Precisely estimating the propagation time error induced by complex terrains and meteorological factors during the propagation of the signal along the ground, also termed the additional secondary phase factor (ASF), is crucial to improve positioning accuracy in the eLoran system. Among all of the ASF estimation algorithms, the well-known kriging-based algorithms, a kind of methods that estimate the ASF only with limited ASF data, often suffer performance reduction, as the fixed basis functions adopted lack adaptability. In this paper, we propose a trend kriging algorithm, which dynamically selects basis functions to construct the optimal trend function, for estimating the ASF under limited ASF data. Specifically, we design an optimization framework that integrates the selection task of basis functions and the search task of the correlation parameter into a single problem for jointly handling these nested tasks. We then develop an objective function based on the weighted covariance model to ensure that the detrended data follows the spatial correlation feature. To tackle this problem with multiple local optima, we employ the niching differential evolution algorithm, a recently developed metaheuristic algorithm. The performance verification results based on the simulated and practical ASF data cautiously exhibit the superiority of our proposed algorithm. For example, based on the practical data, compared with the representative IDW, the OK, the UK 1, and the UK 2, our proposed algorithm reduces the root mean square error by 21.68%, 29.26%, 10.53%, and 23.45%, respectively.

Inter-carrier interference (ICI) and noise mitigation is crucial for precise signal detection in underwater acoustic (UWA) differential orthogonal frequency division multiplexing (DOFDM) communication systems. In this paper, we adopt the Transformer to design a detector, referred to as the TransDetector, which can dramatically mitigate ICI implicitly and noise explicitly, even without requiring any pilot. Compared with the standard Transformer, we come up with three creative designs. Firstly, we break the inner-encoder computation paradigm of the multi-head attention (MHA) in the standard Transformer, and design a brand new inter-encoder attention mechanism, referred to as the interactive MHA, which can significantly improve the performance, as well as accelerate the convergence rate. Secondly, to reduce the noise component attached to the received signal, we design an auto-perception denoising structure, which allows the network to learn the noise distribution in received signals. Thirdly, to better match the characteristics of DOFDM signals and selectively focus on the data at specified locations, we propose a trapezoidal positional encoding (PE), instead of adopting the original sine-cosine PE in the Transformer. The performance verification results across a wide range of signal-to-noise ratios (SNRs) and Doppler shifts exhibit that, the TransDetector outperforms the classical χ-FFT algorithms and the DNNDetector in both the the simulation channels and the realistic underwater channels. For example, based on the simulation channel, the bit error rate (BER) achieved by the TransDetector is reduced by 31.22% and 5.01% when SNR = 0 dB and by 48.21% and 42.01% when SNR = 20 dB against the PS-FFT and the DNNDetector, respectively, while the reduction increases from 8.01% and 9.88% to 44.22% and 18.23%, when the Doppler factor goes from 1 × 10-4to 3 × 10-4.

A crucial prerequisite for orthogonal matching pursuit (OMP), a widely-used channel estimation method in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) communication systems, is the determination of a termination condition. However, the appropriate condition, which is commonly considered equal to the physical sparsity of the UWA channel, actually dramatically varies with the suffered noise, thus possibly leading to extremely unstable estimation performance. Existing OMP-based algorithms attempt to solve this problem by elaborately adjusting iteration numbers to balance the proportion of genuine channel taps and noise in the reconstructed signal based on noise levels, which inevitably increases the dependency on the prior information, i.e., signal-to-noise ratio (SNR). In order to overcome this challenge, an intuitive idea is eliminating the influence of noise to restore the originally sparse signal before implementing the standard OMP, naturally avoiding the variation of termination conditions. Considering the powerful ability of deep learning, we imitate and elegantly modify the feed-forward denoising convolution neural network (DnCNN), one of the most typical neural networks for image denoising, to develop our prior-information-free denoising OMP (DnOMP) algorithm with a constant iteration number. Simulation results validate that, compared to the standard OMP with the dynamic termination condition, the DnOMP can reduce the normalized mean square error (NMSE) by 39.47%.

