Cross-layer Optimization Framework Research Articles

A Cross-Layer Optimization Framework for Index-Coded NOMA in Cache-Aided F-RANs

This paper studies cached-aided multicast transmissions in fronthaul fog radio access networks (F-RANs). While index coding and cached-aided non-orthogonal multiple access (NOMA) are techniques commonly used for utilizing cache contents to save transmit energy, there is a lack of general framework to integrate them. This work proposes index-coded NOMA and dynamic coded-NOMA to investigate energy performance of the integration of index coding and NOMA under whole-file and subfile caching, respectively. Besides, dynamic cache space allocation is applied to both caching schemes, which allocates cache sizes to the fog access points (F-APs) according to their large-scale channel conditions. For index-coded NOMA, the general grouping problem is proved to be NP-hard and optimal solutions for some special cases are given. Furthermore, efficient heuristic grouping algorithms are proposed. For dynamic coded-NOMA, we obtain the closed-form minimum transmit energy. The numerical results validate the good performance of our proposed algorithms. Index-coded NOMA and dynamic coded-NOMA have comparable performance and both of them save much energy than the existing schemes. When there are 12 F-APs under small-cache scenarios, index-coded NOMA saves energy by 70.3% compared to traditional NOMA.

Improve communication network issues for multi UAV

In this paper, we address the problems of power efficiency, real-time routing, and rate allocation in flying ad-hoc networks (FANETs). The study designed a cross-layer optimization framework with delay constraints to solve the proposed problems and then used Lagrangian relaxation and dual decomposition methods to decompose the joint optimization problem into several lower complexity sub-problems. Moreover, Case model 3 is employed that allows each relay node to complete the optimization of different sub-problems through local information. The results show that the proposed algorithm can effectively increase the network throughput and reduce the packet-timeout ratio and power efficiency.

Cross-Layer Optimization for Heterogeneous MU-MIMO/OFDMA Networks.

To enable the full benefits from MU-MIMO (Multiuser-Multiple Input Multiple Output) and OFDMA (Orthogonal Frequency Division Multiple Access) to be achieved, the optimal use of these two technologies for a given set of network resources has been investigated in a rich body of literature. However, most of these studies have focused either on maximizing the performance of only one of these schemes, or have considered both but only for single-hop networks, in which the effect of the interference between nodes is relatively limited, thus causing the network performance to be overestimated. In addition, the heterogeneity of the nodes has not been sufficiently considered, and in particular, the joint use of OFDMA and MU-MIMO has been assumed to be always available at all nodes. In this paper, we propose a cross-layer optimization framework that considers both OFDMA and MU-MIMO for heterogeneous wireless networks. Not only does our model assume that the nodes have different capabilities, in terms of bandwidth and the number of antennas, but it also supports practical use cases in which nodes can support either OFDMA or MU-MIMO, or both at the same time. Our optimization model carefully takes into account the interactions between the key elements of the physical layer to the network layer. In addition, we consider multi-hop networks, and capture the complicated interference relationships between nodes as well as multi-path routing via multi-user transmissions. We formulate the proposed model as a Mixed Integer Linear Programming (MILP) problem, and initially model the case in which each node can selectively use either OFDMA or MU-MIMO; we then extend this to scenarios in which they are jointly used. As a case study, we apply the proposed model to sum-rate maximization and max–min fair allocation, and verify through MATLAB numerical evaluations that it can take appropriate advantage of each technology for a given set of network resources. Based on the optimization results, we also observe that when the two technologies are jointly used, more multi-user transmissions are enabled thanks to flexible resource allocation, meaning that greater use of the link capacity is achieved.

The throughput optimization for wireless sensor networks adopting interference alignment and successive interference cancellation

Interference management has always been a research hotspot in the field of wireless communications. Currently, researches based on interference management mostly focus on an interference management technology, and few studies have combined multiple interference management technologies. This paper proposes an interference management technology that combines Interference Alignment (IA) and Successive Interference Cancellation (SIC) technologies. First, we determine the routing path of the session with the goal to minimize hops and interference and establish the IA-SIC mathematics model in multi-hop networks. Based on this model, a cross-layer optimization framework for multi-hop networks is developed. The goal is to make full use of the advantages of IA and SIC to improve the end-to-end data transmission rate of multi-hop sessions. To evaluate the performance of our algorithm in multi-hop networks, we compare the performance of the network using IA-SIC with that without SIC. Simulation results show that using IA-SIC can significantly increase the throughput of the communication sessions.

