Power Budget Constraints Research Articles

A Simple Method for Throughput Maximization of OFDMA Based CR Networks

This paper investigates reducing the computation complexity in resource allocation of cognitive radio networks. The orthogonal frequency division multiple access scheme is assumed. Secondary users are permitted to capture any portion of the bands while their interference to the primary users remains below a given threshold level. A very simple method with low complexity for resource allocation is proposed in which the throughput logarithmic function is changed into a linear form. The method is applicable in a wide range of problems where the efforts are maximizing the throughput under power budget and interference constraint. Although the proposed method achieves the optimal solution in low channel-gain-to-noise ratio (CNR) cases, the results for high CNRs are also satisfactory. Comparisons between the proposed method and previous works via simulation show the superior performance of the proposed method.

Energy‐efficient power allocation for cognitive radio networks with minimal rate requirements

SummaryBecause the energy consumption is growing rapidly, green radio, which lays emphasis on the energy efficiency (EE) in wireless networks, is becoming increasingly important. As potential paradigms for future wireless network design, cognitive radio (CR) is a promising technology to solve the spectrum shortage and inefficiency issues, while cooperative relay is capable of improving spectral efficiency by combating severe fading in wireless environment. In this paper, we investigate the energy‐efficient power allocation problem in orthogonal frequency division multiplexing (OFDM)‐based relaying CR networks. We develop a general framework to maximize the overall EE of the CR system, under the constraints of transmission power budget, traffic demands, and the interference constraints of the primary users. Our problem formulation is a nonconvex optimization task, and it is hard to obtain the optimal solution. We first convert our formulated problem into a convex optimization problem via its hypograph form, which can be solved by the barrier method. Then we further speed up the computation of Newton step during the barrier method, significantly reducing the complexity of the algorithm by exploiting its special structure. Numerical results validate that our method can exploit the overall EE of CR systems, while the algorithm converges efficiently and stably. Copyright © 2015 John Wiley & Sons, Ltd.

Optimal and suboptimal adaptive algorithms for rate and power transmission in OFDM-based Cognitive Radio systems

This paper investigates an optimal adaptive rate and power transmission algorithms for Orthogonal Frequency Division Multiplexing (OFDM) – based Cognitive Radio (CR) systems. The aim was to study the problem of maximizing the overall rate achieved by the Secondary User (SU), while keeping the interference powers introduced by the SU on the spectrum band of Primary User’s (PU) below the specified thresholds and considering the total transmit power budget constraints. In addition, the novel suboptimal power allocation algorithm was proposed and consequently the maximum modulation level according to allocated power based on maximizing the overall achievable rate was obtained. The performance of the proposed suboptimal algorithm is compared with the optimal and existing algorithms including uniform loading and water filling algorithms. Numerical results revealed that the proposed suboptimal algorithm had a better performance than the uniform loading and water filling algorithms.

Adaptive Sub-Carrier Level Power Allocation in OFDMA Networks

In today’s OFDMA networks, the transmission power is typically fixed and the same for all the sub-carriers that compose a channel. The sub-carriers though, experience different degrees of fading and thus, the received power is different for different sub-carriers; while some frequencies experience deep fades, others are relatively unaffected. In this paper, we make a case for redistributing the power across the sub-carriers (subject to a fixed power budget constraint) to better cope with this frequency selectivity. Specifically, we design a joint power and rate adaptation scheme (called JPRA for short) wherein power redistribution is combined with sub-carrier level rate adaptation to yield significant throughput benefits. We further consider three variants of JPRA: (a) JPRA-Basic where, the power is redistributed across sub-carriers so as to support a maximum common rate across all the sub-carriers (b) JPRA-Intermediate where, the power is redistributed across sub-carriers so as to support a maximum common rate across a “subset” of sub-carriers such that the aggregate rate is maximized. (c) JPRA-Adaptive where, the goal is to redistribute power such that the transmission time of a packet is minimized. While the first two variants decrease transceiver complexity and are simpler, the third is geared towards achieving the maximum throughput possible. We implement all three variants of JPRA on our WARP radio testbed. Our extensive experiments demonstrate that JPRA can provide a 35 percent improvement in total network throughput in testbed experiments compared to FARA, a scheme where only sub-carrier level rate adaptation is used. We also perform simulations to demonstrate the efficacy of JPRA in larger scale networks.

