This paper presents a machine learning based handover management scheme for LTE to improve the Quality of Experience (QoE) of the user in the presence of obstacles. We show that, in this scenario, a state-of-the-art handover algorithm is unable to select the appropriate target cell for handover, since it always selects the target cell with the strongest signal without taking into account the perceived QoE of the user after the handover. In contrast, our scheme learns from past experience how the QoE of the user is affected when the handover was done to a certain eNB. Our performance evaluation shows that the proposed scheme substantially improves the number of completed downloads and the average download time compared to state-of-the-art. Furthermore, its performance is close to an optimal approach in the coverage region affected by an obstacle.

Cognitive networking paradigms may help meet the challenges of operating complex wireless communications networks. In this paper, we contrast the neural network (NN) and the Bayesian network (BN) models to extract information from real-time observations and optimize network performance. In particular, we apply these two models to the problem of call admission control (CAC) for a long term evolution (LTE) system. We simulate a realistic LTE scenario with mobility in ns-3 and we select the most relevant features that can be observed by the base station. Then, we design two new CAC schemes that autonomously learn the network behavior from the observation of the selected features. Furthermore, we propose a performance comparison among these two schemes and a state-of-the-art CAC scheme, showing that the NN and the BN schemes are very promising solutions for CAC in LTE systems.

Packet scheduler at the medium access control (MAC) layer is essential to improve radio resource utilization in the Long Term Evolution (LTE) network. The MAC scheduler allocates resource blocks to user terminals (UEs) according to the priority metric, which varies in different scheduling algorithms. Although there have been many studies on the performance of LTE schedulers at the MAC layer, it is interesting to evaluate the impact of different LTE MAC schedulers on the transport layer, particularly on the transmission control protocol (TCP). In this study, we implement three mainstream LTE MAC schedulers in Network Simulator-3 (NS-3), namely, maximum throughput (MT), blind equal throughput (BET) and proportional fair (PF). Extensive simulations are conducted to examine the different TCP throughput achieved with the frequency domain version and the time domain version of these schedulers in a vehicular environment. The performance difference is attributed to important factors such as the resource allocation granularity, channel-awareness in scheduling, and the number of UEs.

In this paper, we present an open source simulation model for the ns-3 simulator that allows the simulation of LTE handover scenarios, to support the design and evaluation of handover decision algorithms. In addition to the features supported by other publicly available open source LTE simulators, such as mobility, propagation, channel, PHY and MAC modeling, our model provides additional features such as the modeling of the RRC protocol, the MAC random access procedure, and the X2, S1 and S11 interfaces. On top of these features, the LTE handover procedure is modeled, following closely the 3GPP specifications. We present in detail the characteristics of each component of the simulation model, highlighting the modeling assumptions that were made in each case.

In this paper we consider the use of inter-packet encoding at the source node to improve the delay performance of data block transmissions in a multipath communication scenario. First we propose a formal derivation to obtain the coding ratio and the per-path packet allocation that minimizes the data block delay. Then we validate this model through an experimental campaign carried out in a testbed in close-to-real conditions. The proposed encoding scheme is also compared with fixed multiplexing criteria previously proposed in the literature for multipath communications scenarios. Our evaluation shows the benefits of the inter-packet encoding specially when packets travelling through the paths experience unbalanced service rates and heavy tail delays.

Nowadays IEEE 802.11 WLANs are widely deployed; in spite of this, the issue of designing an efficient and practical Access Point selection schemes that can provide the best throughput performance in a variety of link conditions is still open. In this paper we present a Cognitive AP selection scheme that allows the mobile station to learn from its past experience how to select the best AP. In our proposal the mobile station collects measurements regarding the past link conditions and throughput performance, and a cognitive engine based on a Neural Network trained on this data drives the AP selection process. Our performance evaluation shows that the proposed scheme has very good performance in a variety of scenarios, as opposed to other algorithms previously proposed in the literature which perform well only in specific cases and cannot address the non-idealities typical under real conditions.