Joint source coding rate allocation and flow scheduling for data aggregation in collaborative sensing networks

In collaborative sensing networks such as WSNs (Wireless Sensor Networks), due to overlapping converge areas among neighbor nodes, they may percept a large number of similar or identical data, which incurs sensing data redundancy and unnecessary energy consumption. Slepian-wolf theorem based source coding is an effective method to reduce redundancy in data aggregation. However, the exponential growth of constraints prevent the method from practical application. Therefore, in this paper, we propose a cross-layer optimization framework to solve data aggregation problem by jointly considering optimal source encoding rate and flow scheduling. By proving the convex of constraints of Spelian-Wolf theorem, we relax original constraints so that the optimal encoding rate scheme can be adopted. The relaxation makes the optimization problem feasible. Furthermore, we employ dual decomposition to separate the original problem into two sub-problems. By solving the two subproblem distributedly, we provide optimal encoding rate allocation for each node and flow scheduling for each link. Simulation results demonstrate that our framework can reduce data redundancy and network traffic significantly compared to the exiting algorithms.

A Cross-Layer Optimization Framework for Integrated Optical Switches in Data Centers

The advancement of silicon photonics promises integrated optical switches to provide high-bandwidth, low-latency, and low-power communications in data centers. An optical switch’s loss limits its scale and affects the energy efficiency of the switch system. In this paper, we present cross-layer optical switch optimization (CLOSO), a cross-layer optimization (CLO) framework, based on not only photonic device models at the physical layer but also optical switch models at the fabric layer. With the proposed framework, optimal losses of optical switches can be evaluated efficiently, and the corresponding losses and design parameters of photonic devices can be obtained. Using CLOSO, we optimize four categories of integrated optical switches, Crossbar, PILOSS, DRAGON, and FODON, and compare them regarding their optimal worst-case loss with variation of the switch scale and data rate of signals. Furthermore, system-level evaluations of the optimized optical switches are performed, demonstrating a significant improvement of energy efficiency from the CLO. For instance, CLOSO helps to reduce the energy consumption of a 64-port DRAGON and FODON to as low as 6 pJ/bit and that of a 128-port DRAGON and FODON to as low as 10 pJ/bit. The investigation of 128-port switches also shows the necessity of adaptive power control on lasers for high-radix integrated optical switches. Through quantitative analyses and comparisons, CLOSO shows the capability of facilitating initial design exploration of optical switches and paves the way to fair evaluations and comparisons of switch systems in data centers.

Joint Cooling and Server Control in Data Centers: A Cross-Layer Framework for Holistic Energy Minimization

In this paper, we try to address the issue of holistic energy minimization in data centers by designing a cross-layer optimization framework. We formulate a mixed integer nonLinear programming problem named data center holistic energy minimization problem, and develop an efficient JOINT algorithm to solve it. JOINT cuts energy consumption via collaboration of system components across different control layers, and dynamically configures system parameters according to the current workload. To evaluate the performance of the proposed framework, we conduct extensive simulations against synthetic and real-world workload traces. Experimental results suggest that our cross-layer framework is effective in data center holistic energy reduction.

A cross-layer optimization framework for congestion and power control in cognitive radio ad hoc networks under predictable contact

In this paper, we investigate the cross-layer optimization problem of congestion and power control in cognitive radio ad hoc networks (CRANETs) under predictable contact constraint. To measure the uncertainty of contact between any pair of secondary users (SUs), we construct the predictable contact model by attaining the probability distribution of contact. In particular, we propose a distributed cross-layer optimization framework achieving the joint design of hop-by-hop congestion control (HHCC) in the transport layer and per-link power control (PLPC) in the physical layer for upstream SUs. The PLPC and the HHCC problems are further formulated as two noncooperative differential game models by taking into account the utility function maximization problem and the linear differential equation constraint with regard to the aggregate power interference to primary users (PUs) and the congestion bid for a bottleneck SU. In addition, we obtain the optimal transmit power and the optimal data rate of upstream SUs by taking advantage of dynamic programming and maximum principle, respectively. The proposed framework can balance transmit power and data rate among upstream SUs while protecting active PUs from excessive interference. Finally, simulation results are presented to demonstrate the effectiveness of the proposed framework for congestion and power control by jointly optimizing the PLPC-HHCC problem simultaneously.

