Performance modeling and optimization of multiple‐objective cross‐layer design in multi‐flow ad‐hoc networks

In ad‐hoc wireless networks, to achieve good performance, multiple parameters need to be optimized jointly. However, existing literature lacks a design framework that investigates the synchronic impact of several parameters on overall system performance. Among several design parameters, energy conservation, end‐to‐end delay minimization, and improved throughput are considered most important for efficient operation of these networks. In this paper, we propose a novel scheme for multiple‐objective cross‐layer optimization capable of optimizing all these performance objectives simultaneously for reliable, energy‐efficient, and timely transmission of continuous media information across the network. The three global criteria considered for optimization are incorporated in a single programming problem via linear scalarization. Besides, we employ standard convex optimization method and Lagrangian technique to solve the proposed problem to seek optimality. Extensive simulation results are generated accounting for several topologies with multiple concurrent flows in the network. These results are used to validate the analytical results and demonstrate the efficiency of the proposed optimization model. Efficiency of the model is verified by finding the set of Pareto‐optimal solutions plotted in three‐dimensional objective space. These solution points constituting the Pareto front are used as the best possible balance points among maximum throughput, maximum residual energy, and least network delay. Finally, to emphasize the effectiveness and supremacy of our proposed multiple‐objective cross‐layer design scheme, we compare it with the conventional multiple‐objective genetic algorithm. Simulation results demonstrate that our method provides significant performance gain over the genetic algorithm approach in terms of the above specified three objectives.     

Dynamic resource allocation for Device‐to‐Device communication underlaying cellular networks

Future cellular networks such as IMT‐Advanced are expected to allow underlaying direct Device‐to‐Device (D2D) communication for spectrum efficiency. However, enabling D2D communication in a cellular network presents a challenge in resource allocation because of the potentially severe interference it may cause to the cellular network by reusing the spectrum with the cellular users. In this paper, we analyze the resource allocation problem in a single cell system when both cellular users and D2D users are present in the system. We first consider the scenario where cellular users and D2D users are allocated resource independently and propose an optimal algorithm and a heuristic algorithm, and then extend the methods to the scenario where cellular users and D2D users are allocated resource jointly. The number of permitted D2D pairs is selected as a performance measure because it is a more specific performance measure than spectrum efficiency. The proposed schemes maximize the number of permitted D2D communication pairs in a system meanwhile avoiding the strong interference from D2D links to the cellular links. Finally, the performance of the proposed methods is evaluated through the numerical simulation. The simulation results show that the proposed methods enhance the number of permitted D2D communication pairs significantly and that the performance of the proposed scheme for jointly allocation scenario is better than that of the proposed scheme for independently allocation scenario. Copyright © 2012 John Wiley & Sons, Ltd.     

Energy‐efficient cluster division for multi‐cell joint transmission technology

Coordinated Multi‐Point (CoMP) is an effective way to improve user performance in next‐generation wireless cellular networks, such as 3GPP LTE‐Advanced(LTE‐A). The base station cooperation can reduce interference, and increase the signal to interference and noise ratio (SINR) of cell‐edge users and improve the system capacity. However, the base station cooperation also adds additional power consumption for signal processing and sharing information through back‐haul links between cooperative base stations. As such, CoMP may potentially consume more energy. This paper studies such energy consumption issue in CoMP, presents a semi‐dynamic CoMP cluster division algorithm based on energy efficiency (SCCD‐EE) that can effectively adapt to users' real‐time interference, and employs the idea of Maximal Independent Set (MIS) to solve the problem of cluster overlapping. To verify the feasibility of the proposed algorithm, this paper performs comprehensive evaluations in terms of energy efficiency and system capacity. The simulation results show that the proposed semi‐dynamic cluster division algorithm can not only improve the system capacity and the quality of service (QoS) of cell‐edge users, but also achieve higher network energy efficiency compared with static cluster methods and Non‐CoMP approaches. Copyright © 2016 John Wiley & Sons, Ltd.     