This paper addresses the problem of frequency-domain inter-carrier interference (ICI) mitigation for differential orthogonal frequency-division multiplexing (OFDM) systems. The classical fractional fast Fourier transform (F-FFT), adopting the fixed sampling interval, would suffer from the limited accu-racy of ICI mitigation and low adaptability in dynamic Doppler spread. To target the above challenges, we propose an adaptive fractional Fourier transform (A-FFT) demodulation method, in which an estimation algorithm based on the coordinate descent approach is designed to compute the fiducial frequency offset without increasing pilots. By means of compensating ICI at fractions of the fiducial frequency offset adapted to the time-varying Doppler shift, the A-FFT has the capability of tracking Doppler fluctuations over the underwater acoustic channels, thus extending the application range of frequency-domain ICI mitigation. Simulation results show that the A-FFT is significantly superior to the existing classical methods, the partial fast Fourier transform (P-FFT) and the F-FFT, for both medium and high Doppler factors and large carrier numbers in terms of the mean squared error (MSE). Numerically, the MSE of the A-FFT is reduced by 39.88% - 72.14% compared to that of the F-FFT with the input signal-to-noise ratio ranging from 10 dB to 30 dB at a Doppler factor of 2.5 x 10−4and a carrier number of 1024, while the P-FFT even cannot work well.

Suppressing the inter-carrier interference (ICI) is crucial for differentially coherent detection in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) systems due to the fact that the UWA channel is inherently violently Doppler-shifted. In this paper, we propose a new ICI suppression method, referred to as the partially-shifted fast Fourier transform (PS-FFT), which eliminates the ICI from both the time and frequency domains. Specifically, the PS-FFT first divides the received signal in the entire block duration into several short non-overlapping ones to reduce the channel variation in the time domain. It then applies the Fourier transform at several predefined frequencies to the received signal in each of these intervals to compensate Doppler shifts in the frequency domain. Finally, it weightedly combines the multiple demodulator outputs at each carrier as one output for symbol detection, with the combiner weights being solved by the stochastic gradient algorithm. Simulation results show that the PS-FFT dramatically outperforms the existing classical methods, the partial fast Fourier transform (P-FFT) and the fractional fast Fourier transform (F-FFT), for both medium and high Doppler factors and large carrier numbers in terms of the mean squared error (MSE). Numerically, the MSE of the PS-FFT is reduced by 61.83% – 84.89% compared to that of the F-FFT when the input signal-to-noise ratio (SNR) at the receiver ranges from 10 dB to 30 dB at a Doppler factor of 3 × 10−4and a carrier number of 1024 where the P-FFT even cannot work.

In this paper, by exploiting the powerful ability of deep learning, we devote to designing a well-performing and pilot-saving neural network for the channel estimation in underwater acoustic (UWA) orthogonal frequency division multiplexing (OFDM) communications. By considering the channel estimation problem as a matrix completion problem, we interestingly find it mathematically equivalent to the image super-resolution problem arising in the field of image processing. Hence, we attempt to make use of the very deep super-resolution neural network (VD-SR), one of the most typical neural networks to solve the image super-resolution problem, to handle our problem. However, there still exist significant differences between these two problems, we thus elegantly modify the basic framework of the VDSR to design our channel estimation neural network, referred to as the channel super-resolution neural network (CSRNet). Moreover, instead of training an individual network for each considered signal-to-noise ratio (SNR), we obtain an unified network that works well for all SNRs with the help of transfer learning, thus substantially increasing the practicality of the CSRNet. Simulation results validate the superiority of the CSRNet against the existing least square (LS) and deep neural network (DNN) based algorithms in terms of the mean square error (MSE) and the bit error rate (BER). Specifically, compared with the LS algorithm, the CSRNet can reduce the BER by 44.74% even using 50% fewer pilots.