In this work we present a link abstraction model for the simulation of downlink data transmission in LTE networks. The purpose of this model is to provide an accurate link performance metric at a low computational cost by relying solely on the knowledge of the SINR and of the modulation and coding scheme. To this aim, the model combines Mutual Information-based multi-carrier compression metrics with Link-Level performance curves matching, to obtain lookup tables that express the dependency of the Block Error Rate on the SINR values and on the modulation and coding scheme being used. In addition, we propose a 3GPP-compliant Channel Quality Indicator evaluation procedure, based on the proposed Link Abstraction Model, to be used as part of the LTE Adaptive Modulation and Coding mechanisms. Finally, we discuss how these contributions have been tested, validated and integrated in the ns-3 simulator. The link abstraction model described in this paper has been included in the official ns-3 distribution since release 3.14.

In this paper we address the problem of provisioning Quality of Service (QoS) to Voice over IP applications in a Wireless LAN scenario based on the IEEE 802.11 standard. We propose the use of a Cognitive Network approach to design a Call Admission Control (CAC) scheme, according to which each user stores relevant information on its past network experience and then uses such information to build a Bayesian Network (BN), a probabilistic graphical model to describe the statistical relationships among network parameters. The BN is exploited to predict the voice call quality, as a function of the Link Layer conditions in the particular scenario considered. Such prediction on the present and future values of the QoS provided is directly exploited to design the cognitive CAC scheme, which is shown to significantly outperform state of the art CAC techniques in a realistic scenario.

In this paper we present a new simulation module for ns-3 aimed at the simulation of LTE networks. This module has been designed with a product-oriented perspective in order to allow LTE equipment manufacturers to test RRM/SON algorithms in a simulation environment before they are deployed in the field. First, we describe the design of our simulation module, highlighting its novel aspects. Subsequently, we discuss the testing methodology that we adopted to validate its output. Finally, we present some experimental result to assess its performance in terms of execution time and memory usage.

Call Admission Control (CAC) is recognized as one of the key strategies to achieve satisfactory QoS support for VoIP over IEEE 802.11 WLANs. However, most of the prior CAC solutions for VoIP over WLAN are centralized, and the few distributed CAC schemes proposed so far do not account for issues such as the loss of channel time due to medium contention and the coexistence of VoIP traffic with background traffic such as TCP data flows. In this paper, we describe a distributed CAC scheme which aims at addressing these issues by leveraging on channel monitoring techniques, thus being readily implementable in today's consumer devices. The proposed scheme is thoroughly evaluated by means of testbed experiments as well as network simulations in different scenarios, and it is shown to yield a better CAC performance with respect to prior solutions.

We present a framework, based on Markov models, for the analysis of error control techniques in video transmission over wireless channels. We focus on retransmission-based techniques, which require a feedback channel but also enable to perform adaptive error control. Traditional studies of these methodologies usually consider a uniform stream of data packets. Instead, video transmission poses the non-trivial challenge that the packets have different sizes, and, even more importantly, are incrementally encoded; thus, a carefully tailored model is required. We therefore proceed on two different sides. First, we consider a low-level description of the system, where two main inputs are combined, namely, a video packet generation process and a wireless channel model, both described by Markov Chains with a tunable number of states. Secondly, from a highlevel perspective, we represent the whole system evolution with another Markov Chain describing the error control process, which can feed the packet generation process back with retransmissions. The framework is able to evaluate hybrid automatic repeat request with selective retransmission, but can also be adapted to study pure automatic repeat request or forward error correction schemes. In this way, we are able to comparatively evaluate different solutions for video transmission, as well as to quantitatively assess their performance trends in a variety of scenarios. Thus, our framework can be used as an effective tool to understand the behavior of error control techniques applied to video transmission over wireless, and eventually identify design guidelines for such systems.

In this paper we propose a Dynamic Spectrum Access scheme which allows the users to opportunistically and efficiently access the channels available for communications. It addresses the following important aspects of opportunistic spectrum access: 1) implementation of the control channel, 2) multi-channel medium access control, 3) primary user detection, and 4) secondary reuse of spectrum unused by primary users. The main features of the scheme are that it is completely distributed, it does not need dedicated spectrum resources for control purposes, but rather leverages on a virtual control channel which is implemented using Network Coding techniques, and it exploits a cooperative detection strategy to identify unused spectrum. Due to these aspects, our proposal represents a significant improvement with respect to existing Dynamic Spectrum Access solutions. We carry out an evaluation study of the proposed solution to assess its performance with respect to different system and scenario parameters; the obtained results show that the proposed solution is feasible, capable of providing satisfactory performance, and suitable for implementation in real systems.

