CHAPTER 5. QoS RPOVISIONING THROUGH EFFECTIVE RESOURCE ALLOCATION

Transcription

1 CHAPTER 5 QoS RPOVISIONING THROUGH EFFECTIVE RESOURCE ALLOCATION 5.1 PRINCIPLE OF RRM The success of mobile communication systems and the need for better QoS, has led to the development of 3G mobile systems [16]. It guarantees a transmission rate of 2 Mbps so that feasibility for multimedia services, internet and broadband data is promised. However the strong demand for multimedia applications requires higher data rates above 2 Mbps in the downlink so that mobile users will enjoy high speed internet access and broadband services. To present such a connection High Speed Downlink Packet Access (HSDPA) has been evolved and it is expected to achieve higher performance with peak data rate of 1Mbps. Universal Terrestrial Radio Access Network Long-Term Evolution (UTRAN LTE), also known as Evolved UTRAN (E-UTRAN) and currently under development with 3GPP can be able to achieve 2 to 4 times spectral efficiency higher than HSDPA. Here OFDMA is used for downlink and SC-FDMA is used for uplink. AMC, Hybrid ARQ, fast cell selection and fast packet scheduling are the key technologies used to achieve the required QoS. Here channel dependent scheduling is used in which the major portion of the resources is allocated to the users who experience a favorable condition for transmission. This elevates the problem of fairness so that the deprived channel users may not be served resulting in low average throughput as well the retransmitting packets for these users occupies much time in the buffer of the transmitter and receiver. The fair scheduling algorithms are treated in literature for the past decade [84-86] but results in either complexity or become unfair according to user point of view. Search tree based scheduling algorithm [87] proposed in this 79

2 work considers the metric values for user Resource allocation as well the priority of their retransmission i.e. the rank of retransmission in the queue. This improves the fairness among the users so that the user throughput is increased as well the retransmission serving time decreases. The packet scheduler (PS) interacts with the HARQ manager to produce an allocation table which tries to maximize the utility function. PS assigns the portion of the bandwidth which exhibits favorable conditions for the UE. This mechanism is known as Channel Dependent scheduling (CDS). The basic interworking of PS with other Radio Resource Management RRM functionalities [14] is shown in Fig.5.1 In order to perform the packet scheduling process channel state information (CSI) is needed, ideally for all the UEs and overall the frequency band. The CSI manager provides the CSI from the sound referencing signal (SDS) which is processed at enode-b for every TTI over the entire system bandwidth for every UE. Fig.5.1 Basic inter-working between PS and RRM functionalities. The HARQ manager provides the set of UEs that have to undergo retransmission. The PS consists of two units such as Time Domain Packet Scheduler (TDPS) and Frequency Domain Packet Scheduler (FDPS) [87]. Based on the information provided by the HARQ manager, the TDPS identifies the UEs that have to undergo a retransmission in the next TTI. After sorting all the UEs according to TTI, the FDPS find out an allocation table based on the CSI and Retransmission order. 8

3 5.2 PACKET SCHEDULING ALGORITHM In this work proportional allocation (PA) algorithm [84] is used in which the bandwidth is assumed to be fixed and equal for all the UEs. It is indicated as resource chunk (RC) and is constituted by a set of consecutive Physical Resource Blocks (PRB). After allocating UEs with equal number of PRBs, SINR is measured and its ratio to the SINR target is calculated. Using this ratio, the number of PRBs necessary to match the SINR target while transmitting at lowest power is calculated. Similarly, the number of PRBs necessary to match the SINR target while transmitting at highest power is calculated. These minimum and maximum numbers of requested PRBs specifies information regarding the current channel conditions of the UE[99]. To begin with, the packet scheduler tries to guarantee the minimum requested allocation of PRBs to each user. In case the number of available PRBs is less than the requested number, the allocation will take place in proportion to the minimum request from each user. If the number of available PRBs is higher than the minimum requested, the extra PRBs will be distributed among the users who awaits for retransmission in the HARQ system. In HARQ the PRBs are allocated giving more priority to the user waiting for long time without considering the channel quality. Finally, once the allocation is performed, the power is scaled accordingly to meet the target. The allocation of PRBs is performed in the form of matrix with respect to UE and RC. From the matrix after the allocation of resource to the user, the corresponding row and column is deleted and for the next user the dimension of matrix reduces so that the allocation is performed from reduced number of PRBs Proportional Allocation Algorithm Procedure 1. The Bandwidth is divided by the number of users to schedule and the PRBs are allocated equally to all the users. 2. The SNR is calibrated for every users with the allocated PRBs and compared with the SNR target. 81

