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See detailFedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud System
Nguyen, van Dinh UL; Chatzinotas, Symeon UL; Ottersten, Björn UL et al

in IEEE Transactions on Wireless Communications (in press)

Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. To address key challenges of ... [more ▼]

Federated learning (FL) is capable of performing large distributed machine learning tasks across multiple edge users by periodically aggregating trained local parameters. To address key challenges of enabling FL over a wireless fogcloud system (e.g., non-i.i.d. data, users’ heterogeneity), we first propose an efficient FL algorithm based on Federated Averaging (called FedFog) to perform the local aggregation of gradient parameters at fog servers and global training update at the cloud. Next, we employ FedFog in wireless fog-cloud systems by investigating a novel network-aware FL optimization problem that strikes the balance between the global loss and completion time. An iterative algorithm is then developed to obtain a precise measurement of the system performance, which helps design an efficient stopping criteria to output an appropriate number of global rounds. To mitigate the straggler effect, we propose a flexible user aggregation strategy that trains fast users first to obtain a certain level of accuracy before allowing slow users to join the global training updates. Extensive numerical results using several real-world FL tasks are provided to verify the theoretical convergence of FedFog. We also show that the proposed co-design of FL and communication is essential to substantially improve resource utilization while achieving comparable accuracy of the learning model. [less ▲]

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See detailShort-Packet Communications in Multi-Hop Networks with WET: Performance Analysis and Deep Learning-Aided Optimization
Nguyen, Toan Van; Nguyen, van Dinh UL; Costa, Daniel Benevides da et al

in IEEE Transactions on Wireless Communications (in press)

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See detailJoint Optimization of Beam-Hopping Design and NOMA-Assisted Transmission for Flexible Satellite Systems
Wang, Anyue UL; Lei, Lei; Lagunas, Eva UL et al

in IEEE Transactions on Wireless Communications (2022)

Next-generation satellite systems require more flexibility in resource management such that available radio resources can be dynamically allocated to meet time-varying and non-uniform traffic demands ... [more ▼]

Next-generation satellite systems require more flexibility in resource management such that available radio resources can be dynamically allocated to meet time-varying and non-uniform traffic demands. Considering potential benefits of beam hopping (BH) and non-orthogonal multiple access (NOMA), we exploit the time-domain flexibility in multi-beam satellite systems by optimizing BH design, and enhance the power-domain flexibility via NOMA. In this paper, we investigate the synergy and mutual influence of beam hopping and NOMA. We jointly optimize power allocation, beam scheduling, and terminal-timeslot assignment to minimize the gap between requested traffic demand and offered capacity. In the solution development, we formally prove the NP-hardness of the optimization problem. Next, we develop a bounding scheme to tightly gauge the global optimum and propose a suboptimal algorithm to enable efficient resource assignment. Numerical results demonstrate the benefits of combining NOMA and BH, and validate the superiority of the proposed BH-NOMA schemes over benchmarks. [less ▲]

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See detailFlexible Resource Optimization for GEO Multibeam Satellite Communication System
Abdu, Tedros Salih UL; Kisseleff, Steven UL; Lagunas, Eva UL et al

in IEEE Transactions on Wireless Communications (2021)

Conventional GEO satellite communication systems rely on a multibeam foot-print with a uniform resource allocation to provide connectivity to users. However, applying uniform resource allocation is ... [more ▼]

Conventional GEO satellite communication systems rely on a multibeam foot-print with a uniform resource allocation to provide connectivity to users. However, applying uniform resource allocation is inefficient in presence of non-uniform demand distribution. To overcome this limitation, the next generation of broadband GEO satellite systems will enable flexibility in terms of power and bandwidth assignment, enabling on-demand resource allocation. In this paper, we propose a novel satellite resource assignment design whose goal is to satisfy the beam traffic demand by making use of the minimum transmit power and utilized bandwidth. The motivation behind the proposed design is to maximize the satellite spectrum utilization by pushing the spectrum reuse to affordable limits in terms of tolerable interference. The proposed problem formulation results in a non-convex optimization structure, for which we propose an efficient tractable solution. We validate the proposed method with extensive numerical results, which demonstrate the efficiency of the proposed approach with respect to benchmark schemes. [less ▲]

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See detailUAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization
Tran Dinh, Hieu UL; Nguyen, van Dinh UL; Chatzinotas, Symeon UL et al

in IEEE Transactions on Wireless Communications (2021)

