This article proposes and evaluates a technique to predict the level of interference in wireless networks. We design a recursive predictor that computes future interference values at a given location by filtering measured interference at this location. The parametrization of the predictor is done offline by translating the autocorrelation of interference into an autoregressive moving average (ARMA) representation. This ARMA model is inserted into a steady-state Kalman filter enabling nodes to predict with low computational effort. Results show good performance in terms of accuracy between predicted and true values for relevant time horizons. Although the predictor is parametrized for the case of Poisson networks, Rayleigh fading, and fixed message lengths, a sensitivity analysis shows that it also works well in more general network scenarios. Numerical examples for underlay device-to-device communications and a common wireless sensor technology illustrate its broad applicability. The predictor can be applied as part of interference management to improve medium access, scheduling, and resource allocation.
Despite these advances in interference modeling, this new knowledge has not been exploited to actually improve the performance of wireless systems. The state of research is not as advanced as in channel modeling, where knowledge about the channel dynamics, such as coherence time and decorrelation distances, is indeed used in state-of-the-art technologies (e.g., space-time coding and MIMO). This gap from modeling to design is the motivation for our research: the investigation of concepts and solutions for interference prediction . At the core of our work is the fundamental issue: How well can we predict, in a probabilistic manner, the interference power at a given location in a given network into the future? An initial step in this direction is made in @cite_14 , where a simple prediction technique based on low-complexity learning of traffic patterns is proposed. The paper at hand presents a conceptually completely different predictor and discusses the problem more comprehensively.
@cite_14
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Channel uncertainty and co-channel interference are two major challenges in the design of wireless systems such as future generation cellular networks. This paper studies receiver design for a wireless channel model with both time-varying Rayleigh fading and strong co-channel interference of similar form as the desired signal. It is assumed that the channel coefficients of the desired signal can be estimated through the use of pilots, whereas no pilot for the interference signal is available, as is the case in many practical wireless systems. Because the interference process is non-Gaussian, treating it as Gaussian noise generally often leads to unacceptable performance. In order to exploit the statistics of the interference and correlated fading in time, an iterative message-passing architecture is proposed for joint channel estimation, interference mitigation and decoding. Each message takes the form of a mixture of Gaussian densities where the number of components is limited so that the overall complexity of the receiver is constant per symbol regardless of the frame and code lengths. Simulation of both coded and uncoded systems shows that the receiver performs significantly better than conventional receivers with linear channel estimation, and is robust with respect to mismatch in the assumed fading model. We investigate spatial interference statistics for multigigabit outdoor mesh networks operating in the unlicensed 60-GHz "millimeter (mm) wave" band. The links in such networks are highly directional: Because of the small carrier wavelength (an order of magnitude smaller than those for existing cellular and wireless local area networks), narrow beams are essential for overcoming higher path loss and can be implemented using compact electronically steerable antenna arrays. Directionality drastically reduces interference, but it also leads to "deafness," making implicit coordination using carrier sense infeasible. In this paper, we make a quantitative case for rethinking medium access control (MAC) design in such settings. Unlike existing MAC protocols for omnidirectional networks, where the focus is on interference management, we contend that MAC design for 60-GHz mesh networks can essentially ignore interference and must focus instead on the challenge of scheduling half-duplex transmissions with deaf neighbors. Our main contribution is an analytical framework for estimating the collision probability in such networks as a function of the antenna patterns and the density of simultaneously transmitting nodes. The numerical results from our interference analysis show that highly directional links can indeed be modeled as pseudowired, in that the collision probability is small even with a significant density of transmitters. Furthermore, simulation of a rudimentary directional slotted Aloha protocol shows that packet losses due to failed coordination are an order of magnitude higher than those due to collisions, confirming our analytical results and highlighting the need for more sophisticated coordination mechanisms. The equivalent-in-distribution (EiD)-based approach to the analysis of single-input-single-output (SISO) cellular networks for transmission over Rayleigh fading channels has recently been introduced [1]. Its rationale relies upon formulating the aggregate other-cell interference in terms of an infinite summation of independent and conditionally distributed Gaussian random variables (RVs). This approach leads to exact integral expressions of the error probability for arbitrary bi-dimensional modulations. In this paper, the EiD-based approach is generalized to the performance analysis of multiple-input-multiple-output (MIMO) cellular networks for transmission over Rayleigh fading channels. The proposed mathematical formulation allows us to study a large number of MIMO arrangements, including receive-diversity, spatial-multiplexing, orthogonal space-time block coding, zero-forcing reception and zero-forcing precoding. Depending on the MIMO setup, either exact or approximate integral expressions of the error probability are provided. In the presence of other-cell interference and noise, the error probability is formulated in terms of a two-fold integral. In interference-limited cellular networks, the mathematical framework simplifies to a single integral expression. As a byproduct, the proposed approach enables us to study SISO cellular networks for transmission over Nakagami- @math fading channels. The mathematical analysis is substantiated with the aid of extensive Monte Carlo simulations. Consider a MIMO interference channel whereby each transmitter and receiver are equipped with multiple antennas. The basic problem is to design optimal linear transceivers (or beamformers) that can maximize system throughput. The recent work [13] suggests that optimal beamformers should maximize the total degrees of freedom and achieve interference alignment in high SNR. In this paper we first consider the interference alignment problem in spatial domain and prove that the problem of maximizing the total degrees of freedom for a given MIMO interference channel is NP-hard. Furthermore, we show that even checking the achievability of a given tuple of degrees of freedom for all receivers is NP-hard when each receiver is equipped with at least three antennas. Moreover, in case where each transmitter and receiver use at most two antennas, the same problem is polynomial time solvable. Finally, we propose a distributed algorithm for transmit covariance matrix design, while assuming each receiver uses a linear MMSE beamformer. The simulation results show that the proposed algorithm outperforms the existing interference alignment algorithms in terms of system throughput.