4 3. The number of PRBs to match the SNR target at lowest power is calculated as the number of PRBs to match the target with highest power is calculated. 4. With the Ratio of Minimum to Maximum request the prevailing channel condition is understood and the Minimum requested PRBs are allocated to all the users. 5. If the available PRBs are more than the requested, the remaining PRBs are allocated to the Retransmission users in the HARQ system. 6. In retransmission, priority is given to the user depending on the waiting time in the Queue. This approach provides a significant gain over a static scheduling (like a random allocation). As an example let s consider a case with two UEs and two RCs with the metrics. If we apply the algorithm described above we would end up with RC2 allocated to UE1 and RC1 allocated to UE2 as shown in Fig.5.2. The resulting global metric (which we assume to be the sum of the metrics) would be M sum = 23. Performing the opposite allocation (RC to UE1 and RC2 to UE2) it would provide the maximal global me M sum =28. We derive a binary tree from the matrix considering the first highest and second highest metric values. Then from the sub matrix the first highest and second highest is allocated to the UEs. This way we derive binary tree from the allocation is performed based on the branch in the tree that gives maximum sum of the metric values. Fig 5.3 provides an example with three UEs and three RCs. The matrix algorithm, which is equivalent to the tree algorithm with an out-degree of 1 (N out = 1) is shown to the left. The tree built for N out = 2(binary tree) is shown to the right. 82

5 Fig.5.2 Example of proposed algorithm with two UEs and two RCs Fig.5.3 Example of proposed algorithm with three UEs and three RCs. Circles indicate the allocation performed using the matrix algorithm. To the right the thick line indicates the allocation performed using the tree algorithm with N out = 2. 83

6 For the metric values considered, the tree algorithm is able to provide two allocations whose global metric is higher than the one provided by the matrix algorithm. Including the retransmissions, the allocation is performed for the users considering the highest retransmission order the N th retransmission then following the N-1 th order and finally the 1 st retransmission user. The rationale behind this is to optimize their gain in order to reduce the chances of retransmission Performance Results The system bandwidth is fixed to 1 MHz with settings according to the LTE working assumptions. Then the bandwidth is divided into various RCs depending upon the requirements of UEs. In the beginning of a simulation, the location of the UEs is randomly assigned with a uniform distribution within each cell. With the help of HARQ manager the scheduling candidates are identified and initially the total PRBs are equally divided among the UEs based upon the requirements of user. Fig.5.4 depicts the allocation of PRBs equally to all the users in which the total 165 PRBs are equally distributed to all the users at any instant. The allocation for PRBs using search tree based scheduling algorithm is depicted in the same figure in which the available PRBs are allocated unequal at different time instants and it varies depending upon the vicinity of users with respect to Base station. The variable metric values in each branch of the tree are shown in Fig.5.5 Throughput achieved using the proposed algorithm and conventional algorithm is depicted in Fig.5.6 It is inferred that the fair scheduling algorithm can improve the average cell throughput around 5kbps at the user speed of 6kmph. It is evident that the HSDPA performance is robust up to medium terminal speeds on the order of 5 km/h due to the benefit of the feedback for every 2ms and fast HARQ. Fig.5.7 represents the distribution of power allocated for the users based on the available RCs and requested UEs. 84

