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This paper considers optimization of the user and base-station (BS) association in a wireless downlink heterogeneous cellular network under the proportional fairness criterion. We first consider the case where each BS has a single antenna and transmits at fixed power and propose a distributed price update strategy for a pricing-based user association scheme, in which the users are assigned to the BS based on the value of a utility function minus a price. The proposed price update algorithm is based on a coordinate descent method for solving the dual of the network utility maximization problem and it has a rigorous performance guarantee. The main advantage of the proposed algorithm as compared to an existing subgradient method for price update is that the proposed algorithm is independent of parameter choices and can be implemented asynchronously. Further, this paper considers the joint user association and BS power control problem and proposes an iterative dual coordinate descent and the power optimization algorithm that significantly outperforms existing approaches. Finally, this paper considers the joint user association and BS beamforming problem for the case where the BSs are equipped with multiple antennas and spatially multiplex multiple users. We incorporate dual coordinate descent with the weighted minimum mean-squared error (WMMSE) algorithm and show that it achieves nearly the same performance as a computationally more complex benchmark algorithm (which applies the WMMSE algorithm on the entire network for BS association) while avoiding excessive BS handover. [ABSTRACT FROM PUBLISHER]

This paper deals with the problem of efficient resource allocation in dynamic infrastructureless wireless networks. In a reactive interference-limited scenario, at each transmission trial, every transmitter selects a frequency channel from some common pool, together with a power level. As a result, for all transmitters, not only the fading gain, but the number and the power of interfering transmissions as well, vary over time. Due to the absence of a central controller and time varying network characteristics, it is highly inefficient for transmitters to acquire the global channel and network knowledge. Therefore, given no information, each transmitter selfishly intends to maximize its average reward, which is a function of the channel quality, as well as the joint selection profile of all transmitters. This scenario is modeled as an adversarial multiplayer multiarmed bandit game, where players attempt to minimize their so-called regret, while, at the network side, achieving equilibrium in some sense. Based on this model and to solve the resource allocation problem, in this paper, we develop two joint power level and channel selection strategies. We prove that the gap between the average rewards achieved by our approaches and that based on the best fixed strategy converges to zero asymptotically. Moreover, the empirical joint frequencies of the game converge to the set of correlated equilibria, which is characterized for two relaxed versions of the designed game. [ABSTRACT FROM PUBLISHER]

Intelligent reflecting surface (IRS) is deemed as a promising solution to improve the spectral and energy efficiency of wireless communications cost-effectively. In this paper, we consider a wireless network where multiple base stations (BSs) serve their respective users with the aid of distributed IRSs in the downlink communication. Specifically, each IRS assists in the transmission from its associated BS to user via passive beamforming, while in the meantime, it also randomly scatters the signals from other co-channel BSs, thus resulting in additional signal as well as interference paths in the network. As such, a new IRS-user/BS association problem arises pertaining to optimally balance the passive beamforming gains from all IRSs among different BS-user communication links. To address this new problem, we first derive a tractable lower bound of the average signal-to-interference-plus-noise ratio (SINR) at the receiver of each user, termed average-signal-to-average-interference-plus-noise ratio (ASAINR), based on which two ASAINR balancing problems are formulated to maximize the minimum ASAINR among all users by optimizing the IRS-user associations without and with BS transmit power control, respectively. We also characterize the scaling behavior of user ASAINRs with the increasing number of IRS reflecting elements to investigate the different effects of IRS-reflected signal versus interference power. Moreover, to solve the two ASAINR balancing problems that are both non-convex optimization problems, we propose an optimal solution to the problem without BS power control and low-complexity suboptimal solutions to both problems by applying the branch-and-bound method and exploiting new properties of the IRS-user associations, respectively. Numerical results verify our performance analysis and also demonstrate significant performance gains of the proposed solutions over benchmark schemes. [ABSTRACT FROM AUTHOR]

In this paper, we optimize the energy efficiency (bits/s/Hz/J) of device-to-multi-device (D2MD) wireless communications. While the device-to-device scenario has been extensively studied to improve the spectral efficiency in cellular networks, the use of multicast communications opens the possibility of reusing the spectrum resources also inside the groups. The optimization problem is formulated as a mixed integer non-linear joint optimization for the power control and allocation of resource blocks (RBs) to each group. Our model explicitly considers resource sharing by letting co-channel transmission over a RB (up to a maximum of $r$ transmitters) and/or transmission through $s$ different channels in each group. We use an iterative decomposition approach, using first matching theory to find a stable even if sub-optimal channel allocation, to then optimize the transmission power vectors in each group via fractional programming. In addition, within this framework, both the network energy efficiency and the max-min individual energy efficiency are investigated. We characterize numerically the energy-efficient capacity region, and our results show that the normalized energy efficiency is nearly optimal (above 90% of the network capacity) for a wide range of minimum-rate constraints. This performance is better than that of other matching-based techniques previously proposed. [ABSTRACT FROM AUTHOR]

This paper considers a wireless communication network consisting of multiple interfering multicast sessions. Different from a unicast system where each transmitter has only one receiver, in a multicast system, each transmitter has multiple receivers and broadcasts a common message to all of them. It is a well-known result for wireless unicast systems that the feasibility of a signal-to-interference-plus-noise power ratio (SINR) without power constraint is decided by the spectral radius of a nonnegative matrix. We generalize this result and obtain necessary and sufficient conditions for the feasibility of an SINR in a wireless multicast system with and without power constraint. The feasible SINR region and its geometric properties are studied. Besides, an iterative algorithm is proposed, which can efficiently check the feasibility condition and compute the boundary points of the feasible SINR region. [ABSTRACT FROM AUTHOR]

From the perspective of statistical performance, this correspondence presents an analytical framework to study the interference coordination schemes in two-tier networks with closed-access small-cell base stations (SBSs). We assume the SBSs adopt the combination of power control, access control, and coordinated beamforming to suppress the intertier interference to the macro users (MUEs). Using stochastic geometry, we derive the expressions for the average data rate of users in different tiers. Based on the analytical results, we discuss the optimal transmission schemes of SBSs to maximize the average data rate of small-cell users while guaranteeing the performance of the MUEs. We find that access control is always a suboptimal scheme under the network model in this paper. [ABSTRACT FROM AUTHOR]

