Average Transmission Power Research Articles

Goodput Maximization of HARQ-IR Over Arbitrarily Correlated Rician Fading Channels

This paper investigates the optimal design of hybrid automatic repeat request with incremental redundancy (HARQ-IR) over arbitrarily correlated Rician fading channels for goodput maximization. With practical consideration of both the channel time correlation and line-of-sight (LOS) links, theoretical analysis and optimal design of HARQ-IR become very challenging. To address them, asymptotic outage analysis is first carried out to not only gain meaningful insights but also facilitate the optimal design. With simple forms of asymptotic expressions derived, joint optimization of the transmission powers and rate aiming at goodput maximization under average total transmission power constraint is enabled and solved analytically. The optimal goodput is also derived in closed form, with which meaningful insights can then be extracted. Particularly, the scaling law of the optimal goodput with respect to the transmit signal-to-noise ratio is found, and the harmfulness of time correlation and the benefit of LOS links are revealed. Finally, the analytical results are validated through extensive simulations.

Algorithm for the most surviving nodes selection based on adaptive forwarding control under the impact of network storm

In order to improve the survivability of wireless network nodes under the impact of network storm to improve the adaptive forwarding performance of network and control the nodes selection optimisation, an algorithm for the most surviving nodes selection based on adaptive forwarding control under the impact of network storm is proposed in this paper. In this algorithm, a node distribution model under the impact of network storm is constructed; the automatic regulation and control method is adopted for optimal localisation and deployment of target nodes; based on node location, an output link forwarding routing protocol is constructed for interference wave filtering and adaptive forwarding control of output package, so as to select the most surviving nodes and improve the survival rate of nodes. The simulation result shows that this method can realise 100% success rate of data forwarding, reduce the average transmission power by 5 kW than that before optimisation, and stabilise the transmission power. The calculation cost and time delay are 5-20 ms lower than that of other methods, which proves the high efficiency of this method.

Algorithm for the most surviving nodes selection based on adaptive forwarding control under the impact of network storm

In order to improve the survivability of wireless network nodes under the impact of network storm to improve the adaptive forwarding performance of network and control the nodes selection optimisation, an algorithm for the most surviving nodes selection based on adaptive forwarding control under the impact of network storm is proposed in this paper. In this algorithm, a node distribution model under the impact of network storm is constructed; the automatic regulation and control method is adopted for optimal localisation and deployment of target nodes; based on node location, an output link forwarding routing protocol is constructed for interference wave filtering and adaptive forwarding control of output package, so as to select the most surviving nodes and improve the survival rate of nodes. The simulation result shows that this method can realise 100% success rate of data forwarding, reduce the average transmission power by 5 kW than that before optimisation, and stabilise the transmission power. The calculation cost and time delay are 5-20 ms lower than that of other methods, which proves the high efficiency of this method.

Energy-Efficient Optimal Power Allocation in Integrated Wireless Sensor and Cognitive Satellite Terrestrial Networks.

This paper proposes novel satellite-based wireless sensor networks (WSNs), which integrate the WSN with the cognitive satellite terrestrial network. Having the ability to provide seamless network access and alleviate the spectrum scarcity, cognitive satellite terrestrial networks are considered as a promising candidate for future wireless networks with emerging requirements of ubiquitous broadband applications and increasing demand for spectral resources. With the emerging environmental and energy cost concerns in communication systems, explicit concerns on energy efficient resource allocation in satellite networks have also recently received considerable attention. In this regard, this paper proposes energy-efficient optimal power allocation schemes in the cognitive satellite terrestrial networks for non-real-time and real-time applications, respectively, which maximize the energy efficiency (EE) of the cognitive satellite user while guaranteeing the interference at the primary terrestrial user below an acceptable level. Specifically, average interference power (AIP) constraint is employed to protect the communication quality of the primary terrestrial user while average transmit power (ATP) or peak transmit power (PTP) constraint is adopted to regulate the transmit power of the satellite user. Since the energy-efficient power allocation optimization problem belongs to the nonlinear concave fractional programming problem, we solve it by combining Dinkelbach’s method with Lagrange duality method. Simulation results demonstrate that the fading severity of the terrestrial interference link is favorable to the satellite user who can achieve EE gain under the ATP constraint comparing to the PTP constraint.

