3.1 Introduction
Orthogonal frequency division multiplexing (OFDM) has been proposed as a promising technique for future multimedia wireless communication systems due to its ability to mitigate frequency selective fading, intersymbol interference (ISI) and its flexibility for adaptive modulation on each subcarrier. Orthogonal frequency division multiple access (OFDMA) has been adopted for IEEE 802.16 broadband wireless access (BWA) system. Although the medium access control (MAC) signaling has been well defined in the IEEE 802.16 specifications [42], resource management and scheduling are still remained as open issues. Since the wireless channel condition varies with time, adaptive resource allocation has been viewed as one of the key technologies to provide
service (QoS) requirements should be taken into account when developing an efficient resource allocation algorithm. Therefore, an effective resource allocation scheme is required to exploit frequency diversity, multiuser diversity, time diversity, and QoS requirement diversity so that the overall system resource can be efficiently utilized and QoS requirement can be guaranteed.
Subcarrier, bit, and power allocation algorithms for multiuser OFDMA systems to maximize the overall data rate or minimize the total transmitted power under some constraints have been studied in many literatures. Wong et al. [21] proposed a Lagrangian-based algorithm to minimize the total transmission power consumption under user’s QoS requirements, which were defined by a specified data transmission rate and bit error rate (BER). However, a high computational complexity renders it impractical. To reduce the complexity, Zhang and Letaief [25] proposed a near optimum dynamic multiuser subcarrier-and-bit allocation algorithm to maximize the overall spectral efficiency.
Many papers considered the downlink resource allocation [24], [25], [28], but a few papers investigated the uplink resource allocation. Resource allocation of both downlink and uplink is primarily performed by the base station (BS). Das and Mandyam [35] considered the uplink transmission of the OFDMA system and developed an efficient algorithm for subcarrier and bit allocation of each user. The algorithm includs the power distribution over the selected set of subcarriers for every user so that the total used power is minimized. Kim, Han, and Kim proposed a joint subcarrier and power allocation scheme for uplink OFDMA systems to maximize the rate-sum capacity based on Shannon capacity formula [36], where a greedy subcarrier allocation algorithm, based on a marginal rate function, and an iterative water-filling power allocation algorithm were proposed. The scheme was shown to achieve a near optimal solution. Jang and Lee
[59] concluded that the equal power allocation algorithm over assigned subcarriers for each user can achieve similar performance to the water-filling scheme. Hosein [37]
assumed that subchannels made up of a group of contiguous subcarriers are assigned to users in unit of time slots. Also the CSI on subchannels of each SS is assumed to be reported periodically. Then the optimization problem using a utility function was formulated and a practical algorithm was provided to obtain a near-optimal solution.
Singh and Sharma [39] also developed an efficient and fair scheduling (EFS) algorithm for each time slot in IEEE 802.16 OFDMA/TDD system. The EFS algorithm is designed with a fixed priority scheme which gives priorities to service traffic according to their QoS requirements. Chen and Chang proposed a dynamic uplink channel allocation strategy to select a better channel for each SS depending on SS’s SNR value [65].
However, the QoS requirements and power constraint are not considered. Also, an efficient uplink resource allocation for power saving in IEEE 802.16 OFDMA systems was proposed in [38]. It adaptively adjusts the modulation and coding scheme to minimize the required transmissions power while guaranteeing BER. However, multiple services and their differentiated QoS requirements are not taken in account.
In the previous works mentioned above, the QoS requirements and fairness issues are either omitted or simplified. A minimum required transmission data rate or a predefined weight which corresponds to the fixed priority scheme is usually adopted as the QoS requirements. However, with the provision of multimedia real-time traffic, the delay bound and the packet dropping rate, regarded as essential QoS requirements, should be included in the design of radio resource allocation for practical applications.
Besides, realistic traffic models and buffer conditions of different traffic types should be considered. Niyato and Hossain proposed a queue-aware uplink bandwidth allocation
[40]. The bandwidth is allocated according to channel quality and queue state of the traffic.
Also, the tradeoff between system performance and computational complexity is an important issue. The greedy algorithm which performs symbol by symbol allocation can achieve optimal solution [41], but it results in high computational complexity.