This paper aims at developing a power control framework to jointly optimize energy efficiency (measured in bit/joule) and delay in wireless networks. A multi-objective approach is taken dealing with both performance metrics, while ensuring a minimum quality-of-service to each user in the network. Each user in the network is modeled as a rational agent that engages in a generalized non-cooperative game. Feasibility conditions are derived for the existence of each player’s best response, and used to show that if these conditions are met, the game best response dynamics will converge to a unique Nash equilibrium. Based on these results, a convergent power control algorithm is derived, which can be implemented in a fully decentralized fashion. Next, a centralized power control algorithm is proposed, which also serves as a benchmark for the proposed decentralized solution. Due to the non-convexity of the centralized problem, the tool of maximum block improvement is used, to tradeoff complexity with optimality. [ABSTRACT FROM PUBLISHER]

In wireless networks for spectrum sharing among heterogeneous nodes, an issue of significant interest is the interference among coexisting systems, and understanding the essential quantities, such as interference distribution, is fundamental to the design of spatial spectrum sharing methods. This paper analyzes the performance of heterogeneously characterized nodes and investigates efficient spectrum sharing methods for single-input–multiple-output (SIMO) networks where nodes with different capabilities and requirements are spatially distributed according to homogeneous Poisson point processes (PPPs). Transmit power control strategies, such as fixed transmit power control and distance-based transmit power control, are applied, and it is demonstrated that their effects on the system performances depend on the multiple-antenna receiving methods. Furthermore, the underlay and overlay methods are considered for use as spectrum sharing methods. In addition, it is demonstrated that careful resource balancing among heterogeneous nodes contributes to minimizing the total amount of wireless resources required to meet the target outage constraints for all nodes, and the two spectrum sharing methods have the same performance in heterogeneous SIMO networks with low node densities. [ABSTRACT FROM PUBLISHER]

This paper considers the shortest link scheduling problem in wireless networks under the signal-to-interference-plus-noise ratio (SINR) model. We propose an $O({}\log({}l_{\max}/l_{\min}{}){})-approximation algorithm called shortest link scheduling with power control (SLSPC) with oblivious power assignment and an O(\log1+\varphi(l\max/l\min)-approximation algorithm called shortest link scheduling with uniform or mean power assignment (SLSUM) with uniform or mean power control, where \varphi > 0 is a constant serving as a regulatory factor for slight transmit power adjustment, and where l\max and l\min denote the lengths of the longest and shortest links, respectively. We conduct a rigorous theoretical performance analysis to analyze the feasibility and approximation factors of the proposed algorithms. We also carry out an extensive comparison-based simulation study, whose results indicate that the performances of SLSPC and SLSUM are superior over the state of the art as the set of the so-called “black and gray” links, which are difficult to schedule and should be sequentially scheduled, is completely removed by adjusting the transmit power appropriately via $\varphi$. Our numerical analysis demonstrates that the approximation ratios of our algorithms are tighter than the best known ratios. [ABSTRACT FROM PUBLISHER]

Traditional design of wireless networks mainly focuses on system capacity and spectral efficiency. As green networking is an inevitable trend, energy-efficient design for future wireless networks becomes paramount. In this paper, we address energy-efficient resource management in downlink orthogonal frequency division multiple access networks. The focus is targeted toward multi-cell networks, which are composed of multiple base stations (BSs) sharing the available radio resources. Consequently, greater emphasis is given to techniques that take inter-cell interference into account. Resource management in our context refers to the task of allocating the radio resources in order to maximize energy efficiency. We devise resource management techniques that jointly tackle the problems of scheduling and power control. Accordingly, we adopt two different approaches: a centralized approach, where BSs coordinate in order to reach a globally optimal energy-efficient solution, and a distributed approach, where BSs selfishly strive to maximize their own energy efficiency. We portray the centralized approach as a convex optimization problem, whereas we have recourse to non-cooperative game theory to model the distributed approach. In particular, we show that the non-cooperative game converges to a unique Nash equilibrium in low- and high-interference scenarios. We perform thorough numerical simulations to quantify the discrepancy between the centralized and distributed approaches, and identify the conditions where they have precedence over the state of the art. Moreover, the simulation results highlight the fast convergence of our algorithms, which is a precious asset for realistic deployments. [ABSTRACT FROM PUBLISHER]

In this paper, we present a precise comprehensive analysis of the aggregate interference $I$ generated from an underlay network of cognitive radio (CR) nodes employing several transmit power control and receiver association schemes. Importantly, we consider spatial randomness by modeling CR transmitter nodes and receiver nodes as two independent Poisson point processes (PPPs). For the cognitive nodes, we investigate receiver association based on the distance or the instantaneous received power and power control based on the maximum possible transmitter–receiver distance, fixed or location-dependent cutoff power levels, feedback from the primary system, or the maximum number of available receivers. For each of these schemes, the exact moment generating function (mgf) and mean of aggregate $I$ power are derived for links with Rayleigh fading and exponential path loss. The resulting primary outage and the probability of secondary transmitter cutoff are also derived. Numerical results show that the secondary power thresholds and node densities significantly affect the aggregate $I$, the primary receiver (PR) outage, and the secondary transmitter cutoff arising from the different schemes. [ABSTRACT FROM PUBLISHER]