Adapting Rate and Power for Maximizing Secrecy Energy Efficiency

We present the optimum rate and power adaptation rule that maximizes the average secrecy energy efficiency (SEE) subject to an average transmission power constraint. The SEE is defined as the outage secrecy capacity, the largest secrecy rate, such that the outage probability is less than a certain value, divided by the total power consumption (bits per joule). We characterize the SEE gain provided by varying the rate and/or the power, and discuss the impact of the number of antennas on the optimum adaptation rule.

Limited Feedback Scheme for Device-to-Device Communications in 5G Cellular Networks with Reliability and Cellular Secrecy Outage Constraints

In this paper, we propose a device to device (D2D) communication scenario underlaying a cellular network where both D2D and cellular users (CUs) are discrete power-rate systems with limited feedback from the receivers. It is assumed that there exists an adversary which wants to eavesdrop on the information transmission from the base station (BS) to CUs. Since D2D communication shares the same spectrum with cellular network, cross interference must be considered. However, when secrecy capacity is considered, the interference caused by D2D communication can help to improve the secrecy communications by confusing the eavesdroppers. Since both systems share the same spectrum, cross interference must be considered. We formulate the proposed resource allocation into an optimization problem whose objective is to maximize the average transmission rate of D2D pair in the presence of the cellular communications under average transmission power constraint. For the cellular network, we require a minimum average achievable secrecy rate in the absence of D2D communication as well as a maximum secrecy outage probability in the presence of D2D communication which should be satisfied. Due to high complexity convex optimization methods, to solve the proposed optimization problem, we apply Particle Swarm Optimization (PSO) which is an evolutionary approach. Moreover, we model and study the error in the feedback channel and the imperfectness of channel distribution information (CDI) using parametric and nonparametric methods. Finally, the impact of different system parameters on the performance of the proposed scheme is investigated through simulations. The performance of the proposed scheme is evaluated using numerical results for different scenarios.

Joint routing, scheduling and power control for large interference wireless networks

We consider the problem of joint routing, scheduling and power control in multi-hop wireless networks. We use a linear relation between link capacity and signal to interference noise ratio in our formulation. In a previous work, using a duality approach, the optimal link scheduling and power control that minimizes the total average transmission power is found. We formulate this problem as a linear programming problem with exponential number of constraints. To cope with the exponential number of constraints, we propose an iterative algorithm based on the cutting plane method. The separation oracle for the cutting plane algorithm turns out to be an element-wise concave optimization problem that can be effectively solved using branch and bound algorithm. We extend the same method to find the optimal routing scheduling and power control. Simulation results show that this methodology is more efficient and scalable compare to the previously proposed algorithm.

Traffic-Aware Cell Management for Green Ultradense Small-Cell Networks

To reduce the power consumption of fifth-generation ultradense small-cell networks, base stations can be switched to low-power sleep modes when local traffic levels are low. In this paper, a novel sleep mode control algorithm is proposed to control such sleep modes. The algorithm innovates a concept called traffic-aware cell management (TACM). It involves cell division, cell death, and cell migration to represent adaptations of networks, where the state transitions of base stations are controlled. Direction of arrival (DOA) is adopted for distributed decision making. The TACM algorithm aims at reducing the network power consumption while alleviating the impacts of applying sleep modes, such as mitigating system overheads and reducing user transmission power. The TACM algorithm is compared with a recent consolidated baseline scheme by simulation on networks with unbalanced traffic distributions and with base stations at random locations. In contrast, the TACM algorithm shows a significant improvement in mitigating system overheads due to the absence of load information exchange overhead and up to 72 times less switching frequency. Up to 81% network power consumption can be reduced compared with the baseline scheme if considering high energy consumption of switching transient states. In addition, at a low traffic level, average uplink transmission power is reduced by 79% comparatively. Furthermore, the impact of important performance-governing parameters of the TACM algorithm is analyzed. The insensitivity to the estimation accuracy of DOA is also demonstrated. The results show that the proposed TACM algorithm has a comprehensive advantage of power reduction and overhead mitigation over the baseline scheme.