7 6 PRBs/UE without fairness 8 PRBs/UE with fairness PRBS 3 PRBS USERS USERS Fig.5.4 Allocation of PRBs by equal and fair allocation 3 25 fairness comparison of search tree algorithm 1st highest only 2nd highest only 1st-2nd highest 2nd-1st highest 2 metric values USERS Fig.5.5 Metric values using search tree based algorithm. 85

8 Fig.5.6 Throughput performance with and without fairness Fig.5.7 CDF distribution of scheduled SINR after power allocation 86

9 5.3 CROSS LAYER ARCHITECTURE Introduction All wired and wireless networks follow the TCP/IP based protocol architecture that follows the OSI reference model for flow of data. The success of TCP/IP based Internet has led to fast progress and implementation in all networks. The modular architecture of this protocol has been successful in providing modularity interchangeability and standardization. With the emergence of wireless networks and other new networking technologies in the past decade, environments and circumstances have changed. Wireless networks characteristics are quite different from wire line systems. Systems developers and researchers face different problems and challenges than in wire line networks. Wireless channel characteristics generally affect all traditional OSI layers fixing problems locally inside the layers and optimizing layers independently leads to unsatisfactory results. The wireless channel characteristics like noise, dynamically varying channel quality, multi path signal propagation and interference from other devices imparts high BER, Path loss, Fading, Dispersion and Co-channel interference. Although layered architectures have served well for wired networks its modular nature makes it unsuitable for wireless networks. Exploiting the dependencies and interactions between layers has been shown to increase performance in certain scenarios of wireless networking. Sharing knowledge about layer state [91] and conditions proved to be a promising paradigm for performance optimization in wireless systems. To meet the QoS demands, exploiting dependencies of protocol layers and sharing knowledge between layers to achieve highest adaptivity and stability in situations of unstable channel conditions has been shown to be a good solution. Hence Cross Layer design in addition to maintaining the layered approach allows interactions between various protocols of nonadjacent layers without violating the design of TCP/IP architecture. Several issues than can be optimized by means of Cross layer design are optimal resource allocation based on the channel quality, Energy conservation, Congestion aware routing, Power control and Power management. 87

10 Upper Layers Network Layer DataLink Layer Physical Layer Fig. 5.8 Cross layer interaction between Physical and Data link layer In a conventional OSI model the lower layers add header or trailer to the upper layers so that the peer layers in the destination handles the data and the header information has no relation with lower layers. The physical layer in the OSI model communicates directly with the physical media and responsible for activating, maintaining and deactivating the physical link. It handles raw bits stream and defines electrical and optical signaling, voltage levels, data transmission rates. The IEEE refined the standards for WLAN and the Data Link Layer is divided into Logical Link Control (LLC) and Medium Access Control (MAC) sub layers that are responsible for addressing and multiplexing of multiple users data, Error detection, Error Correction and flow control. Automatic Repeat Request (ARQ) is the Error control method used by WLAN which retransmits the packet and repeats the retransmission until successful reception at the destination. Reduction in Packet Error Rate is guaranteed by this technique but there is a compromise in delay as the packets are necessarily to be stored in the Queue till successful transmission. Reduction in PER without compromise in delay is a challenge and need attention. With this motivation the main focus of cross layer design for wireless networks focuses on physical layer interaction with Data Link Layer. Fig.5.8 represents the Cross layer combining of AMC at the physical layer with automatic repeat request 88

11 (ARQ) protocol at the data link layer has been proved to reduce latency and increase throughput compared to adaptation separately at the layers System Model As shown in the Fig.5.9 we consider a point-to-point wireless communication link between a single antenna transmitter and a single-antenna receiver. At both the transmitter and receiver, ARQ controllers are used to regulate the operation of the truncated ARQ protocol at the data link layer that operates the buffer in FIFO mode. Following the ARQ controller at the transmitter end, the packets go through an AMC controller, which updates the AMC pair according to the received SNR through the feedback channel. We assume that user s packet generation is Markovian [94] i.e. the packet arrival process is memory less. Each packet contains N p bits. Transmitter CHANNEL ESTIMATION Receiver ARQ BUFFER AMC CONTROLLER AMC SELECTOR BUFFER Feedback Channel Fig.5.9 System model The system assumes Nakagami-m block-frequency flat-fading model for the propagation channel, according to which the channel remains time invariant during the coherence time interval (CTI) of T f seconds, but is allowed to vary across successive CTI s of T f seconds. Packets with detected errors are dropped after N r retransmissions. 89

