ABSTRACT
In this paper, we model the behavior of a finite-state downlink wireless fading channel bad on the configurations and system parameters provided by the 3GPP LTE. By partitioning the range of the received signal-to-noi ratio into a finite number of intervals, finite-state Markov channel models can be constructed for Rayleigh fading channels. Each state corresponds to a different channel quality indicated by certain modulation scheme. In LTE, Node B is capable of transmitting frames in the downlink with different modulation schemes (QPSK, 16QAM, 64QAM). The model can be ud to provide realistic physical layer input to evaluate the performance of algorithms at the upper layers. For example, the MAC layer will u this data to test the performance of scheduling, admission control, power control, etc. Computer simulations are performed to verify the accuracy of the propod model.
叔嫂传珍Index Terms—LTE; Channel Quality; Modulation Scheme; Modeling.
1.INTRODUCTION
3GPP [1] is defining Long-Term Evolution (LTE), who radio access is called Evolved UMTS Terrestrial Radio Access Network (E-UTRAN). LTE allows 3G operators to u new and wider spectrum (1.25 MHz up to 20 MHz) while achieving higher data rates, lower latency, and higher capacit
y to meet the increasing demand for enhanced broadband rvices by consumers. LTE as a wireless mobile broadband technology will support data rates of up to 100 Mbps for the OFDMA bad downlink and 50 Mbps for the SC-FDMA bad uplink. The LTE downlink continued to u OFDMA methods as in WiMAX and UMB with differences in the details such as the carrier bandwidth, sub-carrier spacing, symbol length, etc. On the other hand, SC-FDMA, a hybrid modulation scheme that combines the low peak to average ratio, ud in single-carrier systems, with multipath resistance and flexible sub-carrier frequency allocation was chon to be ud for the LTE uplink
The LTE physical layer provides shared channels to the higher layers using a 1 ms transmission time interval (TTI), a frame of 10 ms long and a sub-carrier spacing of 15 kHz. LTE relies on hybrid automatic repeat request (HARQ) for rapid adaptation to channel variations and us the concept of a physical resource block (PRB), which is a block of 12 sub-carriers in one slot for bandwidth allocation. A group of resource blocks with a common modulation is defined by LTE as a transport block (TB). Each ur is allocated a number of physical resource blocks in the time–frequency grid, which defines its data bit rate. The more resource blocks a ur is allocated the higher the bit-rate. A scheduling algorithm [2][3] is ud to control the frequency and time allocation of physical resource blocks per ur.
Bad on the success of the classical two-state Gilbert–Elliott model [4][5] for burst noi channels there has been a considerable amount of rearch dealing with the reprentation and analysis of burst-error channels using Markov models. In [6], a finite-state Markov channel (FSMC) was built by partitioning the received instantaneous signal-to-noi ratio (SNR) into K intervals. However, the choice of number of states and SNR partitions was somewhat arbitrary. The received SNR values were partitioned into a finite number of states according to a criterion bad on the average duration of each state. This model was considered uful as it allowed rearchers to avoid slow bit-level simulations and focus on the overall system design. Some other work [7][8][9] has been established at individual layers, including energy-consumption models for hardware and path-loss models for radio propagation at the hardware-radio layer. Examples of path-loss models include Rayleigh, Rician, and Nakagami fading models at the physical layer. At the data link layer, some queuing models are provides at [10].
In this paper, we provide a FSMC for currently emerging broadband wireless network. This model will provide uful metrics for the upper layers to test the performance of their underlying algorithms. In ction 2, we provide a general overview of the LTE architecture. Section 3 prents the system model and assumptions ud in this paper. The FSMC is provided in ction 4. The analytical and simulation results are provided in ction 5. The paper is concluded in ction 6.
2.LTE ARCHITECTURE
LTE aims to provide amless IP bad connectivity between ur equipment (UE) and the packet data network while guaranteeing end-to-end mobile ur application continuity and QoS requirements. This is achieved by LTE
Modeling of Downlink Wireless Fading Channel for 3GPP LTE Cellular
System
Farag Sallabi and Khaled Shuaib
Faculty of Information Technology
United Arab Emirates University
Al Ain, UAE
f.sallabi@uaeu.ac.ae, k.shuaib@uaeu.ac.ae
2011 IEEE GCC Conference and Exhibition (GCC), February 19-22, 2011, Dubai, United Arab Emira
tes
through its Evolved Packet Core (EPC) which is part of the Evolved Packet System (EPS) designed by 3GPP to provide interoperability and amless rvice continuity with existing mobile networks. Figure 1 shows the overall simplified network architecture components. While at a higher level the network is compod of the EPC and E-UTRAN, the access network for LTE consists of Node B which connects to UE. Interfaces between network components are standardized to allow for interoperability between various vendors. Functionalities offered by Node B are across different protocol layers and include radio resource management, curity, admission control, scheduling, and QoS support. Interoperability with other infrastructure networks is provided through rving gateways as illustrated in Figure 1. For mobility control, internal handover or across other access networks a Mobility Management Entity (MME) is ud with ur authentication and authorization for roaming provided via interacting with the Home Subscriber Server (HSS) [1].
