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Long short-term memory (LSTM) is a type of powerful deep neural network that has been widely used in many sequence analysis and modeling applications. However, the large model size problem of LSTM networks make their practical deployment still very challenging, especially for the video recognition tasks that require high-dimensional input data. Aiming to overcome this limitation and fully unlock the potentials of LSTM models, in this paper we propose to perform algorithm and hardware co-design towards high-performance energy-efficient LSTM networks. At algorithm level, we propose to develop fully decomposed hierarchical Tucker (FDHT) structure-based LSTM, namely FDHT-LSTM, which enjoys ultra-low model complexity while still achieving high accuracy. In order to fully reap such attractive algorithmic benefit, we further develop the corresponding customized hardware architecture to support the efficient execution of the proposed FDHT-LSTM model. With the delicate design of memory access scheme, the complicated matrix transformation can be efficiently supported by the underlying hardware without any access conflict in an on-the-fly way. Our evaluation results show that both the proposed ultra-compact FDHT-LSTM models and the corresponding hardware accelerator achieve very high performance. Compared with the state-of-the-art compressed LSTM models, FDHT-LSTM enjoys both order-of-magnitude reduction (more than $1000 \times$ 1000 × ) in model size and significant accuracy improvement (0.6% to 12.7%) across different video recognition datasets. Meanwhile, compared with the state-of-the-art tensor decomposed model-oriented hardware TIE, our proposed FDHT-LSTM architecture achieve $2.5\times$ 2. 5 × , $1.46\times$ 1. 46 × and $2.41\times$ 2. 41 × increase in throughput, area efficiency and energy efficiency, respectively on LSTM-Youtube workload. For LSTM-UCF workload, our proposed design also outperforms TIE with $1.9\times$ 1. 9 × higher throughput, $1.83\times$ 1. 83 × higher energy efficiency and comparable area efficiency. [ABSTRACT FROM AUTHOR]

In this paper, a traction motor for maximum power 76kW electrical vehicle (EV) was designed, and the design results were verified through experiments. One of the important design objectives of the traction motor for EV is to improve energy efficiency. The energy efficiency of a traction motor affects the mileage of electric vehicles (EVs). Among the design factors influencing energy efficiency, shape parameters include torque per rotor volume (TRV), torque density (TD) and shape ratio (SR). These shape parameters have an impact on the motor's phase back EMF, inductance, winding resistance and core loss resistance, eventually affecting the energy efficiency in the entire operating range of the motor. In this paper, a New European Driving Cycle (NEDC) was applied to calculate the energy efficiency of the traction motor for EV. Changes in motor parameters such as phase back electromotive force (EMF), inductance, winding resistance and core loss resistance were identified according to variations in shape parameters. In addition, the motor characteristics according to the changes of these parameters were confirmed, and shape parameters to improve energy efficiency were selected. Since the traction motor designed in this paper adopted a water cooling method, thermal analysis was performed by reflecting the water cooling method in the thermal equivalent circuit (TEC). In order to confirm the reliability of the analysis method and design, a load test was conducted on the entire operating range to calculate energy efficiency, and a temperature test was performed to compare the test results with the analysis results. [ABSTRACT FROM AUTHOR]