Unmanned aerial vehicle (UAV) communication hasemerged as a prominent technology for emergency communi-cations (e.g., natural disaster) in the Internet of Things (IoT)networks to enhance the ability of ... [more ▼]

Unmanned aerial vehicle (UAV) communication hasemerged as a prominent technology for emergency communi-cations (e.g., natural disaster) in the Internet of Things (IoT)networks to enhance the ability of disaster prediction, damageassessment, and rescue operations promptly. A UAV can bedeployed as a flying base station (BS) to collect data from time-constrained IoT devices and then transfer it to a ground gateway(GW). In general, the latency constraint at IoT devices and UAV’slimited storage capacity highly hinder practical applicationsof UAV-assisted IoT networks. In this paper, full-duplex (FD)radio is adopted at the UAV to overcome these challenges. Inaddition, half-duplex (HD) scheme for UAV-based relaying isalso considered to provide a comparative study between twomodes (viz., FD and HD). Herein, a device is considered tobe successfully served iff its data is collected by the UAV andconveyed to GW timely during flight time. In this context,we aim to maximize the number of served IoT devices byjointly optimizing bandwidth, power allocation, and the UAVtrajectory while satisfying each device’s requirement and theUAV’s limited storage capacity. The formulated optimizationproblem is troublesome to solve due to its non-convexity andcombinatorial nature. Towards appealing applications, we firstrelax binary variables into continuous ones and transform theoriginal problem into a more computationally tractable form.By leveraging inner approximation framework, we derive newlyapproximated functions for non-convex parts and then develop asimple yet efficient iterative algorithm for its solutions. Next,we attempt to maximize the total throughput subject to thenumber of served IoT devices. Finally, numerical results showthat the proposed algorithms significantly outperform benchmarkapproaches in terms of the number of served IoT devices andsystem throughput. [less ▲]

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See detailMassive MIMO under Double Scattering Channels: Power Minimization and Congestion Controls
Van Chien, Trinh; Ngo, Hien Quoc; Chatzinotas, Symeon UL et al

in IEEE Transactions on Wireless Communications (2021)

This paper considers a massive MIMO system under the double scattering channels. We derive a closed-form expression of the uplink ergodic spectral efficiency (SE) by exploiting the maximum-ratio combining ... [more ▼]

This paper considers a massive MIMO system under the double scattering channels. We derive a closed-form expression of the uplink ergodic spectral efficiency (SE) by exploiting the maximum-ratio combining technique with imperfect channel state information. We then formulate and solve a total uplink data power optimization problem that aims at simultaneously satisfying the required SEs from all the users with limited power resources. We further propose algorithms to cope with the congestion issue appearing when at least one user is served by lower SE than requested. Numerical results illustrate the effectiveness of our proposed power optimization. More importantly, our proposed congestion-handling algorithms can guarantee the required SEs to many users under congestion, even when the SE requirement is high. [less ▲]

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See detailJoint Radio Resource Management and Link Adaptation for Multicasting 802.11ax-based WLAN Systems
Ha, Vu Nguyen UL; Kaddoum, Georges; Poitau, Gwenael

in IEEE Transactions on Wireless Communications (2021)

Adopting OFDMA and MU-MIMO techniques for both downlink and uplink IEEE 802.11ax will help next-generation WLANs efficiently cope with large numbers of devices but will also raise some research challenges ... [more ▼]

Adopting OFDMA and MU-MIMO techniques for both downlink and uplink IEEE 802.11ax will help next-generation WLANs efficiently cope with large numbers of devices but will also raise some research challenges. One of these is how to optimize the channelization, resource allocation, beamforming design, and MCS selection jointly for IEEE 802.11ax-based WLANs. In this paper, this technical requirement is formulated as a mixed-integer non-linear programming problem maximizing the total system throughput for the WLANs consisting of unicast users with multicast groups. A novel two-stage solution approach is proposed to solve this challenging problem. The first stage aims to determine the precoding vectors under unit-power constraints. These temporary precoders help re-form the main problem into a joint power and radio resource allocation one. Then, two low-complexity algorithms are proposed to cope with the new problem in stage two. The first is developed based on the well-known compressed sensing method while the second seeks to optimize each of the optimizing variables alternatively until reaching converged outcomes. The outcomes corresponding to the two stages are then integrated to achieve the complete solution. Numerical results are provided to confirm the superior performance of the proposed algorithms over benchmarks. [less ▲]