12 5.3.3 Channel Modeling and AMC The quality of the channel can be simply captured by the received SNR γ. For the block-fading model, γ is described by the general Nakagami-m model that prescribes a Gamma probability density function (pdf) p m m1 m m m m exp (5.1) Where (m.5) is the Nakagami fading parameter is the average received SNR m is the Gamma function Given γ, the objective of AMC is to maximize the data rate while maintaining the prescribed packet error rate P o. Let N denote the number of transmission modes available. In addition to the N modes, an additional mode can be chosen in which no transmission takes place under deep fading channel condition. The entire SNR range is partitioned into N+1 consecutive non-overlapping interval. Mode n will be chosen when γ n, n 1. The probability that TM n will be chosen is given by [94] Where m, x m n m n1 m, m, n1 Pr n p d (5.2) m n is the complementary Gamma function. For the given PER, P o let C n denote the channel state corresponding to the SNR region ( n, n 1) in which TM n is chosen. By the slow fading variation of the channel the state transition occurs between adjacent states at the edge of two CTI s. 9

13 The channel can be modeled as a Finite State Markov chain (FSMC) with (N+1)*(N+1) state transition matrix given by P, P, P1, P1, Pc (5.3) PN,N 1 PN, N 5.4 PROPOSED CROSS LAYER DESIGN Queuing Analysis of AMC with ARQ The proposed cross-layer approach jointly models the truncated ARQ protocol at the data link layer and the AMC scheme at the physical layer [98]. At the physical layer, there are N available TMs and thus N + 1 choice available to the AMC selector. In the queuing analysis, we assume that the packet generation adapts Poisson process with intensity λ packets per second. Each packet contains N p bits. The buffer at the transmitter is assumed as finite state buffer. At the beginning of every CTIs the parameters (c i, q i, r i ) denote the channel state, buffer status (no of packets left), ARQ protocol state (no of retransmissions) indices are captured. The Queue is modelled as M/G/1 Queue and the parameters change at the beginning of every transmission time intervals. Based on the above three parameters the state transition between every time slots are modelled as Markovian process to describe the queuing process and channel state. Hence the state transition probability matrix is derived that describes the state transition for every CTI. Hence the resulting transition matrix is modelled as Embedded Markovian process to describe the Queuing process with AMC. The total average delay D for a packet in the slotted system can be decomposed into two parts, namely 91

14 1) The average service time D e represents the delay to transmit the packet until the beginning of transmission interval 2) The average delay D q in the embedded Markov chain. Total Delay D= D e + D q (5.4) The average service time varies with the channel state and is a function of number of slots in one CTI and slot duration. The average delay for the embedded Markov chain [98] is given by D Q Q 1 P b (5.5) Where Q denotes the average number of packets in the transmit queue at t i, P b denotes the probability of having a packet blocked, and thus, λ (1 P b ) is the effective packet arrival rate. evaluated as Thus the total average packet delay of the proposed system can be D = D e + D q (5.6) Result Analysis and Discussion We assume that the Nakagami fading parameter m=1 for the propagation channel (Rayleigh fading) with coherence interval T f =2ms and Doppler frequency f d 1Hz, i.e., T f f d =.2. The number of transmission modes adopted is N=5 and the channel is modeled with 6*6 transmission matrix. By the slow- fading condition of the block-fading channel model, transition happens only between adjacent states at the edge of two coherence time intervals (CTIs). Adaptive modulation and coding technique is done here depending upon the received SNR, which makes the transmitter to choose the modulation order to maintain the prescribed packet loss 92