The energy efficiency of future networks is becoming a significant and urgent issue, calling for greener network designs. However, the increasing complexity in network structure and resource space lead to growing problem scales and coupled resource dimensions, which bring great challenges in obtaining a joint solution in optimizing the energy efficiency. In this paper, we develop a multi-dimensional network model on the basis of tuple-links associated with transmission patterns (TPs) and formulate the optimization problem as a TP based scheduling problem which jointly solves transmission scheduling, routing, power control, radio, and channel assignment. In order to tackle the complexity issues, we propose a novel algorithm by exploiting the delay column generation technique to decompose the coupled problem into recursively solving a master problem for scheduling and a sub-problem for power allocation. Further, we theoretically prove that the performance gap between the proposed algorithm and the optimum is upper bounded by that for the sub-problem solution, where the latter is derived by solving a relaxed version of the sub-problem. Numerical results demonstrate the effectiveness of the multi-dimensional framework and the benefit of the proposed joint optimization in improving network energy efficiency. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communications underlaying cellular networks have been recognized as a promising technology to improve the system capacity and spectral efficiency of 5G networks. In this paper, we investigate the problem of designing the joint uplink subcarrier assignment and power allocation scheme to optimize the sum data rate of D2D pairs, under a general “many-to-many” scenario in which a subcarrier could be shared by multiple D2D pairs and a D2D pair could also reuse multiple subcarriers. In particular, the optimization problem is to maximize the sum data rate of all D2D pairs under the constraints of minimum data rate requirements of cellular user equipments (CUEs). The formulated problem falls into a mixed integer nonlinear programming, which is generally NP-hard. To solve the problem, we decompose it into two subproblems: the subcarrier assignment subproblem and the power allocation subproblem. More specifically, we first propose an effective algorithm to assign subcarriers for both D2D pairs and CUEs under the assumption that the transmit power of each user is equally allocated among all the subcarriers. After that, we apply the successive convex approximation approach to transform the nonconvex power allocation problem into a sequence of convex problems, based on which an effective power allocation algorithm is designed. Simulations validate that the proposed scheme can significantly improve the achievable sum data rate of D2D pairs, compared with the other existing schemes. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the problem of power control in large, random wireless networks that are obtained by “erasing” a finite fraction of nodes from a regular $d$ -dimensional lattice of $N$ transmit-receive pairs. In this model, which has the important feature of a minimum distance between transmitter nodes, we find that when the network is infinite, power control is always feasible below a positive critical value of the users’ signal-to-interference-plus-noise ratio (SINR) target. Drawing on tools and ideas from statistical physics, we show how this problem can be mapped to the Anderson impurity model for diffusion in random media. In this way, by employing the so-called coherent potential approximation method, we calculate the average power in the system (and its variance) for 1-D and 2-D networks. This approach is equivalent to traditional techniques from random matrix theory and is in excellent agreement with the numerical simulations; however, it fails to predict when power control becomes infeasible. In this regard, even though infinitely large systems are always unstable beyond a critical value of the users’ SINR target, finite systems remain stable with high probability even beyond this critical SINR threshold. We calculate this probability by analyzing the density of low lying eigenvalues of an associated random Schrödinger operator, and we show that the network can exceed this critical SINR threshold by at least $ \mathop {{\mathcal O}}\nolimits ((\log N)^{-2/d})$ before undergoing a phase transition to the unstable regime. Finally, using the same techniques, we also calculate the tails of the distribution of transmit power in the system and the rate of convergence of the Foschini–Miljanic power control algorithm in the presence of random erasures. [ABSTRACT FROM PUBLISHER]

This paper proposes a centralized decision making framework at the macro base station (MBS) for device-to-device (D2D) communication underlaying a two-tier cellular network. We consider a D2D pair in the presence of an MBS and a femto access point, each serving a user, with quality of service constraints for all users. Our proposed solution encompasses mode selection (choosing between cellular or reuse or dedicated mode), resource allocation (in cellular and dedicated mode), and power control (in reuse mode) within a single framework. The framework prioritizes D2D dedicated mode if the D2D pair is close to each other and orthogonal resources are available. Otherwise, it allows D2D reuse mode if the D2D satisfies both the maximum distance and an additional interference criteria. For reuse mode, we present a geometric vertex search approach to solve the power allocation problem. We analytically prove the validity of this approach and show that it achieves near optimal performance. For cellular and dedicated modes, we show that frequency sharing maximizes sum rate and solve the resource allocation problem in a closed form. Our simulations demonstrate the advantages of the proposed framework in terms of the performance gains achieved in the D2D mode. [ABSTRACT FROM AUTHOR]

This paper considers a cross-layer optimization framework for video streaming in multinode wireless networks with a time-varying interference environment. We develop a distributed joint power control and rate adaptation framework that exploits the time diversity of the wireless channels, satisfies the hard delay constraints associated with video applications, and respects a certain fairness criterion among the nodes. The proposed framework performs power allocation at the physical/media access control layers to achieve a certain target signal-to-interference-plus-noise ratio, such that the difference between the arrival and the departure rates at the queues is very small, and performs video rate adaptation at the video coding layer (upper layer) according to the nodes' demanded video quality, their channel conditions, and a given fairness criterion. A main challenge here is that the adaptation of the video rate and the power control is not performed at the same time scale. We deal with this issue and model the power and rate variations of the nodes as linear stochastic dynamic equations and formulate a risk-sensitive control problem that captures the hard delay constraints of the video services and the fairness criterion for resource utilization. We provide an optimal solution for this control problem and illustrate the performance of our framework through simulations. [ABSTRACT FROM AUTHOR]

The basic idea of device-to-device (D2D) communication is that pairs of suitably selected wireless devices reuse the cellular spectrum to establish direct communication links, provided that the adverse effects of D2D communication on cellular users are minimized and that cellular users are given higher priority in using limited wireless resources. Despite its great potential in terms of coverage and capacity performance, implementing this new concept poses some challenges, particularly with respect to radio resource management. The main challenges arise from a strong need for distributed D2D solutions that operate in the absence of precise channel and network knowledge. To address this challenge, this paper studies a resource allocation problem in a single-cell wireless network with multiple D2D users sharing the available radio frequency channels with cellular users. We consider a realistic scenario where the base station (BS) is provided with strictly limited channel knowledge, whereas D2D and cellular users have no information. We prove a lower bound for the cellular aggregate utility in the downlink with fixed BS power, which allows for decoupling the channel allocation and D2D power control problems. An efficient graph-theoretical approach is proposed to perform channel allocation, which offers flexibility with respect to allocation criteria (aggregate utility maximization, fairness, and quality-of-service (QoS) guarantee). We model the power control problem as a multiagent learning game. We show that the game is an exact potential game with noisy rewards, which is defined on a discrete strategy set, and characterize the set of Nash equilibria. Q-learning better-reply dynamics is then used to achieve equilibrium. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a spectrum refarming system where an OFDMA system shares the licensed band of a CDMA system. Both systems share the same cell site, but each has different base station (BS) antennas. Joint resource allocation problem is formulated to optimize the CDMA receive power, OFDMA transmit power, and subcarrier assignment. Conventional interference control to protect the primary CDMA system relies on the availability of cross-channel gains (CCGs) from each secondary OFDMA transmitter to the CDMA BS receiver. However, the CCGs are difficult to be obtained due to lack of intersystem cooperation. To address this problem, we first decouple the original problem into higher and lower-level problems via primal decomposition. Then, a robust lower-level resource allocation (R-LRA) scheme, which controls interference without using CCGs is proposed, with which CDMA users can be sufficiently protected. Thereafter, an enhanced R-LRA (ER-LRA) scheme is proposed to decrease the conservation of R-LRA scheme. Assisted by the CDMA inner power control and based on the ER-LRA, efficient algorithm is designed to solve the higher-level problem. Extensions of the ER-LRA for other scenarios are also studied. Simulation results are provided to validate the proposed schemes in facilitating and improving the spectrum sharing performance. [ABSTRACT FROM AUTHOR]

