WNC3 2010

The simultaneous relay channel with collocated relay and destination nodes is investigated. This models the scenario in which the source user is unaware of the channel controlling the communication but it knows the set of all possible relay channels. Our primary focus is the case where the relay node is physically near to the destination so that Compress-and-Forward (CF) coding is the adequate cooperative strategy. A broadcasting scheme for flexible user cooperation is developed. This enables the encoder to send part of the information regardless of which channel is present and additional information intended for each of the different channels. It can be seen that this problem is equivalent to that of sending common and private information to several destinations in presence of helper relays. This scenarios is identified as the broadcast relay channel (BRC). A general achievable rate region is derived and specific results are obtained for the case of Gaussian BRCs. The advantage of the proposed coding scheme is that the source can adapt its coding rate to the different channels that might be present during the communication.

In this paper the novel issue of retransmissions allocation in ARQ relay networks is introduced. In ARQ relay networks, the source and relays repeat a signal in response to a request from the destination. The source and relays can repeat once or multiple times with a constant power. Contrary to the protocols where a node is constrained to transmit once, we allow each node to retransmit multiple times. The goal is to allocate the optimal (minimum) number of retransmissions resulting in successful decoding at the destination. We devise an optimization method which is optimal. Moreover, the performance of the protocol with multiple retransmissions is compared to that of protocols with one retransmission per node or all the retransmissions from a single node. The results reveal that multiple retransmission protocols deliver superior performance over the counterparts.

This paper evaluates and compares different Coordinated MultiPoint (CoMP) Joint Processing (JP) schemes. We consider the LTE-Advanced codebook-based schemes, where the serving beams are taken from a pre-determined codebook. We namely consider Single User Joint Processing (SUJP) and Multiple Users Joint Processing (MUJP) with two variants: Least Interfering Beams (MUJPLIB) and Most Interfering Beams (MUJPMIB). We follow a cross-layer approach where both PHY and MAC layers mechanisms are taken into account by a realistic system-level simulator, whereas the higher level performance is assessed by a Markovian analysis. This latter considers the coupling between the different cells and derives the flow level capacities of the different solutions. Our results show that, globally, MUJP achieves the best performance for low to medium traffic, while all JP schemes do not perform well for large loads.

Based on recent results for the multiple-input multiple-output (MIMO) broadcast channel (BC), the feasibility of the quality of service (QoS) requirements of multi-carrier systems is investigated. Allowing for a precoding operation with arbitrary structure, the feasibility test for multi-carrier systems is analogous to that for MIMO systems with linear transceivers. However, if the precoder is restricted to be applied separately to the different carriers, additional conditions must be fulfilled for feasibility. After stating these conditions, we propose a simple test which is sufficient, but not necessary for feasibility. For a robust QoS formulation that is appropriate for erroneous channel state information, the feasibility results for the MIMO BC can be reused as long as all possible channel realizations fulfill a regularity condition.

We consider the two-user multiple-input single-output (MISO) interference channel and the rate region which is achieved when the receivers treat the interference as additive Gaussian noise and the transmitters have perfect channel state information (CSI). We propose a computationally efficient method for calculating the Pareto boundary of the rate region. We show that the problem of finding an arbitrary Pareto-optimal rate pair, along with its enabling beamforming vector pair, can be cast as a sequence of second-order cone (SOC) feasibility problems. The SOC problems are convex and they are solved very efficiently using standard off-the-shelf algorithms. The number of SOC problems that must be solved, for the computation of a Pareto-optimal point, grows only logarithmically with the desired accuracy of the solution.

By analogy with an approach widely used in physics, we consider a discrete set of base stations (BS) as a continuum of transmitters. This model allows us to establish a simple closed form formula of the interferences and the SINR received by a mobile, whatever its location. As an application of this model, we propose an analytical study of a virtual MIMO network. Indeed, we consider that the useful power received by each user of a cell is the sum of useful powers coming from all base stations of the network. The physical wireless network model enables to quantify, in a simple way, the impact of virtual MIMO on the instantaneous throughput. We moreover show that the outage probability increases.

