Recently, there has been significant interest in the integration and co-existence of Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) with other Radio Access Technologies, like IEEE 802.11 Wireless Local Area Networks (WLANs). Although, the inter-working of IEEE 802.11 WLANs with 3GPP LTE has indicated enhanced network performance in the context of capacity and load balancing, the WLAN discovery scheme implemented in most of the commercially available smartphones is very inefficient and results in high battery drainage. In this paper, we have proposed an energy efficient WLAN discovery scheme for 3GPP LTE and IEEE 802.11 WLAN inter-working scenario. User Equipment (UE), in the proposed scheme, uses 3GPP network assistance along with the results of past channel scans, to optimally select the next channels to scan. Further, we have also developed an algorithm to accurately estimate the UE's mobility state, using 3GPP network signal strength patterns. We have implemented various discovery schemes in Android framework, to evaluate the performance of our proposed scheme against other solutions in the literature. Since, Android does not support selective scanning mode, we have implemented modules in Android to enable selective scanning. Further, we have also used simulation studies and justified the results using power consumption modeling. The results from the field experiments and simulations have shown high power savings using the proposed scanning scheme without any discovery performance deterioration.
UE position estimation solutions @cite_3 @cite_18 based on 3GPP network signal strength require accurate knowledge of path loss characteristics and extensive training data set for each location. The authors, in @cite_20 , have proposed relatively simple algorithm for mobility estimation. The algorithm uses the count of number of base stations for which signal strength crosses a specific threshold. The authors address the issue of signal strength fluctuations by computing the mean and variance of signal strength for each location, which is then used for mobility estimation. The solution assumes availability of significant data for variance computation at each location, which is not possible for a high speed user. Hence, an enhanced solution is required for mobility estimation of the UE, which also takes into account the signal strength fluctuations observed by a mobile UE.
@cite_18 @cite_20 @cite_3
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For an arbitrarily-located user terminal (UE) in a multi-tier heterogeneous cellular wireless network, the joint distribution of the downlink SINR at the UE from the candidate serving base stations (BSs) in the accessible tiers of the network has been derived in closed form for the cases where the candidate serving BS from each accessible tier is chosen as either the one nearest to the UE [1] or the one that is received strongest (equivalently, with maximum SINR) at the UE [2], when the locations of the BSs in the tiers are modeled by independent Poisson Point Processes, and the fading on all links is assumed independent identically distributed (iid) and Rayleigh. The actual serving BS for the UE is chosen as the nearest strongest max-SINR candidate serving BS after imposing selection bias across the tiers. The above joint distributions can be used to yield the distribution of the actual SINR at the UE (i.e., when receiving from this serving BS) when no selection bias exists across tiers. However, for the practically important case of selection bias, analytical calculation of the distribution of the actual SINR presents significant challenges. This work derives and summarizes the distribution of actual downlink SINR for all the above criteria for selection of the serving BS accounting for selection bias. We then explore some implications of these results for design and operation of a heterogeneous network. In this article, we present a novel approach to solving the localization problem in cellular networks. The goal is to estimate a mobile user's position, based on measurements of the signal strengths received from network base stations. Our solution works by building Gaussian process models for the distribution of signal strengths, as obtained in a series of calibration measurements. In the localization stage, the user's position can be estimated by maximizing the likelihood of received signal strengths with respect to the position. We investigate the accuracy of the proposed approach on data obtained within a large indoor cellular network. In wireless networks, a client's locations can be estimated using the signals received from various signal transmitters. Static fingerprint-based techniques are commonly used for location estimation, in which a radio map is built by calibrating signal-strength values in the offline phase. These values, compiled into deterministic or probabilistic models, are used for online localization. However, the radio map can be outdated when the signal-strength values change with time due to environmental dynamics, and repeated data calibration is infeasible or expensive. In this paper, we present a novel algorithm, known as LEMT (Location Estimation using Model Trees), to reconstruct a radio map using real-time signal- strength readings received at the reference points. This algorithm can take into account real-time signal-strength values at each time point and make use of the dependency between the estimated locations and reference points. We show that this technique can effectively accommodate the variations of signal strength over different time periods without the need to rebuild the radio maps repeatedly. We demonstrate the effectiveness of our proposed technique on realistic data sets collected from an 802.11b wireless network and a RFID-based network. With the tremendous growth in wireless network deployment and increasing use of mobile devices, e.g., smartphones and tablets, improving energy efficiency in such devices, especially with communication driven workloads, is critical to providing a satisfactory user experience. Studies show that signal strength plays an important role on energy consumption of cellular data communications. While energy consumption can be minimized by accurately predicting signal strengths and reacting to it in real-time, the dynamic nature of wireless environments makes signal strengths highly unpredictable. In this paper, after analyzing in detail the signal strength variation and its impact on energy consumption, we propose to use crowdsourcing approach to optimize mobile devices’ energy efficiency by utilizing signal strength traces reported shared by other users devices in cellular networks. Via a comprehensive measurement study, we observe that signal strength traces collected from different devices are pseudo-identical, and they even exhibit similar threshold-based behaviors in the relationship between signal strength and device power consumption. Based on our observations, we propose a predictive scheduling algorithm that: (i) selects the right set of signal strength traces based on its location, (ii) applies a filter to smooth out signal strengths and hide abrupt changes, (iii) digitizes the signal strength to “good” and “bad” areas, and (iv) schedules transmissions based on power-throughput characteristics to optimize the transmission energy efficiency. To demonstrate the efficacy of the proposed algorithms, we prototype the crowdsourcing-based predicative scheduling algorithm on Android-based smartphones. Our experiment results from real-life driving tests demonstrate that, by leveraging others’ signal traces, mobile devices can save energy up to 35 percent compared to the conventional opportunistic scheduling, i.e., schedule transmissions only based on instantaneous channel conditions.