Relaying is a promising technique in cellular networks for improving system capacity and coverage. To facilitate the deployment of relays in remote areas without ready access to the electrical grid, energy-harvesting relays may be deployed. Energy-harvesting has been studied extensively for sensor networks, but it is still an open problem for cellular networks. In this paper, we study the problem of the joint allocation of orthogonal frequency division multiplexing access resource blocks and transmission power to users in a cellular network with energy-harvesting relays. The energy-harvesting process is stochastically described by a time-varying Poisson process. We propose a new metric called survival probability as the selection criteria for an energy-harvesting relay to support data transmissions. We propose a survival probability-based resource allocation (SPRA) algorithm. The algorithm solves the joint problem of resource block allocation, power control, and associating relays to users in a cellular network. We show the achievable data rates of SPRA for different energy harvesting rates. [ABSTRACT FROM AUTHOR]

As an important complement and enhancement technique for cellular communication systems, the device-to-device (D2D) technique has exhibited a number of benefits in terms of, for instance, offering an improved spectral efficiency, enabling a reduced transmit power and providing an enhanced channel capacity, etc. However, a severe interference may be imposed on the conventional cellular users by the activated D2D links, thus, substantially eroding the performance of the former. In this paper, we propose an interference limited area (ILA) based D2D-management scheme along with an appropriately designed power control algorithm for mitigating the above-mentioned interference. We first derive the closed-form expressions of the average coverage probability for both the cellular and D2D links under the proposed ILA scheme, followed by proposing a resource-allocation algorithm subject to a power control mechanism. Numerical results show that the proposed ILA scheme is capable of outperforming the conventional scheme in terms of both the coverage probability (i.e., for both cellular and D2D links) and the sum data rate, particularly in the scenarios with a high D2D density and a high signal-to-interference-plus-noise ratio. [ABSTRACT FROM AUTHOR]

This paper studies the optimal joint beamforming and power control strategy for device-to-device (D2D) communication underlaying multiuser multiple-input multiple-output cellular networks. We consider multiple antennas at the base station (BS) and a single antenna at each cellular user (CU), D2D transmitter (DT), and D2D receiver (DR). We aim to minimize the total transmission power of the system by jointly designing the transmit beamforming at the BS and the transmit powers for both BS and DTs, while satisfying the signal-to-interference-plus-noise ratio based quality-of-service constraints for both CUs and DRs. Due to the nonconvex nature of the problem, we apply the semidefinite relaxation technique to find the optimal solution, which always satisfies the rank-one constraint. We also investigate three suboptimal fixed beamforming schemes: zero-forcing (ZF), regularized ZF, and hybrid maximum ratio transmission-ZF, where the focus is to minimize the total transmission power while reducing complexity. When perfect channel information is not available, we propose a robust transmit power minimization strategy with ZF beamforming that only requires limited feedback based channel direction information at the BS. Finally, computer simulation results are presented to demonstrate the effectiveness of the proposed schemes. [ABSTRACT FROM AUTHOR]

Energy efficiency is a key issue in wireless body area networks (WBANs). A number of transmission power control (TPC) schemes have been developed to improve the efficiency of transmission, which is one of the most energy consuming operations in WBAN. To save energy, these schemes only probe the link quality from the received data packets. However, due to large intervals between data packets and fast dynamic on-body link characteristics in WBAN, the obtained link information is usually outdated. In this case, the performance of the current TPC scheme is poor. This paper proposes an accelerometer-assisted TPC (AA-TPC) scheme, which exploits the periodic fluctuations of link qualities to improve the transmission energy efficiency. Consider the relationship between link quality and body movement, AA-TPC makes transmissions at ideal channel points that are identified by using the local accelerometer. We first conduct experiments to investigate the correlation between periodic movements and link quality. Then, we propose an algorithm to locate the time point in each period with the best link quality to transmit packets. The specific transmission power is then determined by the feedback information from the receiver. Finally, we evaluate the energy efficiency of AA-TPC based on a CC2420 platform in both a periodic scenario (without any aperiodic movement to break the periodicity) and a realistic scenario (which has aperiodic movements 20% of the time). The results show that about 26.4% and 18% of total energy consumption can be saved on average in the periodic and realistic scenarios, respectively. [ABSTRACT FROM PUBLISHER]

The paper addresses the problem of interference modeling for wireless networks. Two axiomatic approaches are known from the literature: 1) "standard interference functions" proposed by Yates in 1995, and 2) "general interference functions" proposed by the authors in their previous work. In this paper, both frameworks are thoroughly analyzed and compared. It is shown that every function from framework 1) can be expressed in terms of framework 2). This means that recent structure results for convex interference functions, which were derived for 2), can also be applied to 1). The results provide a bridge between the frameworks 1) and 2), which were studied separately in the literature. Also, new structure results are shown in this paper. For the example of QoS balancing, it is shown that analyzing the structure of interference functions can lead to interesting algorithmic opportunities. The results are potentially useful for the development of physical-layer aware resource allocation algorithms. [ABSTRACT FROM AUTHOR]