An efficient runtime power allocation scheme for many-core systems inspired from auction theory

Design of future many-core chips is experiencing a paradigm shift to the so-called power-budgeting design, due to the widening gap between instantaneous power consumption and the allowed maximum power, referred as the power budget. Critical to these many-core chips is the runtime power allocation mechanism which can help optimizing the overall system performance under a limited power budget constraint. In this paper, the power allocation problem (i.e., maximizing the system performance under a power budget) is modeled by a combinatorial auction. The problem can be transformed to a knapsack problem and the optimal solution reaches a Walrasian equilibrium. To solve the problem efficiently in a decentralized way, a Hierarchal MultiAgent based Power allocation (HiMAP) method is proposed with an optimal bound. In HiMAP, tiles bid for the opportunity to become active based on the chip׳s total power budget. Upon finishing an auction process, certain tiles will be power gated and/or frequency scaled according to the power allocation decision. Experimental results have confirmed that HiMAP can reduce the execution time by as much as 45% compared to four competing methods. The runtime overhead and cost of HiMAP are also small, which makes it scale well with many-core systems.

Optimal spectrum assignment for cognitive radio sensor networks under coverage constraint

Cognitive radios emerged as a solution to spectrum scarcity problem. The integration of cognitive radios and wireless sensor networks enables a new paradigm of communication, in which the sensor nodes can avoid heavily-crowded transmission bands by tuning their transmission parameters to less-crowded bands. The authors consider the problem of spectrum assignment for cognitive radio sensor network (CRSN) under coverage, interference, minimum data rate and power budget constraints. A mixed-integer non-linear programming problem formulation that addresses optimal power allocation, channel selection and node scheduling is presented. Following a practical assumption, that any CRSN node can only access one channel for its transmission with the CRSN base station, the problem is transformed to a binary linear programming (BLP) problem. Using the relaxation techniques, the problem is transformed to a linear programming problem that is solvable in polynomial time, and has the same optimal solution of the BLP problem. Hence, the minimum power algorithm that achieves the optimal solution of our problem is proposed. To further reduce the complexity of the solution, three heuristic lower-complexity algorithms are proposed to solve the problem: random, greedy and two-stage (decoupled) algorithms.

Complexity Analysis of Joint Subcarrier and Power Allocation for the Cellular Downlink OFDMA System

Consider the cellular downlink Orthogonal Frequency Division Multiple Access (OFDMA) system where a single transmitter transmits signals to multiple receivers on multiple discrete subcarriers. To adapt fast channel fluctuations, the transmitter should be able to dynamically allocate subcarrier and power resources. Assuming perfect channel knowledge, we formulate the joint subcarrier and power allocation problem as two optimization problems: the first is the one of minimizing the total transmission power subject to quality of service constraints, and the second is the one of maximizing a system utility function subject to power budget constraints. In this letter, we show that both the aforementioned formulations of the joint subcarrier and power allocation problem are generally NP-hard. We also identify several subclasses of the problem which are polynomial time solvable.

Optimization of long-reach TDM/WDM passive optical networks

Long-Reach Passive Optical Network (LR-PON) using hybrid TDM/WDM techniques is one of the candidates for the future optical access that can solve the expected increase in terms of traffic demand and area coverage. One of its advantages is the possibility to share the capacity of any wavelength among more than one user, through TDM/WDM hybrid multiplexing. However, it is still an open issue which transmission technology (DWDM transmitters, colorless transmitters, coherent detection, direct detection) can more effectively satisfy the requirements and characteristics of the future long-reach access network. In this article we propose a new optimization model based on Mixed Integer Linear Programming (MILP) that formalizes the problem of selecting the most cost-effective transmission technology in LR TDM/WDM PONs while also assigning the wavelength-channels line rate and the splitting ratio of the remote nodes at different levels, under bandwidth and power budget constraints. Using this proposed MILP formulation we can identify the optimal transmission technology for a wide set of possible LR-PON scenarios of interest. In this work we provide an evaluation of the optimal transmission technologies under several PON scenarios with varying traffic loads and area coverage. We also analyze the cost sensitivity of the optimization process for coherent-detection technology since it is still under research and development.

