This paper considers the problem of joint subcarrier assignment and global energy-efficient power allocation (J-SA-GEE-PA) for energy-harvesting (EH) two-tier downlink nonorthogonal multiple-access (NOMA)-based heterogeneous networks (HetNets). Particularly, the HetNet consists of a macro base-station (MBS) and a number of small base-stations (SBSs), which are solely powered via renewable-energy sources. The aim is to solve the joint subcarrier assignment and global energy-efficiency problem subject to quality-of-service (QoS) per user and other practical constraints. However, the formulated J-SA-GEE-PA problem happens to be non-convex and NP-hard, and thus is computationally-prohibitive. In turn, the J-SA-GEE-PA problem is split into two sub-problems: (1) subcarrier assignment via many-to-many matching, and (2) GEE-maximizing power allocation. In the first sub-problem, the subcarriers are assigned to users via the deferred acceptance algorithm. As for the second sub-problem, the GEE-PA problem is solved optimally via a low-complexity algorithm. After that, a two-stage solution procedure is devised to efficiently solve the J-SA-GEE-PA problem. Simulation results are presented to validate the proposed solution procedure, where it is shown to efficiently yield comparable network global energy-efficiency to the J-SA-GEE-PA scheme; however, with lower computational-complexity.
Non-orthogonal multiple access (NOMA) is one of the most promising multiple access schemes, which is anticipated to improve the network spectral efficiency in 5G wireless cellular networks. Together with joint-transmission coordinated multi-point transmission (JT-CoMP), a JT-CoMP-NOMA network will also improve the data rates of cell-edge users, which are prone to severe inter-cell interference. In this paper we propose an implementation of the centralized resource allocation problem for JT-CoMP- NOMA in GAMS, where the aim is to perform joint sub-carrier assignment and power allocation in multi-cell downlink NOMA networks. The implementation serves as a benchmark for evaluating the network sum-rate performance of resource allocation algorithms for JT-CoMP-NOMA systems. Moreover, the provided implementation incorporates practical constraints, such as the maximum number of users multiplexed over each sub-carrier, SIC decoding order, user pairing, intra- and inter-cell interference, and minimum rate requirements. Simulation results are presented as a proof of concept.
The number of mobile connected devices have increased exponentially worldwide. The services provided across mobile networks have also become increasingly demanding for higher network capacity. This has motivated the use of the heterogeneous network (HetNet) architecture. However, despite the low power consumption of small-scale base stations (BSs), their collective power consumption in dense HetNets is significant. The use of green energy to mitigate the power consumption of mobile networks is a trend on the rise. However, traditional association schemes under utilize green energy. Furthermore, in dense HetNets, there is an increased chance of users being on cell-edges and having degraded perceived service. Coordinated Multipoint (CoMP) association can aid in improving the service perceived by cell edge users. In this work, we propose a load balancing scheme that optimizes user latency and green energy utilization. The scheme allows for a fractional solution to the user association problem, enabling CoMP transmissions for cell-edge users. The proposed algorithm is a Quasi-Newton-based approach, which applies the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method of approximating the inverse of Hessian matrices. Performance evaluation shows a reduction in latency of 79% and a reduction of power consumption by 99% in comparison to conventional schemes.
The exponential growth of the Information and Communication Technology (ICT) sector have led to a significant increase in energy consumption, higher electricity bills, and negative environmental and economical impacts. Several researchers, network providers, and manufacturers have been investigating different approaches to improve the energy efficiency of communication networks. Software-defined Networks (SDN) is emerging as a new networking framework that separates data plane from control plane in order to simplify network management, reduce operational costs (OPEX), and facilitate innovation. In this work, we address the centralized integral routing problem in SDN. We propose a greedy heuristic algorithm called Energy Efficient Integral Routing (EEIR) algorithm to minimize power consumption in SDN backbone networks while respecting discreteness of link rates. The performance of EEIR has been evaluated in real topologies, and compared to both optimal and shortest path solutions. Experimental results have shown a significant power saving that is as large as 44.42% can be achieved. Compared to optimal solution, EEIR provides a solution with an optimality gap in the range 7.52%- 12.67%.
The increase in demand for high network bandwidth has significantly increased the network power consumption and hence, capital expenditure and operational expenditure costs. Service providers are investigating various approaches to reduce operational and management costs, while delivering richer ser- vices across their networks. Recently, several centralized power- aware routing heuristic algorithms have been proposed leveraging the centralized control of the Software Defined Networking (SDN) architecture. However, a base solution for benchmarking the performance of these algorithms has not been developed yet. In this paper we propose an implementation of the centralized power-aware routing problem for SDN in GAMS. This implementation facilitates solving the problem using commercial packages and hence serves as a benchmark for accessing the performance of centralized power-aware routing algorithms. Experimental results demonstrate the efficiency of the developed implementation.
