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.
Traffic offloading through heterogenous small-cell networks (HSCNs) has been envisioned as a cost-efficient approach to accommodate the tremendous traffic growth in cellular networks. In this paper, we investigate an energy-efficient dual-connectivity (DC) enabled traffic offloading through HSCNs, in which small cells are powered in a hybrid manner including both the conventional on-grid power-supply and renewable energy harvested from environment. To achieve a flexible traffic offloading, the emerging DC-enabled traffic offloading in 3GPP specification allows each mobile user (MU) to simultaneously communicate with a macro cell and offload data through a small cell. In spite of saving the on-grid power consumption, powering traffic offloading by energy harvesting (EH) might lead to quality of service (QoS) degradation, e.g., when the EH power-supply fails to support the required offloading rate. Thus, to reap the benefits of the DC-capability and the EH power-supply, we propose a joint optimization of traffic scheduling and power allocation that aims at minimizing the total on-grid power consumption of macro and small cells, while guaranteeing each served MU’s traffic requirement. We start by studying a representative case of one small cell serving a group of MUs. In spite of the non-convexity of the formulated joint optimization problem, we exploit its layered structure and propose an algorithm that efficiently computes the optimal offloading solution. We further study the scenario of multiple small cells, and investigate how the small cells select different MUs for maximizing the system-wise reward that accounts for the revenue for offloading the MUs’ traffic and the cost of total on-grid power consumption of all cells. We also propose an efficient algorithm to find the optimal MU-selection solution. Numerical results are provided to validate our proposed algorithms and show the advantage of our proposed DC-enabled traffic offloading through the EH-powered small cells.
The rapidly growing energy consumption of the Internet core network has been a growing concern. In this respect, we have proposed a distributed and load adaptive energy saving router (ESR) mechanism to manage the energy consumption of green routers in our previous work. In this paper, we propose an analytical model to investigate the performance of ESR. The proposed model captures the distribution of the packet service time, the buffer size, and the packet loss probability. Under the low (resp. high) traffic load situation, our numerical results show that the ESR has the ability to save more than 40% (resp. 9%) energy. In addition to evaluating the ESR performance in terms of the energy saving ratio, rerouting probability and average delay, the models provide manufactures and and operators with guidelines for the deployment of green Internet.
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.
Software defined networking (SDN) is a promising networking paradigm for achieving programmability and centralized control in communication networks. These features simplify network management and enable innovation in network applications and services such as routing, virtual machine migration, load balancing, security, access control, and traffic engineering. The routing application can be optimized for power efficiency by routing flows and coalescing them such that the least number of links is activated with the lowest link rates. However, in practice, flow coalescing can generally overflow the flow tables, which are implemented in a size-limited and power-hungry ternary content addressable memory (TCAM). In this paper, a set of practical constraints are imposed to the SDN routing problem, namely size-limited flow table and discrete link rate constraints, to ensure applicability in real networks. Because the problem is NP-hard and difficult to approximate, a low-complexity particle swarm optimization-based and power-efficient routing (PSOPR) heuristic is proposed. Performance evaluation results revealed that PSOPR achieves more than 90% of the optimal network power consumption while requiring only 0.0045% to 0.9% of the optimal computation time in real network topologies. In addition, PSOPR generates shorter routes than the optimal routes generated by CPLEX.
The existing Ethernet networks are designed with high redundancy and over-dimensioning so they can provide reliable services during peak traffic demand periods. However, this has increased the total energy consumption and operational cost. In this paper, we propose an energy saving algorithm (ESA) to reduce the energy consumption of green routers by considering the buffer status and the traffic load. We develop a Network Simulator, version 2, (NS-2)–based simulation model for ESA to evaluate its performance with respect to real traffic traces. Performance bounds of the proposed algorithm are derived. Numerical evaluations are conducted to verify the accuracy of the simulation model against derived bounds. Performance evaluations demonstrate that the proposed algorithm outperforms candidate algorithms, thereby providing greater energy savings with an acceptable packet delay and loss. We show that the introduced delay is bounded by an upper bound that is slightly larger than half of the sleep timer. Furthermore, performance comparisons are extensive and detailed, thus providing insights into the performance of different energy saving functions considered by the candidate algorithms.