Fig. 1. Overall network architecture for 3GPP.
3. SYSTEM MODEL AND ASSUMPTIONS In this study, we consider the wireless downlink interface between the UE and Node B of the 3GPP architecture. We u the system bandwidth of 10 MHz with the number of PRBs equal to 50; each PRB has 12 adjacent sub-carriers of bandwidth 15 KHz each. Modulation schemes for the Physical Downlink Shared Channel (PDSCH) are limited to QPSK, 16QAM, and 64QAM with convolutional coding rate of ¾. The symbol rate per PRB is fixed in the system, each sub-carrier is able to carry data at a maximum rate of 15 ksym/s (symbol per cond), which gives a 10 MHz bandwidth system a raw symbol rate of 9 Msym/s. Different bit rates can be achieved by choosing the transmitted symbols from the appropriate signal constellations. The received signal-to-noi ratio (SNR) by UE fluctuates due to fading and noi. No power control mechanism is applied. Node B transmits frames with its maximum available power. Depending on PRB condition, each UE is rved with the highest possible bit rate. The UE monitors the received S
NR and determines the current channel condition. The UE nds feedback to Node B through the feedback channel only if it decides to switch the rate of the PRB. The feedback channel remains constant per sub-frame (1 ms), but is allowed to vary between sub-frames. The LTE generic frame structure is shown in Figure 2.
Fig. 2. LTE frame structure.
Link adaptation with various modulation schemes and channel coding rates is applied to the shared data channel. The same coding and modulation is applied to all groups of resource blocks belonging to the same L2 PDU scheduled to one ur within one TTI and within single stream, i.e., different modulation schemes and coding rates may be applied to different streams in ca of MIMO [1]. This applies to both localized and distributed transmission. In this model we assume SISO (Single Input/Single Output).
相亲说什么We assume frequency lective and slow fading channel. In frequency lective fading, the transmitted signal has a bandwidth greater than the coherence bandwidth of the channel. In slow fading channel, the channel impul respon changes at a rate much slower than the transmitted baband signal. In this ca, the channel may be assumed to be static over one or veral reciproc
al bandwidth intervals. In the frequency domain, this implies that the Doppler spread of the channel is much less than the bandwidth of the baband signal. The velocity of the mobile (or velocity of objects in the channel) and the baband signaling determines whether a signal undergoes fast fading or slow fading [11].
方向用英语怎么说4. MARKOV MODEL FOR PRB
In Gilbert –Elliot two-state channel model [4][5], each state corresponds to a specific channel quality which is either noiless or totally noisy. When the channel quality varies dramatically, modeling a radio channel as a two-state Gilbert –Elliot channel is not appropriate. This is the ca for urban wireless fading channels. The idea here is to form a finite-state Markov model for such wireless channels. Let S = {0, 1, …, K -1} denote a finite t of states. By partitioning the range of the received SNR into a finite number of intervals, FSMC models can be constructed for Rayleigh fading channels [6][7].
The PRBs are modeled as a FSMC. The states are determined by partitioning the average received SNR range to N+1 intervals, where N is the number of modulation schemes. Let S = s 0, s 1,…, s k-1, denote the state space of a stationary Markov chain with K states. The state space {S k } contain
s K different PRB states with corresponding bit per symbol rate (Constellation size). Let πi be the steady-state probability and p ij be the state transition probability, i, j Є {0, 1, .., K -1}. Since a stationary Markov process has the property of time-invariant transition probabilities, the transition probability is independent of time and can be indicated as:
1 Frame (10 mc)
p ij ≡ Pr(S n+1 = j|S n = i), n = 0, 1, ….i, (1) Due to slow fading, the transitions happen only between adjacent states, the probability of transition exceeding two states is zero.
