Abstract-Spatial wireless channel prediction is important for future wireless networks, and in particular for proactive resource allocation at different layers of the protocol stack. Various sources of uncertainty must be accounted for during modeling and to provide robust predictions. We investigate two channel prediction frameworks, classical Gaussian processes (cGP) and uncertain Gaussian processes (uGP), and analyze the impact of location uncertainty during learning/training and prediction/testing, for scenarios where measurements uncertainty are dominated by large-scale fading. We observe that cGP generally fails both in terms of learning the channel parameters and in predicting the channel in the presence of location uncertainties. In contrast, uGP explicitly considers the location uncertainty. Using simulated data, we show that uGP is able to learn and predict the wireless channel.Index Terms-Gaussian processes, uncertain inputs, location uncertainty, spatial predictability of wireless channels.

ilot contamination, defined as the interference during the channel estimation process due to reusing the same pilot sequences in neighboring cells, can severely degrade the performance of massive multiple-input multiple-output systems.ilot contamination, defined as the interference during the channel estimation process due to reusing the same pilot sequences in neighboring cells, can severely degrade the performance of massive multiple-input multiple-output systems.P In this paper, we propose a location-based approach to mitigating the pilot contamination problem for uplink multiple-input multiple-output systems. Our approach makes use of the approximate locations of mobile devices to provide good estimates of the channel statistics between the mobile devices and their corresponding base stations. Specifically, we aim at avoiding pilot contamination even when the number of base station antennas is not very large, and when multiple users from different cells, or even in the same cell, are assigned the same pilot sequence. First, we characterize a desired angular region of the target user at the serving base station based on the number of base station antennas and the location of the target user, and make the observation that in this region the interference is close to zero due to the spatial separability. Second, based on this observation, we propose pilot coordination methods for multi-user multi-cell scenarios to avoid pilot contamination. The numerical results indicate that the proposed pilot contamination avoidance schemes enhance the quality of the channel estimation and thereby improve the percell sum rate offered by target base stations.

Abstract-Ultra-wide bandwidth (UWB) systems allow for accurate positioning in environments where global navigation satellite systems may fail, especially when complemented with cooperative processing. While cooperative UWB has led to centimeter-level accuracies, the communication overhead is often neglected. We quantify how accuracy and delay trade off in a wide variety of operation conditions. We also derive the asymptotic scaling of accuracy and delay, indicating that in some conditions, standard cooperation offers the worst possible tradeoff. Both avenues lead to the same conclusion: indiscriminately targeting increased accuracy incurs a significant delay penalty. Simple countermeasures can be taken to reduce this penalty and obtain a meaningful accuracy/delay trade-off.

Massive MIMO systems, while being a promising technology for 5G systems, face a number of practical challenges. Among those, pilot contamination stands out as a key bottleneck to design high-capacity beamforming methods. We propose and analyze a location-aided approach to reduce the pilot contamination effect in uplink channel estimation for massive MIMO systems. The proposed method exploits the location of user terminals, scatterers, and base stations. The approach removes the need for direct estimation of large covariance matrices and provides good channel estimation performance in the large antenna regime.

Mobile data traffic has surpassed that of voice to become the main component of the system load of today's wireless networks. Recent studies indicate that the data demand patterns of mobile users are predictable. Moreover, the channel quality of mobile users along their navigation paths is predictable by exploiting their location information. This work aims at fusing the statistically predictable demand and channel patterns in devising proactive caching strategies that alleviate network congestion. Specifically, we establish a fundamental bound on the minimum possible cost achievable by any proactive scheduler under time-invariant demand and channel statistics as a function of their prediction uncertainties, and develop an asymptotically optimal proactive service policy that attains this bound as the prediction window grows. In addition, the established bound yields insights on how the demand and channel statistics affect proactive caching decisions. We reveal some of these insights through numerical investigations.