Semi‐distributed dynamic inter‐cell interference coordination scheme for interference avoidance in heterogeneous networks

Inter‐cell interference (ICI) is a major problem in heterogeneous networks, such as two‐tier femtocell (FC) networks, because it leads to poor cell‐edge throughput and system capacity. Dynamic ICI coordination (ICIC) schemes, which do not require prior frequency planning, must be employed for interference avoidance in such networks. In contrast to existing dynamic ICIC schemes that focus on homogeneous network scenarios, we propose a novel semi‐distributed dynamic ICIC scheme to mitigate interference in heterogeneous network scenarios. With the goal of maximizing the utility of individual users, two separate algorithms, namely the FC base station (FBS)‐level algorithm and FC management system (FMS)‐level algorithm, are employed to restrict resource usage by dominant interference‐creating cells. The distributed functionality of the FBS‐level algorithm and low computational complexity of the FMS‐level algorithm are the main advantages of the proposed scheme. Simulation results demonstrate improvement in cell‐edge performance with no impact on system capacity or user fairness, which confirms the effectiveness of the proposed scheme compared to static and semi‐static ICIC schemes.     

Energy‐Efficiency Power Allocation for Cognitive Radio MIMO‐OFDM Systems

This paper studies energy‐efficiency (EE) power allocation for cognitive radio MIMO‐OFDM systems. Our aim is to minimize energy efficiency, measured by “Joule per bit” metric, while maintaining the minimal rate requirement of a secondary user under a total power constraint and mutual interference power constraints. However, since the formulated EE problem in this paper is non‐convex, it is difficult to solve directly in general. To make it solvable, firstly we transform the original problem into an equivalent convex optimization problem via fractional programming. Then, the equivalent convex optimization problem is solved by a sequential quadratic programming algorithm. Finally, a new iterative energy‐ efficiency power allocation algorithm is presented. Numerical results show that the proposed method can obtain better EE performance than the maximizing capacity algorithm.     

Novel schemes for interference‐resilient OFDM wireless communication

This research work introduces some novel techniques for interference‐resilient OFDM wireless communication. Firstly, novel schemes for spatial multiplexing and interference cancelation based on signal subspace estimation are proposed. Secondly, the OFDM system is designed such that it meets three main objectives simultaneously, namely, (i) interference‐resiliency, (ii) throughput maximization, and (iii) energy consumption minimization. Interference‐cancelation techniques in prior art mostly consider maximization of throughput without any focus on energy consumption. On the other hand, the literature in energy minimization is limited to interference‐free environments. Most of the previous techniques also have a drawback that they cannot operate in smart or dynamically changing interference environments. The focus here is on interference‐resilient communication with equal attention to both throughput and energy‐efficiency maximization in dynamic hostile environments. The optimization problem is formulated, and then optimal policies and switching thresholds are found for throughput and energy‐efficient interference‐resilient communication. Methodologies for estimating the channel and jammer conditions and then adapting the transmission strategies accordingly are proposed. Moreover, to have an effective defense against smart jamming scenario, a constant‐payoff scheme is also introduced. Simulation results are compared with previous techniques that demonstrate the efficacy of proposed research. Copyright © 2015 John Wiley & Sons, Ltd.     

A self‐organized resource allocation scheme for heterogeneous macro‐femto networks