In this letter, we consider the varying detection environments to address the problem of detecting small targets within sea clutter. We first extract three simple yet practically discriminative features from the returned signals in the time and frequency domains and then fuse them into a 3-D feature space. Based on the constructed space, we then adopt and elegantly modify the support vector machine to design a learning-based detector that enfolds the false alarm rate (FAR). Most importantly, our proposed detector can flexibly control the FAR by simply adjusting two introduced parameters, which facilitates to regulate detector's sensitivity to the outliers incurred by the sea spikes and to fairly evaluate the performance of different detection algorithms. Experimental results demonstrate that our proposed detector significantly improves the detection probability over several existing classical detectors in both low signal to clutter ratio (up to 58%) and low FAR (up to 40%) cases.

In wireless virtualization communication markets, mobile virtual network operators (MVNOs) lease spectrum resources from mobile network operators (MNOs) and offer certain wireless services to end users. In order to attract more users, one MVNO may provide better service (i.e., more spectrum inventory thus higher QoS) and/or set lower retail price through taking its competitors' decisions into account. In this paper, we study the price strategy and spectrum inventory decision for MVNOs to optimize profits. First, we integrate these price and inventory factors into the utility function of end users and the revenue function of MVNOs, respectively, and analyze the impacts of the price factor on profits given each MVNO's inventory. With non-cooperative game theory, we derive the Cournot Nash equilibrium (C-NE) and further reveal several effective conclusions with guidance on price decision. Significantly, we discover that the increase of one MVNO's inventory would reduce others' revenues even though they adjust their prices to the C-NEs. Based on this observation, we propose to consider the price strategy and inventory decision together. Specifically, for a duopoly MVNOs market, we develop an ordinary differential equation (ODE) based evolutionary model that only contains its own price and inventory, which is very helpful for further analysis. However, since there is no explicit expression for the ODE, we figure out the boundary condition and initial value to facilitate a numerical solution. Overall, the conclusions in this study provide insights into price and inventory decision, and simulation results also validate our analysis.

In this paper, we consider the spectrum sharing planning problem for a full-duplex unmanned aerial vehicle (UAV) relaying systems with underlaid device-to-device (D2D) communications, where a mobile UAV employed as a full-duplex relay assists the communication link between separated nodes without direct link. Our design aims to maximize the sum throughput under the transmit power budget, while guaranteeing the coexistence with terrestrial D2D pairs, satisfying the information causality and UAV's trajectory constraints. First, the transmit power planning with a given trajectory is investigated, where a successive convex algorithm is developed by leveraging the D.C. (difference of two convex) programming. Then, we propose a two-step trajectory design method for the given transmit power since the constraints of D2D pairs result in a non-convex feasible set. Furthermore, an efficient spectrum sharing method for an aerial UAV and terrestrial D2D communications is designed by alternately optimizing the transmit power and UAV's trajectory. Finally, simulation results under various parameter configurations are provided to show the effectiveness of the proposed algorithms.

Existing works have widely studied relay-aided underwater acoustic networks under some specialized relay distributions, e.g., equidistant and rectangular-grid. In this paper, we investigate two fundamental problems that under which conditions a relay should be deployed and where to deploy it if necessary in terms of the energy and delay performance in linear underwater acoustic networks. To address these two problems, we first accurately approximate the complicated effective bandwidth and transmit power in the logarithm domain to formulate an energy consumption minimization problem. By analyzing the formulation, we discover a critical transmission distance, defined as open distance, and explicitly show that a relay should not be deployed if the transmission distance is less than the open distance and should be otherwise. Most importantly, we derive a closed-form and easy-to-calculate expression for the open distance and also strictly prove that the optimal placing position is at the middle point of the link when a relay should be introduced. Moreover, although this paper considers a linear two-hop relay network as the first step, our derived results can be applied to construct energy-efficient and delay-friendly multi-hop networks. Simulation results validate our theoretical analysis and show that properly introducing a relay can dramatically reduce the network energy consumption almost without increasing the end-to-end delay.

