and final service altitudes in the sky. When n UAVs with different initial energy storages are dispatched from different initial locations, we prove this problem is NP-hard in general by reduction from partition problem. Despite of this, we propose to preserve the UAVs’ location order and successfully design an approximation algorithm of complexity n log 1/ε to arbitrarily approach the optimum with error ε. Inspiring by this proposed (1−ε)-approximation algorithm, we present a heuristic algorithm to solve the general case by balancing the efficiency and computation complexity well. [ABSTRACT FROM AUTHOR]

This article considers the dual-use unimodular signal design for a novel dual-functional radar-communication (DFRC) architecture. The information of downlink communication is modulated via the ambiguity function (AF) sidelobe nulling in the prescribed range-Doppler cells. At the same time, the sidelobe around the AF mainlobe is suppressed to ensure the radar detection performance. User equipment (UE) computes the AF of the received signal to make a judgment of the position of nulling for achieving demodulation. To this end, an objective function to evaluate the depth of nulling in AF is developed as figure of merit accounting for the worst-case sidelobe level ratio over the range-Doppler cells of interest. The resultant design is very challenging to solve due to the non-convex and non-smooth quartic fractional objective function and the NP-hard constant modulus constraint. Herein, we develop a fractional-alternating direction penalty method (FADPM) algorithm that invokes the fractional program theory and the ADPM framework. Specifically, we formulate a new ADPM form through introducing an auxiliary variable and resorting to the Dinkelbach's procedure. In each iteration, we transform the original quartic fractional program into two quadratic optimization subproblems both of which are approximated successively through a series of convex subproblems. We also provide the analytical convergence guarantee of the proposed FADPM algorithm. The simulation results verify the performance of the proposed algorithm and exhibit the effectiveness of the DFRC framework in modulation and demodulation process guaranteeing that a high data rate is achievable. [ABSTRACT FROM AUTHOR]

In this work, we study the uplink (UL) of a cloud radio access network (C-RAN), where the central processing unit (CU) utilizes remote radio units (RU)s belonging to the same operator, i.e., the trusted RUs, as well as the RUs belonging to other operators or private owners, i.e., the untrusted RUs. In order to preserve information privacy against the untrusted RUs, the trusted RUs are enabled with full-duplex (FD) capability and transmit a jamming signal towards the exotic RUs, while receiving and forwarding UL signal to the CU. Note that the transmitted jamming signal degrades the decoding capability at the untrusted RUs, however, it can be later subtracted from the UL communication as it is apriori known by the CU. An optimization problem is then formulated to maximize the sum uplink private information rate by jointly designing the fronthaul compression, as well as the information and jamming transmission strategies. Due to the intractability of the resulting mathematical problem, an iterative solution is proposed with convergence to a point satisfying the Karush-Kuhn-Tucker (KKT) optimality conditions. Numerical simulations illustrate a notable gain obtained via the proposed sharing mechanism under the consideration of information privacy. [ABSTRACT FROM AUTHOR]

This paper focuses on the use of a reinforcement learning (RL) approach to find two online power allocation policies in a point to point EH-MIMO wireless communication system. In our study, we train the power allocation policies in order to learn the map between the environment and the agent. Particularly, in order to avoid “dimension disaster” problem which may happen in our proposed SARSA power allocation policy, we introduce a linear approximation method to get an approximate SARSA power allocation policy. The linear approximation can handle infinite number of states and trade-off between complexity and performance of power allocation is significantly improved. The simulation results show that the proposed SARSA and approximate SARSA power allocation policies have a considerable throughput increase compared with the benchmark policies, such as greedy, random and conservative policies. [ABSTRACT FROM AUTHOR]

This paper investigates UAV-to-UAV (unmanned aerial vehicle) communications with a turning angle constraint, in which the information transmission time is minimized by joint UAV flight path planning and transmit power control. Considering the communication throughput requirement, peak transmit power, energy consumption, UAV flight speed, collision avoidance, and turning angle constraints, the joint UAV flight path planning and transmit power control problem is formulated as a non-convex optimization. Different from majority of prior studies that separately optimize the UAV flight path and transmit power, we develop a successive convex approximation based time minimization algorithm that simultaneously updates the UAV path and transmit power. We further prove that the proposed algorithm is guaranteed to converge. In addition, an initialization scheme is designed to start the proposed time minimization algorithm by solving an energy minimization problem. Simulation results verify that the proposed time minimization algorithm can shorten the completion time length and reduce the computational complexity compared with state of the art methods. [ABSTRACT FROM AUTHOR]

