Traditionally, wireless cellular systems have been designed to operate in frequency division duplexing (FDD) paired bands that allocate the same amount of spectrum for both downlink (DL) and uplink (UL) communications. Such design is very convenient under symmetric DL/UL traffic conditions, as it used to be the case when voice transmission was predominant. However, due to the overwhelming advent of data services, which involves large asymmetries between DL and UL, the conventional FDD solution becomes inefficient. In this regard, flexible duplexing concepts aim to derive procedures to improve spectrum utilization by adjusting resources to actual traffic demand. In this work, we review these concepts and propose the introduction of time division duplexing (TDD) small eNBs (SeNB) to operate in the unused resources of an FDD-based system. This proposal alleviates the saturated DL/UL transmission commonly found in FDD-based systems through user offloading towards a TDD system based on SeNBs. In this context, the flexible duplexing concept is analyzed from three points of view: a) regulation, b) long term evolution (LTE) standardization, and c) technical solutions.
We propose a novel stochastic radio-resource-allocation strategy that achieves long-term fairness considering backhaul and air-interface capacity limitations. The base station (BS) is powered only with a finite battery that is recharged by an energy harvester. The energy harvesting is also taken into account in the proposed resource-allocation strategy. The constrained scenario is often found in remote rural areas where the backhaul connection is limited, and the BSs are fed with solar panels of reduced size. Our results show that the proposed scheme achieves higher fairness among the users and provides greater worst user rate and sum rate if an average backhaul constraint is considered.
We consider the problem of estimating the coefficients in a multivariable linear model by means of a wireless sensor network which may be affected by anomalous measurements. The noise covariance matrices at the different sensors are assumed unknown. Treating outlying samples, and their support, as additional nuisance parameters, the Maximum Likelihood estimate is investigated, with the number of outliers being estimated according to the Minimum
Description Length principle. A distributed implementation based on iterative consensus techniques is then proposed, and it is shown effective for managing outliers in the data.
Providing femto access points (FAPs) with computational capabilities will allow (either total or partial) offloading of highly demanding applications from smartphones to the so-called femto-cloud. Such offloading promises to be beneficial in terms of battery savings at the mobile terminal (MT) and/or in latency reduction in the execution of applications. However, for this promise to become a reality, the energy and/or the time required for the communication process must be compensated by the energy and/or the time savings that result from the remote computation at the FAPs. For this problem, we provide in this paper a framework for the joint optimization of the radio and computational resource usage exploiting the tradeoff between energy consumption and latency. Multiple antennas are assumed to be available at the MT and the serving FAP. As a result of the optimization, the optimal communication strategy (e.g., transmission power, rate, and precoder) is obtained, as well as the optimal distribution of the computational load between the handset and the serving FAP. This paper also establishes the conditions under which total or no offloading is optimal, determines which is the minimum affordable latency in the execution of the application, and analyzes, as a particular case, the minimization of the total consumed energy without latency constraints.
In this paper, we present a procedure for switching
on and off base stations (BSs) that are powered with solar panels
and have finite batteries. In the scenario under consideration it
is considered that the BSs are placed at the same site with fully
overlapped coverage areas and using different frequencies. We
propose a decision strategy where we assume perfect knowledge
of the traffic profile and a second approach where a robust
Bayesian strategy is considered in order to account for possible
error modeling in the traffic profile information
A precise knowledge of the MIMO channel
between the serving node and the user equipment (UE) is
important for attaining good data rates in downlink
transmissions (DL) in cellular systems. The interfering point-tomultipoint
(I-P2MP) channel, consisting of multiple transmitters
coexisting in the same area, where each transmitter is intended to
serve multiple users, is a model that subsumes most of the
scenarios that can be found in wireless cellular networks with a
dense deployment of small cells (SCs). In conventional channel
estimation procedures, resources allocated to each SC for
training tend to be orthogonal, negatively impacting in the
efficiency of the whole system. Reusing resources for training
allows releasing resources for data transmission, but at the cost
of degrading the channel estimation due to interference. We
propose a decentralized algorithm for interference management
that enforces the coordination among SCs in the design of the
training sequences for de DL. Results in this work elucidate how
to reuse resources for training and significantly improve the
throughput of the system.
his paper addresses the interference management in a MIMO interference channel (MIMO-IC) by proposing a decentralized transmit and receive beamformer optimization using improper (or circularly asymmetric complex) Gaussian signaling. For the ease of exposition, the downlink (DL) of a cellular network is considered. In order to generate improper Gaussian signals, widely linear precoding (WLP) is adopted at transmission, while at reception we consider that users might apply either widely linear estimation (WLE) or linear estimation. The coordination between transmitters for WLP design is attained by taking into account the received signal in the uplink (UL), provided that propagation channel reciprocity can be assumed and that transmit filters in the UL are appropriately designed. In this way the estimation of the interfering channels is avoided, while we can take advantage of both DL transmit coordination to adjust transmit power and beamformers and the use of improper Gaussian signaling to exploit the real and imaginary dimensions of the MIMO channel. Simulations show that the proposed decentralized technique allows reducing the mean square error (MSE) and increasing user throughput in highly interfered scenarios
The degrees of freedom (DoF) of the 3-user multiple-input multiple-output (MIMO) interference channel (IC) with full channel state information and constant channel coefficients are investigated when (p, p + 1) antennas are deployed at the transmitters and receivers, respectively. The point of departure of this paper is the work of Wang et al., which conjectured but did not prove the DoF for the antenna settings with p > 1. Here the achievability of the DoF outer bound is formally proved using linear methods, thereby avoiding the use of the rational dimensions framework. The proposed transmission scheme exploits asymmetric complex signaling together with symbol extensions in time and space interference alignment concepts. While the paper deals with the p = 2, 3, ... , 6 cases, providing the specific transmit and receive filters, there are also provided the tools needed for proving the achievability of the optimal DoF for p > 6, whose DoF characterization is conjectured.
Molina, M.; Muñoz, O.; Pascual Iserte, A.; Vidal, J. IEEE International Symposium on Personal Indoor and Mobile Radio Communications p. 1093-1098 Data de presentació: 2014-09 Presentació treball a congrés
We consider a system where multiple users are
connected to a small cell base station enhanced with
computational capabilities. Instead of doing the computation
locally at the handset, the users offload the computation of full
applications or pieces of code to the small cell base station. In this
scenario, this paper provides a strategy to allocate the uplink,
downlink, and remote computational resources. The goal is to
improve the quality of experience of the users, while achieving
energy savings with respect to the case in which the applications
run locally at the mobile terminals. More specifically, we focus on
minimizing a cost function that depends on the latencies
experienced by the users and provide an algorithm to minimize
the latency experienced by the worst case user, under a target
energy saving constraint per user.