Cooperative Interference Neutralization in Multi-Hop Wireless Networks

Interference neutralization (IN) is regarded as a promising interference management techniques for multi-hop wireless networks. Yet most existing results of IN are limited to two-hop networks such as the relay-aided cellular network. Little progress has been made so far in the exploration of IN in generic multi-hop (more than two hops) networks. This paper aims to bridge this gap by developing an optimization framework for IN in a generic multi-hop network with the objective of maximizing the end-to-end throughput of multiple coexisting communication sessions. We first derive a mathematical model for IN in a special one-hop network to characterize the capability of IN, and then generalize this model to a multi-hop network. Based on the IN model, we develop a cross-layer optimization framework for a multi-hop network with the objective of fully translating the benefits of IN to the end-to-end throughput of the multi-hop sessions. To evaluate the performance of IN in multi-hop networks, we compare its performance against the case where IN is not employed. Simulation results show that the use of IN can significantly (more than 50%) increase the session throughput and, more notably, the throughput gain of IN increases with the node density and traffic intensity in the network.

Cross-Layer Optimization for Ultra-Reliable and Low-Latency Radio Access Networks

In this paper, we propose a framework for cross-layer optimization to ensure ultra-high reliability and ultra-low latency in radio access networks, where both transmission delay and queueing delay are considered. With short transmission time, the blocklength of channel codes is finite, and the Shannon capacity cannot be used to characterize the maximal achievable rate with given transmission error probability. With randomly arrived packets, some packets may violate the queueing delay. Moreover, since the queueing delay is shorter than the channel coherence time in typical scenarios, the required transmit power to guarantee the queueing delay and transmission error probability will become unbounded even with spatial diversity. To ensure the required quality-of-service (QoS) with finite transmit power, a proactive packet dropping mechanism is introduced. Then, the overall packet loss probability includes transmission error probability , queueing delay violation probability , and packet dropping probability . We optimize the packet dropping policy, power allocation policy, and bandwidth allocation policy to minimize the transmit power under the QoS constraint. The optimal solution is obtained, which depends on both channel and queue state information. Simulation and numerical results validate our analysis, and show that setting the three packet loss probabilities as equal causes marginal power loss.

Adaptive resource allocation framework for user satisfaction maximization in multi-service wireless networks

In order to capture and maintain a representative share of the wireless communication market, effective ways to manage the scarce physical resources of cellular networks are fundamental for cellular network operators. In this context, this paper proposes an adaptive Radio Resource Allocation algorithm that targets the user satisfaction maximization in cellular networks with multiple services. The proposed algorithm is mathematically derived from a utility-based cross-layer optimization framework and employs user weights as well as an innovative service weight that is adapted to meet the satisfaction target of the most prioritized service. Furthermore, the proposed algorithm is scalable to several services classes and can be employed in the current and future generations of wireless systems that guarantee orthogonality among the allocable resources. The performance evaluation is conducted in realistic scenarios of the downlink of an Orthogonal Frequency Division Multiple Access based cellular network serving video and Constant Bit Rate flows, where we assume imperfect Channel State Information at the transmitter. Significant gains in the joint system capacity were obtained, demonstrating that the adaptability and service prioritization allow the accomplishment of simultaneously maximizing the user satisfaction for distinct services.

Cross-Layer Resource Allocation for Scalable Video Over OFDMA Wireless Networks: Tradeoff Between Quality Fairness and Efficiency

This work addresses the tradeoff between quality fairness and system efficiency for scalable video delivery to multiple users over OFDMA wireless networks. We consider a cross-layer optimization framework seeking to maximize the sum-PSNR corresponding to average user rates, subject to relaxed PSNR-fair constraints. More specifically, a pure quality-fairness (PF) problem is solved first to determine the maximum PSNR value obtained by imposing the same PSNR level to all users. Next the constraints in the PF problem are relaxed by allowing the relative difference between the PSNR of each video and the PF PSNR value to be within some range $[0,\sigma ]$ . Thus, the parameter $\sigma$ controls the tradeoff between quality fairness and system efficiency. The PF problem is equivalent to the quality fairness problem proposed by Cicalo and Tralli, which was solved using a vertical decomposition approach. Further, we convert the optimization problem with the relaxed fairness constraints into a convex problem and solve it using established techniques. Our simulation results show that by varying the value of $\sigma$ , a wide range, densely populated, of trade-off points between quality fairness and efficiency can be achieved. Additionally, a subjective quality assessment reveals that while the maximum efficiency scheme (ME), i.e., when $\sigma =\infty$ , may compromise the quality of the high demanding videos, the PF scheme may sacrifice the quality of the low demanding videos. On the other hand, by providing a trade-off between PF and ME, the proposed scheme has the potential of finding a middle ground where all users are satisfied.