Compressed sensing (CS) is becoming a hot topic in recent years for its advantages such as low-power consumption, low memory requirement, and low sampling frequency. However, high-dimensional nonlinear signals will inevitably introduce notable complexity and low efficiency in signal recovery. Generalized turbo signal recovery (G-Turbo-SR) is a cutting-edge method, which efficiently reduces complexity with a partial discrete Fourier transform (DFT) sensing matrix. However, in practical applications, G-Turbo-SR still suffers from high complexity for probability computations and matrix multiplications. This article optimizes the algorithm of G-Turbo-SR in scheduling to reduce the matrix multiplications by half. High-precision numerical approximation method is proposed to replace the complex integral calculation, which efficiently reduces the hardware cost with acceptable performance degradation. Based on the data-flow graph (DFG) analysis, detailed hardware architecture is proposed with module designs. Proper quantization scheme is selected according to the mean square error (MSE) performance. Pipelining, folding, and variable precision quantization (VPQ) scheme are employed for higher hardware efficiency. FPGA implementation on Xilinx 7k325tffg900-2 shows a higher throughput and hardware efficiency compared to existing recovery methods for CS. [ABSTRACT FROM AUTHOR]

This paper studies the problem of user association, deadline scheduling and channel allocation in dense cellular networks with energy harvesting base stations. The objective is to maximize the number of associated and scheduled users while allocating the available channels to the users and respecting the energy and deadline constraints. First, the computational complexity of this problem is characterized by studying its NP-hardness in different cases. Next, efficient algorithms are proposed in each case. The case of single channel and single base station is solved by proposing polynomial-time optimal algorithms. The case of single channel and multiple base stations is solved by proposing an efficient constant-factor approximation algorithm. The case of multiple channels is solved by proposing efficient heuristic algorithms. Our theoretical analysis are supplemented by simulation results to illustrate the performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Reinforcement learning is regarded as a potential method to be applied in automated vehicles, but the stability and efficiency of algorithms are concerns. To improve them, the deterministic promotion reinforcement learning method is put forward, which can promote the policy determinately. Correspondingly, the policy evaluation in critic and the exploration in actor are improved, which combines a normalization-based evaluation and a model-free search guide. The aim is finding the right action exploration direction by critic, then the direction is used to update and guide action exploration in actor. The modified method decreases the dependencies of exploring a good action for promotional updating and only makes deterministic promotion in policy. Consequently, the efficiency of the algorithm is improved without loss in stability. More notably, it can relieve the cold-start and circumvent the limitations in learning with constrained physical systems. To verify the proposed method, the longitudinal velocity control problem for automated vehicles is considered, which contains car-following and non-car-following conditions in a unitized form. The learning system is established in Carsim. Furthermore, some different reinforcement learning technologies are used to accelerate learning. Real-vehicle experiments for validation are also given. The results indicate that the proposed method can achieve permissible learning performance in the longitudinal velocity continuous control problem. [ABSTRACT FROM AUTHOR]

Resource management plays a crucial role in improving sum rate of non-orthogonal multiple access (NOMA) networks. However, the traditional resource management methods have considerable complexity, creating a huge challenge in computational efficiency. To handle this challenge, a resource management method is proposed based on a deep neural network (DNN). The key advantage of the method is that the DNN can perform in almost real-time resource allocation because it requires a very simple operation. In this paper, the resource management problem of the NOMA system adopting imperfect successive interference cancellation (SIC) technology at the receivers is studied, including the power allocation stage and the user scheduling stage. For power allocation stage, the generic fully-connected DNN is trained to approximate the power allocation of interior point method (IPM), which not only greatly improves the computational efficiency but also increases the sum rate of the system. Based on the allocated power, the user scheduling algorithm is performed to further increase the system sum rate. Finally, simulation results verify some performances of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates the sensor selection problem in time difference of arrival (TDOA) tracking scenario to find the optimal sensor activation strategy for the upcoming time step. We propose a multiobjective optimization framework to minimize two conflicting objectives, i.e., tracking accuracy and quantity budget, which are represented by the trace of the conditional posterior Cramér Rao lower bound (CPCRLB) and the number of selected sensors, respectively. Due to the reduced measurement dimension and correlated noise caused by the common reference sensor (CRS), sensor selection algorithms for the general nonlinear model in existing literature cannot be applied to the TDOA tracking directly as they mostly assume that each sensor produces an independent measurement. Therefore, we introduce two Boolean vectors to indicate the CRS and ordinary sensors respectively. The sensor selection problem is then formulated as a multiobjective optimization problem (MOP), which can be further transformed as a single objective optimization problem (SOOP) using the linear weighted-sum method. We prove that the SOOP satisfies the rules of discrete monotonic optimization (DMO), and propose the polyblock outer approximation (POA)-based algorithms to seek for a globally optimal solution. For comparison, we introduce the conventional semidefinite program (SDP)-based algorithm to solve the SOOP with multi-step relaxation. Simulation results demonstrate that the proposed POA-based algorithms can considerably outperform the SDP-based ones in solving the optimization problem. [ABSTRACT FROM AUTHOR]