According to the frame structures of DL-MAP and UL-MAP defined in IEEE 802.16 for downlink and uplink, respectively, the symbol-by-symbol allocation algorithm costs high transmission overhead. Besides, most resource allocation algorithms are designed for downlink and claimed to be compatible with uplink as well. However, the downlink frame structure (DL-MAP) and uplink frame structure (UL-MAP) are differently defined in IEEE 802.16 specifications [42]. Thus an efficient and feasible resource allocation algorithm for either downlink or uplink needs to be specifically designed to meet its individual frame structure.
In this chapter, we propose a dynamic priority resource allocation (DPRA) scheme for IEEE 802.16 uplink communication systems. The goal of the proposed DPRA scheme is to maximize system throughput while satisfying various QoS requirements of multimedia traffic. Four types of service traffic for users are taken into account, including unsolicited grant service (UGS), real-time polling service (rtPS), non-real-time polling service (nrtPS), and best effort (BE) service. A priority value for every service type of each user is defined and adaptively adjusted frame by frame according to its urgency related with individual QoS requirements and buffer condition. Then the BS will dynamically allocate the uplink subchannel, modulation order, and power to each SS according to its priority value and the CSI. Furthermore, in order to meet the uplink frame structures defined in IEEE 802.16 specifications and reduce the computational complexity and the transmission overhead, a consistent allocation mechanism is
designed in the proposed DPRA scheme. Simulation results show that the proposed DPRA scheme performs close to the optimal method, which is by exhaustive search, in system throughput. Also, it outperforms the EFS conventional algorithm [39] in system throughput and rtPS packet dropping rate. Besides, the DPRA scheme can take much less computational complexity than the optimal method and the EFS algorithm, where the DPRA scheme is just 1/1000 of the optimal method and 1/10 of the EFS algorithm.
The chapter 4 is organized as follows. The system model of the considered uplink OFDMA system is introduced in section 4.2. Section 4.3 presents the details of the proposed DPRA scheme. Section 4.4 discusses the performance of the DPRA scheme, compared to the efficient and fair scheduling [39]. Finally, conclusions are given in section 4.5.
3.2 System Model
Suppose that there are N subchannels in the uplink of the IEEE 802.16 OFDMA system, and each subchannel consists of q adjacent subcarriers. There are K subscriber stations (SSs) going to communicate with one BS in one cell. Each SS can be viewed as a single user containing different service types of traffic to transmit and each service type in an SS has its individual queue. Also, based on IEEE 802.16 uplink rectangle frame structure, traffic data are transmitted in fixed length of frames and each frame contains L OFDMA slots. Then, the total number of resource units in each frame is L N× slots, which is in sequence from the most left of the top subchannel to the most right of the bottom subchannel.
IEEE 802.16 defines the following four service types, and each of them has different QoS requirements: (i) Unsolicited Grant Service (UGS): The UGS supports
allocates a fixed amount of bandwidth for this type of service. (ii) Real-time Polling Service (rtPS): It is designed to support real-time service which generates variable size data packets. It is a delay sensitive traffic so that the delay requirement is an important QoS issue. The amount of bandwidth granted to this type of service needs to be determined dynamically according to its priority based on the QoS requirements and traffic models. (iii) Non-real-time Polling Service (nrtPS): It is designed to support delay-tolerant data streams while a minimum data transmission rate is required. Also the bandwidth granted to nrtPS needs to be determined dynamically according to its priority based on the QoS requirement and the buffer condition. (iv) Best effort (BE): BE service is designed to support data streams which have no QoS requirement. It will be transmitted when system resource is available. Thus the bandwidth left after serving the UGS, rtPS and nrtPS traffic is allocated to BE service.
The priority value of service type s (s∈
{
UGS, rtPS nrtPS BE ) for user k, , ,}
denoted by γk s, , is here defined in term of the minimum number of bits required to transmit per frame. The γk UGS, remains constant in each frame since the system needs to grant a constant amount of bandwidth to UGS. The γk rtPS, and γk nrtPS, are dynamically adjusted frame by frame so that the QoS requirements can be satisfied and the radio resource will be efficiently utilized. The γk BE, is set to be
, ,
0≤γk BE ≤γk s,s∈{UGS rtPS nrtPS, , } since there is no delay or transmission rate requirement. Usually, γk BE, is the smallest and γk UGS, is the largest but not necessarily.