QoE-Driven Cross-Layer Downlink Scheduling for Heterogeneous Traffics Over 4G Networks

With the soaring demands for high speed data communication, as well as transmission of various types of services with different requirements over cellular networks, having a decent radio resource management is considered vital in Long Term Evolution (LTE) system. In particular, satisfying the quality of service (QoS) requirements of different applications is one of the key challenges of radio resource management that needs to be dealt by the LTE system. In this paper, we propose a cross-layer design scheme that jointly optimizes three different layers of wireless protocol stack, namely application, Medium Access Control, and physical layer. The cross-layer optimization framework provides efficient allocation of wireless resources across different types of applications (i.e., real-time and non real-time) run by different users to maximize network resource utilization and user-perceived QoS, or also known as Quality of Experience (QoE). Here, Mean Opinion Score is used as a unified QoE metric that indicates the user-perceived quality for real-time or multimedia services notably video applications. Along with multimedia services, the proposed framework also takes care of non-real-time traffic by ensuring certain level of fairness. Our simulation, applied to scenarios where users simultaneously run different types of applications, confirms that the proposed QoE-oriented cross-layer framework leads to significant improvement in terms of maximizing user-perceived quality as well as maintaining fairness among users.

On the integration of SIC and MIMO DoF for interference cancellation in wireless networks

Recent advances in MIMO degree-of-freedom (DoF) models allowed MIMO research to penetrate the networking community. Independent from MIMO, successive interference cancellation (SIC) is a powerful physical layer technique used in multi-user detection. Based on the understanding of the strengths and weaknesses of MIMO DoF and SIC, we propose to have DoF-based interference cancellation (IC) and SIC help each other so that (i) precious DoF resources can be conserved through the use of SIC and (ii) the stringent SINR threshold criteria can be met through the use of DoF-based IC. In this paper, we develop the necessary mathematical models to realize the two ideas in a multi-hop wireless network. Together with scheduling and routing constraints, we develop a cross-layer optimization framework with joint DoF IC and SIC. By applying the framework on a throughput maximization problem, we find that SIC and DoF IC can indeed work in harmony and achieve the two ideas that we propose.

Proportional Fairness-Based Resource Allocation for LTE-U Coexisting With Wi-Fi

To further boost the performance of LTE to meet the ever-increasing mobile traffic demand in a cost-effective way, applying LTE in unlicensed spectrum, known as LTE-U technology, is considered as a promising complementary solution for achieving the ultra-capacity foreseen in 5G and beyond. In the unlicensed spectrum, LTE-U will share the channel with other unlicensed networks, e.g., Wi-Fi. However, the centralized control architecture of LTE networks is inherently different from the distributed channel access of Wi-Fi network, which poses great challenges to achieve fair coexistence of the two networks. To this end, in this paper, we propose a cross-layer proportional fairness (PF)-based framework to jointly optimize the protocol parameters of the medium access control layer and physical layer of an LTE-U network. Specifically, to achieve throughput-oriented PF between the two heterogeneous networks, the cross-layer optimization framework can be decoupled into a device number weighted time occupation ratio-oriented PF optimization problem and a channel-power allocation-based instantaneous transmission rate-oriented PF optimization problem. Given that LTE-U base stations adopt a listen-before-talk-based channel access scheme, the interactions between the LTE-U and the Wi-Fi networks are modeled by two interactive Markov chains. The effectiveness and the superior performance of the proposed cross-layer PF-based optimization framework are demonstrated and verified by simulations.