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See detailJoint Symbol Level Precoding and Combining for MIMO-OFDM Transceiver Architectures Based on One-Bit DACs and ADCs
Domouchtsidis, Stavros UL; Tsinos, Christos UL; Chatzinotas, Symeon UL et al

in IEEE Transactions on Wireless Communications (2021), 20(7), 4601-4613

Herein, a precoding scheme is developed for orthogonal frequency division multiplexing (OFDM) transmission in multiple-input multiple-output (MIMO) systems that use one-bit digital-to-analog converters ... [more ▼]

Herein, a precoding scheme is developed for orthogonal frequency division multiplexing (OFDM) transmission in multiple-input multiple-output (MIMO) systems that use one-bit digital-to-analog converters (DACs) and analog-to-digital converters (ADCs) at the transmitter and receiver, respectively, as a means to reduce the power consumption. Two different one-bit architectures are presented. In the first, a single user MIMO system is considered where the DACs and ADCs of the transmitter and the receiver are assumed to be one-bit and in the second, a network of analog phase shifters is added at the receiver as an additional analog-only processing step with the view to mitigate some of the effects of coarse quantization. The precoding design problem is formulated and then split into two NP-hard optimization problems, which are solved by an algorithmic solution based on the Cyclic Coordinate Descent (CCD) framework. The design of the analog post-coding matrix for the second architecture is decoupled from the precoding design and is solved by an algorithm based on the alternating direction method of multipliers (ADMM). Numerical results show that the proposed precoding scheme successfully mitigates the effects of coarse quantization and the proposed systems achieve a performance close to that of systems equipped with full resolution DACs/ADCs. [less ▲]

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See detailMachine Learning-Enabled Joint Antenna Selection and Precoding Design: From Offline Complexity to Online Performance
Vu, Thang Xuan UL; Chatzinotas, Symeon UL; Nguyen, van Dinh UL et al

in IEEE Transactions on Wireless Communications (2021), 20(6), 3710-3722

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial ... [more ▼]

We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M < N. Upon receiving pilot sequences to obtain the channel state information (CSI), the BS determines the best subset of M antennas for serving the users. We propose a joint antenna selection and precoding design (JASPD) algorithm to maximize the system sum rate subject to a transmit power constraint and quality of service (QoS) requirements. The JASPD overcomes the non-convexity of the formulated problem via a doubly iterative algorithm, in which an inner loop successively optimizes the precoding vectors, followed by an outer loop that tries all valid antenna subsets. Although approaching the (near) global optimality, the JASPD suffers from a combinatorial complexity, which may limit its application in real-time network operations. To overcome this limitation, we propose a learning-based antenna selection and precoding design algorithm (L-ASPA), which employs a deep neural network (DNN) to establish underlaying relations between the key system parameters and the selected antennas. The proposed L-ASPD is robust against the number of users and their locations, BS's transmit power, as well as the small-scale channel fading. With a well-trained learning model, it is shown that the L-ASPD significantly outperforms baseline schemes based on the block diagonalization and a learning-assisted solution for broadcasting systems and achieves higher effective sum rate than that of the JASPA under limited processing time. In addition, we observed that the proposed L-ASPD can reduce the computation complexity by 95% while retaining more than 95% of the optimal performance. [less ▲]

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See detailTransfer Learning and Meta Learning Based Fast Downlink Beamforming Adaptation
Yuan, Yi; Zheng, G.; Wong, K.-K. et al

in IEEE Transactions on Wireless Communications (2021)

This paper studies fast adaptive beamforming optimization for the signal-to-interference-plus-noise ratio balancing problem in a multiuser multiple-input single-output downlink system. Existing deep ... [more ▼]

This paper studies fast adaptive beamforming optimization for the signal-to-interference-plus-noise ratio balancing problem in a multiuser multiple-input single-output downlink system. Existing deep learning based approaches to predict beamforming rely on the assumption that the training and testing channels follow the same distribution which may not hold in practice. As a result, a trained model may lead to performance deterioration when the testing network environment changes. To deal with this task mismatch issue, we propose two offline adaptive algorithms based on deep transfer learning and meta-learning, which are able to achieve fast adaptation with the limited new labelled data when the testing wireless environment changes. Furthermore, we propose an online algorithm to enhance the adaptation capability of the offline meta algorithm in realistic non-stationary environments. Simulation results demonstrate that the proposed adaptive algorithms achieve much better performance than the direct deep learning algorithm without adaptation in new environments. The meta-learning algorithm outperforms the deep transfer learning algorithm and achieves near optimal performance. In addition, compared to the offline meta-learning algorithm, the proposed online meta-learning algorithm shows superior adaption performance in changing environments. [less ▲]