On relay selection and power allocation in cooperative free-space optical networks

Drawing increasing attention, free-space optics (FSO) is a cost-effective technology to support data intensive communications. Cooperative diversity is considered to be an effective means for combating weather turbulence in FSO networks. In this paper, we consider the challenging problem of joint relay selection and power allocation in FSO networks. The objective was to maximize the FSO network-wide throughput under constraints of a given power budget and a limited number of FSO transceivers. The problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which is NP-hard. We first adopt the reformulation-linearization technique (RLT) to derive an upper bound for the original MINLP problem. Due to the relaxation, the solutions obtained from RLT are infeasible. We then propose both centralized and distributed algorithms using bipartite matching and convex optimization to obtain highly competitive solutions. The proposed algorithms are shown to outperform the noncooperative scheme and an existing relay selection protocol with considerable gains through simulations.

When is FDMA Sum-Rate Optimal in 2x2 Gaussian Interference Channel?

Consider a 2x2 Gaussian interference channel system in a frequency flat environment. We aim to derive sufficient conditions under which frequency division multiple access (FDMA) is optimal, in the objective of sum-rate maximization under power budget constraints of individual users. Two types of conditions are obtained. Briefly, the former one states that the normalized interference channel gain has to be large enough. The corresponding formula can be further extended to Kx K (K>2) multi-user scenario. The latter one is that the power budget has to be large enough, even if the normalized interference channel gain is small.

Maximizing Sum Rates in Cognitive Radio Networks: Convex Relaxation and Global Optimization Algorithms

A key challenge in wireless cognitive radio networks is to maximize the total throughput also known as the sum rates of all the users while avoiding the interference of unlicensed band secondary users from overwhelming the licensed band primary users. We study the weighted sum rate maximization problem with both power budget and interference temperature constraints in a cognitive radio network. This problem is nonconvex and generally hard to solve. We propose a reformulation-relaxation technique that leverages nonnegative matrix theory to first obtain a relaxed problem with nonnegative matrix spectral radius constraints. A useful upper bound on the sum rates is then obtained by solving a convex optimization problem over a closed bounded convex set. It also enables the sum-rate optimality to be quantified analytically through the spectrum of specially-crafted nonnegative matrices. Furthermore, we obtain polynomial-time verifiable sufficient conditions that can identify polynomial-time solvable problem instances, which can be solved by a fixed-point algorithm. As a by-product, an interesting optimality equivalence between the nonconvex sum rate problem and the convex max-min rate problem is established. In the general case, we propose a global optimization algorithm by utilizing our convex relaxation and branch-and-bound to compute an ε-optimal solution. Our technique exploits the nonnegativity of the physical quantities, e.g., channel parameters, powers and rates, that enables key tools in nonnegative matrix theory such as the (linear and nonlinear) Perron-Frobenius theorem, quasi-invertibility, Friedland-Karlin inequalities to be employed naturally. Numerical results are presented to show that our proposed algorithms are theoretically sound and have relatively fast convergence time even for large-scale problems

Joint sub-carrier pairing and resource allocation for cognitive networks with adaptive relaying

Relayed transmission in a cognitive radio (CR) environment could be used to increase the coverage and capacity of communication system that benefits already from the efficient management of the spectrum developed by CR. Furthermore, there are many types of cooperative communications, including decode-and-forward (DAF) and amplify-and-forward (AAF). In this paper, these techniques are combined in an adaptive mode to benefit from its forwarding advantages; this mode is called adaptive relaying protocol (ARP). Moreover, this work focuses on the joint power allocation in a cognitive radio system in a cooperative mode that operates ARP in multi-carrier mode. The multi-carrier scenario is used in an orthogonal frequency division multiplexing (OFDM) mode, and the problem is formulated to maximize the end-to-end rate by searching the best power allocation at the transmitters. This work includes, besides the ARP model, a sub-carrier pairing strategy that allows the relays to switch to the best sub-carrier pairs to increase the throughput. The optimization problem is formulated and solved under the interference and power budget constraints using the sub-gradient algorithm. The simulation results confirm the efficiency of the proposed adaptive relaying protocol in comparison to other relaying techniques. The results show also the consequence of the choice of the pairing strategy.