For rtPS, denote Dk∗ the maximum delay tolerance of user k with a rtPS head-of-line (HOL) packet and D the current delay of the rtPS HOL packet of user k k experienced, which is the time duration from the arrival frame of the packet to the present frame. Both Dk∗ and D are in unit of frame. The remaining time for the rtPS k HOL packet of user k before being dropped, denoted by Δ , is given by Dk
k k k.
where Bk rtPS, is the number of residual bits of the rtPS HOL packet buffered at the queue of user k, and D is a predefined delay threshold for warning in order to th guarantee QoS requirements. If Δ is smaller than or equal to the threshold Dk D , it th means that the rtPS HOL packet of user k is very urgent and all of the residual bits in the buffer had better finish transmission in the current frame. Otherwise, the priority value
, k rtPS
γ can be set lower based on the average transmission rate, Bk rtPS, /Δ . We further Dk add its denominator with a bias of log(ΔDk) to lessen its served transmission bits and make room for other possible high priority users since its residual time before QoS violation is still long. Note that the larger the D , the earlier the warning and the better th the QoS satisfaction of RT services. But in this situation, the system will reserve or consume more resource to protect these RT services, and the system throughput will be reduced. Therefore, the D should be properly set. th
For nrtPS, the average transmission rate should be larger than the requirement of the minimum transmission rate, denoted by Rk nrtPS∗, . Denote Bk nrtPS, the number of residual bits of the user k’s nrtPS HOL packet buffered at the current frame and Δ Tk the maximum number of frames left for the nrtPS HOL packet of user k at the current frame so that the requirement Rk nrtPS∗, can be fulfilled. Similarly, the γk rtPS, is designed as
,
where T is a predefined threshold for nrtPS to make obvious priority distinction, the th log(ΔTk) is a bias by the same concept as that for rtPS, and a is a weighting constant, 0< ≤a 1, which is used to depress the priority of nrtPS traffic as compared to that of rtPS traffic. Similar to D , the th T should be properly determined. th
The transmitted signal of user k on subchannel n at the A th OFDMA slot, denoted by sk n( )A, , is given as transmitted data symbol of user k on subchannel n at the A th slot. Note that the normalized M-QAM modulation is used so that the data symbol has unitary mean energy.
We assume that the coherence time of the wireless channel is larger than the duration of one frame. Hence the CSI is assumed to remain constant over one frame.
Besides, perfect estimation of CSI on each subchannel of each user is assumed in this paper. Since in IEEE 802.16 uplink system, the SSs only report the uplink CSI on each subchannel, the channel gain of each adjacent subcarrier which a subchannel contains is assumed to be the same. Let h be the uplink channel gain between user k and the k n, considered base station on subchannel n. Note that the channel gain is not a function of slot time A since it remains fixed during one frame time. The received signal of user k on subchannel n at the A th OFDMA slot, denoted by yk n( )A, , is given by
where 'K is the set of users which use the same subchannel n at the A th OFDMA slot in other cells, and zk n( )A, is the complex white Gaussian noise of user k on subchannel n with zero mean and variance σ2. The second term at the right-hand side of (3.5) is the co-channel interference from other cells. Therefore, the received signal-to-interference-plus-noise-ratio (SINR) of user k on subchannel n at the A th OFDMA slot, denoted by SINRk n( )A, , can be obtained as [66]
An approximated bit error rate (BER) when using M-QAM modulation has been given by [60]
Therefore, based on the BER of user k, the minimum power allocated to user k on k* each subcarrier of subchannel n can be obtained by
( ) * 2
Besides, the allocated power on each subcarrier of subchannel n will be equally distributed. Thus the total allocated power to user k on subchannel n, which contains q subcarriers, at the A th OFDMA symbol, denoted by pk n( ),A , can be obtained by