Location awareness is becoming essential for various wireless applications, and cooperation among nodes is highly beneficial for localization performance. However, the existing cooperative localization (CL) algorithms may suffer from major performance loss when applied to the mobile environments with fast changing network topologies. In this paper, we extend the joint spatial and temporal cooperation (JSTC) model to the range-based localization problem. Based on mobility constraints and the maximum likelihood estimation (MLE) criterion, we formulate the CL problems in both centralized and distributed cases. Different from the conventional distributed CL problems, the proposed one takes the transmission delay and the cooperation among multi-hop nodes into account, which is more practical. Moreover, the performance limits of the considered problems are investigated by the Cramér-Rao lower bound (CRLB). Finally, these two problems are solved by the Sequential Quadratic Programming (SQP) algorithm. Both simulation and experiment results show that the extended JSTC model can effectively improve the localization reliability and accuracy. [ABSTRACT FROM AUTHOR]

By enabling energy redistribution among nodes in Wireless Sensor Networks (WSNs), the Wireless Power Transfer (WPT) technology provides a new approach to prolong life time of WSNs or a fundamental block for building perceptual WSNs. In this paper, we attempt to deal with the underlying WPT based Energy ReDistribution (WPTERD) problem, which is targeted for finding an energy transmission schedule that satisfies the nodes’ energy expectations, meanwhile achieves minimum energy loss and shortest time span. In this paper, we propose a two-step approach for WPTERD by splitting it into two sub-problems named WPTERD-Egy and WPTERD-Time, which focus on the optimization in energy loss and in time span respectively. Basing on some proved properties, we transform WPTERD-Egy to a Linear Programming problem and thus can obtain an optimal time-length list of the nodes’ energy transmissions leading to minimum energy loss. Finding that WPTERD-Time is NP-hard, we propose an approximate algorithm to solve the WPTERD problem by exploiting the concurrent energy transmission opportunities. The algorithm is named as Greedy Energy ReDistribution Scheduling (GERDS) algorithm, which is proved to finish in limited loops. Simulations validate the algorithm's ability in minimizing energy loss, and meanwhile return near-optimal schedules with time span in range $100{\sim }125$ % of the optimal value. Simulation results also show that GERDS outperforms a typical flow-based algorithm in terms of the tested performance metrics. [ABSTRACT FROM AUTHOR]

With the increasing penetration of smart devices, device-to-device (D2D) communications offer a promising paradigm to accommodate the ever-growing mobile video traffic and unremitting demands for fast content distribution. The redundant storage and communication capacities of smart devices can be exploited for collaborative content caching and distribution. Such united resources at the network edge can serve end users with low delivery cost and high performance. In this paper, we analyze a heuristic distance-based scheme, which appropriately pairs a device requesting a content item with a cache device within a collaboration distance and providing the requested content. An analytical framework is developed to derive the optimal collaboration distance so as to maximize the likelihood that the paired cache devices successfully fulfill the content requests. The simulation results validate the analytical framework and the effectiveness of the heuristic distance-based scheme for D2D-assisted content distribution. We also investigate the effects of various system parameters on the performance in terms of expected satisfaction probability. [ABSTRACT FROM AUTHOR]