This paper investigates the radio resource management (RRM) issues in a heterogeneous macro‐femto network. The objective of femto deployment is to improve coverage, capacity, and experienced quality of service of indoor users. The location and density of user‐deployed femtos is not known a‐priori. This makes interference management crucial. In particular, with co‐channel allocation (to improve resource utilization efficiency), RRM becomes involved because of both cross‐layer and co‐layer interference. In this paper, we review the resource allocation strategies available in the literature for heterogeneous macro‐femto network. Then, we propose a self‐organized resource allocation (SO‐RA) scheme for an orthogonal frequency division multiple access based macro‐femto network to mitigate co‐layer interference in the downlink transmission. We compare its performance with the existing schemes like Reuse‐1, adaptive frequency reuse (AFR), and AFR with power control (one of our proposed modification to AFR approach) in terms of 10 percentile user throughput and fairness to femto users. The performance of AFR with power control scheme matches closely with Reuse‐1, while the SO‐RA scheme achieves improved throughput and fairness performance. SO‐RA scheme ensures minimum throughput guarantee to all femto users and exhibits better performance than the existing state‐of‐the‐art resource allocation schemes.Copyright © 2014 John Wiley & Sons, Ltd.     

Interference management schemes for multi‐user cooperative wireless networks

Cooperative communication is a promising technique for future wireless networks. It can be used in improving communication reliability and enhancing spectrum efficiency by using the broadcast nature of radio communication and exploiting cooperative diversity. However, its performance gain degrades in the presence of co‐channel interference, which makes it essential to propose interference mitigation schemes. In this paper, we introduce three cooperative communication schemes with interference management for multi‐user cooperative wireless networks. The first scheme (best relay selection) is used as a performance benchmark because it completely avoids the interference problem by using the Frequency‐Division Multiple Access technique. The second scheme (best available relay selection) maximizes the received signal‐to‐noise ratio while keeping the interference levels below a certain threshold, and the third scheme (General Order Relay and User Selection) is based on iterative resource allocation algorithm. We derive exact closed‐form expressions of average bit error probability, outage probability, and average consumed power for the proposed schemes. Simulations are used to validate the analytical results. The results confirm the advantage of the proposed cooperation schemes in enhancing the system performance and improving the interference management. Copyright © 2015 John Wiley & Sons, Ltd.     

A novel approach for energy‐efficient resource allocation in double threshold‐based cognitive radio network

This paper mainly focuses on solving the energy efficiency (EE) maximization problem in double threshold‐based soft decision fusion (SDF) cooperative spectrum sensing (CSS) in the cognitive radio network (CRN). The solution to this objective problem starts with the selection of suitable secondary users (SUs) both for the spectrum sensing and data transmission. Here, energy efficiency is maximized under the constraints of interference to the primary user (PU), an acceptable outage of SUs, the transmission power of the SUs and the probability of false alarm. We propose a novel algorithm called iterative Dinkelbach method (IDM) which jointly optimizes the sensing time and transmission power allocation to the SUs. Further, Lagrangian duality theorem is employed to find the exact power assigned to the SUs. Finally, simulation results are carried out to validate the effectiveness of our proposed scheme by comparing with the other existing schemes. The performance is also analyzed for different system parameters.     

Multiuser two‐way relay processing and power control methods for cognitive radio networks

We consider a cognitive radio system where a secondary network shares the spectrum band with a primary network. Aiming at improving the frequency efficiency of the secondary network, we set a multiantenna relay station in the secondary network to perform two‐way relaying. Three linear processing schemes at the relay station based on zero forcing, zero forcing‐maximum ratio transmission, and minimum mean square error criteria are derived to guarantee the quality of service of primary users and to suppress the intrapair and interpair interference among secondary users (SUs). In addition, the transmit power of SUs is optimized to maximize the sum rate of SUs and to limit the interference brought to PUs. Numerical results show that the proposed multiuser two‐way relay processing schemes and the optimal power control policies can efficiently limit the interference caused by the secondary network to primary users, and the sum rate of SUs can also be greatly improved. Copyright © 2011 John Wiley & Sons, Ltd.     