We consider the problem of minimizing the routing power consumption in software-defined networks. The network is composed of software-defined networking (SDN) nodes and a central controller where routing decisions are centralized. More specifically, the central controller minimizes the routing power consumption by routing flows on the minimum number of active links with the lowest discrete link rates. Thus, it maximizes the number of inactive links and the level of link rates, which reflects significant saving in power consumption. This problem is a mixed-integer programming problem and known to be NP- hard. Therefore, we propose a low-complexity greedy heuristic to minimize the number of active links and link rates by rerouting flows and aggregating them on common links. Numerical results show that the proposed algorithm achieves 17.18% to 32.97% power saving in real network topologies relative to a base shortest path algorithm. The savings are achieved with minimal increase in average path length that is less than 0.2 hops.
Power consumption and CO2 emission have become a major concern over the last few years. Several recent studies have shown that servers and network equipments consume up to 45% of the energy consumption of data centers. Software-defined networking is a new networking paradigm that decouples the control and data functionalities; thus, makes networks easily manageable and programmable. In software-defined networks (SDNs), the central controller has a global view of the network topology, traffic matrices and QoS requirements, which allows it to optimize the energy consumption of the network through energy-aware routing. In this paper, we investigate the impact of practical constraints, discreteness of link rates and limitation of flow rule space, on the performance of energy-aware routing schemes in SDN. The energy-aware routing problem is modeled as an integer linear program (ILP) with discrete cost function. The problem is modeled in GAMS and solved by CPLEX under real network settings and practical constraints. Results show that considering these constraints is critical in order to exploit the energy saving margin of SDNs.
The communication among entities in any network is administered by a set of rules and technical specifications detailed in the communication protocol. All communicating entities adhere to the same protocol to successfully exchange data. Most of the rules are expressed in an algorithm format that computes a decision based on a set of inputs provided by communicating entities or collected by a central controller. Due to the increasing number of communicating entities and large bandwidth required to exchange the set of inputs generated at each entity, distributed implementations have been favorable to reduce the control overhead. In such implementations, each entity self-computes crucial protocol decisions; therefore, can alter these decisions to gain unfair share of the resources managed by the protocol. Misbehaving users degrade the performance of the whole network in-addition to starving well-behaving users. In this work we develop a framework to derive the optimal penalty strategy for penalizing misbehaving users. The proposed framework considers users learning of the detection mechanism techniques and the detection mechanism tracking of the users behavior and history of protocol offenses. Analysis indicate that escalating penalties are optimal for deterring repeat protocol offenses.
In this paper, we present three network evolution models for generating fault-tolerant and energy- efficient large-scale peer-to-peer wireless sensor networks (WSNs) based on complex networks theory. Being scale-free is one of the intrinsic features of complex networks-based evolution models that generates fault- tolerant topologies. In this work, we argue that fault- tolerant topologies are not necessarily energy efficient. The three proposed energy-aware evolution models are energy-aware common neighbors (ECN), energy- aware large degree promoted (ELDP) and energy-aware large degree demoted (ELDD). ECN considers neighborhood overlap, whereas ELDP and ELDD consider topological overlap for node attachment. The ELDP model promotes the establishment of links to nodes with a large degree, whereas the ELDD model demotes this strategy. Performance evaluations demonstrate that the proposed models outperform a candidate clustering-based model, thereby providing greater energy savings and fault- tolerance. Among the proposed models, ECN is the winner in-terms of energy efficiency, ELDD performs best in- terms of fault-tolerance, and ELDP conveniently provides balance between the two.
This work contributes to better understanding of the optimal penalties for the deterrence of academic dishonesty. Academic dishonesty has recently become one of the most prevalent issues in higher education. In order to address this issue, researches have focused on understanding cheating methods and techniques, determinants, punishment, and consequences. Unlike common descriptive, exploratory or regression-based approaches proposed in the literature, this work proposes a mathematical framework for analyzing the optimal academic dishonesty penalties. Results suggest that expected penalties should be more than the possible gain in grades obtained by committing an offense. Moreover, it shows that increasing penalties are not always optimal for dealing with repeat cheats.
