Eduard Calvo

Efficient Channel-Estimation-Based Multiuser Detection for Underwater CDMA Systems

Motivated by finding reduced complexity versions of the maximum-likelihood (ML) detector for highly distorted underwater channels, a multiuser detection (MUD) algorithm for joint data detection and channel estimation based on the cyclic coordinate descent method is proposed. Assuming that the data symbols are available, they are used to estimate the channel responses, which, in turn, are used to refine the symbol estimates. Adaptive estimation is performed using minimum mean square error as the overall optimization criterion. The receiver is implemented in a multichannel configuration, which provides the array processing gain necessary for many of the underwater acoustic channels. The complexity of the detection algorithm is linear in the number of receive elements and it does not depend on the modulation level of the transmitted signals. The algorithm has been tested using real data obtained over a 2-km shallow-water channel in a 20-kHz band, demonstrating good results.

Optimal Resource Allocation in Relay-Assisted Cellular Networks With Partial CSI

Emerging cellular networks are likely to handle users with heterogeneous quality of service requirements attending to the nature of their underlying service application, the quality of their wireless equipment, or even their contract terms. While sharing the same physical resources (power, bandwidth, transmission time), the utility they get from using them may be very different and arbitrage is needed to optimize the global operation of the network. In this respect, resource allocation strategies maximizing network utility under practical constraints are investigated in this paper. In particular, we focus on a cellular network with half-duplex, MIMO terminals and relaying infrastructure in the form of fixed and dedicated relay stations. Whereas orthogonal-frequency-division multiple access is assumed, it is seen as a frequency diversity enabler since path loss is the only channel state information (CSI) known at the transmitters, which is refreshed periodically. With this setup, the performance of a state-of-the art relay-assisted transmission protocol is characterized in terms of the ergodic achievable rates, for which novel concave lower bounds are developed. The use of these bounds allows us to derive two efficient algorithms computing resource allocations in polynomial time, which address the optimization of the uplink and downlink directions jointly. First, a global optimization algorithm providing one Pareto optimal solution maximizing network utility during the validity period of one CSI is studied, which acts as a performance upper bound. Second, a sequential optimization algorithm maximizing network utility frame by frame is considered as a simpler alternative. The performance of both schemes has been compared in practical scenarios, giving special attention to the performance-complexity and throughput-fairness tradeoffs.

On the totally asynchronous interference channel with single-user receivers

The performance characterization of decentralized wireless networks with uncoordinated sender-destination pairs motivates the study of the totally asynchronous interference channel with single-user receivers. Since this channel is not information stable, its capacity region is determined resorting to information density, although more amenable single-letter inner and outer bounds are provided as well. Aiming at numerical evaluation of the achievable rates, we subsequently concentrate on the inner bound for the Gaussian case. We show that taking Gaussian inputs is not the best choice in general and derive analytical conditions under which other input distributions may be optimal. Essentially, these conditions require the channel to be interference-limited. Finally, the existence of such non-Gaussian distributions with superior performance is validated numerically in different scenarios.

Coupling noise effect in self-synchronizing wireless sensor networks

Recent results have shown that the mathematical tools considered for modelling populations of coupled oscillators appearing in nature provide an appealing framework for designing self-syncronizing sensor networks. Trendy signal processing applications take advantage of these works by coupling the sensors in order to design reliable decision/estimation networks based on cheap and unreliable sensors. In this work, we extend those results to take into account that the coupling function might suffer from noise due to the need of estimating the states of the nearby sensors. The novelty of this paper is the introduction of the concept of frustration in the design of wireless sensor networks. Frustration implies that synchronization is only possible up to a certain variance standstill floor. We provide the analytic expression of this floor and discuss some limiting cases. In order to assess the performance of the self-synchronizing network, we propose a simple signal model for the transmission of states from node to node and study its Cramer-Rao Bound and the asymptotically efficient Maximum Likelihood estimator. Taking into consideration these achieved estimation variances, computer simulation results are provided discussing the coupling noise effect and the obtained theoretical lower bound.

Sourcing Strategies and Supplier Incentives for Short-Life-Cycle Goods

Multiple sourcing with quick response has been recognized as a useful tool to manage demand risk for short-life-cycle goods. However, general wisdom has traditionally ignored the effect of these practices on supplier incentives. In this paper we find that, when suppliers make pricing decisions, dual sourcing does not always lead to higher supply chain efficiency or buyer profits as compared to single sourcing. This loss takes place when suppliers commit to prices up front, before any possible forecast change, but not when they delay the price quotes after demand forecasts have been updated. Specifically, with up-front price commitment, dual sourcing leads to inflation of supplier prices because expensive suppliers will still receive part of the business if they are sufficiently quick. Thus, when supplier prices are endogenous, double marginalization may offset the additional buyer profit enabled by higher ordering flexibility. In contrast, with delayed price quotes, a buyer will find dual sourcing benefic...

