Introduction
Chapter 1 Introduction
he orthogonal frequency division multiplexing (OFDM) technique is a promising modulation scheme for high data rate wireless communications. The main idea of the OFDM scheme is that the high rate data streams are split into a number of lower rate streams, and transmitted by a set of orthogonal subcarriers. The OFDM system has many compelling properties, such as: (1) flat fading per subcarrier (2) comparatively short inter-symbol interference (ISI) (3) N short equalizers needed if there are N subcarriers (4) maximum spectral efficiency (Nyquist rate). In a single user OFDM system, when the channel state information (CSI) is available at the transmitter, the transmit power for each subcarrier can be adapted according to the CSI in order to increase the data rate. There were many related works have studied on this issue.
In a multi-user environment, the OFDM scheme has many choices to implement multiple access, such as code division multiple access (CDMA), time division multiple access (TDMA), and frequency division multiple access (FDMA). Accordingly, there are various kinds of OFDM systems: OFDM-CDMA, OFDM-TDMA and OFDM-FDMA. The OFDM-CDMA has processing gain due to frequency diversity, but it has the negative effect of multiple access interference (MAI) because of sharing common subcarries. Moreover, it has another drawback of no adaptation to channel characteristics. Unlike the OFDM-CDMA, the OFDM-TDMA has no MAI because each user has a specified time slot to transmit. For the OFDM-FDMA, Reference [1] has proved that the data rate of a multi-user OFDM system is maximized when each subcarrier is assigned to only one user with the best channel gain and the transmit power is distributed over the subcarriers by the water-filling policy.
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In downlink OFDMA systems, many papers show that it is advantageous to use a well-designed radio resource allocation (RRA) algorithm to fully utilize power and bandwidth of the system. The main idea of the RRA algorithms is exploiting the multi-user diversity, which means that the system can efficiently allocate power and bandwidth among users having different CSI profiles.In the OFDMA system, however, the best subcarrier for a user can also be the best subcarrier for another user, so a conflict exists in the allocation of radio resource. Recently, many studies focused on how to propose efficient RRA algorithm with low computational complexity in OFDMA systems.
M. Ergen et. al. [2] considered the resource allocation problem of assigning a set of subcarriers and determining the number of bits to be transmitted for each subcarrier in OFDMA systems with computational simplicity and fairness between users. C. Y. Wang et. al.
[3] applies Lagrangian relaxation (LR) to this problem, in which the QoS requirement defined as achieving a specified data transmission rate and bit error rate (BER) of each user in each transmission was taken in account. However, Kivanc et. al. challenged the feasibility and validity of the LR method. [4] Instead, the author [4] proposed two different approaches, called rate-craving greedy (RCG) and amplitude-craving greedy (ACG) algorithms, to achieve the near-optimal solution with the decrease on computational complexity.
For the same reason, [5] also proposed another adaptive bit allocation (ABA) algorithm and adaptive subcarrier allocation (ASA) algorithm to reduce computational complexity with the same QoS requirements and goal of minimizing total transmission power. Furthermore, the authors in [6] introduced the concept of a utility function for cross-layer optimization, which is based on maximizing the sum of the utilities over all active users in multi-user frequency-selective fading wireless environments.
More precisely, the utility function in [6] was only a function of transmission rate of each user, which is not appropriate applied to fulfill different kinds of service requirements, such as delay tolerance, maximum dropping ratio, fairness, and priority among users.
Moreover, a buffer management is another important issue on cross-layer design because of
the further consideration of users’ source traffic.
In previous works, the user’s QoS requirements are only required transmission rate and required bit error rate (BER), and the goal of their RRA algorithms is to minimize transmission power and to satisfy the two QoS requirements. However, when radio resources are not enough for all the users, their RRA algorithms do not work well because no compensation is made for unsatisfied users. As a result, parts of users cannot be satisfied with their QoS requirements and the unfairness among users arises. Moreover, in more realistic environments, users haves different service types, and their traffic model should be taken into account. In addition, the related buffer management should also be embedded, such as a packet-dropping mechanism or a packet-queuing mechanism.
Base on the motivations mentioned above, this thesis formulates a utility function to enhance transmission efficiency and compensate for unsatisfied users. Besides, the utility function is designed with three factors: the allocation preference of BS, the satisfaction of user’s QoS requirements, and the subscription preference of user. With the proposed utility function, a utility-based RRA (URRA) framework is proposed in this thesis. The goal of the URRA algorithms is to maximize the system throughput and to satisfy user’s QoS requirements; accordingly, the URRA framework is designed in two-phase operations. The first phase allocates the radio resources with the consideration of satisfying user’s QoS requirements, while the second phase allocates the residual radio resources for non-real-time users to maximize the system throughput. In order to find an optimal subcarrier allocation vector to maximize the sum of assigned utilities, a simulated-annealing (SA) method is implemented in the first phase (SA-URRA), and another suboptimal heuristic method is proposed in the first phase (H-URRA) to replace the SA method for reducing computational complexity.
The organization is described as follows. In chapter 2, the OFDMA system model is introduced in section 2.1-2.2, and four constraints required to be considered for the RRA problem are discussed in section 2.3. The channel model assumed in this thesis is formulated
in section 2.4, and the services types, including real-time (RT) service and non-real-time (NRT) service, and their traffic models are mentioned in section 2.5.
In chapter 3, a conventional link-gain-based RRA (LRRA) algorithm is described in section 3.1. The proposed URRA framework is discussed in detail in section 3.2. The main concept of the URRA framework is described in section 3.2.1, and the utility function is formulated in section 3.2.2. In section 3.2.3, a SA method is implemented in the URRA algorithm to find an optimal allocation subcarrier vector. For reducing the computational complexity of the SA-URRA algorithm, a heuristic method is implemented in the URRA algorithm to find a suboptimal allocation subcarrier vector. The H-URRA algorithm is discussed in section 3.2.4.
In chapter 4, the simulation results are presented. The system throughput and the satisfaction of users are evaluated for the LRRA, the SA-URRA, and the H-URRA algorithms. It is shown that the SA-URRA and the H-URRA algorithms outperforms the conventional LRRA algorithm both in the the system throughput and the QoS satisfaction of users. In chapter 5, concluding remarks are provided to this thesis.