This paper investigates a new scenario of spectrum sharing between unmanned aerial vehicle (UAV) and terrestrial wireless communication, in which a cognitive/secondary UAV transmitter communicates with a ground secondary receiver (SR), in the presence of a number of primary terrestrial communication links that operate over the same frequency band. We exploit the UAV’s mobility in three-dimensional (3D) space to improve its cognitive communication performance while controlling the co-channel interference at the primary receivers (PRs), such that the received interference power at each PR is below a prescribed threshold termed as interference temperature (IT). First, we consider the quasi-stationary UAV scenario, where the UAV is placed at a static location during each communication period of interest. In this case, we jointly optimize the UAV’s 3D placement and power control to maximize the SR’s achievable rate, subject to the UAV’s altitude and transmit power constraints, as well as a set of IT constraints at the PRs to protect their communications. Second, we consider the mobile UAV scenario, in which the UAV is dispatched to fly from an initial location to a final location within a given task period. We propose an efficient algorithm to maximize the SR’s average achievable rate over this period by jointly optimizing the UAV’s 3D trajectory and power control, subject to the additional constraints on UAV’s maximum flying speed and initial/final locations. Finally, numerical results are provided to evaluate the performance of the proposed designs for different scenarios, as compared to various benchmark schemes. It is shown that in the quasi-stationary scenario the UAV should be placed at its minimum altitude while in the mobile scenario the UAV should adjust its altitude along with horizontal trajectory, so as to maximize the SR’s achievable rate in both scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hierarchical game approach to model the energy efficiency maximization problem, where transmitters individually choose their channel assignment and power control. We conduct a thorough analysis of the existence, uniqueness, and characterization of the Stackelberg equilibrium. Interestingly, we formally show that a spectrum orthogonalization naturally occurs when users decide sequentially about their transmitting carriers and powers, delivering a binary channel assignment. Both analytical and simulation results are provided for assessing and improving the performances in terms of energy efficiency and spectrum utilization between the simultaneous-move game (with synchronous decision makers), the social welfare (in a centralized manner), and the proposed Stackelberg (hierarchical) game. For the first time, we provide tight closed-form bounds on the spectral efficiency of such a model, including correlation across carriers and users. We show that the spectrum orthogonalization capability induced by the proposed hierarchical game model enables the wireless network to achieve the spectral efficiency improvement while still enjoying a high energy efficiency. [ABSTRACT FROM PUBLISHER]

The increasing densification of telecommunications mobile networks raises the compelling problem of exposure to electromagnetic fields (EMF). In this paper, we address the particular concept of heterogeneous networks, where small cells (SCs) are deployed to build up a second layer to the existing macrocell network. SC deployment is shown to be particularly effective in reducing EMF exposure, in addition to capacity enhancement. Previous studies on EMF have focused on the uplink (UL) and downlink (DL) components separately, although they are strongly correlated. Relying on the definition of the exposure index (EI) from the European FP7 Lexnet project, we evaluate the impact on EMF exposure due to SC deployment. Guidelines for EMF-efficient deployment of SCs are first presented and evaluated. Then, a new enhanced intercell interference coordination (eICIC) algorithm using UL and DL metrics to optimize capacity and exposure to EMF is proposed. Static and self-optimizing dynamic implementations of the solution are considered, using a utility function related to the sojourn time of the users. Time-domain and frequency-domain self-optimized eICIC are compared in terms of EMF exposure reduction and capacity enhancement. The eICIC algorithms are combined with a self-optimized coverage range expansion function leading to further reduction of the EI. The proposed solution for optimizing both capacity and exposure to EMF is compared with a capacity-driven solution based on the DL metrics only. Simulation results illustrate the performance gains obtained for the different SC deployment scenarios. [ABSTRACT FROM PUBLISHER]

In this paper, using stochastic geometry, we investigate the average energy efficiency (AEE) of the user terminal (UT) in the uplink of a two-tier heterogeneous network, where the two tiers are operated on separate carrier frequencies. In such a deployment, a typical UT must periodically perform the interfrequency small-cell discovery (ISCD) process to discover small cells in its neighborhood and benefit from the high data rate and traffic offloading opportunity that small cells present. We assume that the base stations of each tier and UTs are randomly located, and we derive the average ergodic rate and UT power consumption, which are later used for our AEE evaluation. The AEE incorporates the percentage of time that a typical UT missed small-cell offloading opportunity as a result of the periodicity of the ISCD process. The additional power consumed by the UT due to the ISCD measurement is also included. Moreover, we derive the optimal ISCD periodicity based on the UT's average energy consumption (AEC) and AEE. Our results reveal that ISCD periodicity must be selected with the objective of either minimizing the UT's AEC or maximizing the UT's AEE. [ABSTRACT FROM PUBLISHER]

In wireless network resource allocation, the radio power control problems are often solved by fixed point algorithms. Although these algorithms give feasible problem solutions, such solutions often lack notion of problem optimality. This paper reconsiders well-known fixed-point algorithms, such as those with standard and type-II standard interference functions, and investigates the conditions under which they give optimal solutions. The optimality is established by the recently proposed fast-Lipschitz optimization framework. To apply such a framework, the analysis is performed by a logarithmic transformation of variables that gives tractable fast-Lipschitz problems. It is shown how the logarithmic problem constraints are contractive by the standard or type-II standard assumptions on the power control problem, and how sets of cost functions fulfill the fast-Lipschitz qualifying conditions. The analysis on nonmonotonic interference function allows establishing a new qualifying condition for fast-Lipschitz optimization. The results are illustrated by considering power control problems with standard interference function, problems with type-II standard interference functions, and a case of subhomogeneous power control problems. Given the generality of fast-Lipschitz optimization compared to traditional methods for resource allocation, it is concluded that such an optimization may help to determine the optimality of many resource allocation problems in wireless networks. [ABSTRACT FROM AUTHOR]

In an interference limited network, joint power and admission control (JPAC) aims at supporting a maximum number of links at their specified signal-to-interference-plus-noise ratio (SINR) targets while using minimum total transmission power. Various convex approximation deflation approaches have been developed for the JPAC problem. In this paper, we propose an effective polynomial time non-convex approximation deflation approach for solving the problem. The approach is based on the non-convex \ell q~(0< q< 1) approximation of an equivalent sparse \ell 0 reformulation of the JPAC problem. We show that, for any instance of the JPAC problem, there exists a \bar q\in (0,1) such that it can be exactly solved by solving its \ell q approximation problem with any q\in (0, \bar q]. We also show that finding the global solution of the \ell q approximation problem is NP-hard. Then, we propose a potential reduction interior-point algorithm, which can return an \epsilon -KKT solution of the NP-hard \ell q approximation problem in polynomial time. The returned solution can be used to check the simultaneous supportability of all links in the network and to guide an iterative link removal procedure, resulting in the polynomial time non-convex approximation deflation approach for the JPAC problem. Numerical simulations show that the proposed approach outperforms the existing convex approximation approaches in terms of the number of supported links and the total transmission power, particularly exhibiting a quite good performance in selecting which subset of links to support. [ABSTRACT FROM PUBLISHER]