Joint Optimization of Collaborative Sensing and Radio Resource Allocation in Small-Cell Networks

The current trend towards heterogeneous networks requires some sort of self-organization capability, in terms of self-optimization and self-configuration. The basic steps enabling self-optimization typically require listening to the environment, learning and adapting resource allocation consequently. In this work, we propose a joint optimization of sensing parameters and radio resource allocation in order to maximize the opportunistic throughput, under the constraint of limiting undue interference towards primary users. The method applies to wireless networks where the sensing nodes are allowed to cooperate to improve their local decision capabilities using a fully decentralized approach based on distributed consensus. The proposed optimization maximizes the opportunistic throughput, taking into account decision errors, sensing time and time necessary to achieve a consensus over the sensed variables. Because of the lack of knowledge of the channel between the cognitive users and the macro-users, the interference constraint is formulated in probabilistic form, depending on the spatial distribution of the nodes and on the channel fading model. Our formulation allows us to find the optimal false alarm rate and, consequently, the optimal decision thresholds jointly with the optimal power/bit allocation. Finally, we show that, given a total power budget constraint, there is an optimal way of distributing the available power between the power spent to achieve consensus and the power spent for transmitting the data payload.

Cognitive Radio Network Duality and Algorithms for Utility Maximization

We study a utility maximization framework for spectrum sharing among cognitive secondary users and licensed primary users in cognitive radio networks. All the users maximize the network utility by adapting their signal-to-interference-plus-noise ratio (SINR) assignment and transmit power subject to power budget constraints and additional interference temperature constraint for the secondary users. The utility maximization problem is challenging to solve optimally in a distributed manner due to the nonconvexity and the tight coupling between the power budget and interference temperature constraint sets. We first study a special case where egalitarian SINR fairness is the utility, and a tuning-free distributed algorithm with a geometric convergence rate is developed to solve it optimally. Then, we answer the general utility maximization question by developing a cognitive radio network duality to decouple the SINR assignment, the transmit power and the interference temperature allocation. This leads to a utility maximization algorithm that leverages the egalitarian fairness power control as a submodule to maintain a desirable separability in the SINR assignment between the secondary and primary users. This algorithm has the advantage that it can be distributively implemented, and the method converges relatively fast. Numerical results are presented to show that our proposed algorithms are theoretically sound and practically implementable.

A Multi-Objective Perspective for Operator Scheduling Using Finegrained DVS Architectures

The stringent power budget of fine grained power managed digital integrated circuits have driven chip designers to optimize power at the cost of area and delay, which were the traditional cost criteria for circuit optimization. The emerging scenario motivates us to revisit the classical operator scheduling problem under the availability of DVFS enabled functional units that can trade-off cycles with power. We study the design space defined due to this trade-off and present a branch-and-bound(B/B) algorithm to explore this state space and report the pareto-optimal front with respect to area and power. The scheduling also aims at maximum resource sharing and is able to attain sufficient area and power gains for complex benchmarks when timing constraints are relaxed by sufficient amount. Experimental results show that the algorithm that operates without any user constraint(area/power) is able to solve the problem for most available benchmarks, and the use of power budget or area budget constraints leads to significant performance gain.

Joint Sensing and Power Allocation in Nonconvex Cognitive Radio Games: Quasi-Nash Equilibria

In this paper, we propose a novel class of Nash problems for Cognitive Radio (CR) networks composed of multiple primary users (PUs) and secondary users (SUs) wherein each SU (player) competes against the others to maximize his own opportunistic throughput by choosing jointly the sensing duration, the detection thresholds, and the vector power allocation over a multichannel link. In addition to power budget constraints, several (deterministic or probabilistic) interference constraints can be accommodated in the proposed general formulation, such as constraints on the maximum individual/aggregate (probabilistic) interference tolerable from the PUs. To keep the optimization as decentralized as possible, global interference constraints, when present, are imposed via pricing; the prices are thus additional variables to be optimized. The resulting players' optimization problems are nonconvex and there are price clearance conditions associated with the nonconvex global interference constraints to be satisfied by the equilibria of the game, which make the analysis of the proposed game a challenging task; none of classical results in the game theory literature can be successfully applied. To deal with the nonconvexity of the game, we introduce a relaxed equilibrium concept, the Quasi-Nash Equilibrium (QNE), and study its main properties, performance, and connection with local Nash equilibria. Quite interestingly, the proposed game theoretical formulations yield a considerable performance improvement with respect to current centralized and decentralized designs of CR systems, which validates the concept of QNE.