Distributed and Dynamic Resource Management for Wireless Service Delivery to High-Speed Trains

With the fast development of high-speed railway (HSR), how to provide high-quality and cost-effective wireless services for HSR users has attracted increasing attention in recent years. A key issue is to design an efficient resource management scheme for wireless service delivery between the train and the ground. In this paper, we first provide an overview of the existing resource management schemes and some unsolved challenges are identified. To address these challenges, a cross-layer optimization framework is developed for facilitating the design and optimization of dynamic resource management. Then, the resource management problem is formulated as a stochastic optimization problem, which jointly considers the quality-of-service requirements and dynamic characteristics of HSR wireless communications. The stochastic network optimization theory is applied to transform the intractable stochastic optimization problem into a tractable deterministic optimization problem, which can be further decomposed into two separate subproblems: admission control and resource allocation. A fully distributed admission control scheme is proposed for the admission control subproblem, and a cooperative distributed resource allocation scheme is developed for the mixed-integer resource allocation subproblem with guaranteed global optimality. Finally, a distributed dynamic resource management algorithm is proposed to solve the original stochastic optimization problem with high reliability and robustness against central node failure. The performance of the proposed algorithm is analyzed theoretically and further validated by numerical simulations under realistic conditions for HSR wireless communications.

A Cross-Layer Optimization Framework for Energy Efficiency in Wireless Sensor Networks

We consider the extension of network lifetime of battery driven wireless sensor networks by splitting the sensing area into uniform clusters and implementing heterogeneous modulation schemes at different members of the clusters. A cross-layer optimization has been proposed to reduce total energy expenditure of the network; at network layer, routing is done through uniform clusters; at MAC layer, each sensor node of the cluster is assigned fixed or variable time slots and at physical layer different member of the clusters is assigned different modulation techniques. MATLAB simulation proved substantial network lifetime gains.

A cross-layer optimization framework for joint channel assignment and multicast routing in multi-channel multi-radio wireless mesh networks

ABSTRACTExisting literature on multicast routing protocols in wireless mesh networks (WMNs) from the view point of the links involved in routing are divided into two categories: schemes are aimed at multicast construction with minimal interference which is known as NP hard problem. In contrast, other methods develop network-coding-based solutions with the main objective of throughput maximization, which can effectively reduce the complexity of finding the optimal routing solution from exponential to polynomial time. The proposed framework in this paper is placed in the second category. In multi-channel multi-radio WMNs (MCMR WMNs), each node is equipped with multiple radios, each tuned on a different channel. In this paper, for the first time, we propose a cross-layer convex optimization framework for joint channel assignment and multicast throughput maximization in MCMR WMNs. The proposed method is composed of two phases: in the first phase, using cellular learning automata, channels are assigned to the links established between the radios of the nodes in a distributed fashion such that the minimal interference coefficient for each link is provided. Then, the resultant channel assignment scheme is utilized in the second phase for throughput maximization within an iterative optimization framework based on Lagrange relaxation and primal problem decomposition. We have conducted many experiments to contrast the performance of our solution against many representative approaches.

Advanced Technique for Improved Mobile Multimedia Communication Services

The proliferation of smartphones is associated with the development of multimedia applications. Mobile multimedia applications involve audio, data, speech, image, video processing and distribution of over mobile platform. However, compressed media packets are vulnerable to channel errors, thus making it difficult to sustain good perceived video quality performance within limited resources. In this research work, the weight length and impact of different video packets are analyzed. Based on the analysis an advanced technique to enhance mobile multimedia communication services is proposed. The technique involves cross-layer optimization framework, utilizing Network Abstraction Layer Units Length and flexibility of IP-based mobile network in exchanging network information for error protection of sensitive media packets. The simulation results carried out with compatible multiple media streams of different priority levels and Network Abstraction Layer Units Length show overall significant improvement in the received media services.

Cross-Layer Optimization for Multi-Hop Wireless Networks With Successive Interference Cancellation

The classical approach to interference management in wireless medium access is based on avoidance. Recently, there is a growing interest in exploiting interference (rather than avoiding it) to increase network throughput. This was made possible by a number of advances at the physical layer. In particular, the so-called successive interference cancellation (SIC) scheme appears very promising, due to its ability to enable concurrent receptions from multiple transmitters as well as interference rejection. Although SIC has been extensively studied as a physical layer technology, its research and advances in the context of multi-hop wireless network remain limited. In this paper, we aim to close this gap by offering a systematic study of SIC in a multi-hop wireless network. After gaining a fundamental understanding of SIC’s capability and limitation, we propose a cross-layer optimization framework for SIC that incorporates variables at physical, link, and network layers. We use numerical results to affirm the validity of our optimization framework and give insights on how SIC behaves in a multi-hop wireless network.