Machine-type communications (MTC) is a key technology for Internet-of-Things (IoT) services in 5G mobile communications and beyond. An essential design problem for an MTC network is the efficient and scalable data collection from low-power machine-type communication devices (MTCDs). This paper uses unmanned aerial vehicles (UAVs) to facilitate data collection from a clustered MTC network on the ground. The notion of artificial energy map (AEM) is introduced as a novel modeling technique for energy efficiency analysis, which is critical to the subject of investigation here considering the limited energy of battery-powered MTCDs and UAVs. The proposed design framework first determines the number of MTCD clusters $L_\mathrm{{opt}}$ according to a certain criterion. A greedy learning clustering (GLC) algorithm is then employed to divide the MTCDs into $L_\mathrm{{opt}}$ clusters. For each MTCD cluster, an AEM is constructed, and the optimal UAV hovering strategy within the cluster can be obtained accordingly. Finally, the UAV stations travel across the clusters and collect data from each cluster while hovering above it. This AEM-based modeling technique leads to a solution that can effectively improve the energy efficiency (EE) of UAV-enabled data collection. However, the MTCD clustering strategy, UAV hovering strategy, and UAV flying strategy all have impacts on the overall energy efficiency, which results in a coupled optimization problem that is difficult to solve. The GLC-AEM method is proposed to decouple the original EE optimization problem into sub-problems that can be handled easily by standard optimization techniques. Simulation results show that the GLC-AEM algorithm can be applied to UAV-enabled data collection scenarios with single and multiple UAV stations, and it can improve the overall EE effectively. Besides, the GLC-AEM algorithm shows good scalability and consistent performance in clustered MTC networks. The more MTCDs, the higher the achieved EE. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surface (RIS) has been introduced as a promising emerging paradigm to design a smart radio environment for improving wireless capacity. It has the potential to reshape wave propagation via its tunable and low-cost reflecting elements. Therefore, it is regarded as a propitious solution that provides alternative paths for the propagation of high-frequency signals, which are vulnerable to blockages and suffer from penetration losses. Because of those features, RIS has been considered to be integrated into the wireless vehicular network (WVN), which is characterized by high mobility and high quality of service (QoS) requirements. Hence, similar to relaying systems, a vehicle needs to select the best available RIS that maximizes the signal-to-noise ratio (SNR) at the receiver. Inspired by that, in this paper, we introduce the partial RIS selection for enhancing capacity and reducing error rate. We derive closed-form expressions for the ergodic capacity, symbol error probability (SEP), and energy efficiency for a partial RIS selection design. Besides, we compare its performances to the partial relaying design while considering: decode-and-forward (DF) and amplify-and-forward fixed gain (AF). Moreover, we consider a wireless attack scenario, where an eavesdropper intends to overhear the transmitted signal at the receiver’s end. Then, we compare partial RIS to partial relay selection from this security perspective, where we derive closed-form expressions for the average secrecy capacity. Furthermore, we compare the partial RIS selection to a single RIS architecture scenario from the literature, where our solution outperforms the related existing work. [ABSTRACT FROM AUTHOR]

This paper studiesan unmanned aerial vehicle (UAV)-assisted wireless network, where a UAV is dispatched to gather information from ground sensor nodes (SN) and transfer the collected data to a depot. The information freshness is captured by the age of information (AoI) metric, whilst the energy consumption of the UAV is seen as another performance criterion. Most importantly, the AoI and energy efficiency are inherently competing metrics, since decreasing the AoI requires the UAV returning to the depot more frequently, leading to a higher energy consumption. To this end, we design UAV paths that optimize these two competing metrics jointly and reveal the Pareto frontier. To formulate this problem, a multi-objective mixed integer linear programming (MILP) is proposed with a flow-based constraint set and we apply Bender’s decomposition on the proposed formulation. Numerical results show that the proposed method allows deriving non-dominated solutions among two competing metrics when designing the UAV path. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the energy-efficient resource allocation problem in device-to-device (D2D) communications underlaying unmanned aerial vehicle (UAV)-enabled networks. The UAV is deployed as a flying base station to communicate with wireless users in the presence of an eavesdropper in the cell. We consider two types of users: the ground users (GUs) served by the UAV and the D2D users that communicate directly with one another. Our aim is to maximize the total energy efficiency (TEE) of all D2D pairs while guaranteeing the quality of service (QoS) requirements and secrecy rates of all GUs and D2D users via joint power control and channel allocation. The considered TEE maximization problem is a mixed-integer nonlinear programming (MINLP) problem, which is difficult to solve. Therefore, we propose a method that consists of outer and inner loops. In the outer loop, Dinkelbach's algorithm is utilized to transform the original fractional programming problem into a subtractive form. In the inner loop, we employ the alternating optimization method and divide the equivalent optimization problem into two sub-problems: power allocation and channel allocation. Solving the two sub-problems directly using standard convex optimization software may have high complexity. Therefore, we also propose a low-complexity algorithm using the Lagrangian dual and Kuhn—Munkres algorithm to obtain the optimal power allocation in closed-form and the optimal channel allocation, respectively. Simulation results show that the proposed algorithm converges in a small number of iterations. Furthermore, the proposed approach shows its superior performance compared with other benchmark methods. [ABSTRACT FROM AUTHOR]