The incorporation of Cognitive Radio (CR) and Energy Harvesting (EH) capabilities in wireless sensor networks enables spectrum and energy efficient heterogeneous cognitive radio sensor networks (HCRSNs). The new networking paradigm of HCRSNs consists of EH-enabled spectrum sensors and batterypowered data sensors. Spectrum sensors can cooperatively scan the licensed spectrum for available channels, while data sensors monitor an area of interest and transmit sensed data to the sink over those channels. In this work, we propose a resource allocation solution for the HCRSN to achieve the sustainability of spectrum sensors and conserve energy of data sensors. The proposed solution is achieved by two algorithms that operate in tandem, a spectrum sensor scheduling algorithm and a data sensor resource allocation algorithm. The spectrum sensor scheduling algorithm allocates channels to spectrum sensors such that the average detected available time for the channels is maximized, while the EH dynamics are considered and PU transmissions are protected. The data sensor resource allocation algorithm allocates the transmission time, power and channels such that the energy consumption of the data sensors is minimized. Extensive simulation results demonstrate that the energy consumption of the data sensors can be significantly reduced while maintaining the sustainability of the spectrum sensors.
The smart electricity grid introduces new opportunities for fine-grained consumption monitoring. Such functionality, however, requires the constant collection of electricity data that can be used to undermine consumer privacy. In this work, we address this problem by proposing two decentralized protocols to securely aggregate the measurements of n smart meters. The first protocol is a very lightweight one, it uses only symmetric cryptographic primitives and provides security against honest-but-curious adversaries. The second protocol is public-key based and considers the malicious adversarial model; malicious entities not only try to learn the private measurements of smart meters but also disrupt protocol execution. Both protocols do not rely on centralized entities or trusted third parties to operate. Additionally, we show that they are highly scalable owning to the fact that every smart meter has to interact with only a few others, thus requiring only O(1) work and memory overhead. Finally, we implement a prototype based on our proposals and we evaluate its performance in realistic deployment settings.
The energy efficiency of wired networks has received considerable attention over the past decade due to its economic and environmental impacts. However, because of the vertical integration of the control and data planes in conventional networks, optimizing energy consumption in such networks is challenging. Software-defined networking (SDN) is an emerging networking paradigm that decouples the control plane from the data plane and introduces network programmability for the development of network applications. In this work, we propose an energy-aware integral flow-routing solution to improve the energy efficiency of the SDN routing application. We consider discreteness of link rates and pose the routing problem as a Mixed Integer Linear Programming (MILP) problem, which is known to be NP complete. The proposed solution is a heuristic implementation of the Benders decomposition method that routes additional single and multiple flows without resolving the routing problem. Performance evaluations demonstrate that the proposed solution achieves a close-to-optimal performance (within 3.27% error) compared to CPLEX on various topologies with less than 0.056% of CPLEX average computation time. Furthermore, our solution outperforms the shortest path algorithm by 24.12% to 54.35% in power savings.
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.
Due to the limited battery power of sensor nodes and harsh deployment environment, it is of fundamental importance and great challenge to achieve high energy efficiency and strong robustness in large-scale wireless sensor networks (LS-WSNs). To this end, we propose two self-organizing schemes for LS-WSNs. The first scheme is the energy-aware common neighbor (ECN) scheme, which considers the neighborhood overlap in link establishment. The second scheme is energy-aware low potential-degree common neighbor (ELDCN) scheme, which takes both neighborhood overlap in topology formation and the potential degrees of common neighbors into consideration. Both schemes generate clustering-based and scale-free-inspired LS-WSNs, which are energy-efficient and robust. However, the ELDCN scheme shows higher energy efficiency and stronger robustness to node failures because it avoids establishing links to hub-nodes with high potential connectivity. Analytical and simulation results demonstrate that our proposed schemes outperform the existing scale-free evolution models in terms of energy efficiency and robustness.
