Page 137 |
Save page Remove page | Previous | 137 of 175 | Next |
|
small (250x250 max)
medium (500x500 max)
Large (1000x1000 max)
Extra Large
large ( > 500x500)
Full Resolution
All (PDF)
|
This page
All
|
for allocation (i.e., N) is greater than or equal to what is needed by the MSs (i.e., M
number of MSs), so that we can just give each MS its own subchannel. In this case, the
algorithm terminates at Step 2 with the optimal solution.
If M > N, the algorithm proceeds by rst assigning N arbitrarily chosen nodes to N
clusters, one in each cluster (Step 2). Then, the rest MN nodes are iteratively assigned
at each step to the cluster for which the increased intra-cluster weight is minimized (Steps
3 & 4). Once the new assignment is done, the intra-cluster weight of the cluster is updated
(Step 5). The iteration repeats until all nodes are assigned into a cluster (Step 6).
The complexity of this heuristic method is proportional to the sum of the number
of edges, nodes and clusters in the graph. For our particular case with M nodes and N
clusters, this heuristic method is of complexity O(M2=2 +M=2 + N).
5.5.2 Properties of Algorithm A1
Some discussions on the properties of the clustering algorithm A1 are presented here. The
objective is to show that algorithm A1 along with the weight assignment in Sec. 5.4.2
will indeed produce desirable results.
Property 1: (BSC-weight Assignment) An MS node can be connected by a BSC (wB)
weighted edge with other MSs in at most two neighbor cells.
Proof: (Property 1) First, we note that BSC weight is assigned to the edge between
two MSs when each MS's anchor BS is in each other MS's neighbor BS set (Sec. 5.4.2).
Second, by the Property of Forming the Diversity Set in Sec. 5.2.2, an MS has at most
two neighbor BSs in its diversity set (denoted here for convenience by BS a and BS b).
123
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 137 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | for allocation (i.e., N) is greater than or equal to what is needed by the MSs (i.e., M number of MSs), so that we can just give each MS its own subchannel. In this case, the algorithm terminates at Step 2 with the optimal solution. If M > N, the algorithm proceeds by rst assigning N arbitrarily chosen nodes to N clusters, one in each cluster (Step 2). Then, the rest MN nodes are iteratively assigned at each step to the cluster for which the increased intra-cluster weight is minimized (Steps 3 & 4). Once the new assignment is done, the intra-cluster weight of the cluster is updated (Step 5). The iteration repeats until all nodes are assigned into a cluster (Step 6). The complexity of this heuristic method is proportional to the sum of the number of edges, nodes and clusters in the graph. For our particular case with M nodes and N clusters, this heuristic method is of complexity O(M2=2 +M=2 + N). 5.5.2 Properties of Algorithm A1 Some discussions on the properties of the clustering algorithm A1 are presented here. The objective is to show that algorithm A1 along with the weight assignment in Sec. 5.4.2 will indeed produce desirable results. Property 1: (BSC-weight Assignment) An MS node can be connected by a BSC (wB) weighted edge with other MSs in at most two neighbor cells. Proof: (Property 1) First, we note that BSC weight is assigned to the edge between two MSs when each MS's anchor BS is in each other MS's neighbor BS set (Sec. 5.4.2). Second, by the Property of Forming the Diversity Set in Sec. 5.2.2, an MS has at most two neighbor BSs in its diversity set (denoted here for convenience by BS a and BS b). 123 |