IP traffic has increased significantly in recent years, and it is expected that this progress will continue. Recent studies report that the viewing of online video content accounts for a share of 1% of the global greenhouse gas emissions. To reduce the data traffic of video streaming, the new standard Versatile Video Coding (VVC) has been finalized in 2020. In this paper, the energy efficiency of two different VVC decoders is analyzed in detail. Furthermore, we propose a design space exploration that uses an algorithm based on a greedy strategy to derive coding tool profiles that optimize the energy demand of the decoder. We show that the algorithm derives optimal coding tool profiles for a subset of coding tools. Additionally, we propose profiles that reduce the energy demand of VVC decoders and provide energy savings of more than 50% for sequences with 4K resolution. Thereby, we will also show that the proposed profiles can have a lower decoding energy demand than comparable HEVC-encoded bit streams while also having a significantly lower bit rate. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) and Terahertz (THz) technology are envisioned to play paramount roles in next-generation wireless communications. In this paper, we present a novel secure UAV-assisted mobile relaying system operating at THz bands for data acquisition from multiple ground user equipments (UEs) towards a destination. We assume that the UAV-mounted relay may act, besides providing relaying services, as a potential eavesdropper called the untrusted UAV-relay (UUR). To safeguard end-to-end communications, we present a secure two-phase transmission strategy with cooperative jamming. Then, we devise an optimization framework in terms of a new measure $-$ secrecy energy efficiency (SEE), defined as the ratio of achievable average secrecy rate to average system power consumption, which enables us to obtain the best possible security level while taking UUR’s inherent flight power limitation into account. For the sake of quality of service fairness amongst all the UEs, we aim to maximize the minimum SEE (MSEE) performance via the joint design of key system parameters, including UUR’s trajectory and velocity, communication scheduling, and network power allocation. Since the formulated problem is a mixed-integer nonconvex optimization and computationally intractable, we decouple it into four subproblems and propose alternative algorithms to solve it efficiently via greedy/sequential block successive convex approximation and non-linear fractional programming techniques. Numerical results demonstrate significant MSEE performance improvement of our designs compared to other known benchmarks. [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]

This paper studies a scheduling problem in decentralized coded caching considering sequential and random request arrivals. The asynchrony between the requests begets the question of when to launch network-coded multicast to serve the requests when both the energy efficiency and the delay penalty are of concern (one can be traded for the other). To attain an optimal energy-delay tradeoff, we formulate this scheduling problem as an optimal stopping problem. In particular, for either the hard deadline or the linear penalty functions, we derive the structure of an optimal stopping rule and propose an iterative method to approximate the optimal rule. Simulation results demonstrate the effectiveness of the derived optimal stopping rules under various conditions, comparing them with several baseline scheduling strategies. [ABSTRACT FROM AUTHOR]

In the face of the unprecedented demand for higher data rates while coping with the constraints from physical size (i.e., windward area), it is profitable to deploy compact planar arrays (CPAs) on the base stations (BSs). But massively densified antenna deployment not only leads to antenna mutual coupling (MC), but also consumes additional energy due to the equipment of more radio frequency (RF) chains. This paper studies energy efficiency (EE) optimization for massive multi-input multi-output (MIMO) wireless systems with a CPA in the presence of MC. In comprehensive consideration of the spectral efficiency (SE) and power consumption, a joint optimization problem of beamforming and antenna subset (AS) is proposed. Our motivation is to relieve the effects of MC and reduce the relevant energy consumption by constructing irregular AS. Particularly, considering the nonconvexity of the objective and the discrete 0-1 constraints, an available iterative algorithm, named the energy-efficient beamforming and AS (EE-BF&AS) algorithm, is developed. Numerical results illustrate that when MC is modeled, the proposed EE-BF&AS algorithm outperforms conventional related approaches in EE. By using the AS switching strategy, the irregular AS can be constructed to make MC less serious and let the relevant energy consumption decrease. [ABSTRACT FROM AUTHOR]

This paper investigates and analyzes the number of reflecting elements for guaranteed energy- and spectral-efficient intelligent reflecting surface (IRS)-assisted communication systems. As opposed to previous works where the energy efficiency (EE)/the spectral efficiency (SE) maximization or the EE-SE tradeoff was considered, our goal is to minimize the number of reflecting elements in the IRS-assisted system. Besides, both the EE and the SE constraints are considered in the number minimization problem, which has not been addressed in existing works. However, both the EE and the SE performance do not admit exact closed-form expressions due to the non-convexity incurred by joint beamforming and phase shift design. In order to make the optimization problem tractable, we resort to their performance bounds for problem reformulation. By decomposing the problem into two sub-problems, we are able to derive closed-form expressions for the minimum number of reflecting elements, and both the coherent phase shift (CPS)-oriented solution and the random phase shift (RPS)-oriented solution are proposed for comparison. In order to shed light on the practical design, the relationship between the derived number of reflecting elements is established for both schemes, and the upper bounds on both the EE and the SE thresholds and the placement of the IRS to achieve only one reflecting element are obtained. Simulation results confirm the validity of our analysis on the minimum number of reflecting elements and effectiveness of both schemes. [ABSTRACT FROM AUTHOR]