To afford effective service of real-time monitoring and responses for smart cities, it is desired to provide ubiquitous network connections and high data rate services. However, the huge demands for ubiquitous high data rate wireless communications have caused a sharp increase in energy consumption and green house gas emission. In order to realize a sustainable smart city, it is critical to incorporate green communication technique into smart city developments. Device-to-device (D2D) communication has been recognized as one of the key technologies to improve data rate and reduce power consumption, which allows two physically nearby located user equipments to communicate directly with each other. In this paper, with the target of achieving green communications through D2D, we investigate the joint optimization of uplink subcarrier assignment (SA) and power allocation (PA) in D2D underlying cellular networks. Specifically, the problem formulation is to minimize the energy cost of all users in the system while guaranteeing the required data rate of both the D2D user equipments (DUEs) and cellular user equipments. Such an optimization problem is in general a mixed-integer nonlinear programming problem that is NP-hard. To make this problem tractable, we decompose it into the SA and PA subproblems. In particular, we design a heuristic algorithm to assign subcarrier by assuming that the transmit power is evenly allocated over all subcarriers. After that, we solve the PA subproblem by exploiting the difference between the concave function (D.C.) structure of the constraints and transform it into a convex optimization problem. Simulation results demonstrate the remarkable improvement in terms of power consumption by using our algorithms.

Existing works have explored anchor deployment for autonomous underwater vehicles (AUVs) localization under the assumption that sound propagates straightly underwater at a constant speed. Considering that the underwater acoustic waves propagate along bent curves at varying speeds in practice, it becomes much more challenging to determine a proper anchor deployment configuration. In this paper, taking the practical variability of underwater sound speed into account, we investigate the anchor-AUV geometry problem in a 3-D time-of-flight-based underwater scenario from the perspective of localization accuracy. To address this problem, we first rigorously derive the Jacobian matrix of measurement errors to quantify the Cramer–Rao lower bound (CRLB) with a widely-adopted isogradient sound speed profile. We then formulate an optimization problem that minimizes the trace of the CRLB subject to the angle and range constraints to figure out the anchor-AUV geometry, which is multivariate and nonlinear, and thus generally hard to handle. For mathematical tractability, by adopting tools from the estimation theory, we interestingly find that this problem can be equivalently transformed into a more explicit univariate optimization problem. By this, we obtain an easy-to-implement anchor-AUV geometry that yields satisfactory localization performance, referred to as the uniform sea-surface circumference (USC) deployment. Extensive simulation results validate our theoretical analysis and show that our proposed USC scheme outperforms both the cube and the random deployment schemes in terms of localization accuracy under the same parameter settings.

In this paper, we investigate the fundamental energy- efficient power allocation problem in spectrum-sharing wireless networks. We introduce an energy-rate tradeoff index (EI) to characterize how efficiently the energy is consumed and formulate an optimization problem to maximize the EI subject to the power budget constraints. We first show that the EI maximization problem is intimately connected to the extensively investigated energy efficiency (EE) maximization problems. Due to the nonconvexity and NP-hardness of the formulation, we focus on designing cost-efficient and easy-implementation algorithms instead of finding globally optimal but exponential-complexity solutions. Leveraging the high signal-to-interference-plus-noise ratio (SINR) approximation method, we develop a simple fixed-point algorithm with fast convergence, fully distributed framework, and tuning-free properties. Simulation results exhibit the effectiveness of the proposed algorithm.

In this paper, we jointly consider mode selection, admission control, partner assignment, and power allocation to investigate the sum rate maximization problem in underlay SCMA device-to-device (D2D) networks. Due to its mixed combinatory, we first decouple the problem to devise efficient algorithms. In particular, we propose a channel gain based mode selection criterion and a greedy-style partner assignment scheme, and obtain closed-form solutions for both admission control and power allocation. To further reduce the computational cost, we also exploit the structure of the formulation to devise a much faster heuristic algorithm. Simulation results exhibit the superiority of the proposed algorithms against other schemes.