p ij = 0, |i-j| >1, i, j Є {0, 1, 2, 3} (2) Multipath propagation environment is best modeled by Rayleigh distribution. With additive Gaussian noi, the received instantaneous SNR (γ) is distributed exponentially with probability density function specified as given in [6]:
γ ≥0
(3)
Where : = E {γ} is the average received SNR. Assume one-step transition in the model corresponds to the channel state transition after one sub-frame time period T f (1 ms). Modulation schemes as specified by 3GPP specifications [1] are QPSK, 16QAM and 64QAM. Therefore, we should have four intervals and four PRB states including the deep fade state. Let γ0 < γ1 < γ2 < γ3 be the thresholds of the received SNR. Then the PRB is in state πk , k=0, 1, 2, 3, if the SNR values in the sub-frame varies in the range [γk , γk+1]. To avoid deep fading, we assume that no data are nt when γ0≤γ≤γ1.
Figure 3 shows how the K-state noisy wireless channel can be modeled by FSMC.
Fig. 3. K-state wireless channel.
The steady-state probabilities of the channel states are
given by[6]:
k=0, 1, 2, 3 (4)
Transitions are allowed between two adjacent states only, therefore, the transitions states for the FSMC can be determined as:
, k = 0, 1, 2, 3 (5)
,
k = 0, 1, 2, 3
(6)
Where N (.) is the level crossing function given by:
(7)
Where f d is the maximum Doppler frequency defined
as:
, v is the velocity and λ is the wavelength.
絮叨的意思5. PERFORMANCE EVALUATION
大鹿儿歌This ction prents the computation results of the steady-state probabilities and the state transitions probabilities. Simulation experiments, using discrete-event simulator, are then conducted to monitor the state changes of PRBs. Transmission modes provided in Table I will be ud throughout the simulation process. The modes are lected to comply with the 3GPP specifications provided by [1].
TABLE I
T RANSMISSION MODES
Coding Rate ¾ ¾ ¾ R s (bits/symbol) 1.5 3 4.5 Symbol rate per PRB (ksym/s) 180 180 180 Bit rate R b per PRB (kbits/s) 270 540 810 Bit rate R bf per PRB (bits/1ms) 270 540
810
Table II below, shows the number of bits per symbol as a function of approximate received γ for a target Bit Error Rate (BER) of 10-3 and average SNR ( 20 dB [9].
TABLE II
N UMBER OF BITS ACCORDING TO RECEIVED AVERAGE SNR γk < γk+1 Range γk Threshold Modulation Scheme Number of
bits
010γ1 < γ2 (10<17) γ1(10) QPSK 2 γ2 < γ3 (17<24) γ2(17) 16QAM 4 γ3 (24-) γ3(24) 64QAM 6
According to the 3GPP specifications for LTE, the ur must maintain the lected modulation scheme for sub-frame duration. In addition, the modulation scheme should be the same for all assigned PRBs for each ur.
Table III prents the calculated values of state transition and steady-state probabilities of the ud FSMC model according to equations (4), (5), and (6) with f d =10 Hz, T f = 1 ms, ( = 20 dB.
TABLE III
S TEADY -STATE AND STATE TRANSITIONS PROBABILITIES k k,k-1k,k k,k+11 0.2773 0.0377 0.9267 0.0356 2 0.1262 0.0783 0.8562 0.0655 3 0.1659 0.0499 0.9501 -
Figure 4 shows the constellation size versus the simulation time. The simulation was run for 100,000 simulation conds (only 32,000 shown). As can be en, the constellation size changes very fast which reflects the condition of the PRB s’ sub-carriers. To show the life span of each state, a snapshot of the simulation is prented in Figur 5.
Fig. 4. Bit rate per symbol for one PRB versus simulation time.
Fig. 5. Snapshot of bit rate per symbol for one PRB versus simulation time.
The average constellation rate for the 50 PRBs during the simulation time is shown in Figure 6 and a snapshot is shown in Figure 7.
Fig. 6. Average bit rate per symbol for 50 PRBs versus simulation time.
Fig. 7. Snapshot of average bit rate per symbol for 50 PRBs versus simulation time.
Finally, the average constellation size for each PRBs during the entire simulation time is depicted in Figure 8.
Fig. 8. Average bit rate per symbol per PRB.
6.CONCLUSION
In this paper, we conducted analytical and simulation work to provide indicative realistic data to be ud in cross-layer modeling and performance evaluations. We provided an overview of emerging broadband wireless networks (LTE). Then the system model that relies on data from LTE specifications was described. The obtained results show the fluctuations of PRBs with respect to received SNR and the long term average for each PRB. This work will provide input for our future work on upper layers algorithms.
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