The Sign-Definiteness Lemma and Its Applications to Robust Transceiver Optimization for Multiuser MIMO Systems

We formally generalize the sign-definiteness lemma to the case of complex-valued matrices and multiple norm-bounded uncertainties. This lemma has found many applications in the study of the stability of control systems, and in the design and optimization of robust transceivers in communications. We then present three different novel applications of this lemma in the area of multi-user multiple-input multiple-output (MIMO) robust transceiver optimization. Specifically, the scenarios of interest are: (i) robust linear beamforming in an interfering adhoc network, (ii) robust design of a general relay network, including the two-way relay channel as a special case, and (iii) a half-duplex one-way relay system with multiple relays. For these networks, we formulate the design problems of minimizing the (sum) MSE of the symbol detection subject to different average power budget constraints. We show that these design problems are non-convex (with bilinear or trilinear constraints) and semi-infinite in multiple independent uncertainty matrix-valued variables. We propose a two-stage solution where in the first step the semi-infinite constraints are converted to linear matrix inequalities using the generalized sign-definiteness lemma, and in the second step, we use an iterative algorithm based on alternating convex search (ACS). Via simulations we evaluate the performance of the proposed scheme.

On Opportunistic Power Control for Alamouti and SM MIMO Systems

Transmit antenna diversity and single user spatial multiplexing have become attractive in practical systems, because they achieve performance gains without requiring sophisticated channel state information (CSI) feedback mechanisms. On the other hand, when fast and accurate CSI at the transmitter is available, opportunistic power control (OPC) is an attractive alternative to signal-to-interference-and-noise ratio (SINR) target following approaches, because it maximizes throughput by taking advantage of fast channel variations. In this paper we examine the question whether OPC is worth the pain of obtaining fast CSI by evaluating the gains of OPC for the downlink of a system employing multiple input multiple output (MIMO) systems with Alamouti and open loop spatial multiplexing (SM). We formulate the OPC problem as a throughput maximization task subject to power budget and fairness constraints. We solve this task by the Augmented Lagrangian Penalty Function and find that without fairness constraints, OPC in concert with SM provides superior throughput. With increasingly tight fairness constraints, Alamouti along with equal power allocation becomes a viable alternative to the SM OPC scheme. Both from fairness and throughput perspectives, Alamouti along with OPC is particularly efficient when adaptive MCS is employed and users with large differences in channel qualities have to share the total transmit power.

Joint Sensing-Channel Selection and Power Control for Cognitive Radios

We consider joint optimization for sensing-channel selection and ensuing power control problem with cognitive radios over time-varying fading channels. It is shown that this joint design can be judiciously formulated as a convex optimization problem. Optimal joint sensing-channel selection and power control scheme is then derived in closed-form under the constraints of average power budget and maximum allowable probability of collisions with the primary communications. In addition, we develop a stochastic optimization algorithm that can operate without a-priori knowledge of the fading channel statistics. It is rigourously established that the proposed stochastic scheme is capable of dynamically learning the intended wireless channels on-the-fly to approach the optimal strategy almost surely. Numerous results are also provided to evaluate the proposed schemes for cognitive transmissions over block fading channels.

CDIT-Based Constrained Resource Allocation for Mobile WiMAX Systems

Adaptive resource allocation has been shown to provide substantial performance gain in OFDMA-based wireless systems, such as WiMAX, when full channel state information (CSI) is available at the transmitter. However, in some fading environments (e.g., fast fading), there may not be a feedback link sufficiently fast to convey the CSI to the transmitter. In this paper, we consider resource allocation strategies for downlink multiuser mobile WiMAX systems, where the transmitter has only the channel distribution information (CDI), but no knowledge of the instantaneous channel realization. We address the problem of subchannel assignment and power allocation, to maximize the ergodic weighted-sum rate under long-term fairness, minimum data rate requirement and power budget constraints. This problem is formulated as a nonlinear stochastic constrained optimization problem. We provide an analytical solution based on the Lagrange dual decomposition framework. The proposed method has a complexity of (KM) for K users and M subchannels. Simulation results are provided to compare the performance of this method with other allocation schemes and to illustrate the tradeoff between maximized weighted-sum rate and the constraints.