In this paper, we investigate the energy-efficient transmission problem by resource allocation in SCMA networks. We formulate it as an optimization problem to maximize the network energy efficiency (EE) subject to quality-of-service (QoS) requirements, codebook assignment, power allocation, and subcarrier reuse constraints. Due to its mixed combinatory, we separate codebook assignment and power allocation to devise suboptimal but cost- efficient algorithms. With power equally distributed, we first propose a novel scheme to assign codebooks. We then develop a derivative- bisection based algorithm to optimally solve the resultant power allocation problem by exploiting its quasiconcave structure. Simulation results exhibit the superiority of the proposed algorithms against the existing classical schemes and of SCMA over OFDMA in terms of the network EE.

As a promising multiple access technique for 5G wireless networks, sparse code multiple access (SCMA) has been put forward to support massive connectivity and enhance network performance. In this paper, we develop a theoretical framework using stochastic geometry to evaluate the performance of SCMA ad hoc networks. Under this framework, we first derive an explicit matrix form for the successful transmission probability. We then consider two area spectral efficiency (ASE) maximization problems without and with link reliability constraint to investigate the ASE gain of SCMA over OFDMA networks and the tradeoff between the ASE and link reliability, respectively. In particular, we obtain the optimal medium access probability (MAP) to solve both problems. Both numerical and simulation results exhibit that, compared to OFDMA networks, an asymptotically 160% gain in the ASE and a nearly 91% improvement in the transmission opportunity can be achieved by SCMA networks with typical settings.

Device-to-device (D2D) communications underlaying a cellular infrastructure have been recognized as an important network-organization architecture in 5G networks. In these scenarios, existing works have explored the impacts of one or several factors among mode selection, admission control, partner assignment, and power allocation on the network performance. In this paper, we put forward an optimization framework that considers all of these coupled factors to investigate the spectrum sharing problem in D2D networks. In particular, we introduce an objective that combines the access rate and the network sum rate and then maximize it subject to users' quality-of-service requirements and resource allocation constraints. Due to its mixed combination, we focus on designing cost-efficient and easy-to-implement algorithms instead of finding globally optimal but exponentially complex solutions. By decomposition, we first devise two novel mode selection criteria and an admission-prioritized partner assignment scheme and obtain closed-form solutions for both admission control and power allocation. Moreover, we present a simple but interesting geometric interpretation on the physical implication of admission conditions. To further reduce the computational cost, we also exploit the structure of the formulation to devise a much faster heuristic algorithm, which usually runs at an order of millisecond. Simulation results show the low computational complexity of the proposed algorithms and exhibit their superiority against other schemes in terms of the access rate and the sum rate.

Network control strategies for energy-efficient operation of HetNets need to match the dynamics of spatial and temporal traffic loads and to stabilize the network. In this paper, we develop a stochastic optimization framework, which formulates spatially inhomogeneous traffic distributions and time-varyingly random traffic arrivals and guarantees network stability, to investigate the energy conservation problem in HetNets. In particular, we jointly optimize base station (BS) operation, user association, subcarrier assignment, and power allocation to minimize the average energy consumption. We devise an algorithm without requiring any prior-knowledge of traffic distributions, referred to as the Steerable Energy ExpenDiture algorithm (SEED), to solve the problem. To deal with a highly coupled and mixed combinational subproblem in the SEED, we separate optimization variables for suboptimal but cost-efficient and easy-to-implement algorithm design. By this, we develop closed-form solutions for both user association and subcarrier assignment, a fast and tuning-free algorithm that provably achieves at least local optimality for power allocation, and a greedy-style heuristic algorithm for BS operation with polynomial complexity. Simulation results exhibit that the SEED usually converges fast, can flexibly tune the power-delay tradeoff, and can significantly reduce energy consumption against other existing schemes.

This paper investigates the problem of revealing the tradeoff between energy efficiency (EE) and delay in device-to-device (D2D) communications underlaying cellular networks. Considering both stochastic traffic arrivals and time-varying channel conditions, we formulate it as a stochastic optimization problem, which optimizes EE subject to the average power, interference-control, and network stability constraints. With the help of fractional programming and the Lyapunov optimization technique, we develop an algorithm, referred to as the TRADEOFF, to solve the problem. To deal with the nonconvex and NP-hard power allocation subproblem in the TRADEOFF, we adopt the prismatic branch and bound algorithm to find its globally optimal solution, where only a linear programming needs to be solved in each iteration. Thus, the TRADEOFF serves as an important benchmark to evaluate performance of other heuristic algorithms and is usually cost-efficient. The theoretical analysis and simulation results show that the TRADEOFF achieves an EE-delay tradeoff of [O(1/V),O(V)] with V being a control parameter and can strike a flexible balance between them by simply tuning V.

