High Spectral Efficient and Flexible Next Generation Mobile Communications

In this article we identify and describe the requirements and challenges of next generation mobile communications systems. The so-called fourth generation (4G) aims at throughput rates of more than 100 MBit/s outdoors with high mobility and 1GBit/s indoors. This requires new advanced techniques in the air interface of such a system. We outline new possible techniques and introduce a future basic physical layer concept. The adaptivity to the channel of all the modules in such a possible concept is highlighted. Since the needed large bandwidth for high data rates is a very valuable resource, future communications systems need a high spectrum flexibility. Furthermore, 4G systems will face severe inter-cell interference scenarios that have to be tackled. Both challenges, i.e., spectrum flexibility and inter-cell interference, are discussed in more detail.

Human control of complex objects: towards more dexterous robots

Manipulation of objects with underactuated dynamics remains a challenge for robots. In contrast, humans excel at ‘tool use’ and more insight into human control strategies may inform robotic control architectures. We examined human control of objects that exhibit complex – underactuated, nonlinear, and potentially chaotic dynamics, such as transporting a cup of coffee. Simple control strategies appropriate for unconstrained movements, such as maximizing smoothness, fail as interaction forces have to be compensated or preempted. However, predictive control based on internal models appears daunting when the objects have nonlinear and unpredictable dynamics. We hypothesized that humans learn strategies that make these interactions predictable. Using a virtual environment subjects interacted with a virtual cup and rolling ball using a robotic visual and haptic interface. Two different metrics quantified predictability: stability or contraction, and mutual information between controller and object. In point-to-point displacements subjects exploited the contracting regions of the object dynamics to safely navigate perturbations. Control contraction metrics showed that subjects used a controller that exponentially stabilized trajectories. During continuous cup-and-ball displacements subjects developed predictable solutions sacrificing smoothness and energy efficiency. These results may stimulate control strategies for dexterous robotic manipulators and human–robot interaction.     

Resource allocation under uncertainty using the maximum entropy principle

In this paper, we formulate and solve a problem of resource allocation over a given time horizon with uncertain demands and uncertain capacities of the available resources. In particular, we consider a number of data sources with uncertain bit rates, sharing a set of parallel channels with time-varying and possibly uncertain transmission capacities. We present a method for allocating the channels so as to maximize the expected system throughput. The framework encompasses quality-of-service (QoS) requirements, e.g., minimum-rate constraints, as well as priorities represented by a user-specific cost per transmitted bit. We assume only limited statistical knowledge of the source rates and channel capacities. Optimal solutions are found by using the maximum entropy principle and elementary probability theory. The suggested framework explains how to make use of multiuser diversity in various settings, a field of recently growing interest in communication theory. It admits scheduling over multiple base stations and includes transmission buffers to obtain a method for optimal resource allocation in rather general multiuser communication systems.     

Analysis of stability and performance of adaptation algorithms with time-invariant gains

Adaptation laws that track parameters of linear regression models are investigated. The considered class of algorithms apply linear time-invariant filtering on the instantaneous gradient vector and includes least mean squares (LMS) as its simplest member. The asymptotic stability and steady-state tracking performance for prediction and smoothing estimators is analyzed for parameter variations described by stochastic processes with time-invariant statistics. The analysis is based on a novel technique that decomposes the inherent feedback of adaptation algorithms into one time-invariant loop and one time-varying loop. The impact of the time-varying feedback on the tracking error covariance can be neglected under certain conditions, and the performance analysis then becomes straightforward. Performance analysis in the presence of a non-negligible time-varying feedback is performed for algorithms that use scalar measurements. Convergence in mean square error (MSE) and the MSE tracking performance is investigated, assuming independent consecutive regression vectors. Closed-form expressions for the tracking MSE are thereafter derived without this independence assumption for a subclass of algorithms applied to finite impulse response (FIR) models with white inputs. This class includes Wiener LMS adaptation.