In this paper, we study resource management and allocation for Energy Harvesting Cognitive Radio Sensor Networks (EHCRSNs). In these networks, energy harvesting supplies the network with a continual source of energy to facilitate self-sustainability of the power-limited sensors. Furthermore, cognitive radio enables access to the underutilized licensed spectrum to mitigate the spectrum-scarcity problem in the unlicensed band. We develop an aggregate network utility optimization framework for the design of an online energy management, spectrum management and resource allocation algorithm based on Lyapunov optimization. The framework captures three stochastic processes: energy harvesting dynamics, inaccuracy of channel occupancy information, and channel fading. However, a priori knowledge of any of these processes statistics is not required. Based on the framework, we propose an online algorithm to achieve two major goals: first, balancing sensors’ energy consumption and energy harvesting while stabilizing their data and energy queues; second, optimizing the utilization of the licensed spectrum while maintaining a tolerable collision rate between the licensed subscriber and unlicensed sensors. Performance analysis shows that the proposed algorithm achieves a close-to-optimal aggregate network utility while guaranteeing bounded data and energy queue occupancy. Extensive simulations are conducted to verify the effectiveness of the proposed algorithm and the impact of various network parameters on its performance.
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.
Although academic dishonesty has a long history in academia, its pervasiveness has recently reached an alarming level. Academic dishonesty not only undermines the purpose of education and the assessment process but also threatens the creditability of academic records. We propose a framework for analysing students’ behaviour with respect to academic policies and honour codes. We draw an analogy between law enforcement and academic integrity enforcement and highlight similarities and differences. The proposed framework captures major determinants of academic dishonesty reported in the literature, namely detection probability, punishment severity, class average and record of academic deviance. The framework models both students’ development of nonacademic skills to improve their grades and teaching assistants’ development of detection skills, which both affect the detection probability. Our analysis demonstrates that the optimality of escalating penalties is conditional on the offenders and academic policy enforcers learning. Use-case scenarios are presented to facilitate the implementation of our results in classrooms.
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.
The above paper (Awad M. K. and Wong K. T., Recursive Least-Squares Source Tracking using One Acoustic Vector Sensor, IEEE Transactions on Aerospace and Electronic Systems, 48, 4 (Oct. 2012), 3073—3083) was published with incomplete figures. The correct figures appears below.
An acoustic vector-sensor (a.k.a. vector-hydrophone) is composed of three acoustic velocity-sensors, plus a collocated pressure-sensor, all collocated in space. The velocity-sensors are identical, but orthogonally oriented, each measuring a different Cartesian component of the three-dimensional particle-velocity field. This acoustic vector-sensor offers an azimuth-elevation response that is invariant with respect to the source’s centre frequency or bandwidth. This acoustic vector-sensor is adopted here for recursive least-squares (RLS) adaptation, to track a single mobile source, in the absence of any multi path fading and any directional interference. A formula is derived to preset the RLS forgetting factor, based on the prior knowledge of only the incident signal power, the incident source’s spatial random walk variance, and the additive noise power. The work presented here further advances a multiple-forgetting-factor (MFF) version of the RLS adaptive tracking algorithm, that requires no prior knowledge of these aforementioned source statistics or noise statistics. Monte Carlo simulations demonstrate the tracking performance and computational load of the proposed algorithms.
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.