Cooperative spectrum sensing may cause a high sensing overhead to secondary users (SUs) and induce much interference to primary users (PUs) due to detection result reporting, although it can improve the detection probability. In this paper, we propose two novel selective-reporting-based cooperative spectrum sensing strategies, called random-selection reporting (RSR) and best-selection reporting (BSR), aimed at reducing the sensing overhead of SUs and limiting the induced interference to PUs. By considering the interference impact on decision reporting from PUs, we derive closed-form expressions of false-alarm probability and detection probability over Rayleigh fading channels for traditional, RSR, and BSR strategies, respectively. We also analyze the interference impact on PUs from cooperative SUs and then show that such interference can be limited by power control to satisfy a given quality-of-service (QoS) requirement of primary transmissions. Finally, numerical and simulation results reveal that the proposed strategies can remarkably reduce the sensing overhead without degrading the detection performance compared with the traditional case. Moreover, we show that both RSR and BSR can achieve minimized miss detection probability and sensing overhead by adjusting the sensing time allocation. [ABSTRACT FROM PUBLISHER]

In a heterogeneous network (HetNet) with a large number of low power base stations (BSs), proper user-BS association and power control is crucial to achieving desirable system performance. In this paper, we systematically study the joint BS association and power allocation problem for a downlink cellular network under the max-min fairness criterion. First, we show that this problem is NP-hard. Second, we show that the upper bound of the optimal value can be easily computed, and propose a two-stage algorithm to find a high-quality suboptimal solution. Simulation results show that the proposed algorithm is near-optimal in the high-SNR regime. Third, we show that the problem under some additional mild assumptions can be solved to global optima in polynomial time by a semi-distributed algorithm. This result is based on a transformation of the original problem to an assignment problem with gains \log(gij), where \gij\ are the channel gains. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of power control for sum-rate maximization on multiple interfering links [transmitter–receiver pairs (Tx–RX)] under a sum power constraint. We consider a single-frequency network, where all pairs are operating in the same frequency band, thereby creating interference with each other. We study the power-allocation problem for sum-rate maximization with and without quality-of-service (QoS) requirements on individual links. When the objective is only sum-rate maximization without QoS guarantees, we develop an analytic solution to decide optimal power allocation for a two-TX–RX-pair problem. We also develop a low-complexity iterative algorithm for a three-TX–RX-pair problem. For a generic N>\3 TX–RX pair problem, we develop two low-complexity suboptimal power-allocation algorithms. The first algorithm is based on the idea of making clusters of two or three TX–RX pairs and then leveraging the power-allocation results obtained for the two- and three-TX–RX-pair problems. The second algorithm is developed by using a high signal-to-interference-plus-noise ratio (SINR) approximation, and this algorithm can be also implemented in a distributed manner by individual TXs. We then consider the same problem but with additional QoS guarantees for individual links. We again develop an analytic solution for the two-TX–RX-pair problem and a distributed algorithm for N>\2 TX–RX pairs. [ABSTRACT FROM PUBLISHER]

With the increasing demand for high-data mobile traffic and applications, Device-to-Device (D2D) communication has emerged as a promising technique for next generation of cellular networks to increase the network capacity. In this paper, to offload traffic from network backhaul, we investigate the problem of relaying data through multihop nodes, using D2D communication, for a set of transmitter-receiver pairs, by sharing cellular resource blocks. Furthermore, the problem is extended to schedule the transmission links within a maximum time frame to obey a delay constraint. If the transmission path could not be scheduled within the maximum time frame for any D2D communication pair, the communication is redirected through the base station similar to cellular mode. Our problem aims at minimizing the overall D2D transmission power by giving priority to maximizing the number of paths that go through D2D relaying. We mathematically formulate the above problem and find optimal solutions for small networks. We show the NP-hardness of the problem, and owing to its complexity, we propose an algorithmic method for more extensive networks. Through numerical results, the performance of our mathematical optimization model and algorithm is compared and the efficiency of the proposed algorithmic method is shown. [ABSTRACT FROM AUTHOR]

Femtocell technology has shifted beyond indoor residential applications to cover a wider range of scenarios including metropolitan and rural areas. The term “small cell” has hence been used to denote such low-power transmission points deployed for enhancing macrocell coverage and/or capacity. While deployment of femto BSs has typically followed the bottom-up paradigm driven by the ad hoc demand of users, more and more studies have prompted a move toward a more managed deployment model for better tradeoff between performance and cost. In this paper, we investigate an optimization problem for femtocell deployment in a dense network with arbitrary topology. The goal is to determine deployment locations and operation parameters of femtocells for maximizing the number of customers supported with QoS constraints. Since the formulated problem belongs to mixed-integer non-linear programming (MINLP), we propose an anytime algorithm that transforms the joint problem into a cluster formation sub-problem (involving location selection and cell coverage) and a resource management sub-problem (involving power control and resource allocation) for effectively solving all optimization variables in an iterative fashion. Compared with other approaches for femtocell deployment, our evaluation results show that the proposed algorithm can effectively solve the target problem while striking a better performance tradeoff between computation complexity and solution quality. [ABSTRACT FROM AUTHOR]

Heterogeneous networks (HetNets) are envisioned to enable next-generation cellular networks by providing higher spectral and energy efficiency. A HetNet is typically composed of multiple radio access technologies where several low-power low-cost operators or user-deployed small-cell base stations (SBSs) complement the macrocell network. In this paper, we consider a two-tier HetNet where the SBSs are arranged around the edge of the reference macrocell such that the resultant configuration is referred to as cell-on-edge (COE). Each mobile user in a small cell is considered capable of adapting its uplink transmit power according to a location-based slow power control mechanism. The COE configuration is observed to increase the uplink area spectral efficiency (ASE) and energy efficiency while reducing the cochannel interference power. A moment-generating-function (MGF)-based approach has been exploited to derive the analytical bounds on the uplink ASE of the COE configuration. The derived expressions are generalized for any composite fading distribution, and closed-form expressions are presented for the generalized- \cal K fading channels. Simulation results are included to support the analysis and to show the efficacy of the COE configuration. A comparative performance analysis is also provided to demonstrate the improvements in the performance of cell-edge users of the COE configuration compared with that of macro-only networks (MoNets) and other unplanned deployment strategies. [ABSTRACT FROM PUBLISHER]