Future mobile communication networks (5G) are envisioned to support a wide range of use cases with diverse service requirements. Network slicing as a key architectural enabling technology for 5G can provide users with customized services and meet the requirement of economic efficiency, where resource allocation is of vital importance. In this paper, we propose an online auction-based resource allocation scheme for network slicing (ORANS) to meet the diverse service requirements and improve network economic efficiency. We first propose a bidding information construction method to provide customized bidding information for users based on service types. Next, we model the slicing resource allocation problem as an online winner determination problem, with aim to maximize the social welfare of auction participants. We then implement a resource allocation strategy and payment rule to accomplish the service-oriented optimal resource allocation. Extensive simulation experiments are conducted to evaluate the performance of the proposed ORANS and the numerical results show that ORANS significantly outperforms the greedy algorithm and the primal-dual approximation algorithm in network economic efficiency while satisfying the diverse service requirements of users. [ABSTRACT FROM AUTHOR]

The future 5G network is expected to provide throughput 1000 times and spectrum efficiency 10 times those of the current 4G network. In-band full-duplex (IBFD) is a promising technique for 5G that can conceivably improve spectrum efficiency. Drone-mounted base stations (DBSs) provide high mobility and high flexibility, and leveraging DBSs to provision services to UEs can facilitate the quality of service of the wireless network and enlarge the coverage area. Therefore, the throughput and the spectrum efficiency can be improved by leveraging IBFD-enabled DBSs. We formulate the drone-mounted base station placement with IBFD communications (DBSP-IBFD) problem, which consists of two sub-problems: the joint bandwidth, power allocation and UE association (joint-BPU) problem and the DBS placement problem. We first propose an approximate algorithm to solve the joint-BPU problem that provides guaranteed performance. Then, an exhaustive search method is employed to obtain the optimal locations for DBSs. After that, we propose a $\frac{1}{2}(1-\frac{1}{2^{l}})$ -approximation algorithm to solve the DBSP-IBFD problem, where $l$ is the number of simulation runs. Simulation results have demonstrated that the throughput of the proposed approximate algorithm is superior to benchmark algorithms in solving the DBSP-IBFD problem. [ABSTRACT FROM AUTHOR]

We consider the energy-efficient mobility-aware content caching problem with clustered small cells (SCs) in ultra-dense networks (UDNs). The UDN is regarded as one of the key solutions to overcome the explosive growth of mobile data traffic and consequential energy consumption. In addition, caching the popular contents at the edge of UDNs can further mitigate the challenges of traffic and energy. In this paper, we group the SCs into disjoint clusters and model the mobility of users between clusters as a Markov chain. We formulate the caching problem aimed at minimizing the overall energy consumption for content delivery to the moving users. Then we decompose the complicated task of energy consumption optimization in cache-enabled UDNs into two sub-problems. In the first sub-problem, we formulate the inter-cluster caching problem as minimizing the energy consumption for serving a user request by the macro base station. We show that the first sub-problem is NP-hard, and develop a polynomial-heuristic algorithm. Further, we define the mobility-oblivious version of the sub-problem, and provide a ${\textstyle {1 \over {2k }}}$ -approximation solution for the mobility-aware based on this mobility-oblivious version. In the second sub-problem, we consider the heterogeneity of small cells and define the intra-cluster caching problem. The aim of this sub-problem is the placement of content in different SCs within the cluster so that the energy consumption for the content delivery in that cluster is minimized. In particular, the second sub-problem fits the general framework of the generalized assignment problem, which allows us to exploit its rich literature, to determine the optimal solution for our problem. Through extensive simulations based on real wireless data and human mobility patterns, we demonstrate the benefits of our approach that is even up to 56% better compared to the conventional caching schemes. [ABSTRACT FROM AUTHOR]

Caching at base stations (BSs) can enhance the performance of a mobile network, which has gained much attention in the past few years. In this paper, we investigate the energy-saving issue in the cache-enabled mobile network, where we try to minimize the total system power consumption with limited radio resource and storage capacity of the BSs. We decouple the formulated difficult optimization task into a series of tractable subproblems and develop efficient algorithms to solve them with reasonable computation load. The key idea of our proposed scheme is to associate as many as possible users with the BSs that have stored the users’ requested files in their caches while considering the spectrum and power budgets of the BSs. Numerical results show our proposed scheme can significantly reduce the system power consumption compared with others. [ABSTRACT FROM AUTHOR]