15 rate (P =.1), average SNR =1db. The transition Matrix obtained after modeling the channel using Markov chain is given by [P] = P(,) =.8175 denotes the probability when the current transmission state is in mode and the channel remains in the same state.and P(,1)=.1825 denotes the probability that current transmission is in mode and the next state will be mode 1. Since the channel is modeled as slow fading the probability of adjacent channel is high compared to other states. When the current transmission mode is 1,it can either move to mode or mode 2. Therefore probability of choosing modes 3,4,5 is zero. According to the channel state probability matrix the Markov model for the channel as a state flow diagram is shown in Fig

16 Fig.5.1 Markov State diagram The CTIs L n are configured as follows for assumed 5 channel states. Consider a point-to-point link communication system with N p =18 bits. Therefore the frame duration for the channel states are shown in Table 5.1. Table 5.1 Frame duration for the assumed channel states. L 1 L 2 L 3 L 4 L 5 2ms 1ms.667ms.333ms.222ms 94

17 Under the assumption that the packets are fed from the finite buffer with a poisson process with intensity λ =.1 and the maximum retry limit at the data link layer set to 2. The state transition matrix T n between time slots under a particular channel condition C n is defined by embedded markov chain. For the simplicity of analysis, we have assumed B (number of packets in buffer) = 2 and N r = 2. The allocation of AMC based on the channel state condition and the Retranmission order in the buffer is illustrated in the Table 5.2 In the transmission mode 2, the modulation order is QPSK (1/2). Since the channel is slowly varying, the transition can happen either to mode 1 BPSK (1/2) or to mode 3 QPSK (3/4). The mode is chosen depending upon the retransmission limit. If the retransmission number is 1 and the channel state goes in favor, higher order modulation is utilized. When the packet is retransmitted for the second time the modulation order is reduced to BPSK (1/2) as the packet will be dropped in case of failure. Thus depending on the receiver SNR and the retransmission number adaptive modulation and coding scheme are done to maintain the prescribed packet loss rate. Based on the above scenario for transceiver the Queuing delay has been calibrated with and without cross layer design. The Queuing delay has been plotted for WLAN environment and an improvement of 5ms and above is shown in the Fig The Queuing delay reduces with increase in the received average SNR in the feedback channel. The Queuing delay reduces as the retransmission packets are served taking the channel quality into account. 95

18 Table 5.2 AMC Selection based on channel matrix and Retransmission order RETRANSMISSION STATE TRANSITION AMC SELECTION NUMBER (,) (,) Mode (,) (1,) (1,) (,) (1,) (1,) (1,) (1,1) (1,) (2,1) (1,1) (,) (1,1) (1,) (1,1) (1,2) (1,1) (2,2) (1,2) (,) (1,2) (1,) (2,) (1,) (2,) (2,1) (2,1) (1,) (2,1) (2,2) (2,2) (1,) Mode2 QPSK (1/2) Mode2 QPSK(1/2) Mode 2 QPSK(1/2) Mode3 QPSK(3/4) Mode 3 QPSK(3/4) Mode 2 QPSK(1/2) Mode 2 QPSK(1/2) Mode 1 BPSK(1/2) Mode1 BPSK(1/2) Mode 2 QPSK(1/2) Mode2 QPSK(1/2) Mode2 QPSK(1/2) Mode3 QPSK(3/4) Mode2 QPSK(1/2) Mode1 BPSK(1/2) Mode2 QPSK(1/2) 96

19 Fig 5.11 Buffer Queuing Delay performance 5.5 SUMMARY The average throughput of the system can be increases with the individual user s throughput. To increase the user throughput Fair scheduling with less are the key methods that has identified in the chapter. Search Tree based fair scheduling algorithm has been worked out which shows the increases in user throughput up to 1.5 Mbps compared to 1Mbps by conventional equal allocation of bandwidth. To reduce the retransmission delay cross layer design of the physical and data link layer parameters are modeled so that the AMC parameters of the physical layer are considered by the ARQ protocol for fast retransmission. Embedded Markov chain modeling has been designed to jointly optimize the layer parameters. It is inferred that the queuing delay reduction of 5ms and above is achieved by the cross layer design. 97