In this paper, the problem of distributed mode selection, resource allocation, and interference management is studied for device-to-device (D2D) communications underlaying future cellular networks. In existing underlaid D2D communications, the base station (BS) typically assists D2D pairs in selecting a transmission mode to operate in (network-assisted), that is, either cellular, dedicated, or reuse mode. Network-assisted D2D communications assumes the BS has global knowledge of the network, which includes complete channel state information (CSI). However, acquiring global network information causes signaling overhead and network complexity to increase, due to the large number of users in the network. Therefore, we investigate user-assisted D2D communications as a viable solution for D2D communications, in which the BS has partial knowledge of the network (and partial CSI). To address this problem, a cross-layer coalitional (CLC) game is formulated, where D2D pairs and cellular users are clustered into coalitions based on resource allocation/sharing and mode selection, in a fully distributed and autonomous manner. Clustering D2D pairs allows resources to be shared fairly amongst users in a cluster, while also ensuring D2D pairs operate efficiently. To coordinate the interference between cellular and D2D pairs, an interference management subproblem is proposed that schedules D2D pairs reusing cellular user resources, as well as minimizing the transmission power of all users. To solve this game, a distributed algorithm is proposed, which converges to a Nash stable coalition partition that is socially efficient and has a nonempty core. Simulation results show that the proposed approach can effectively cluster D2D pairs into coalitions based on resource allocation and mode selection, which, in turn, reduces network sum power and increases network sum rate across all D2D pairs. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problems of minimizing sum power and maximizing sum rate for multicell networks with load coupling, where coupling relation occurs among cells due to intercell interference. This coupling relation is characterized by the signal-to-interference-plus-noise ratio (SINR) coupling model with cell load vector and cell power vector as the variables. Due to the nonlinear SINR coupling model, the optimization problems for multicell networks with load coupling is nonconvex. To solve these nonconvex problems, we first consider the optimization problems for single-cell networks. Through variable transformations, the optimization problems can be equivalently transformed into convex problems. By solving the Karush–Kuhn–Tucker, the optimal solutions to power minimization and rate maximization problems can be obtained in closed form. Based on the theoretical findings of optimization problems for single-cell networks, we develop a distributed time allocation and power control algorithm with low complexity for the sum power minimization in multicell networks. This algorithm is proved to be convergent and globally optimal by using the properties of standard interference function. For sum rate optimization in multicell networks, we also provide a distributed algorithm that yields suboptimal solution. Besides, the convergence for this distributed algorithm is proved. Numerical results illustrate the theoretical findings, showing the superiority of our solutions compared to the conventional solution of allocating uniform power for users in the same cell. [ABSTRACT FROM PUBLISHER]

Cognitive radio has received great attention recently for its ability to improve spectrum efficiency by letting secondary users to access spectrum resource that is unoccupied by primary users. However, cognitive radio also brings new challenges in the design of future wireless networks. In this paper, we investigate the problem of resource allocation in cognitive radio networks. Specifically, we consider the problem of proportional fair scheduling in cognitive radio relay networks. Our problem formulation takes into account the fluctuations of usable spectrum resource, channel quality variations caused by frequency selectivity, and interference caused by different transmit power levels. We prove that the problem is NP-hard and is computationally infeasible to be solved in a timely manner by using brute force algorithms. An easy-to-compute upper bound for the formulated problem is also derived. We then propose two heuristic algorithms that are easy-to-implement, yet achieve performance close to the upper bound. The proposed algorithms can be executed and finished within 1 millisecond. Thus, they can meet the requirement of real-time scheduling. Simulation experiments verify that the proposed algorithms can achieve good proportional fairness among users and enhance system throughput by proper power control. [ABSTRACT FROM PUBLISHER]

This paper considers the max-min weighted signal-to-interference-plus-noise ratio (SINR) problem subject to multiple weighted-sum power constraints, where the weights can represent relative power costs of serving different users. First, we study the power control problem. We apply nonlinear Perron-Frobenius theory to derive closed-form expressions for the optimal value and solution and an iterative algorithm which converges geometrically fast to the optimal solution. Then, we use the structure of the closed-form solution to show that the problem can be decoupled into subproblems each involving only one power constraint. Next, we study the multiple-input-single-output (MISO) transmit beamforming and power control problem. We use uplink-downlink duality to show that this problem can be decoupled into subproblems each involving only one power constraint. We apply this decoupling result to derive an iterative subgradient projection algorithm for the problem. [ABSTRACT FROM PUBLISHER]

This paper studies power control strategies in interference-limited wireless networks with Poisson distributed nodes. We concentrate on two sets of strategies: single-node optimal power control (SNOPC) strategies and Nash equilibrium power control (NEPC) strategies. SNOPC strategies maximize the expected throughput of the power-controllable link given that all the other transmitters do not use power control. Under NEPC strategies, no individual node of the network can achieve a higher expected throughput by unilaterally deviating from these strategies. We show that under mean and peak power constraints at each transmitter, the SNOPC and NEPC strategies are ALOHA-type random on-off power control policies, whose transmit powers and transmit probabilities depend on the knowledge about the network at each transmitter. Moreover, the resulting NEPC strategies achieve a higher spatial average throughput of the network than constant power transmission. These results suggest that ALOHA can be viewed not only as a MAC scheme but also as a stable and efficient power control scheme. [ABSTRACT FROM PUBLISHER]

In this paper, we formulate a delay-power control (DPC) scheme for wireless networking, which efficiently balances delay against transmitter power on each wireless link. The DPC scheme is scalable, as each link autonomously updates its power based on the interference observed at its receiver; no cross-link communication is required. It is shown that DPC converges to a unique equilibrium power and several key properties are established, concerning the nature of channel bandwidth sharing achieved by the links. The DPC scheme is contrasted to the well-known Foschini–Miljanic (FM) formulation for transmitter power control in wireless networks, and some key advantages are established. Based on the DPC and FM schemes, two protocols are developed, which leverage adaptive tuning of DPC parameters. One of them is inspired by TCP and exhibits analogous behavior. This paper primarily focuses on the theoretical underpinnings of DPC and their practical implications for efficient protocol design. The DPC dynamics are also investigated numerically. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the game theoretical approach in designing the multiuser downlink beamformers in multicell systems. Sharing the same physical resource, the base-station of each cell wishes to minimize its transmit power subject to a set of target signal-to-interference-plus-noise ratios (SINRs) at the multiple users in the cell. In this context, at first, the paper considers a strategic noncooperative game (SNG) where each base-station greedily determines its optimal downlink beamformer strategy in a distributed manner, without any coordination between the cells. Via the game theory framework, it is shown that this game belongs to the framework of standard functions. The conditions guaranteeing the existence and uniqueness of a Nash Equilibrium (NE) in this competitive design are subsequently examined. The paper then makes a revisit to the fully coordinated design in multicell downlink beamforming, where the optimal beamformers are jointly designed between the base-stations. A comparison between the competitive and coordinated designs shows the benefits of applying the former over the latter in terms of each design's distributed implementation. Finally, in order to improve the efficiency of the NE in the competitive design, the paper considers a more cooperative game through a pricing mechanism. The pricing consideration enables a base-station to steer its beamformers in a more cooperative manner, which ultimately limits the interference induced to other cells. The study on the existence and uniqueness of the new game's NE is then given. The paper also presents a condition on the pricing factors that allow the new NE point to approach the performance established by the coordinated design, while retaining the distributed nature of the multicell game. [ABSTRACT FROM PUBLISHER]