In next-generation wireless networks, energy efficiency optimization needs to take individual link fairness into account. In this paper, we investigate a max-min energy efficiency-optimal problem (MEP) to ensure fairness among links in terms of energy efficiency in OFDMA systems. In particular, we maximize the energy efficiency of the worst-case link subject to the rate requirements, transmit power, and subcarrier assignment constraints. Due to the nonsmooth and mixed combinatorial features of the formulation, we focus on low-complexity suboptimal algorithms design. Using a generalized fractional programming theory and the Lagrangian dual decomposition, we first propose an iterative algorithm to solve the problem. We then devise algorithms to separate the subcarrier assignment and power allocation to further reduce the computational cost. Our simulation results verify the convergence performance and the fairness achieved among links, and particularly reveal a new tradeoff between the network energy efficiency and fairness by comparing the MEP with the existing algorithms.

Energy harvesting is a promising technique to reduce conventional grid energy consumption, which caters for 5G visions on the green evolution of current cellular networks. To fully exploit the harvested energy, an inefficient factor caused by the battery leakage must be taken into account to tackle the energy dissipation problem, which triggers a new dimensional optimization related to the transmission time. However, most approaches are studied for perfect battery models and neglect the optimization for the transmission time. In this paper, we formulate the battery leakage process into our model to explore the grid energy conservation problem by jointly considering admission control, power allocation, subcarrier assignment, and transmission time determination in cellular networks powered by grid and renewable energy. To tackle this problem, we exploit the Lyapunov optimization technique to develop an online algorithm, referred to as leakage-aware dynamic resource allocation policy (LADRA). Specifically, the LADRA only needs to track the current system states (e.g., channel and energy conditions) but without requiring their prior-knowledge. Furthermore, we prove that the minimum grid energy consumption value can be achieved by our proposed algorithm asymptotically. Simulation results verify the correctness of the theoretical analysis, as well as exhibit the performance improvement against other algorithms in terms of grid energy consumption and queue backlog.

Existing works have addressed the tradeoffs between any two of the three performance metrics: throughput, energy efficiency (EE), and delay. In this paper, we unveil the intertwined relations among these three metrics under a unifying framework and particularly investigate the problem of EE-guaranteed throughput-delay tradeoff in interference-free wireless networks. We first propose two admission control schemes, referred to as the first-out and first-in schemes. We then formulate it as two stochastic optimization problems, aiming at throughput maximization (in the first-out scheme) or dropping rate minimization (in the first-in scheme) subject to requirement of EE (RoE), stability, admission control, and transmit power. To solve the problems, the EE-Guaranteed algorithm for throUghput-delAy tRaDeoff (eGuard), respectively called eGuard-I and eGuard-II in the first-out and first-in schemes, is devised. Moreover, with guaranteed RoE, we theoretically show that the eGuard (I and II) can not only push the throughput arbitrarily close to the optimal with tradeoffs in delay but also quantitatively control the throughput-delay performance on demand. Simulation results consolidate the theoretical analysis and particularly show the pros and cons of the two schemes.

Green communications are becoming an inevitable trend for future wireless network design, meanwhile, as a promising technique, distributed antenna systems (DAS) cater for this evolution. In this paper, we focus on the problem of devising globally optimal antenna selection and power allocation algorithm in downlink DAS to achieve energy efficiency (EE) maximization. We formulate it as a mixed-integer nonlinear programming (MINLP), which maximizes EE subject to rate requirements, transmit power, and antenna selection constraints. By equivalent transformation, an iterative antenna selection and power allocation algorithm is proposed based on nonlinear fractional programming theory, and branch and bound methods. Our algorithm ensures global optimality and thus, it provides an important benchmark for performance evaluation of other heuristic algorithms targeting the same problem. Simulation results show that the computation complexity can be dramatically reduced comparing with exhaustive search, as well as demonstrate that a significant gain can be obtained in terms of EE against the schemes without antenna selection.