The computation of the capacity region of the discrete degraded BC is a nonconvex DC problem

While the capacity region of the discrete memoryless broadcast channel is in general unknown, it admits a computable single-letter characterization when it is degraded. In this case, we pose its computation as an optimization problem and analyze its structure. We show that the computation of the capacity region of the two-user discrete memoryless degraded broadcast channel can be characterized as a difference of convex optimization problem, a non-convex problem in general. For this problem, which cannot be solved optimally in polynomial time, we obtain necessary conditions for optimality which substantially reduce the set of potential capacity-achieving candidate distributions. As an application of this result, the capacity region of the BEC-BSC degraded broadcast channel is derived by maximizing the achievable rates over this set of reduced dimensionality.

Toward a reconfigurable MIMO downlink air interface and radio resource management: the SURFACE concept

This article presents a reconfigurable multiple-input multiple-output air interface design combined with radio resource management algorithms applicable to multi-user MIMO transmission in downlink orthogonal frequency-division multiple access systems. A low-complexity, adaptive, and channel-aware single-user and multi-user MIMO transmission solution is proposed based on the findings of the SURFACE European Commission funded research project. The resulting cross-layer design covers the reconfigurable air interface, and practical layer 1 and layer 2 RRM mechanisms for time-frequency packet scheduling. System-level performance analysis, including the effects of limited and imperfect feedback from the terminals, shows that the SURFACE air interface provides an attractive practical solution for operations with high-rate adaptive MIMO transmission schemes in the context of next-generation wireless communication systems.

Downlink Coordinated Radio Resource Management in Cellular Networks With Partial CSI

We explore decentralized coordination of sectored cellular networks to adapt the usage of downlink resources to the instantaneous network conditions. The transmission frame consists of an orthogonal bandwidth usage phase, where sectors perform FDMA and power control over an agreed frequency chunk, and a shared bandwidth usage phase where each sector performs FDMA over the full available bandwidth without power control (interference is not controlled in this phase by any means). Decentralized network utility maximization with global optimality guarantee is enabled by fixing this structure of the transmission frame, which does not cause significant network-wide losses. Thus, the ability to better balance the resources gained from coordination generates some slack that can be used to either i) provide higher-quality access, ii) increase the number of active users, or iii) reduce deployment and maintenance costs by operating larger cells.

The Computation of the Capacity Region of the Discrete MAC is a Rank-One Non-Convex Optimization Problem

The computation of the channel capacity of discrete memoryless channels is a convex problem that can be efficiently solved using the Arimoto-Blahut (AB) iterative algorithm. However, the extension of this algorithm to the computation of capacity regions of multiterminal networks is not straightforward since its computation gives rise to non-convex problems. In this context, the AB algorithm has been only successfully extended to the calculation of the sum-capacity of the discrete memoryless multiple-access channel. However, the computation of the capacity region still requires the use of computationally demanding random search algorithms or brute force (full search) methods. In this paper, we first give an alternative reformulation of the problem that identifies the non-convexity as a rank-one constraint. We then propose an efficient algorithm to compute outer and inner bounds on the capacity region by relaxing the original problem and then by projecting the relaxed solution onto the original space variable via a minimum divergence criterion. There exists a class of channels for which the proposed algorithm can be shown to compute exactly the capacity region. As an illustration, we analyze two particular channels, the binary adder MAC and the binary switching MAC, in detail. In the general case, the algorithm is able to compute very tight bounds as shown by simulation.

On the Computation of the Capacity Region of the Discrete MAC

The computation of the channel capacity of discrete memoryless channels is a convex problem that can be efficiently solved using the Arimoto-Blahut (AB) iterative algorithm. However, the extension of this algorithm to the computation of capacity regions of multiterminal networks is not straightforward since it gives rise to non-convex problems. In this context, the AB algorithm has only been successfully extended to the calculation of the sum-capacity of the discrete memoryless multiple-access channel (DMAC). Thus, the computation of the whole capacity region still requires the use of computationally demanding search methods. In this paper, we first give an alternative reformulation of the capacity region of the DMAC which condenses all the non-convexities of the problem into a single rank-one constraint. Then, we propose efficient methods to compute outer and inner bounds on the capacity region of the two-user DMAC by solving a relaxed version of the problem and projecting its solution onto the original feasible set. Targeting numerical results, we first take a randomization approach. Focusing on analytical results, we study projection via minimum divergence, which amounts to the marginalization of the relaxed solution. In this case we derive sufficient conditions and necessary and sufficient conditions for the bounds to be tight. Furthermore, we are able to show that the class of channels for which the marginalization bounds match exactly the capacity region includes all the two-user binary-input deterministic DMACs as well as other non-deterministic channels. In general, however, both methods are able to compute very tight bounds as shown for various examples.