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In this section, we shall discuss the performance of the capacity-optimized cross-layer scheduling algorithms for multiuser systems with multiple antennas at the base station. We rst consider the system performance (network capacity) based on two commonly used system utilities: the total throughput and the proportional fairness. For each of the two system utilities, we compare the performance of the optimal cross-layer scheduling with respect to the greedy scheduling as well as the genetic-based scheduling. To highlight the contribution of multiuser selection diversity, we also compare the performance with respect to the round-robin scheduler, where the K users share the channel evenly in time irrespective of their channel matrices at every fading block. In addition, we shall discuss the effect of nT on the overall network capacity. Next, we shall discuss the performance of the coverage-optimized crosslayer scheduling. Similarly, we compare the coverage performance of the optimal algorithm versus the conventional round-robin, greedy scheduling as well as genetic-based scheduling. Finally, we shall discuss the tradeoff between the capacity and coverage of multiuser systems. Note that in the simulations, each data point consists of 5000 realizations of channel fading. Channel fading of the K users are generated in accordance with an independent complex Gaussian distribution with unit variance. We assume 0 dB antenna gain in the transmit and receive antennas and 2 GHz carrier frequency. The data rate is expressed in terms of bits per second per hertz, and network coverage is expressed in terms of cell radius (m). 6.6.1 System Capacity Performance

Interim IT destination provides new capabilities to move to the next destination Look for new opportunities or threats Assess ways to use new capabilities to seize opportunity and counter threat

Figure 6.9a d illustrates the scheduling performance of the maximal throughput scheduler with respect to nT and SNR. Mobile users are assumed to be homogeneous in terms of path loss and transmit power constraint. We observe

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where yj =< y(t), Yj(t) >= - y(t)Y*(t)dt and sj =< s(t), Yj(t) >= - s(t)Y*(t)dt. j j Let y and s be the vectors corresponding to y(t) and s(t) = h(t)x(t) with respect to the signal spaces y and s, respectively. De ne v as the projection of y over the signal space s. Since p(y|w, h(t)) = p(v|w, h(t)), v forms a suf cient statistics for the unknown parameter w. Therefore, it is suf cient to project the received signal y(t) over the signal space s, and there is no loss of information with respect to the detection of the message index w. The vector representation of s(t) = h(t)x(t) is given by (s1, . . . , sDs), where s j =< h(t ) x(t ), Yj (t ) >= h(t ) x(t )Yj* (t )dt = xn h(t )g(t - nTs )Yj* (t )dt

3. ACT EFFECTIVELY TO MANAGE RISK sequence of interim IT destinations Determine how to reach the next interim destination in the sequence

- n -

that a company with unhappy customers is not on a path to survival. Curiously, there is proof. A company s American Customer Satisfaction Index score has been shown to be predictive of both consumer spending and stock market growth, among other important indicators of economic growth (www.theacsi.org). Yes, that s right, happier customers make for increased stock prices.

In the special case of slow fading where Tc > Ts, we have h(t) > t [(n - 1)Ts, nTs]. The vector representation of s(t) becomes s j = xn h(t )g(t - nTs )Yj* (t )dt

n -

Reach successive interim destinations every 6 9 months Interim destination provides value in its own right Changes in staffing, operations, and technology are manageable

n n -

" j [1, Ds ]

where gn,j = - g(t - nTs)Y*(t)dt, Ds = WNTs and N is the number of transj mitted symbols. Therefore, we have s = Snxnhngn, where gn is the vector representation of the time-delayed transmit pulse g(t - nTs). The equivalent discrete-time at fading model with channel input s and channel output v (the projection of y(t) onto the signal space s) is therefore given by

more work in order to be of any tangible value. In the context of IT this means that the interim destination must be a functioning system or operating process, not just an analysis document or a set of specifications. The system delivered at an interim destination must be able to go into production and begin repaying the cost of the work to produce it. Design each three- to nine-month step so that changes in staffing, operations, and technology are affordable and manageable. Remember that systems are people, procedures, and technology combined in a coordinated way. Do not attempt changes in these three areas that are beyond the capability and capacity of your organization. Be honest about what is possible and what is probable. Evaluate changes happens upon completion of each three- to nine-month step. The interim destination at the end of each step provides a base from which to take the next step but does not lock the IT organization into any rigid, preset sequence of next steps. The world will probably have changed in the past three to nine months. Take the opportunity to pause for a moment and check out the lay of the land again. Reaching an interim destination gives the CIO a new position and new

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