Power allocation scheme based on sum capacity maximization for signal‐to‐leakage‐and‐noise ratio precoded multiuser multiple‐input single‐output downlink

This paper proposes a power allocation scheme to maximize the sum capacity of all users for signal‐to‐leakage‐and‐noise ratio (SLNR) precoded multiuser multiple‐input single‐output downlink. The designed scheme tries to explore the effect of the power allocation for the SLNR precoded multiuser multiple‐input single‐output system on sum capacity performance. This power allocation problem can be formulated as an optimization problem. With high signal‐to‐interference‐plus‐noise ratio assumption, it can be converted into a convex optimization problem through the geometric programming and hence can be solved efficiently. Because the assumption of high signal‐to‐interference‐plus‐noise ratio cannot be always satisfied in practice, we design a globally optimal solution algorithm based on a combination of branch and bound framework and convex relaxation techniques. Theoretically, the proposed scheme can provide optimal power allocation in sum capacity maximization. Then, we further propose a judgement‐decision algorithm to achieve a trade‐off between the optimality and computational complexity. The simulation results also show that, with the proposed scheme, the sum capacity of all the users can be improved compared with three existing power allocation schemes. Meanwhile, some meaningful conclusions about the effect of the further power allocation based on the SLNR precoding have been also acquired. The performance improvement of the maximum sum capacity power allocation scheme relates to the transmit antenna number and embodies different variation trends in allusion to the different equipped transmit antenna number as the signal‐to‐noise ratio (SNR) changes.Copyright © 2013 John Wiley & Sons, Ltd.     

无线供电混合多址接入网络的最优能效资源分配研究

在能量受限的通信系统中，能量效率是衡量系统性能的关键指标.本文研究由一个基站和多个分簇用户组成的无线供电混合非正交多址接入系统.在该网络中，基站通过无线能量传输方式给用户供能，用户则利用收集到的能量向基站传输各自的信息.为降低基站的接收解码复杂度，用户采用分簇的方式进行信息传输：簇间用户的信息传输采用时分多址方式，而簇内用户的信息传输采用非正交多址方式.通过联合分配能量传输与信息传输的时间长度以及控制基站和用户的发射功率来实现网络能量效率的最大化.由于涉及的优化问题是非凸的，本文先通过寻找问题最优解的结构，然后根据分式规划理论，提出了一种新的迭代资源分配算法来求解该问题.仿真结果表明，与"吞吐量最大化策略"和 "固定时间分配策略"两种基准策略相比，所提出的算法显著提高了网络的能量效率.     

Pricing and power control for energy‐efficient radio resource management in cognitive femtocell networks

Cognitive femtocell has been considered as a promising technique that can improve the capacity and the utilization of spectrum efficiency in wireless networks because of the short transmission distance and low transmit power. In this paper, we study the win–win solution of energy‐efficient radio resource management in cognitive femtocell networks, where the macrocell tries to maximize its revenue by adjusting spectrum utilization price while the femtocells try to maximize their revenues by dynamically adjusting the transmit power. When the spectrum utilization price is given by macrocell, we formulate the power control problem of standalone femtocells as an optimization problem and introduce a low‐complexity iteration algorithm based on gradient‐assisted binary search algorithm to solve it. Besides, non‐cooperative game is used to formulate the power control problem between collocated femtocells in a collocated femtocell set, and then low complexity and widely used gradient‐based iteration algorithm is applied to obtain the Nash‐equilibrium solution. Specially, asymptotic analysis is applied to find the approximate spectrum utilization price in macrocell, which can greatly reduce the computational complexity of the proposed energy‐efficient radio resource management scheme. Finally, extensive simulation results are presented to verify our theoretical analysis and demonstrate the performance of the proposed scheme. Copyright © 2013 John Wiley & Sons, Ltd.     

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA). Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well‐known NP‐hard problem. To resolve this problem, this paper proposes an energy‐efficient task‐scheduling algorithm based on best‐worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision‐making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization.     