A Topology Control Approach Reducing Construction Cost for Lossy Wireless Sensor Networks

In lossy wireless sensor networks, many links suffer from significant quality variation with time and environments. Topology control approaches need to consider such stochastic nature to yield different topologies for different application requirements. However, the metric of links must be timely obtained to speed up the topology construction. In fact, the existing approaches address it by passive monitoring, which is not timely adaptive to link quality variation. Also, timely access to the metric of all links at all power levels causes a large burden on topology control operation. We do not insist on getting the link metrics of all power levels at a time. Most urgently needed link metrics are firstly obtained by an active probing mode in this paper. If these link metrics do not meet the topology performance requirements, sub-urgently needed link metrics will be obtained on demand. At the same time, each node performs a topology control process based on the information in a smaller range (e.g., 1-hop neighborhood). Therefore, our approach has the low construct cast, which is proved in this paper. The simulation results also show that our approach outperforms the existing typical works in terms of average transmission power level, though it is slightly less efficient in terms of average delivery rate, average end-to-end delay and total energy consumption. In addition, our approach has advantage in terms of standard deviation of remaining energy under the relatively smaller required path quality bound or lower node density.

An Effective Timing Synchronization Scheme for DHTR UWB Receivers

In performing short-distance, high-data-rate wireless communications using ultra-wideband (UWB) systems, it is necessary to reduce the average transmission power and ensure a fixed synchronization error between the transmitter and receiver ends. Furthermore, to reduce multipath interference, the UWB system should be sometimes implemented using a rake receiver. However, for such receivers, the delay time and attenuation loss over each transmitted route must be known in advance. In the present study, the complexity of rake receivers with a large number of “fingers” is reduced by means of a delay-hopped transmitted-reference (DHTR) scheme, in which the delayed signal is correlated with the original signal; thereby avoiding the need for a separate template signal. The synchronization performance of the proposed DHTR receiver is analyzed both theoretically and numerically. An effective timing synchronization scheme, designated as “parallel signal acquisition with shared looped delay-line” (PS-SLD), is then proposed. The simulation results show that for multipath environments with a single user, the proposed synchronization scheme achieves a higher detection probability and a lower normalized mean square error (MSE) than the traditional timing dirty templates (TDT) algorithm.

On Adaptive Power Control for Energy Harvesting Communication Over Markov Fading Channels

We study a continuous-time power policy to maximize the ergodic channel throughput of an energy harvesting transmitter over a Markov fading channel. In particular, we consider transmission power policies that are adapted to the fading process of the channel as well as the storage process of the battery. We obtain a set of equations that determine the probability density of the energy in the battery at each channel state. Specifically, for an ergodic battery storage process, these equations describe the relation between the probability density of stored energy and the transmission power at each channel state. From these equations, we derive an upper bound on the average transmission power and an upper bound on the average transmission rate. To compute a lower bound on the average transmission rate, we apply a calculus of variations technique to a non-linear throughput maximization problem. As a result, we obtain a system of coupled ordinary differential equations for locally optimal power policies. We then focus on the Gilbert–Elliot channel as a special case and derive some structural results for specific classes of fast and slow fading channels. Furthermore, we numerically find a locally optimal transmission power policy for the two channel state scenario.

Traffic-Aware Green Cognitive Radio

Cognitive Radio Network (CRN) has emerged as an effective solution to the spectrum under-utilization problem, by providing secondary users (SUs) an opportunistic access to the unoccupied frequency bands of primary users (PUs). Most of the current research on CRN are based on the assumption that the SU always has a large amount of data to transmit. This leads to the objective of SU throughput maximization with a constraint on the allowable interference to the PU. However, in many of the practical scenarios, the data arrival process of the SU closely follows an ON–OFF traffic model, and thus the usual throughput optimization framework may no longer be suitable. In this paper, we propose an intelligent data scheduling strategy which minimizes the average transmission power of the SU while maintaining the transmission delay to be sufficiently small. The data scheduling problem has been formulated as a finite horizon Markov Decision Process (MDP) with an appropriate cost function. Dynamic programming approach has been adopted to arrive at an optimal solution. Our findings show that the average transmitted power for our proposed approach can be as small as 36.5% of the power required for usual throughput maximization technique with insignificant increase in average delay.

Delay-Power Tradeoff of Fixed-Rate Wireless Transmission With Arbitrarily Bursty Traffics

Wireless communication system is expected to provide service with low latency and high energy efficiency. To improve the energy efficiency, the transceiver prefers sending packets, when the channel states are good. However, such opportunistic transmission may induce undesirably large latency. Therefore, a fundamental tradeoff exists between the average transmission power and average queuing delay, and is studied in this paper via cross-layer probabilistic scheduling. In particular, we consider the delay-power tradeoff when the packet arrivals have arbitrary probabilistic distributions. A Markov reward model is adopted to model the queue of the backlogged packets. Based on that, we formulate a nonlinear optimization problem and convert it into a linear programming (LP) problem by using variable substitution. The optimal solution to the LP problem allows us to derive the optimal scheduling parameters. Based on the optimal solution, we can derive the optimal scheduling policy, which turns out to be threshold-based. Besides, we consider the source scheduling with the specific packet arrival distribution being unknown. Adaptive algorithms are proposed to achieve the corresponding delay-power tradeoff.