In subway systems, increasing attention has been paid to improve energy efficiency due to remarkable energy consumption for rail transportation operation. The optimization of single-train driving control and multi-train operation synergy are two critical and closely related aspects for energy saving, but they are rarely solved in an integrative manner. The comprehensive study on these two aspects is a complex, large-scale, multi-variable and constrained task, which involves nested, coupled and mutual-input optimization processes. This paper proposes an energy-efficient integrated model, which includes optimizing single-train operation control in an inner model and multi-train operation synergy, committing to the utilization of regenerative braking energy (RBE) in an outer model. A parallelized particle swarm optimization based on Map-Reduce (PPSO-MR) algorithm is proposed to solve the integrated model to reduce the execution time. Two concepts of temporal-spatial record and time shift are introduced to derive the movement of a train at inter-stations for the operation adjustment of multiple trains on a subway line. Experimental results show that the proposed control model solved by the holistic optimization approach can achieve lower energy consumption with fast execution time. This study provides the novel model and algorithm to obtain energy-saving schedules serving the multi-train operation coordination. [ABSTRACT FROM AUTHOR]

The heterogeneous fog radio access networks (Fog-RAN), the integration of fog computing, and traditional heterogeneous radio access networks can be implemented through the next-generation wireless communication networks. However, most of the solutions are limited to the spectrum efficiency optimization, and cross-tier interference existing in the fog access points (F-APs) could affect the network performance seriously. In this paper, the user association, resource allocation (including bandwidth and power), and caching deployment are investigated in the heterogeneous Fog-RAN to consider energy efficiency and cross-tier interference mitigation. Specifically, the user association, resource allocation, and caching strategy are formulated as a non-convex optimization problem and then transformed into a convex problem, which can be solved by a proposed algorithm based on the concept of the alternating direction method of multipliers (ADMM). Then an ADMM-based algorithm is proposed to enhance the energy efficiency of the Fog-RAN. Compared with the current solutions, simulation results illustrate the proposed algorithm’s convergence and effectiveness. [ABSTRACT FROM AUTHOR]

In 5G wireless networks, cooperative non-orthogonal multiple access (NOMA) and wireless power transfer (WPT) are efficient ways to improve the spectral efficiency (SE) and energy efficiency (EE). In this paper, a new cooperative NOMA scheme with WPT is proposed, where EE optimization with a constrained maximum transmit power and minimum required SE is considered for the user grouping and transmit power allocation of users. We obtain a sub-optimal solution by decoupling the original problem in two sub-problems: an iterative algorithm is considered for the user grouping, while, in addition, we utilize the Bat Algorithm (BA) for solving the power allocation problem, where BA was proved to be able to achieve a higher accuracy and efficiency with respect to other meta-heuristic algorithms. Furthermore, to validate the performance of the proposed system, analytical expressions for the energy outage probability and outage probability of users are derived, confirming the effectiveness of the simulation results. It is demonstrated that the proposed cooperative NOMA with WPT offers a considerable improvement in terms of SE and EE of the network compared to other methods. Finally, the effectiveness of BA in solving the EE optimization problem is demonstrated through a high convergence speed by comparing it with other methods. [ABSTRACT FROM AUTHOR]

Thanks to the strong ability against the inter-cell interference, cell-free network is considered as a promising technique to improve network capacity. However, further capacity improvement requires to deploy more base stations (BSs) with high cost and power consumption. To address this issue, inspired by the recently developed reconfigurable intelligent surface (RIS) technique, we propose the concept of RIS-aided cell-free network to improve the capacity with low cost and power consumption. The key idea is to replace some of the required BSs by low-cost and energy-efficient RISs. Then, in a wideband RIS-aided cell-free network, we formulate the problem of joint precoding design at BSs and RISs to maximize the network capacity. Due to the non-convexity and high complexity of the formulated problem, we develop an alternating optimization framework to solve this challenging problem. In particular, we decouple this problem via fractional programming, and solve the subproblems alternatively. Note that most of the scenarios considered in existing works are special cases of the general scenario studied in this paper, and the proposed joint precoding framework can serve as a general solution to maximize the capacity in most existing RIS-aided scenarios. Finally, simulation results demonstrate that, compared with the conventional cell-free network, the network capacity under the proposed scheme can be improved significantly. [ABSTRACT FROM AUTHOR]