On the performance of two‐user full‐duplex network‐coded cooperation

In this paper, we propose two schemes based on a full‐duplex network‐coded cooperative communication (FD‐NCC) strategy, namely, full‐duplex dynamic network coding (FD‐DNC) and full‐duplex generalized dynamic network coding (FD‐GDNC). The use of full‐duplex communication aims at improving the spectrum efficiency of a two‐user network where the users cooperatively transmit their independent information to a common destination. In the proposed FD‐NCC schemes, the self‐interference imposed by full‐duplexing is modeled as a fading channel, whose harmful effect can be partially mitigated by interference cancellation techniques. Nevertheless, our results show that, even in the presence of self‐interference, the proposed FD‐NCC schemes can outperform (in terms of outage probability) the equivalent half‐duplex network‐coded cooperative (HD‐NCC) schemes, as well as traditional cooperation techniques. Moreover, the ?‐outage capacity, that is, the maximum information rate achieved by the users given a target outage probability, is evaluated. Finally, we examine the use of multiple antennas at the destination node, which increases the advantage of the FD‐NCC (in terms of the diversity‐multiplexing trade‐off and ?‐outage capacity).     

Simultaneous Information and Power Transfer for Multi‐antenna Primary‐Secondary Cooperation in Cognitive Radio Networks

In this paper, cognitive radio and simultaneous wireless information and power transfer (SWIPT) are effectively combined to design a spectrum‐efficient and energy‐efficient transmission paradigm. Specifically, a novel SWIPT‐based primary‐secondary cooperation model is proposed to increase the transmission rate of energy/spectrum constrained users. In the proposed model, a multi‐antenna secondary user conducts simultaneous energy harvesting and information forwarding by means of power splitting (PS), and tries to maximize its own transmission rate under the premise of successfully assisting the data delivery of the primary user. After the problem formulation, joint power splitting and beamforming optimization algorithms for decode‐and‐forward and amplify‐and‐forward modes are presented, in which we obtain the optimal PS factor and beamforming vectors using a golden search method and dual methods. Simulation results show that the proposed SWIPTbased primary‐secondary cooperation schemes can obtain a much higher level of performance than that of non‐SWIPT cooperation and non‐cooperation schemes.     

Energy‐efficient resource allocation in D2D communications for energy harvesting–enabled NOMA‐based cellular networks

In this paper, we propose an energy‐efficient power control and harvesting time scheduling scheme for resource allocation of the subchannels in a nonorthogonal multiple access (NOMA)–based device‐to‐device (D2D) communications in cellular networks. In these networks, D2D users can communicate by sharing the radio resources assigned to cellular users (CUs). Device‐to‐device users harvest energy from the base station (BS) in the downlink and transmit information to their receivers. Using NOMA, more than one user can access the same frequency‐time resource simultaneously, and the signals of the multiusers can be separated successfully using successive interference cancellation (SIC). In fact, NOMA, unlike orthogonal multiple access (OMA) methods, allows sharing the same frequency resources at the same time by implementing adaptive power allocation. Our aim is to maximize the energy efficiency of the D2D pairs, which is the ratio of the achievable throughput of the D2D pairs to their energy consumption by allocating the proper subchannel of each cell to each device user equipment (DUE), managing their transmission power, and setting the harvesting and transmission time. The constraints of the problem are the quality of service of the CUs, minimum required throughput of the subchannels, and energy harvesting of DUEs. We formulate the problem and propose a low‐complexity iterative algorithm on the basis of the convex optimization method and Karush‐Kuhn‐Tucker conditions to obtain the optimal solution of the problem. Simulation results validate the performance of our proposed algorithm for different values of the system parameters.     