Analog Network-Coded Modulation With Maximum Euclidean Distance: Mapping Criterion and Constellation Design

Physical-layer network coding holds the great potential of improving the power efficiency and the spectral efficiency for the two-stage transmission scheme. The first stage is the multiple access stage, where two source nodes (SN1 and SN2) simultaneously transmit to the relay node (RN). The second stage is the Broadcast stage, where the RN broadcasts to the two destination nodes (DN1 and DN2), after a denoising-and-mapping operation. In this paper, we investigate the joint network-coded modulation design of the two stages. A universal modulation framework is built, referred to as analog network-coded modulation strategy, which is more general than the former modulation design mechanism. More explicitly, we propose a joint design criterion to guarantee the forwarding reliability at the RN. The criterion ensures that the neighboring constellation points superposed at the RN are mapped to an identical constellation point for broadcasting if their Euclidean distance (ED) is less than a given threshold. This yields a non-convex polynomial optimization problem by minimizing the average transmission power and constraining the ED among the constellation points. By solving the problem, we propose two joint modulation design algorithms, termed as the Enhanced Semidefinite Relaxation Algorithm and the Fast-Relaxation Algorithm, respectively. The two algorithms can achieve the tradeoff between the communication performance and the computation resources. As for the Fast-Relaxation Algorithm, the theoretical performance boundary is derived in detail. Simulation results demonstrate the effectiveness of both the proposed algorithms by comparing symbol error rate performance with the existing modulation design methods.

Statistical Delay QoS Driven Energy Efficiency and Effective Capacity Tradeoff for Uplink Multi-User Multi-Carrier Systems

In this paper, the total system effective capacity (EC) maximization problem for the uplink transmission, in a multi-user multi-carrier orthogonal frequency division multiple access system, is formulated as a combinatorial integer programming problem, subject to each user’s link-layer energy efficiency (EE) requirement as well as the individual’s average transmission power limit. To solve this challenging problem, we first decouple it into a frequency provisioning problem and an independent multi-carrier link-layer EE–EC tradeoff problem for each user. In order to obtain the subcarrier assignment solution, a low-complexity heuristic algorithm is proposed, which not only offers close-to-optimal solutions, while serving as many users as possible, but also has a complexity linearly relating to the size of the problem. After obtaining the subcarrier assignment matrix, the multi-carrier link-layer EE–EC tradeoff problem for each user is formulated and solved by using Karush–Kuhn–Tucker conditions. The per-user optimal power allocation strategy, which is across both frequency and time domains, is then derived. Further, we theoretically investigate the impact of the circuit power and the EE requirement factor on each user’s EE level and optimal average power value. The low-complexity heuristic algorithm is then simulated to compare with the traditional exhaustive algorithm and a fair-exhaustive algorithm. Simulation results confirm our proofs and design intentions, and further show the effects of delay quality-of-service exponent, the total number of users, and the number of subcarriers on the system tradeoff performance.

A Game-Based Localized Multi-Objective Topology Control Scheme in Heterogeneous Wireless Networks

In wireless networks, network topology may change at any time. Therefore, topology control is one of the effective methods to get and keep the desired topology performance. The most existing topology control methods assume that nodes are altruistic. Although there are some game-based topology control schemes to stimulate cooperation between nodes, they only consider a single objective (e.g., energy consumption or network lifetime), which cannot be adaptive to the variation of demand on topology performance. To address these weaknesses, we present the notion of link lifetime and model the multi-objective weight sum of any link as the function with respect to transmission power, link delay and link lifetime. Then the proposed game-based localized multi-objective topology control ensures that the desired topology property exists in resulting topology, in which the presented Improved LOCAL $\delta $ -Improvement Algorithm (LDIA) algorithm not only stimulates nodes’ cooperation on topology control operation and ensures network’s convergence to a steady state, but also has the better performance with respect to executing time and communication overhead than a classic algorithm, i.e., LDIA. Finally, the simulation results show that, by employing appropriate weight values, when compared with some typical schemes considering only energy efficiency, the proposed scheme is the most efficient in regard to average link delay and link lifetime. When compared with a typical scheme considering only network lifetime, the proposed scheme has advantage over average link lifetime, but it is slightly worse in terms of average link delay. Although the proposed scheme is less efficient in terms of average transmission power, where the shortage may be alleviated by adjusting weight values, it satisfies diversified demands for applications due to its flexibility.

