Chapter 1 Introduction
1.2 Thesis Organization
Because there is no connection between MeNB and HeNB, and the available radio resource is insufficient, the interference induced by the closed access HeNBs would degrade performance of its nearby MUEs. Recently, the development of hybrid access femtocell is proposed to solve this problem [32]-[35], and thus HeNB can serve some of its nearby MUEs to offload the traffic of macrocell system, while the hybrid access HeNB has provided service to its HUEs. However, in order to utilize the available radio resource efficiently, and mitigate interfering with neighbor eNBs and UEs, we adopt the cognitive radio technology into our resource management scheme. Also, in order to guarantee the QoS requirements of HUEs, we adopt the priority-based service discipline into our resource management scheme. Therefore, the proposed cognitive priority-based resource management (CPRM) scheme aims to maximize the system throughput of hybrid access HeNB with guaranteeing the QoS requirements of all traffic types.
The remainder of this thesis is organized as follows. Chapter 2 introduces the considered system environment. And, Chapter 3 formulates the resource management
7
problem of cognitive HeNB in the hybrid access policy, as well as presents the proposed CPRM scheme. Then, the simulation results are provided and analyzed in Chapter 4, and we would conclude our work in Chapter 5.
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Chapter 2
System Model
2.1 Macro-Femto Networks
In LTE-A system [36], the macrocell system is a hexagonal grid structure with an MeNB located in the center of coverage, and can be partitioned into three equivalent sectors as illustrated in Figure 2.1. This partition architecture can prevent cell-edge UEs from interference induced by contiguous cell, while we adopt different frequency bands in the three sectors. Since we assume each sector is operated on different frequency bands, and there is the same number of femto blocks randomly overlaid in each sector, we would only focus on one sector of macrocell. Besides, we define the cell-edge region of MeNB as the region where is apart from MeNB by more than four-fifths of its radius.
Figure 2.1: Macro-Femto networks.
: femto block : MeNB
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Furthermore, the femto block is based on dual-strips model and has only one floor for simplicity as shown in Figure 2.2. The femto block consists of 40 apartments, and each apartment can be randomly deployed with one HeNB at most. The deployment ratio of a femto block, denoted by rd, indicates a ratio of the deployed number of HeNBs over the 40 apartments. As illustrated in Figure 2.2, HUEs are uniformly distributed inside apartments, and MUEs are uniformly distributed outside apartments, while there are a set of Ψf HUEs and a set of Ψm MUEs in the K serving UEs of hybrid access HeNB.
10m 10m
10m
10m 10m
: HeNB : MUE : HUE
Figure 2.2: Femto block.
Moreover, we consider the frequency division duplexing (FDD) frequency operation mode, and only focus on the downlink transmission bandwidth, denoted by BW. The downlink transmission frame structure is shown in Figure 2.3 [37]. Each frame comprises L sub-frames, one sub-frame lasts 2 time slots, and there are 7 symbols in a time slot. The BW is divided into N sub-channels, each sub-channel is grouped by 12 adjacent orthogonal sub-carriers, and each sub-carrier individually spaces 15 kHz. The basic resource unit is sub-frame, and each resource unit contains
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Ne allocation units. We assume each sub-channel has flat channel response bandwidth, denoted by BWsc, in a frame duration. Thus, the fading effect coefficients remain constant in each frame, and change from one frame to another one. Under the assumption of the perfect frame synchronization, the cross-talk between frames and inter-symbol interference (ISI) inside frame are neglected. And, the precise OFDM receiver is also supposed to ignore the inter-carrier interference by the property of
Figure 2.3: Downlink transmission frame structure.
2.2 Channel Model
The wireless fading channel is composed of path-loss, short-term fading, and long-term fading. However, we adopt the path-loss model from [36], and express the path-loss (PL) between receiver R and transmitter T by
1 2log ( )10
11
where d is the distance in meters between receiver R and transmitter T, L1= 15.3 and L2= 37.6 when receiver R is not in the same apartment stripe as transmitter T, L1= 38.46 and L2= 20 when receiver R and transmitter T are in the same apartment stripe, and Lw is the penetration loss caused by the walls in the path between receiver R and transmitter T. The short-term fading (JM) between receiver R and transmitter T on the sub-channel n at time t is modeled by Jakes model [38] with Mo oscillators as:
,
where fD is the Doppler frequency, while θ, ϕ, and φ are statistically independent and uniformly distributed on [-π, π). Moreover, the long-term fading (SF) between receiver R and transmitter T on the sub-channel n is modeled by shadow fading model, which is a log-normal distribution with mean of zero and standard deviation (STD) of
SH as:
where δ is a standard normal distributed variable. Therefore, the channel gain between the receiver R and transmitter T on sub-channel n can be expressed by
, , , ,
R T R T R T R T
n n n
h PL JM SF . (2.4)
2.3 Radio Sensing and Power Allocation Mechanism
By CR technique, HeNB would sample the radio environment many times in a sub-frame duration, and the sensed received signal strength (RSS) on sub-channel n is denoted by RSSn and given by
12
where Ln is the number of sub-frames required for HeNB to sense sub-channel n, Ω is the set of total eNBs in a sector, P n lT, is the transmitted signal power of transmitter T on the sub-channel n in the l-th sub-frame, and N0 is the background noise power per sub-channel. Besides, the channel state of sub-channel n, denoted by sn, is set to be 1 if the signal power on sub-channel n is less than or equal to the threshold of RSS on sub-channel, denoted by RSSth, and 0 otherwise.