In this paper, by jointly considering antenna selection and power allocation, we address the energy efficiency (EE) maximization problem with delay performance taken into account in downlink distributed antenna systems (DAS). To characterise system EE, we first define a revenue-cost (RC) function as the weighted difference between sum transmit rate and total energy consumption. We then formulate the problem as a stochastic optimization model, which maximizes the long-term average RC value subject to network stability (used to depict delay performance) and average power constraints. An Energy-Efficient Antenna selection and Power allocation Algorithm (EE-APA) is proposed based on Lyapunov optimization technique. The EE-APA adapts to time-varying channel conditions and stochastic traffic arrivals without requiring any corresponding prior-knowledge. Moreover, the theoretical analysis shows that the EE-APA can not only push the EE arbitrarily close to the optimal at the cost of delay performance, but also quantitatively control the EE-delay performance. Numerical results validate the adaptiveness of the EE-APA and the correctness of the theoretical analysis.

In this paper, by jointly considering subchannels, power, and Modulation and Coding Scheme (MCS) allocation, we address the sum-rate maximization problem in OFDMA downlink systems. We formulate the problem as an integer linear programming (ILP), which maximizes the system sum-rate subject to the minimum rate requirements of users and total transmit power constraint of base station. To solve the formulation with low complexity, we propose a two-level iterative Subchannels, Power, and MCS allocation Algorithm (SPMA) by exploiting Tabu Search (TS). At each iteration, the SPMA firstly assigns MCS to users and then allocates subchannels and power based on a SubChannels and Power allocation Algorithm (SCPA). Particularly, the SCPA maximizes the system sum-rate by first satisfying the minimum rate requirements with the least transmit power. Simulation results show that the SPMA outperforms the existing algorithms in terms of sum-rate and average rate per user, as well as demonstrate that the sum-rate is distributed flexibly among users in instantaneous channel conditions with the SPMA.

In this paper, we address the energy conservation problem with the quality of service (QoS) requirements taken into account for the physical downlink shared channel (PDSCH) in LTE/LTE-Advanced networks. By jointly allocating the modulation and coding schemes (MCS), resource blocks (RB), and power, we first propose a standards-compliant QoS-oriented power control algorithm (SQPC) for realistic systems to save energy. Specifically, with an appropriate MCS allocation, the proposed algorithm can tailor the power to match the QoS requirements. However, the SQPC saves energy at the cost of RB utilization. To this end, we further devise an Enhanced SQPC algorithm (ESQPC) to strike a balance between RB allocation and energy consumption. Finally, simulation results show that the proposed algorithms have the advantage of reducing nearly half of energy consumption compared to the existing algorithm, as well as demonstrate that the ESQPC can improve RB utilization against the SQPC.

In this paper, we investigate the fundamental tradeoff between energy efficiency (EE) and delay for time-varying and interference-free wireless networks. We formulate the problem as a stochastic optimization model, which optimizes the system EE subject to network stability and the average and peak transmit power constraints. By adopting the fractional programming theory and Lyapunov optimization technique, a general and effective algorithm, referred to as the EE-based dynamic power allocation algorithm (EE-DPAA), is proposed. The EE-DPAA does not require any prior knowledge of traffic arrival rates and channel statistics, yet yields an EE that can arbitrarily approach the theoretical optimum achieved by a system with complete knowledge of future events. Most importantly, we quantitatively derive the EE-delay tradeoff as$[O(1/V),O(V)]$with$V$as a control parameter for the first time. This result provides an important method for controlling the EE-delay performance on demand. Simulation results validate the theoretical analysis on the EE-delay tradeoff, as well as show the adaptiveness of the EE-DPAA.