Even if massive multiple-input multiple-output (MIMO) can theoretically bring huge benefits, it incurs substantial hardware complexity and expensive hardware costs. To address these issues while maintaining the system performance simultaneously, we develop an asymmetrical transceiver architecture for massive MIMO systems in this paper by releasing the shackles on radio frequency (RF) chains. Specifically, we first develop the architecture for the asymmetrical transceiver where the number of receive RF chains is different from that of the transmit RF chains. Due to this unequal number of RF chains, channel inconsistency appears. To overcome the channel inconsistency and thus fully harness the system's downlink data transmission capability, we propose two uplink-to-downlink channel transfer algorithms. The cost and power consumption models for the asymmetrical transceiver are also developed and the uplink signal-to-noise loss due to the channel inconsistency is investigated. Through analyzing system spectral, cost, and energy efficiency, we demonstrate that the proposed asymmetrical transceiver-based massive MIMO system can achieve excellent downlink spectral efficiency while maintaining a reasonable energy efficiency. [ABSTRACT FROM AUTHOR]

New radio in unlicensed spectrum (NR-U) and network slicing have been emerged as two promising technologies to meet with the demand of enormous traffic volume, extreme transmission speed, and highly heterogeneous service requirements in future 5 G networks. Additionally, spurred by both economic considerations and environmental concerns, green communication becomes a new prominent figure of merit in the design of future 5 G networks. In this paper, we investigate an energy consumption scheduling for unlicensed heterogeneous networks, namely NR-U and WiFi, by leveraging the network slicing technology. First, we introduce a network slice broker, which enables virtual heterogeneous network operators to request and lease resources cooperatively via signaling means. Then, we investigate a joint network selection and resource allocation scheduling to minimize the network energy consumption in real time environment, taking into account various user data rate and delay requirements. An online Lyapunov optimization algorithm has been introduced to solve the proposed problem with low computational complexity and rapid convergence. We show that there exists a tradeoff between energy consumption and traffic delay, which can be adjusted through control parameters. Numerical results are presented to confirm our analyses and demonstrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on the performance analysis of point-to-point communication, where the source node is powered with a fixed-power battery as well as a harvest-use battery. A time-switching scheme is implemented such that in each time frame the source either harvests energy or sends information to the destination node. Since switching takes place within a communication time frame, energy harvesting may not necessarily be beneficial in terms of improving the maximum achievable spectral efficiency or the energy efficiency of system. Here, we investigate and provide conditions under which harvesting can improve the system performance. We consider a scenario where the transmission power from the fixed battery is constant. Closed-form expressions for the conditions under which energy harvesting is proven beneficial are provided. The effects of important parameters, namely, time switching parameter, circuit powers, and harvested energy, on the system performance and on the condition for harvesting beneficialness, are investigated and discussed. [ABSTRACT FROM AUTHOR]

Age of information (AoI) and energy efficiency (EE), as two important performance metrics in status update systems, usually cannot be simultaneously optimized. This paper investigates the age and energy tradeoff for multicast networks with retransmissions, where each sensed status update is encoded as a short blocklength packet and is broadcasted to multiple destinations via independent and identically distributed error-prone channels. By considering the stopping threshold, the average AoI and EE expressions for stopping at earliest- $\ell $ , stopping at preselected- $\ell $ , and wait-for-all schemes are derived as a function of the packet length. On this basis, the average AoI-EE ratio is introduced and is minimized to trade off age and energy by optimizing the packet length. Numerical results indicate that among the three schemes, earliest- $\ell $ scheme attains the minimum AoI when the packet length is small, and preselected- $\ell $ scheme attains the maximum EE when the packet length is large. An optimal packet length can always be found for each scheme to minimize age-energy ratio. Moreover, the selection of stopping threshold $\ell $ will also impact the age and energy performance. [ABSTRACT FROM AUTHOR]