Fair weights have been implemented to maintain fairness in recent resource allocation schemes. However, designing fair weights for multiservice wireless networks is not trivial because users’ rate requirements are heterogeneous and their channel gains are variable. In this paper, we design fair weights for opportunistic scheduling of heterogeneous traffic in orthogonal frequency division multiple access (OFDMA) networks. The fair weights determine each user’s share of rate for maintaining a utility notion of fairness. We then present a scheduling scheme which enforces users’ long term average transmission rates to be proportional to the fair weights. The proposed scheduler takes the advantage of users’ channel state information and the inherent flexibility of OFDMA resource allocation for efficient resource utilization. Furthermore, using the fair weights allows flexibility for realization of different scheduling schemes which accommodate a variety of requirements in terms of heterogeneous traffic types and user mobility. Simulation based performance analysis is presented to demonstrate efficacy of the proposed solution in this paper.
This paper presents a novel scheme for the allocation of subcarriers, rates, and power in orthogonal frequency-division multiple-access (OFDMA) networks. The scheme addresses practical implementation issues of resource allocation in OFDMA networks: the inaccuracy of channel-state information (CSI) available to the resource allocation unit (RAU) and the diversity of subscribers’ quality-of-service (QoS) requirements. In addition to embedding the effect of CSI imperfection in the evaluation of the subscribers’ expected rate, the resource-allocation problem is posed as a network utility maximization (NUM) one that is solved via decomposing it into a hierarchy of subproblems. These subproblems coordinate their allocations to achieve a final allocation that satisfies aggregate rate constraints imposed by the call-admission control (CAC) unit and OFDMA-related constraints. A complexity analysis shows that the proposed scheme is computationally efficient. In addition, performance evaluation findings support our theoretical claims: A substantial data rate gain can be achieved by considering the CSI imperfection, and multiservice classes can be supported with QoS guarantees.
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.
This paper presents a novel approach to investigate ergodic mutual information of OFDMA Selection-Decode-and-Forward (SDF) cooperative relay networks with imperfect channel state information (CSI). Relay stations are either dedicated or non-dedicated (i.e., subscriber stations assisting other subscriber stations). The CSI imperfection is modeled as an additive random variable with known statistics. Numerical evaluations and simulations demonstrate that by considering the CSI imperfection based on a priori knowledge of the estimation error statistics, a substantial gain can be achieved in terms of ergodic mutual information which makes channel adaptive schemes closer to practical implementations.
The worldwide interoperability for microwave access (WiMAX) extends the transmission rate and range of wireless communications beyond the limits of existing technologies while allowing for heterogeneous traffic transmissions. To achieve all these goals, qualified protocols for WiMAX should effectively utilize the spectrum and overcome the deficits of wireless channel while maintaining a satisfactory level of heterogeneous services for users. WiMAX supports air interfaces based on orthogonal frequency division multiplexing (OFDM) which is a robust and flexible technique for transmissions and resource allocations, respectively, over wireless channel. The basic characteristics of OFDM that mitigate the wireless channel impairments are stated in this chapter.Moreover, several resource allocation schemes for subcarrier and power allocation problem in OFDM-based networks are surveyed that provides a deep insight into the problem. Besides, a resource allocation scheme for WiMAX is presented that considers the heterogeneous requirements of WiMAX users and the utilization of scarce resources simultaneously.
A comprehensive and integrative overview (excluding ultrawideband measurements) is given of all the empirical data available from the open literature on various temporal properties of the indoor radiowave communication channel. The concerned frequency range spans over 0.8-8 GHz. Originally, these data were presented in about 70 papers in various journals, at diverse conferences, and in different books. Herein overviewed are the multipaths’ amplitude versus arrival delay, the probability of multipath arrival versus arrival delay, the multipath amplitude’s temporal correlation, the power delay profile and associated time dispersion parameters (e.g., the RMS delay spread and the mean delay), the coherence bandwidth, and empirically ldquotunedrdquo tapped-delay-line models. Supported by the present authors’ new analysis, this paper discusses how these channel-fading metrics depend on the indoor radiowave propagation channel’s various properties, (e.g., the physical environment, the floor layout, the construction materials, the furnishing’s locations and electromagnetic properties) as well as the transmitted signal’s carrier-frequency, the transmitting-antenna’s location, the receiving-antenna’s location, and the receiver’s detection amplitude threshold.
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.