Prediction‐based MAC‐layer sensing in cognitive radio networks

We consider a cognitive radio network which coexists with multiple primary users (PUs) and secondary users (SUs) transmit over time‐varying channels. In this scenario, one problem of the existing work is the poor performances of throughput and fairness due to variances of SUs' channel conditions and PUs' traffic patterns. To solve this problem, we propose a novel prediction‐based MAC‐layer sensing algorithm. In the proposed algorithm, the SUs' channel quality information and the probability of the licensed channel being idle are predicted. Through the earlier predicted information, we schedule the SUs to sense and transmit on different licensed channels. Specifically, multiple significant factors, including network throughput and fairness, are jointly considered in the proposed algorithm. Then, we formulate the prediction‐based sensing scheduling problem as an optimization problem and solve it with the Hungarian algorithm in polynomial time. Simulation results show that the proposed prediction‐based sensing scheduling algorithm could achieve a good tradeoff between network throughput and fairness among SUs. Copyright © 2014 John Wiley & Sons, Ltd.     

Cache‐at‐relay: energy‐efficient content placement for next‐generation wireless relays

Uploading and downloading content have recently become one of the major reasons for the growth of Internet traffic volume. With the increasing popularity of social networking tools and their video upload/download applications, as well as the connectivity enhancements in wireless networks, it has become a second nature for mobile users to access on‐demand content on‐the‐go. Urban hot spots, usually implemented via wireless relays, answer the bandwidth need of those users. On the other hand, the same popular contents are usually acquired by a large number of users at different times, and fetching those from the initial content source each and every time makes inefficient use of network resources. In‐network caching provides a solution to this problem by bringing contents closer to the users. Although in‐network caching has been previously studied from latency and transport energy minimization perspectives, energy‐efficient schemes to prolong user equipment lifetime have not been considered. To address this problem, we propose the cache‐at‐relay (CAR) scheme, which utilizes wireless relays for in‐network caching of popular contents with content access and caching energy minimization objectives. CAR consists of three integer linear programming models, namely, select relay, place content, and place relay, which respectively solve content access energy minimization, joint minimization of content access and caching energy, and joint minimization of content access energy and relay deployment cost problems. We have shown that place relay significantly minimizes the content access energy consumption of user equipments, while place content provides a compromise between the content access and the caching energy budgets of the network. Copyright © 2015 John Wiley & Sons, Ltd.     

Cooperative Q‐learning techniques for distributed online power allocation in femtocell networks

In this paper, we address the problem of distributed interference management of femtocells that share the same frequency band with macrocells using distributed multi‐agent Q‐learning. We formulate and solve two problems representing two different Q‐learning algorithms, namely, femto‐based distributed and sub‐carrier‐based distributed power controls using Q‐learning (FBDPC‐Q and SBDPC‐Q). FBDPC‐Q is a multi‐agent algorithm that works on a global basis, for example, deals with the aggregate macrocell and femtocell capacities. Its complexity increases exponentially with the number of sub‐carriers in the system. Also, it does not take into consideration the sub‐carrier macrocell capacity as a constraint. To overcome these problems, SBDPC‐Q is proposed, which is a multi‐agent algorithm that works on a sub‐carrier basis, for example, sub‐carrier macrocell and femtocell capacities. Each of FBDPC‐Q and SBDPC‐Q works in three different learning paradigms: independent (IL), cooperative (CL), and weighted cooperative (WCL). IL is considered the simplest form for applying Q‐learning in multi‐agent scenarios, where all the femtocells learn independently. CL and WCL are the proposed schemes in which femtocells share partial information during the learning process in order to strike a balance between practical relevance and performance. We prove the convergence of the CL paradigm when used in the FBDPC‐Q algorithm. We show via simulations that the CL paradigm outperforms the IL paradigm in terms of the aggregate femtocell capacity, especially in networks with large number of femtocells and large number of power levels. In addition, we propose WCL to address the CL limitations. Finally, we evaluate the robustness and scalability of both FBDPC‐Q and SBDPC‐Q, against several typical dynamics of plausible wireless scenarios (fading, path loss, random activity of femtocells, etc.). We show that the CL paradigm is the most scalable to large number of femtocells and robust to the network dynamics compared with the IL and WCL paradigms. Copyright © 2014 John Wiley & Sons, Ltd.