The paper presents a methodology that combines statistical learning with constraint optimization by locally optimizing Radio Resource Management (RRM) or system parameters of poorly performing cells in an iterative manner. The statistical learning technique used is Logistic Regression (LR) which is applied on the data in the form of RRM-KPI (Key Performance Indicator) pairs. LR extracts closed form (functional) relations, known as the model, between KPIs and RRM parameters. This model is then processed by an optimization engine which proposes a new RRM parameter value. The RRM parameter value is reinserted in the network/simulator to generate corresponding KPI vector constituting generated RRM-KPI pair. First, only the a priori RRM-KPI pairs which are based upon the a priori model information are used for the model extraction. Then, as the optimization iterations progress, the generated pairs are given more importance in model extraction and the model is iteratively refined. The use of the a priori knowledge has the advantage of avoiding wrong initial models due to noisy data, allows much faster convergence and makes it more suitable for the off-line implementation. The proposed method is applied to troubleshoot an Inter-Cell Interference Coordination (ICIC) process in a LTE network which is based on soft-frequency reuse scheme.

The convergence between Wireless Sensor Network and RFID technology enables the development of flexible and integrated architectures that could currently represent a competitive solution for several application scenarios. This paper proposes an advanced heterogeneous wireless network designed for industrial environments: typical sensor applications (personal and environmental parameters monitoring), RFID based services (e.g. objects identification) and convergent applications (localization and tracking) are merged. Several research topics are addressed (resource optimization, low power communication within hostile environments, etc). Proposed model would be a generalized solution that assures high performance in terms of reliability, robustness, and flexibility: main architecture component, Multi-Modal Wireless Sensor Node (MM-WSNode), is provided with multiple sensing, communication and data process resource that allows several working modes in function of environmental conditions detected.

The paper studies the routing in the network shared by several users. Each user seeks to optimize either its own performance or some combination between its own performance and that of other users, by controlling the routing of its given flow demand. We parameterize the degree of cooperation which allows to cover the fully non-cooperative behavior, the fully cooperative behavior, and even more, the fully altruistic behavior, all these as special cases of the parameter's choice. A large part of the work consists in exploring the impact of the degree of cooperation on the equilibrium. Our first finding is to identify multiple Nash equilibria with cooperative behavior that do not occur in the non-cooperative case under the same conditions (cost, demand and topology). We then identify Braess like paradox (in which adding capacity or adding a link to a network results in worse performance to all users) in presence of user's cooperation. We identify another type of paradox in cooperation scenario: when a given user increases its degree of cooperation while other users keep unchanged their degree of cooperation, this may lead to an improvement in performance of that given user. We then pursue the exploration and carry it on to the setting of Mixed equilibrium (i.e. some users are non atomic-they have infinitesimally small demand, and other have finite fixed demand). We finally obtain some theoretical results that show that for low degree of cooperation the equilibrium is unique, confirming the results of our numerical study.

A set of mobile wireless sensors observe the environment as they move about and make decisions based on their observations. They send/relay their decisions to a sensor, called the Cluster-Head (CH), that has requested all decisions made about observations from a given region during a specified time interval. There are two sources of error facing the multi-hop cluster of sensors that results from this scenario: observations are corrupted by noise and transmissions suffer communication errors. Once the sensors' decisions have reached the CH, the optimal maximum a posteriori (MAP) detector is known to be a weighted order statistic of these noisy decisions.

We characterize the performance and energy usage of this decision fusion algorithm by: determining when local fusion reduces the CH's decision error rate and characterizing the trade-off between the energy saved by compression of local decisions and the performance of the decision algorithm. Large deviation techniques, simulations and direct calculation are used to determine the performance of these strategies and to demonstrate that hybrids of them perform best.

In a wireless network, transmissions from the various nodes have to be scheduled so as to avoid mutual interference. The pattern of interference induced by active transmissions depends on the routes along which the link-transmissions have to be scheduled; i.e., the interference that scheduling has to accommodate depends on the routing. Both routing and scheduling are mechanisms to promote the efficient use of network capacity, and in view of their interdependence, it is important to consider their joint optimization. Considering one without the other can create limitations for each function and is non-optimal. We develop an algorithm based on the column-generation technique of Linear Programming for the joint optimization of routing and scheduling for multicast flows for maximizing network capacity, and demonstrate the benefit of the joint optimization in increased capacity over the case where the routing and scheduling are separately considered.

In this paper we discuss the MANIAC Challenge, a cooperative and competitive approach to MANET networking research. Our goal was to create an opportunity for researchers to come together and compete in a MANET-based competition where points were awarded for received traffic and deducted for use of node resources, including packet forwarding. Using software we created, each team built a participation strategy that allowed them to decide how much they would participate in forwarding traffic for other nodes. This exercise turned out to be a resounding success and a wealth of data was gathered about traffic patterns, network behavior, node behavior, and the impact of node participation strategies on the MANET. The major observations of this work are that location and hardware can affect node performance, node participation can affect the larger network in some circumstances, and node mobility patterns can vary based on the goal of the node.