In this paper, we present an application of the cognitive networking paradigm to the problem of dynamic channel selection in infrastructured wireless networks. We first discuss some of the key challenges associated with the cognitive control of wireless networks. Then we introduce our solution, in which a Neural Network-based cognitive engine learns how environmental measurements and the status of the network affect the performance experienced on different channels, and can therefore dynamically select the channel which is expected to yield the best performance for the mobile users. We carry out performance evaluation of the proposed system by experimental measurements on a testbed implementation; the obtained results show that the proposed cognitive engine is effective in achieving performance enhancements with respect to state-of-the-art channel selection strategies.

Traffic sensing and characterization is an important building block of cognitive networking systems; however, it is very challenging in multi-channel multi-radio wireless networks. The contributions of this paper include the following: (i) a discussion of packet sampling for traffic sensing in multi-channel wireless networks, (ii) a comparison of various time-based sampling strategies using the Kullback-Leibler Divergence (KLD) measure, (iii) a study of the effect of the sampling parameters on the accuracy of the sampling strategies, (iv) the proposal of a new metric (Traffic Intensity) which estimates the busyness of channels by taking into consideration not only the successfully received packets but also corrupt or broken packets, and (v) some preliminary results on the characterization of a campus 802.11 network environment in a spatio-temporal fashion.

In this paper we propose a Multi-Channel Medium Access scheme with the aim to enable efficient Dynamic Spectrum Access. Two are the remarkable features of our solution: first, it is completely distributed, and second, it does not need dedicated spectrum resources for control purposes, but rather it leverages on a virtual control channel which is implemented by having users exchange control information whenever they meet in a particular channel, and by retrieving the control information using Network Coding techniques. We carry out an evaluation study of the proposed solution to assess its performance with respect to different system and scenario parameters; the obtained results show that the proposed solution is feasible and suitable for implementation in real Dynamic Spectrum Access systems.

We consider a scenario in which wireless users want to connect to the Internet using one of several available network access opportunities, possibly using different radio technologies. We propose a distributed cognitive network access scheme with the aim of providing the best quality of service with respect to both radio link and core network performance and user application requirements. Knowledge of the service quality experienced by active connections is shared, and prospective users use Fuzzy Logic techniques to process cross-layer communication quality metrics and to estimate the expected transport-layer performance. These estimates are compared to the Quality of Service requirements of the application using Fuzzy Decision Making techniques to choose the most suitable access opportunity. This scheme naturally fits into the recently proposed Cognitive Network paradigm in that it defines a cognition process leveraging on end-to-end and cross-layer performance evaluation techniques as well as information sharing among users; moreover, it offers a significant amount of flexibility and extensibility, thanks to its modularity and its independence from the particular technology and application being used. The proposed scheme is shown to outperform state-of-the-art solutions in several multi-technology and multi-application scenarios, while at the same time achieving similar performance to application-specific omniscient schemes that we introduce in this paper as a benchmark.

The estimation of the communication performance achievable with respect to environmental factors and configuration parameters plays a key role in the optimization process performed by a Cognitive Radio according to the original definition by Mitola [1]. In this paper we propose the use of Multilayered Feedforward Neural Networks as an effective technique for real-time characterization of the communication performance which is based on measurements carried out by the device and therefore offers some interesting learning capabilities.

