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Kozat et al. [41] formulate the joint problem of power control and scheduling similar to that of ElBatt and Ephremides [40]. The authors also try to minimize the total transmit power in each timeslot while guaranteeing the end-to-end quality of service for sessions in terms of bandwidth and bit error rate probabilities. They prove that the problem of nding feasible power assignment and schedule under given source data rate constraint and SINR requirements is NP-completeness, hence focus should be put on nding the near-optimal and ef cient algorithms to perform the joint scheduling and power allocation. Two heuristics are proposed: One is top-down design and the other is bottom-up design. The rst approach tries to nd the feasible maximal matching of links and then tries to further reduce the energy consumption by moving links from congested timeslots to the empty or less congested one. The procedure of this algorithm is shown in Figure 11.5. The second approach is a bottom-up method by adding one link at each iteration. The adding of links follows the water- lling method, where links are added to timeslots where the links are still feasible and the total power level increased is minimized. Behzad and Co-workers [42, 43] propose a cross-layer design of power control and scheduling based on a novel interference graph (the Power-Based Interference Graph). The set of vertices in this graph is the set of links in the network, and two vertices (i1 , j1 ) and (i2 , j2 ) in the graph are connected if and only if one of the following conditions hold: r Nodes i1 , j1 , i2 , and j2 are not distinct. r Transmissions i1 j1 and i2 j2 cannot begin simultaneously.



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Highly Iterative Top-Down/Bottom-Up/Lateral Design Highly Iterative Top-Down/Bottom-Up/Lateral Design

System Performance Specification (SPS)

Figure 11.6. Flowchart of the joint scheduling and power control algorithm in Behzad and Co-workers [42, 43].

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In contrast to calculating the profit from the past month, quarter, or year a descriptive view calculating projected financial returns involves discounted cash flow (DCF) math that considers both the timing of future cash inflows and outflows as well as the cost of capital If the financial interest rate is 10 percent, then $110 a year from now equates to $100 today Exhibit 610 displays an equation for computing a customer s value score as being the sum of the CLVs for both existing and future customers Challenges with Using CLV Math There are some questions, challenges, and difficulties involved with computing CLV For example, the CLV equation works assuming that the start time for measuring it begins with today.

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Thus, any connected pair of nodes in the graph means that they cannot start transmissions simultaneously. With this interference graph, a distributed power control and scheduling algorithm is introduced. The scheduling policy decides whether two links can activate at the same time according to the graph, and the distributed power control algorithm nds the transmit power level for the scheduled links in the way similar to the one used in reference 40. The procedure of this cross-layer design is given in Figure 11.6. Fang and Rao [44] formulate the resource allocation (power) problem of maximizing the sum of transmitter utilities subject to a minimum and maximum data rate constraint per link and peak power level constraint per node in a wireless multihop network. The Additive Gaussian White Noise (AWGN) channel is assumed to be the channel model, so the system utilities de ned as functions of link rates enjoy the same properties as functions of transmission powers. Because the system utilities are functions of the transmit power levels of nodes, they propose a distributed power control algorithm based on the gradient method. The update of transmit power is as follows: Pi (t + 1) = Pi (t) L(P(t), qk ) Pi

Requirements Traceability 19 CSCI B22 Software Requirements Specification (SRS)

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Pmax (i) 0

For system integration we reverse this strategy by integrating the PART level solutions into higher levels of complexity. Thus we establish the fundamental strategy for SITE. The SITE Process is implemented by integrating into successively higher levels physical components that have been veri ed at a given level of abstraction. We refer to each integration node as an Integration Point (IP). Figure 25.3 provides an illustrative example of the time-based graphical sequence of our discussion.

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