Transmit Power Minimization for MIMO Systems of Exponential Average BER with Fixed Outage Probability

This paper is concerned with a wireless multiple-antenna system operating in multiple-input multiple-output (MIMO) fading channels with channel state information being known at both transmitter and receiver. By spatiotemporal subchannel selection and power control, it aims to minimize the average transmit power (ATP) of the MIMO system while achieving an exponential type of average bit error rate (BER) for each data stream. Under the constraints on each subchannel that individual outage probability and average BER are given, based on a traditional upper bound and a dynamic upper bound of Q function, two closed-form ATP expressions are derived, respectively, which can result in two different power allocation schemes. Numerical results are provided to validate the theoretical analysis, and show that the power allocation scheme with the dynamic upper bound can achieve more power savings than the one with the traditional upper bound.

How to Increase Energy Efficiency in Cognitive Radio Networks

In this paper, we investigate the achievable energy efficiency of cognitive radio networks where two main modes are of interest, namely, spectrum sharing (known as underlay paradigm) and spectrum sensing (or interweave paradigm). In order to improve the energy efficiency, we formulate a new multiobjective optimization problem that jointly maximizes the ergodic capacity and minimizes the average transmission power of the secondary user network while limiting the average interference power imposed on the primary user receiver. The multiobjective optimization will be solved by first transferring it into a single objective problem (SOP), namely, a power minimization problem, by using the $\varepsilon$ -constraint method. The formulated SOP will be solved using two different methods. Specifically, the minimum power allocation at the secondary transmitter in a spectrum sharing fading environment are obtained using the iterative search-based solution and augmented Lagrangian approach for single and multiple secondary links, respectively. The significance of having extra side information and also imperfect side information of cross channels at the secondary transmitter are investigated. The minimum power allocations under perfect and imperfect sensing schemes in interweave cognitive radio networks are also found. Our numerical results provide guidelines for the design of future cognitive radio networks.

Analysis of a Subset Selection Scheme for Wireless Sensor Networks in Time-Varying Fading Channels

One of the main challenges facing wireless sensor networks (WSNs) is the limited power resources available at small sensor nodes. It is therefore desired to reduce the power consumption of sensors while keeping the distortion between the source and its estimate at the fusion centre (FC) below a specific threshold. In this paper, we analyze a subset selection strategy to reduce the average transmission power of the WSN. We consider a two-hop network and assume the channels between the source and the relay sensors to be time-varying fading channels, modeled as Gilbert-Elliott channels. We show that when these channels are known at the FC, a subset of sensors can be selected by the FC to minimize transmit power while satisfying the distortion criterion. Through analysis, we derive the probability distribution of the size of this subset. We also consider practical aspects of implementing the proposed scheme, including channel estimation at relays. Through simulations, we compare the performance of the proposed scheme with schemes appearing in the literature. Simulation results confirm that for a certain range of end-to-end bit-error rates (BERs), the proposed scheme succeeds to achieve a superior power reduction compared to other schemes.

Resource Allocation in Decode-and-Forward Cooperative Communications Networks with Limited Rate Feedback Channel

In this paper, we propose a limited rate feedback scheme for decode-and-forward (DF) relay-assisted networks. It is assumed that the cellular user is far from the base station such that the direct communication is impossible and communication is performed with the help of a DF relay. To study the limited feedback channel, two cases are proposed: Perfect channel distribution information (CDI)-quantized channel state information (CSI) and quantized CDI-quantized CSI. While in the former, the space of channel gains is quantized into a finite number of regions, in the latter case, the space of CDI parameters is also quantized. We formulate the proposed resource allocation into an optimization problem whose objective is to maximize the average achievable rate under an average transmission power constraint. To solve the proposed optimization problems, we adopt the block coordinated descent algorithm, which is based on the space of variables partitioning into finite sets, and particle swarm optimization (PSO), which is an evolutionary computation technique. To solve the optimization problem for quantized CDI-quantized CSI case, we consider an approach based on the Lloyd algorithm. We also model and study the error in the feedback channel. Finally, the impact of different system parameters on the performance of the proposed scheme is investigated through simulations.