Although cooperative diversity (CD) systems are widely considered in recent wireless communications standards, little research has been undertaken on their performance under practical conditions. In this study, we study the impact of the channel estimation error on the performance of the multilevel quadrature amplitude modulation (M-QAM) based regenerate-and-forward CD systems. In addition, we investigate the effect of user mobility on channel estimation. After a pilot symbol assisted modulation is proposed for CD systems, the analytical BER performance of the proposed CD system is derived and results are verified via simulations. Numerical results demonstrate that estimation error degrades the performance of CD systems. Furthermore, the inter-user channel estimation error has greater impact on the performance of CD systems than that of the user-to-base station channel. Moreover, the choice of pilot symbol insertion period is crucial and it trades effective data rate for BER performance.
This paper addresses practical implementation issues of resource allocation in OFDM A networks: inaccuracy of channel state information (CSI) available to the resource allocation unit (RAU) and diversity of subscribers’ quality of service (QoS) requirements. The resource allocation problem in the considered point-to-multipoint (PMP) network is modeled as a network utility maximization (NUM) problem that allocates subcarriers, rate and power while satisfying orthogonal frequency division multiple access (OFDMA) constraints and QoS constraints defined in the service level agreement. Performance evaluation findings support our theoretical claims: a substantial data rate gain is achieved by considering the CSI imperfection and multiservice classes are supported with QoS guarantees by coordinating with a call admission control (CAC) scheme.
In this paper, a novel Kalman filter-based power allocation scheme is developed for cooperative networks with inaccurate channel state information (CSI). The channel estimation error is embedded in the power allocation model which results in uncertain linear and time varying system. The robust and constrained Kalman filter (RCKF) scheme adapts the allocated power to channel variation while satisfying the average bit error probability (BEP) requirements of each subscriber. The proposed scheme’s low complexity and robustness to channel estimation inaccuracy make it practical for real networks implementations. Simulation results show that the proposed scheme converges to the optimal allocation with light computational burden despite of the inaccuracy in the received CSI.
Maintaining fairness using weighting factors is a common approach in resource allocation. However, computing weighting factors for multiservice wireless networks is not trivial because users’ rate requirements are heterogeneous and their channel gains are variable. In this paper, we propose weighting factor computation and scheduling schemes for orthogonal frequency division multiple access (OFDMA) networks. The weighting factor computation scheme determines each user’s share of rate for maintaining a utility notion of fairness. We then present a scheduling scheme which takes the users’ weighting factors into consideration to allocate sub-carriers and power in OFDMA networks. The simulation results demonstrate that the proposed scheduling scheme outperforms an opportunistic scheme in terms of fairness performance in different scenarios, where the users are fixed or mobile.
In this paper, we focus on the resources allocation for the OFDMA based two-hop relay network which consists of a single base station, dedicated fixed relay stations and subscriber stations. Subscriber stations are allocated the subcarriers and relay stations that are required to satisfy their minimum rate requirements in either non-cooperative mode (i.e., direct communication with the base station) or in cooperative mode with one of the available relay stations. The cooperation is limited to one relay station to reduce the complexity incurred by the need for synchronization with multiple relays and with the base station at the PHY layer. The subcarriers and relay stations allocation problem is formulated as a Binary Integer Programming (BIP) problem with QoS constraints (minimum rate) and a practical synchronization constraint (cooperation with a single relay). Since the formulated problem is NP-complete, a simple sub-optimal algorithm is proposed to manage the multi-service network resources. Simulations and complexity analysis show that the presented algorithm achieves a network near optimal resources allocation with low computational complexity.
This paper analyzes the ergodic mutual information of OFDMA cooperative relay networks when the Channel State Information (CSI) imperfection is considered at resource allocation unit. Based on the derived ergodic mutual information, a quantitative characterization of the impact of CSI inaccuracy on the ergodic mutual information is presented. A network with multiple subscriber stations, relay stations and one base station that employ a Selection-Decode-and-Forward (SDF) relaying scheme is considered in the uplink mode. Subscriber stations act as relay stations to assist other transmitting subscriber stations. Numerical evaluations illustrate that considering the CSI imperfection based on priori knowledge of the error statistics brings substantial gain to the network in terms of ergodic mutual information, and takes the MAC layer resource allocation algorithms a step ahead towards practical implementations.
A vector-hydrophone (a.k.a. acoustic vector-sensor) is composed of two or three spatially collocated but orthogonally oriented velocity-hydrophones, possibly plus a collocated pressure-hydrophone. A vector-hydrophone may form azimuth-elevation spatial beams that are invariant with respect to the sources’ frequencies, bandwidths and radial locations (i.e., in near field as opposed to the far field). This paper adopts a multiple-forgetting-factor recursive-least-squares (RLS) adaptive algorithm to a single vector-hydrophone for source tracking, without needing any prior knowledge of the source power and/or the noise powers.