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See detailDynamic Bandwidth Allocation and Precoding Design for Highly-Loaded Multiuser MISO in Beyond 5G Networks
Vu, Thang Xuan UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in IEEE Transactions on Wireless Communications (2021)

Multiuser techniques play a central role in the fifth-generation (5G) and beyond 5G (B5G) wireless networks that exploit spatial diversity to serve multiple users simultaneously in the same frequency ... [more ▼]

Multiuser techniques play a central role in the fifth-generation (5G) and beyond 5G (B5G) wireless networks that exploit spatial diversity to serve multiple users simultaneously in the same frequency resource. It is well known that a multi-antenna base station (BS) can efficiently serve a number of users not exceeding the number of antennas at the BS via precoding design. However, when there are more users than the number of antennas at the BS, conventional precoding design methods perform poorly because inter-user interference cannot be efficiently eliminated. In this paper, we investigate the performance of a highly-loaded multiuser system in which a BS simultaneously serves a number of users that is larger than the number of antennas. We propose a dynamic bandwidth allocation and precoding design framework and apply it to two important problems in multiuser systems: i) User fairness maximization and ii) Transmit power minimization, both subject to predefined quality of service (QoS) requirements. The premise of the proposed framework is to dynamically assign orthogonal frequency channels to different user groups and carefully design the precoding vectors within every user group. Since the formulated problems are non-convex, we propose two iterative algorithms based on successive convex approximations (SCA), whose convergence is theoretically guaranteed. Furthermore, we propose a low-complexity user grouping policy based on the singular value decomposition (SVD) to further improve the system performance. Finally, we demonstrate via numerical results that the proposed framework significantly outperforms existing designs in the literature. [less ▲]

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See detailUplink Power Control in Massive MIMO with Double Scattering Channels
Trinh, van Chien UL; Ngo, Quoc Hien; Chatzinotas, Symeon UL et al

in IEEE Transactions on Wireless Communications (2021)

Massive multiple-input multiple-output (MIMO) is a key technology for improving the spectral and energy efficiency in 5G-and-beyond wireless networks. For a tractable analysis, most of the previous works ... [more ▼]

Massive multiple-input multiple-output (MIMO) is a key technology for improving the spectral and energy efficiency in 5G-and-beyond wireless networks. For a tractable analysis, most of the previous works on Massive MIMO have been focused on the system performance with complex Gaussian channel impulse responses under rich-scattering environments. In contrast, this paper investigates the uplink ergodic spectral efficiency (SE) of each user under the double scattering channel model. We derive a closed-form expression of the uplink ergodic SE by exploiting the maximum ratio (MR) combining technique based on imperfect channel state information. We further study the asymptotic SE behaviors as a function of the number of antennas at each base station (BS) and the number of scatterers available at each radio channel. We then formulate and solve a total energy optimization problem for the uplink data transmission that aims at simultaneously satisfying the required SEs from all the users with limited data power resource. Notably, our proposed algorithms can cope with the congestion issue appearing when at least one user is served by lower SE than requested. Numerical results illustrate the effectiveness of the closed-form ergodic SE over Monte-Carlo simulations. Besides, the system can still provide the required SEs to many users even under congestion. [less ▲]

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See detailConstant Envelope MIMO-OFDM Precoding for Low Complexity Large-Scale Antenna Array Systems
Domouchtsidis, Stavros UL; Tsinos, Christos UL; Chatzinotas, Symeon UL et al

in IEEE Transactions on Wireless Communications (2020)

Herein, we consider constant envelope precoding in a multiple-input multiple-output orthogonal frequency division multiplexing system (CE MIMO-OFDM) for frequency selective channels. In CE precoding the ... [more ▼]

Herein, we consider constant envelope precoding in a multiple-input multiple-output orthogonal frequency division multiplexing system (CE MIMO-OFDM) for frequency selective channels. In CE precoding the signals for each transmit antenna are designed to have constant amplitude regardless of the channel realization and the information symbols that must be conveyed to the users. This facilitates the use of power-efficient components, such as phase shifters (PS) and nonlinear power amplifiers, which are key for the feasibility of large-scale antenna array systems because of their low cost and power consumption. The CE precoding problem is firstly formulated as a least-squares problem with a unit modulus constraint and solved using an algorithm based on coordinate descent. The large number of optimization variables in the case of the MIMO-OFDM system motivates the search for a more computationally efficient solution. To tackle this, we reformulate the CE precoding design into an unconstrained nonlinear least-squares problem, which is solved efficiently using the Gauss-Newton algorithm. Simulation results underline the efficiency of the proposed solutions and show that they outperform state of the art techniques. [less ▲]

