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A simple link adaptation algorithm would directly follow the CQI values reported by the UE. However, there may be a need to adjust the UE-reported CQI by adding an o set for the following reasons. The HS-DSCH transmit power from the Node B to the user might be di erent from the assumed HS-DSCH transmit power by the UE at the time it derived the CQI report. The UE assumes that HSDPA power equals the power of the primary common pilot channel (P-CPICH) plus G, where G is a power o set parameter signalled to the UE via RRC signalling from the RNC. The e ect of feedback delays on link adaptation performance has previously been addressed in [11], [12]. The conclusion from those studies indicate a need for an outer loop HS-DSCH link adaptation algorithm to further adjust the CQI index received from a user before applying it for adjustment of the HS-DSCH transmission format. The outer loop algorithm can be based on ACKs/NACKs from past transmissions. The algorithm adjusts the o set values to ascertain the average targetted retransmission probability. Too many retransmissions add an unnecessary delay while too few indicate that the transport block sizes used are not large enough, unnecessarily lowering throughput. Outer loop HS-DSCH link adaptation can be based on the same principle as the Release 99 outer loop power control algorithms that were rst published in [13]. Examples of outer loop HS-DSCH link adaptation algorithms are studied in [14], [15]. The HS-DSCH link adaptation algorithm is summarized in the simpli ed block diagram in Figure 6.14.

There is an abundance of probabilistic evaluation measures, although only a few of them are commonly used for learning probabilistic graphical models from data. One reason for this may be that many probabilistic evaluation measures were originally developed either as independence tests in statistics or for the purposes of feature selection and decision tree induction in machine learning [Borgelt and Kruse 1998b] and that the connection to learning graphical models was simply not recognized. However, since all that is required of an evaluation measure in order to make it usable for learning graphical models is that it yields an assessment of the strength of the dependence of two attributes, there is no reason why one should not consider using them.

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Reaction data from 158 LMP participants using a written evaluation form. Job application data from a 360-degree assessment (pre- and posttraining data from 16 out of 34 participants). Business impact data from an LMP review survey of 128 participants. The survey instrument was designed to isolate the impact of coaching from the overall training program so it could distinguish the difference in results. A leadership mastery ROI survey (24 of 36 LMP participants responded) that identified tangible benefits and actual program costs. Step 4. Data analysis. Hodges and Wentz used a return-on-training analysis team, with representatives from coaching and program design, to compile and analyze the results. Throughout the impact study process, they used the Jack Phillips Center for Research ROI Process (briefly, evaluation planning, data collection, data analysis, ROI and reporting). Step 5. Calculating the results. Reaction and job application results were high: Participants saw improvement in 13 of 18 competencies. In four areas:(1) communicates with impact, (2) fresh thinking, (3) positive disposition, and (4) decisionmaking the group reported significant improvement. Both training and coaching were perceived to have a positive impact on meeting business and performance objectives. Participants that received more coaching perceived a higher impact to business/performance objectives (20 to 50% higher impact with five or more coaching sessions). These results were based on a pre- and posttraining 360-degree assessment that 16 participants agreed to complete. Step 6. Calculating tangible benefits. Wentz and Hodges were able to isolate tangible benefits and convert them to dollar values based on participants analysis of 10 business benefits that the earlier focus groups had identified (see example in Exhibit 6.3). The eight business benefits were:

52322 Mobile Station Execution Environment (MExE) and Wireless Application Protocol (WAP) SMG#22 agreed in June 1997 to establish a small project team to elaborate a work item description backed by a feasibility study on the mobile station application execution environment (MExE) This work item intended to use the intelligence of the mobile station to enable a comfortable Internet access In parallel the WAP Forum was established by a small number of companies It became obvious that the work of this forum was of great relevance to the MExE work item But the forum was not fully open to all companies SMG#23 approved in October 19997 the work item and a feasibility study under the condition that the relationship between the SMG work and the WAP consortium is clari ed before WAP is taken into account.

GSM 1, working on service aspects; a subgroup SIMEG, the SIM expert group, was formed in summer 1991; GSM 2, working on radio aspects; GSM 3, working on network aspects, with subgroups WPA (radio interface protocols), WPB (supplementary services protocols) and WPC (network protocols); GSM 4, working on data aspects.

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At SMG#24 in December 1997 the work item description 11 was revised But no full clari cation regarding the co-operation with the WAP Forum could be achieved The complaints of two companies continued at SMG#25 in March 1998 SMG4 was asked to organise a technical workshop to review the WAP Forum results This workshop came to the conclusion that WAP has the potential to ful l the MExE requirements SMG4 proposed a co-operation method between SMG and the WAP Forum SMG4 and 9 should liase directly with the WAP Forum The Chairmen of these groups should explore with representatives of the WAP Forum the most appropriate way to standardise WAP It was clari ed that (parts of) the WAP documents should be approved directly as SMG documents.

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