Simultaneous wireless information and power transfer (SWIPT) has been advocated as a highly promising technology to provide near-perpetual operation to low-powered wireless devices in Internet-of-Things (IoT)-based wireless networks. In this paper, a SWIPT-enhanced cell-free massive MIMO network is proposed. In such a network, a large set of spatially distributed access points (APs) interconnected via a central processing unit (CPU) can collaboratively serve a large number of both energy harvesting mobile stations (MSs) (requiring wireless energy transfer) and conventional MSs (not requiring wireless energy transfer) on the same time-frequency resources. We consider spatially correlated Rician fading channels and the use of different precoding schemes that are based on different channel estimators differing on the assumed knowledge of the line-of-sight component. Mathematically manageable expressions are derived for the harvested energy during the downlink (DL) energy harvesting phase and the achievable spectral and energy efficiencies during the uplink (UL) payload transmission phase. A coupled UL/DL optimization problem is formulated aiming at finding the power control coefficients that maximize the minimum of the weighted achievable UL signal-to-interference-plus-noise ratios (SINRs) of all MSs. Extensive numerical results are presented that serve to highlight the existing trade-offs among the achievable spectral and energy efficiencies, the harvested energy, the energy dedicated to UL pilot transmission or the system configuration. [ABSTRACT FROM AUTHOR]

This paper investigates the energy efficiency (EE) optimization in downlink multi-cell massive multiple-input multiple-output (MIMO). In our research, the statistical channel state information (CSI) is exploited to reduce the signaling overhead. To maximize the minimum EE among the neighbouring cells, we design the transmit covariance matrices for each base station (BS). Specifically, optimization schemes for this max-min EE problem are developed, in the centralized and distributed ways, respectively. To obtain the transmit covariance matrices, we first find out the closed-form optimal transmit eigenmatrices for the BS in each cell, and convert the original transmit covariance matrices designing problem into a power allocation one. Then, to lower the computational complexity, we utilize an asymptotic approximation expression for the problem objective. Moreover, for the power allocation design, we adopt the minorization maximization method to address the non-convexity of the ergodic rate, and use Dinkelbach’s transform to convert the max-min fractional problem into a series of convex optimization subproblems. To tackle the transformed subproblems, we propose a centralized iterative water-filling scheme. For reducing the backhaul burden, we further develop a distributed algorithm for the power allocation problem, which requires limited inter-cell information sharing. Finally, the performance of the proposed algorithms are demonstrated by extensive numerical results. [ABSTRACT FROM AUTHOR]

Wireless-powered communication and reconfigurable intelligent surface (RIS) can complement each other for increasing energy utilization and spectrum efficiency by reconfiguring the surrounding radio environment, however, which has not been sufficiently studied by the existing works. In this paper, we propose a joint radio resource and passive beamforming optimization scheme for a downlink RIS-assisted wireless-powered communication network with a harvest-then-transmit protocol to improve system energy efficiency (EE). In the considered model, the single-antenna wireless devices (WDs) harvest wireless energy from a multi-antenna dedicated power station (PS) through the RIS in the downlink and transmit their independent information to a single-antenna receiver in the uplink by a time-division-multiple-access mode. Our goal is to maximize the total EE of all WDs. To make full use of the beamforming gain provided by both the PS and the RIS, we jointly optimize the active beamforming of the PS and the passive beamforming of the RIS. To deal with the challenging non-convex optimization problem with multiple coupled variables, we first consider fixing the passive beamforming, and converting the remaining radio resource allocation problem into an equivalent convex problem which is solved by using Lagrange dual theory. Then, we fix the optimized resource allocation parameters and optimize the passive beamforming of the RIS by using a semidefinite programming method. Simulation results demonstrate that the proposed algorithm achieves higher EE compared to the conventional schemes. [ABSTRACT FROM AUTHOR]

Billions of mobile sensing devices in Internet of Things (IoT) collect data from surrounding environment in which the Medium Access protocol is employed. Quorum-based protocols are most important medium access control protocol (MAC) for wake-up based devices. However, existing quorum-based protocols cannot achieve both low latency and long lifetime at the same time. In this paper, a novel Element Shift Quorum (ESQ) based MAC is proposed in the sensor based systems. Data transmission is usually launched from the periphery of the network through multi hop routing to the data collector (DC), thus the data operation time of devices are different based on the distance from the DC. In the ESQ protocol, the Quorum time slot (QTS) of sensing devices is scheduled in the time when the devices just have data to process, then the latency and energy consumption can be reduced. In this way, sufficient QTS are allocated for nodes when data operation is needed, and nodes can stay at sleeping state when no data forwarding is required, thus enhancing the energy efficiency and lowering the network delay. Theoretical analysis and experimental results show that, the lifetime is increased by 16% using the proposed method. Meanwhile, the latency is reduced by a factor between 9.08%-29.32%. [ABSTRACT FROM AUTHOR]