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See detailJoint User Grouping, Scheduling, and Precoding for Multicast Energy Efficiency in Multigroup Multicast Systems
Bandi, Ashok UL; Mysore Rama Rao, Bhavani Shankar UL; Chatzinotas, Symeon UL et al

in IEEE Transactions on Wireless Communications (2020)

This paper studies the joint design of user grouping, scheduling (or admission control) and precoding to optimize energy efficiency (EE) for multigroup multicast scenarios in single-cell multiuser MISO ... [more ▼]

This paper studies the joint design of user grouping, scheduling (or admission control) and precoding to optimize energy efficiency (EE) for multigroup multicast scenarios in single-cell multiuser MISO downlink channels. Noticing that the existing definition of EE fails to account for group sizes, a new metric called multicast energy efficiency (MEE) is proposed. In this context, the joint design is considered for the maximization of MEE, EE, and scheduled users. Firstly, with the help of binary variables (associated with grouping and scheduling) the joint design problem is formulated as a mixed-Boolean fractional programming problem such that it facilitates the joint update of grouping, scheduling and precoding variables. Further, several novel optimization formulations are proposed to reveal the hidden difference of convex/ concave structure in the objective and associated constraints. Thereafter, we propose a convex-concave procedure framework based iterative algorithm for each optimization criteria where grouping, scheduling, and precoding variables are updated jointly in each iteration. Finally, we compare the performance of the three design criteria concerning three performance metrics namely MEE, EE, and scheduled users through Monte-Carlo simulations. These simulations establish the need for MEE and the improvement from the system optimization. [less ▲]

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See detailRandom Access based Reliable Uplink Communication and Power Transfer using Dynamic Power Splitting
Kisseleff, Steven UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in IEEE Transactions on Wireless Communications (2020)

Large communication networks, e.g. Internet of Things (IoT), are known to be vulnerable to co-channel interference. One possibility to address this issue is the use of orthogonal multiple access (OMA ... [more ▼]

Large communication networks, e.g. Internet of Things (IoT), are known to be vulnerable to co-channel interference. One possibility to address this issue is the use of orthogonal multiple access (OMA) techniques. However, due to a potentially very long duty cycle, OMA is not well suited for such schemes. Instead, random medium access (RMA) appears more promising. An RMA scheme is based on transmission of short data packets with random scheduling, which is typically unknown to the receiver. The received signal, which consists of the overlapping packets, can be used for energy harvesting and powering of a relay device. Such an energy harvesting relay may utilize the energy for further information processing and uplink transmission. In this paper, we address the design of a simultaneous information and power transfer scheme based on randomly scheduled packet transmissions and reliable symbol detection. We formulate a prediction problem with the goal to maximize the harvested power for an RMA scenario. In order to solve this problem, we propose a new prediction method, which shows a significant performance improvement compared to the straightforward baseline scheme. Furthermore, we investigate the complexity of the proposed method and its vulnerability to imperfect channel state information. [less ▲]

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See detailFull-Duplex Enabled Mobile Edge Caching: From Distributed to Cooperative Caching
Vu, Thang Xuan UL; Chatzinotas, Symeon UL; Ottersten, Björn UL et al

in IEEE Transactions on Wireless Communications (2020)

Mobile edge caching (MEC) has received much attention as a promising technique to overcome the stringent latency and data hungry requirements in future generation wireless networks. Meanwhile, full-duplex ... [more ▼]

Mobile edge caching (MEC) has received much attention as a promising technique to overcome the stringent latency and data hungry requirements in future generation wireless networks. Meanwhile, full-duplex (FD) transmission can potentially double the spectral efficiency by allowing a node to receive and transmit in the same time/frequency block simultaneously. In this paper, we investigate the delivery time performance of full-duplex enabled MEC (FD-MEC) systems, in which the users are served by distributed edge nodes (ENs), which operate in FD mode and are equipped with a limited storage memory. Firstly, we analyse the FD-MEC with different levels of cooperation among the ENs and take into account a realistic model of self-interference cancellation. Secondly, we propose a framework to minimize the system delivery time of FD-MEC under both linear and optimal precoding designs. Thirdly, to deal with the non-convexity of the formulated problems, two iterative optimization algorithms are proposed based on the inner approximation method, whose convergence is analytically guaranteed. Finally, the effectiveness of the proposed designs are demonstrated via extensive numerical results. It is shown that the cooperative scheme mitigates inter-user and self interference significantly better than the distributed scheme at an expense of inter-EN cooperation. In addition, we show that minimum mean square error (MMSE)-based precoding design achieves the best performance-complexity trade-off, compared with the zero-forcing and optimal designs. [less ▲]

