Achieving Frequency Reuse 1 in WiMAX Networks with Beamforming

In this chapter, we examine the performance of adaptive beamforming in connection with three different subcarrier permutation schemes (PUSC, FUSC and AMC) in WiMAX cellularnetwork with frequency reuse 1. Performance is evaluated in terms of radio quality parameters and system throughput. We show that organization of pilot subcarriers in PUSC Majorgroups has a pronounced effect on system performance while considering adaptive beamforming. Adaptive beamforming per PUSC group offers full resource utilization without need of coordination among base stations. Though FUSC is also a type of distributed subcarrier permutation, its performance in terms of outage probability is somewhat less than that of PUSC. We also show that because of lack of diversity, adjacent subcarrier permutation AMC has theleast performance as far as outage probability is concerned. Results in this chapter are based on Monte Carlo simulations performed in downlink.</p

Network-Based UE Mobility Estimation in Mobile Networks

International audienceThe coexistence of small cells and macro cells is a key feature of 4G and future networks. This heterogeneity, together with the increased mobility of user devices can generate a high handover frequency that could lead to unreasonably high call drop probability or poor user experience. By performing smart mobility management, the network can pro-actively adapt to the user and guarantee seamless and smooth cell transitions. In this work, we introduce an algorithm that takes as input sounding reference signal (SRS) measurements available at the base station (eNodeB in 4G systems) to estimate with a low computational requirement the mobility level of the user and with no modification at the user device/equipment (UE) side. The performance of the algorithm is showcased using realistic data and mobility traces. Results show that the classification of UE speed to three mobility classes can be achieved with accuracy of 87% for low mobility, 93% for medium mobility, and 94% for high mobility, respectively

Online Mobile User Speed Estimation: Performance and Tradeoff Considerations

International audienceThis paper presents an online algorithm for mobile user speed estimation in 3GPP Long Term Evolution (LTE)/LTE-Advanced (LTE-A) networks. The proposed method leverages on uplink (UL) sounding reference signal (SRS) power measurements performed at the base station, also known as eNodeB (eNB), and remains effective even under large sampling period. Extensive performance evaluation of the proposed algorithm is carried out using field traces from realistic environment. The on-line solution is proven highly efficient in terms of computational requirement, estimation delay, and accuracy. In particular, we show that the proposed algorithm can allow for the first speed estimation to be obtained after 10 seconds and with an average speed underestimation error of 14 kmph. After the first speed acquisition, subsequent speed estimations can be obtained much faster (e.g., each second) with limited implementation cost and still provide high accuracy

Avoiding call drop

A mobile terminal, in a cellular communication network, comprising a freezing means able, when an on-going call is about to be dropped, to put said on-going call in frozen mode: freezing said on-going call by suspending the data traffic of said on-going call, instead of having said on-going call dropped. A cell, in a cellular communication network, comprising an admission means (AC) able, when an incoming call requests to be hosted, to make a decision by selecting among: - accepting said incoming call in normal mode (NM), - rejecting (RE) said incoming call, - accepting said incoming call in silent mode (SM): servicing all signalling aspects of said incoming call while suspending data traffic of said incoming call. A method, in a cellular communication network for avoiding a call drop

Method and system for user speed estimation in wireless networks

A method for estimating the speed of a user equipment connected to a base station of a wireless network, the method comprising the following steps: - performing signal strength measurements (S) of a radio signal transmitted between the user equipment and the base station; - performing a spectral analysis (11) of the signal strength measurements; - determining the frequency of a local maximum in the power spectrum of the signal strength measurements; - estimating (12), from previously established reference data, the speed of the user equipment that corresponds to the determined frequency, the reference data associating a given user equipment speed with a certain determined frequency

Cell partitioning for high-speed users

The present invention relates to a radio access arrangement for operating a radio cell (C), and comprising at least one wireless transceiver (210) configured to set up and operate radio communication channels with mobile devices. In accordance with an embodiment of the invention, the radio access arrangement further comprises speed determination logic (240) configured to characterize a particular mobile device (320) as belonging to a high-speed or lower-speed category (HS; LS) according to speed information of the particular mobile device, and a radio resource controller (250) configured to assign a particular radio communication channel, for communication with the particular mobile device, within a first or second disjoint radio resource partition of the radio cell (B_DL1; B_UL1; B_DL2; B_UL2) if the particular mobile device is characterized as belonging to the lower-speed or high-speed category. The radio access arrangement further comprises a transmit power controller (213) configured to control a transmit power used for communication over the particular radio communication channel as being lower than a first or second substantially higher maximum transmit power level (PTXMAX_DL1; PTXMAX_UL1; PTAX_MAX_DL2; PTXMAX_UL2) if the particular radio communication channel is assigned within the first or second radio resource partition respectively, thereby yielding a long-reach radio coverage area (340) for high-speed mobile devices and a shorter-reach radio coverage area (330) for lower-speed mobile devices. The present invention also relates to a method for operating a radio cell, and to a method for configuring radio resources of radio cells

Network-based UE mobility estimation in mobile networks

International audienceThe co-existence of small cells and macro cells is a key feature of 4G and future networks. This heterogeneity, together with the increased mobility of user devices can generate a high handover frequency that could lead to unreasonably high call drop probability or poor user experience. By performing smart mobility management, the network can pro-actively adapt to the user and guarantee seamless and smooth cell transitions. In this work, we introduce an algorithm that takes as input sounding reference signal (SRS) measurements available at the base station (eNodeB in 4G systems) to estimate with a low computational requirement the mobility level of the user and with no modification at the user device/equipment (UE) side. The performance of the algorithm is showcased using realistic data and mobility traces. Results show that the classification of UE speed to three mobility classes can be achieved with accuracy of 87% for low mobility, 93% for medium mobility, and 94% for high mobility, respectively

Temporal analysis for user speed estimation in wireless networks

A method for estimating the speed of a user equipment connected to a base station of a wireless network, the method comprising the following steps: - measuring the power of a signal transmitted between the user equipment and the base station; - computing the derivative of the measured signal power with respect to time; - computing the standard deviation of the computed derivative; - estimating, from previously established reference data, the speed of the user equipment that corresponds to the computed standard deviation, the reference data associating a given user equipment speed with a certain computed standard deviation