This paper provides novel analysis methods and detailed simulations for determining the impact of out-of-band emissions (OOBE) of adjacent band wireless services on mobile receive-only wireless services. Using the interference from the adjacent cellular bands on an operational digital wireless receive-only system as an example, we demonstrate the impact of the interference from adjacent cellular band transmitters and determine the performance impact such as outage on satellite receivers in realistic roadway conditions. This paper presents new methods for determining appropriate OOBE spectral masks for use by new cellular subscriber transmitters. This paper considers realistic propagation and traffic conditions in five cities throughout the United States and offers approaches that the engineering community may use to determine interference conditions between new cellular and fixed broadband mobile services in adjacent spectrum bands to existing receive-only mobile receiver systems, such as satellite radio. [ABSTRACT FROM PUBLISHER]

Distributed power control over interference limited network has received an increasing intensity of interest over the past few years. Distributed solutions (like the iterative water-filling, gradient projection, etc.) have been intensively investigated under quasi-static channels. However, as such distributed solutions involve iterative updating and explicit message passing, it is unrealistic to assume that the wireless channel remains unchanged during the iterations. Unfortunately, the behavior of those distributed solutions under time-varying channels is in general unknown. In this paper, we shall investigate the distributed scaled gradient projection algorithm (DSGPA) in a K pairs multicarrier interference network under a finite-state Markov channel (FSMC) model. We shall analyze the convergence property as well as tracking performance of the proposed DSGPA. Our analysis shows that the proposed DSGPA converges to a limit region rather than a single point under the FSMC model. We also show that the order of growth of the tracking errors is given by \cal O(1 / \barN), where \barN is the average sojourn time of the FSMC. Based on the analysis, we shall derive the tracking error optimal scaling matrices via Markov decision process modeling. We shall show that the tracking error optimal scaling matrices can be implemented distributively at each transmitter. The numerical results show the superior performance of the proposed DSGPA over three baseline schemes, such as the gradient projection algorithm with a constant stepsize. [ABSTRACT FROM PUBLISHER]

Power control is important in maintaining the communication link quality under fading and interference situations. In recent years, research on power control has been conducted toward reducing power consumption while maintaining reliable link quality. These previous works, however, did not try to optimize power control algorithms under practical constraints. In this paper, we first observe and provide some insights into the previous works on different aspects of power control schemes. These observations motivate us to propose a new power control scheme which has several novel features: it uses an adaptive optimal quantizer at the receiver for transmitting discrete feedback information and an adaptive quantization scaler/restorer followed by a loop filter at the transmitter. Optimal quantization minimizes the feedback information loss and the additional power control error caused by it, while the loop filter can be designed to achieve the lowest power control error. Optimization of the loop filter requires some computational power. Fortunately, filter self-design capability can be implemented. In that case, closed-loop power control can be seen as an instance of a general channel identification problem. Intuitive explanation, together with analysis and simulation results of the proposed scheme will be given [ABSTRACT FROM PUBLISHER]

This paper considers a multipair two-way amplify-and-forward (AF) relaying system, where multiple pairs of full-duplex users are served via a full-duplex relay with massive antennas, and the relay adopts maximum-ratio combining/maximum-ratio transmission (MRC/MRT) processing. The orthogonal pilot scheme and the least square method are first exploited to estimate the channel state information (CSI). When the number of relay antennas is finite, we derive an approximate sum rate expression which is shown to be a good predictor of the ergodic sum rate, especially with a large number of antennas. Then, the corresponding achievable rate expression is obtained by adopting another pilot scheme which estimates the composite CSI for each user pair to reduce the pilot overhead of channel estimation. We analyze the achievable rates of the two pilot schemes and then show the relative advantages of the two methods. Furthermore, power allocation strategies for users and the relay are proposed based on sum rate maximization and max-min fairness criterion, respectively. Finally, numerical results verify the accuracy of the analytical results and show the performance gains achieved by the proposed power allocation. [ABSTRACT FROM AUTHOR]

This paper develops power control algorithms for energy efficiency (EE) maximization (measured in bit/Joule) in wireless networks. Unlike previous related works, minimum-rate constraints are imposed and the signal-to-interference-plus-noise ratio takes a more general expression, which allows one to encompass some of the most promising 5G candidate technologies. Both network-centric and user-centric EE maximizations are considered. In the network-centric scenario, the maximization of the global EE and the minimum EE of the network is performed. Unlike previous contributions, we develop centralized algorithms that are guaranteed to converge, with affordable computational complexity, to a Karush–Kuhn–Tucker point of the considered non-convex optimization problems. Moreover, closed-form feasibility conditions are derived. In the user-centric scenario, game theory is used to study the equilibria of the network and to derive convergent power control algorithms, which can be implemented in a fully decentralized fashion. Both scenarios above are studied under the assumption that single or multiple resource blocks are employed for data transmission. Numerical results assess the performance of the proposed solutions, analyzing the impact of minimum-rate constraints, and comparing the network-centric and user-centric approaches. [ABSTRACT FROM AUTHOR]

In this paper, we consider the dynamic power control for delay-aware D2D communications. The stochastic optimization problem is formulated as an infinite horizon average cost Markov decision process. To deal with the curse of dimensionality, we utilize the interference filtering property of the CSMA-like MAC protocol and derive a closed-form approximate priority function and the associated error bound using perturbation analysis. Based on the closed-form approximate priority function, we propose a low-complexity power control algorithm solving the per-stage optimization problem. The proposed solution is further shown to be asymptotically optimal for a sufficiently large carrier sensing distance. Finally, the proposed power control scheme is compared with various baselines through simulations, and it is shown that significant performance gain can be achieved. [ABSTRACT FROM PUBLISHER]