In this paper we present APOS, a method for dynamically adapting the parameters of IEEE 802.11 g to the estimated system state, with the aim of enhancing the quality of a voice communication between a mobile station and a remote peer node. The system state is estimated based on a number of counters that are collected by the MAC layer of the mobile station, regarding the number of successful and unsuccessful transmission/reception events, channel busy periods and idle slots. These statistics are processed to estimate the collision probability and the signal to noise ratio at the receiver side. Hence, a mathematical model is used to get the expected end-to-end network performance in terms of throughput, delay and packet error rate, for different settings of some PHY and MAC parameters, such as the modulation/coding scheme and the retransmission limit. The setting that is estimated to maximize the quality of service for the end user is then selected. Unlike other optimization mechanisms proposed in literature, APOS is totally stand-alone and standard compliant. In fact, APOS makes use of local information that can be collected from the Network Interface Card, and no explicit interactions with the other devices in the network is required.

Rate adaptation for 802.11 has been deeply investigated in the past, but the problem of achieving optimal rate adaptation with respect not only to channel-related errors but also to contention-related issues (i.e., collisions and variations in medium access times) is still unsolved. In this paper we address this issue by proposing (1) a practical definition of the medium status in a multi-user 802.11 scenario in terms of channel errors, MAC collisions and packet service times, and a method for its estimation based on measurements; (2) an analytical model of the goodput performance as a function of the Medium Status; (3) a rate adaptation algorithm, called goodput optimal rate adaptation (GORA), which is based on this model. Unlike other rate adaptation schemes proposed in literature, which require either modifications to the IEEE 802.11 standard or cooperation among nodes, GORA is totally stand-alone and standard compliant. In fact, the Medium Status Estimation used by GORA is obtained by using standard MAC counters that are commonly collected by commercial MAC drivers, and no explicit interactions with the other devices in the network is required. Therefore, GORA offers the advantage of being readily deployable on real devices. The performance of GORA is evaluated through NS2 simulations which reveal that, as expected, GORA outperforms other well- known rate adaptation algorithms in several scenarios and can be used as a new reference benchmark.

Interest in underwater acoustic networks has grown rapidly with the desire to monitor the large portion of the world covered by oceans. Fundamental differences between underwater acoustic propagation and terrestrial radio propagation may call for new criteria for the design of networking protocols. In this paper, we focus on some of these fundamental differences, including attenuation and noise, propagation delays, and the dependence of usable bandwidth and transmit power on distance (which has not been extensively considered before in protocol design studies). Furthermore, the relationship between the energy consumptions of acoustic modems in various modes (i.e., transmit, receive, and idle) is different than that of their terrestrial radio counterparts, which also impacts the design of energy-efficient protocols. The main contribution of this work is an in-depth analysis of the impacts of these unique relationships. We present insights that are useful in guiding both protocol design and network deployment. We design a class of energy-efficient routing protocols for underwater sensor networks based on the insights gained in our analysis. These protocols are tested in a number of relevant network scenarios, and shown to significantly outperform other commonly used routing strategies and to provide near optimal total path energy consumption. Finally, we implement in ns2 a detailed model of the underwater acoustic channel, and study the performance of routing choices when used with a simple MAC protocol and a realistic PHY model, with special regard to such issues as interference and medium access.

The search for the ultimate architecture for crosslayer optimization in cognitive radio networks is characterized by challenges such as modularity, scalability, complexity and interpretability constraints. In this paper we propose Fuzzy Logic as an effective means of meeting these challenges, as far as both knowledge representation and control implementation are concerned.

In this paper, we consider a scenario in which wireless users want to connect to the Internet using one of several available network access opportunities, possibly using different radio technologies. We propose a distributed cognitive network access scheme with the aim of providing the best quality of service with respect to both radio link and core network performance and user application requirements. Knowledge of the service quality experienced by active connections is shared, and prospective users use Fuzzy Logic techniques to process cross-layer communication quality metrics and to estimate the expected transport-layer performance. These estimates are compared to the Quality of Service requirements of the application, and Fuzzy Decision Making is used to choose the most suitable access opportunity. The proposed scheme is compared by simulation to commonly used algorithms as well as omniscient decision schemes in a variety of scenarios, and is shown to have superior performance and much better flexibility.

A new transmission rate control for wireless multimedia streaming is introduced using RTCP feedback about the status of the streaming client buffer. Here, extended RCTP feedback is used as described within the 3GPP PSS streaming standard. Compared with algorithms from the literature buffer overflows are always avoided and higher buffer utilization is achieved.