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See detailJoint Data Compression and Computation Offloading in Hierarchical Fog-Cloud Systems
Nguyen, Ti Ti; Ha, Vu Nguyen UL; Le, Long Bao et al

in IEEE Transactions on Wireless Communications (2019)

Data compression (DC) has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the ... [more ▼]

Data compression (DC) has the potential to significantly improve the computation offloading performance in hierarchical fog-cloud systems. However, it remains unknown how to optimally determine the compression ratio jointly with the computation offloading decisions and the resource allocation. This optimization problem is studied in this paper where we aim to minimize the maximum weighted energy and service delay cost (WEDC) of all users. First, we consider a scenario where DC is performed only at the mobile users. We prove that the optimal offloading decisions have a threshold structure. Moreover, a novel three-step approach employing convexification techniques is developed to optimize the compression ratios and the resource allocation. Then, we address the more general design where DC is performed at both the mobile users and the fog server. We propose three algorithms to overcome the strong coupling between the offloading decisions and the resource allocation. Numerical results show that the proposed optimal algorithm for DC at only the mobile users can reduce the WEDC by up to 65% compared to computation offloading strategies that do not leverage DC or use sub-optimal optimization approaches. The proposed algorithms with additional DC at the fog server lead to a further reduction of the WEDC. [less ▲]

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See detailA Joint Solution for Scheduling and Precoding in Multiuser MISO Downlink Channels
Bandi, Ashok UL; Shankar, Bhavani UL; Chatzinotas, Symeon UL et al

in IEEE Transactions on Wireless Communications (2019)

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See detailRelay Selection and Resource Allocation for SWIPT in Multi-User OFDMA Systems
Gautam, Sumit UL; Lagunas, Eva UL; Chatzinotas, Symeon UL et al

in IEEE Transactions on Wireless Communications (2019)

We investigate the resource allocation and relay selection in a two-hop relay-assisted multi-user Orthogonal Frequency Division Multiple Access (OFDMA) network, where the end-nodes support Simultaneous ... [more ▼]

We investigate the resource allocation and relay selection in a two-hop relay-assisted multi-user Orthogonal Frequency Division Multiple Access (OFDMA) network, where the end-nodes support Simultaneous Wireless Information and Power Transfer (SWIPT) employing a Power Splitting (PS) technique. Our goal is to optimize the end-nodes’ power splitting ratios as well as the relay, carrier and power assignment so that the sum-rate of the system is maximized subject to harvested energy and transmitted power constraints. Such joint optimization with mixed integer non-linear programming structure is combinatorial in nature. Due to the complexity of this problem, we propose to solve its dual problem which guarantees asymptotic optimality and less execution time compared to a highly-complex exhaustive search approach. Furthermore, we also present a heuristic method to solve this problem with lower computational complexity. Simulation results reveal that the proposed algorithms provide significant performance gains compared to a semi-random resource allocation and relay selection approach and close to the optimal solution when the number of OFDMA sub-carriers is sufficiently large. [less ▲]

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See detailAchievable Data Rate of DCT-based Multicarrier Modulation Systems
Cruz-Roldán, Fernando; Alves Martins, Wallace UL; Sergio Ramirez Diniz, Paulo et al

in IEEE Transactions on Wireless Communications (2019), 18(3), 1739-1749

This paper aims at studying the achievable data rate of discrete cosine transform (DCT)-based multicarrier modulation (MCM) systems. To this end, a general formulation is presented for the full ... [more ▼]

This paper aims at studying the achievable data rate of discrete cosine transform (DCT)-based multicarrier modulation (MCM) systems. To this end, a general formulation is presented for the full transmission/reception process of data in Type-II even DCT and Type-IV even DCT-based systems. The paper focuses on the use of symmetric extension (SE) and zero padding (ZP) as redundancy methods. Furthermore, three cases related to the channel order and the length of the redundancy are studied. In the first case, the channel order is less than or equal to the length of the redundancy. In the second and third cases, the channel order is greater than the length of the redundancy; the interference caused by the channel impulse response is calculated, and theoretical expressions for their powers are derived. These expressions allow studying the achievable data rate of DCT-based MCM systems, besides enabling the comparison with the conventional MCM based on the discrete Fourier Transform. [less ▲]

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