Page 37 |
Save page Remove page | Previous | 37 of 175 | Next |
|
small (250x250 max)
medium (500x500 max)
Large (1000x1000 max)
Extra Large
large ( > 500x500)
Full Resolution
All (PDF)
|
This page
All
|
0
1
2
3
0
1
2
(a)
(b)
0
1
2
3
(c)
Figure 2.2: Examples of (a) the reducible, (b) the periodic, (c) the irreducible and ape-
riodic Markov chains.
where Pi;j is called the transition probability from state i to j, and is xed regardless of
t. The transition probability matrix, P, is de ned as having Pi;j as its (i; j) entry.
We de ne two terms that describe a Markov chain. First, a Markov chain is called
irreducible if there is a path of nonzero probability from every state to every other state,
and reducible if otherwise. For instance, the Markov chain in Fig. 2.2(a) is reducible as
state 2 does not reach state 1. Second, the period of any state i is de ned to be the
greatest common divisor (g.c.d.) of the number of hops from state i to itself considering
all paths from state i to itself. Then, a Markov chain is called aperiodic if none of its
states have period 2, and periodic, otherwise. A periodic chain is shown in Fig. 2.2(b)
in which the period of all states is 2. It can be shown that, for an irreducible Markov
chain, all states have the same period. Therefore, if an irreducible Markov chain has self
transitions with any states, the chain is aperiodic, such as the one in Fig. 2.2(c).
23
Object Description
| Title | Resource allocation in OFDM/OFDMA cellular networks: protocol design and performance analysis |
| Author | Chang, Yu-Jung |
| Author email | yujungc@usc.edu; yjrchang@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2008-09-09 |
| Date submitted | 2008 |
| Restricted until | Unrestricted |
| Date published | 2008-10-29 |
| Advisor (committee chair) | Kuo, C.-C. Jay |
| Advisor (committee member) |
Neely, Michael J. Govindan, Ramesh |
| Abstract | Orthogonal frequency division multiplexing (OFDM) and orthogonal frequency division multiple access (OFDMA) are two promising technologies adopted in the IEEE 802.16 standard to support broadband wireless access as well as multimedia quality-of-service (QoS). In this dissertation, we discuss several important topics regarding OFDM/OFDMA: cross-layer performance analysis of OFDM and OFDMA downlinks in terms of several QoS metrics; the medium access control (MAC) protocol design for the OFDMA uplink; and the inter-cell interference (ICI) management in multi-cell OFDMA networks through a systematic approach.; First, performance analysis of OFDM-TDMA and OFDMA networks is performed in terms of cross-layer QoS measures which include the bit rate and the bit error rate (BER) in the physical layer, and packet average throughput/delay and packet maximum delay in the link layer. We adopt a cross-layer QoS framework similar to that in IEEE 802.16, where service classification, flow control and opportunistic scheduling with different subcarrier/bit allocation schemes are implemented. Our analysis provides important insights into the performance differences of these two multiaccess systems. In addition, it is shown by analysis and simulation that OFDMA outperforms OFDM-TDMA in QoS metrics of interest. Thus, we conclude that OFDMA has higher potential in supporting multimedia services.; Second, a distributed MAC algorithm for uplink OFDMA networks under the IEEE 802.16 framework is proposed and analyzed. We present a simple yet efficient algorithm to enhance the system throughput by integrating opportunistic medium access and collision resolution through random subchannel backoff. Consequently, the resulting algorithm is called the opportunistic access with random subchannel backoff (OARSB) scheme. OARSB not only achieves distributed coordination among users but also reduces the amount of information exchange between the base station and users. The throughput and delay performance analysis of OARSB is conducted, and the superior performance of OARSB over an existing scheme is demonstrated by analysis as well as computer simulation. Besides, the proposed OARSB scheme can be easily implemented in 802.16 due to its simplicity.; Lastly, a practical and low-complexity multi-cell OFDMA downlink channel assignment method using a graphic framework is proposed. Our solution consists of two phases: 1) a coarse-scale inter-cell interference (ICI) management scheme and 2) a fine-scale channel-aware resource allocation scheme. In the first phase, the task of managing the performance-limiting ICI in cellular networks is accomplished by a graphic approach, in which no ICI measurement is needed and state-of-the-art ICI management schemes such as ICI coordination (ICIC) and base station cooperation (BSC) can be incorporated easily. In the second phase, channel assignment is accomplished by taking instantaneous channel conditions into account. Heuristic algorithms are proposed to solve both phases of the problem efficiently. Extensive simulation is conducted for various practical scenarios to demonstrate the superior performance of the proposed solution against the conventional OFDMA allocation scheme. Thanks to its practicality and low complexity, the proposed scheme can be used in next generation cellular systems such as the 3GPP Long Term Evolution (LTE) and IEEE 802.16m. |
| Keyword | OFDM; OFDMA; MAC protocol design; performance analysis; resource allocation; interference management; IEEE 802.16; cellular networks |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m1704 |
| Contributing entity | University of Southern California |
| Rights | Chang, Yu-Jung |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Chang-2392 |
| Archival file | uscthesesreloadpub_Volume40/etd-Chang-2392.pdf |
Description
| Title | Page 37 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 0 1 2 3 0 1 2 (a) (b) 0 1 2 3 (c) Figure 2.2: Examples of (a) the reducible, (b) the periodic, (c) the irreducible and ape- riodic Markov chains. where Pi;j is called the transition probability from state i to j, and is xed regardless of t. The transition probability matrix, P, is de ned as having Pi;j as its (i; j) entry. We de ne two terms that describe a Markov chain. First, a Markov chain is called irreducible if there is a path of nonzero probability from every state to every other state, and reducible if otherwise. For instance, the Markov chain in Fig. 2.2(a) is reducible as state 2 does not reach state 1. Second, the period of any state i is de ned to be the greatest common divisor (g.c.d.) of the number of hops from state i to itself considering all paths from state i to itself. Then, a Markov chain is called aperiodic if none of its states have period 2, and periodic, otherwise. A periodic chain is shown in Fig. 2.2(b) in which the period of all states is 2. It can be shown that, for an irreducible Markov chain, all states have the same period. Therefore, if an irreducible Markov chain has self transitions with any states, the chain is aperiodic, such as the one in Fig. 2.2(c). 23 |