HeNB may misjudge the channel state and thus cause false alarm or mis-detection problem. False alarm indicates that HeNB misjudges the available radio resource is unable to use, and mis-detection indicates that HeNB misjudges the unavailable radio resource can be used. Therefore, the probability of false alarm and mis-detection, denoted by Pf and Pm, can be expressed as Pf = Prob{RSSn > RSSth | sn HeNB F on sub-channel n at the l-th sub-frame is determined by
*
Since the storage capacity of eNBs can be enhanced easily by hardware devices, we basically assume that each serving UEs of eNB has infinite buffer. Because multiple services are provided by operators, various service traffics are proposed with
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individual characteristics and requirements [39]. We focus on the four most common traffic types, which are voice traffic of real-time (RT) service, video traffic of RT packet would be dropped if the packet violates its maximum delay tolerance.
Voice traffic is modeled as a two-state voice activity in Figure 2.4, which corresponds to active and inactive states. The state update is made at the speech encoder periodically with changing probability from active to inactive state of Pa, and that from inactive to active state of Pi. However, it provides different fixed-sized packets to UE in active and inactive states with different fixed periods.
1-Pi
Video traffic is modeled as a stable video frame flow in Figure 2.5. It provides a fixed duration and a constant number of packets in one video frame duration.
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Moreover, the packet size and the inter-arrival time between packets in a video frame are both based on the truncated Pareto distribution.
time
... ...
frame duration frame duration
inter-arrival time
Figure 2.5: Video traffic model.
HTTP traffic is modeled as a web-browsing behavior in Figure 2.6, which includes web-page downloads, reading and parsing duration. The web-page contains a main object and several embedded objects, and the number of embedded objects is based on the truncated Pareto distribution. Besides, the sizes of main and embedded objects are based on the truncated log-normal distribution. The reading duration is the time interval between two web-page downloads, and the parsing duration is the time interval between two objects in a web-page download, while the duration of reading and parsing are both based on the exponential distribution.
web-page downloads web-page downloads
main object embedded objects reading
duration
parsing duration
time Figure 2.6: HTTP traffic model.
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FTP traffic is modeled as a sequence of file downloads in Figure 2.7. Its file size is based on the truncated log-normal distribution, and the inter-arrival time between two successive files is based on the exponential distribution.
file download
inter-arrival time
file download file download
time Figure 2.7: FTP traffic model.
However, the detail parameters of each traffic model would be described in Chapter 4.
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Chapter 3
Cognitive Priority-based Resource Management Scheme for Macro- Femto Networks
Since the HeNB has the ability of cognitive radio, it could sense all sub-channels and choose the most suitable sub-channels to avoid interfering with MeNB and the neighboring HeNBs. Once the HeNB determines the available sub-channels to transmit data, it allocates sub-channels, modulation order, and power to its serving UEs. Moreover, the HeNB could serve HUEs and MUEs since it adopts the hybrid access policy. Each UE has its QoS requirements, which should be guaranteed by the resource management scheme. Therefore, the problem of radio resource management for the hybrid access HeNB with the ability of cognitive radio is very complex due to its multiple degree of freedom.
In this thesis, we adopt the priority-based service discipline to design a resource management scheme, which determines the priority value to each UE and then serves UEs based on their priority values. In order to maximize the throughput of HeNB and guarantee QoS requirements of its serving UEs, we first formulate the resource management problem of hybrid access HeNB to an optimal problem in the following.
After that, we propose a cognitive priority-based resource management (CPRM) scheme to find a sub-optimal solution for this optimal problem.
17 modulation order of QPSK, 16-QAM, or 64-QAM, respectively, on sub-channel n at the l-th sub-frame. Therefore, the total throughput of UE k at the current frame can be obtained by constraints to satisfy the limitation of HeNB.
(i) QoS Fulfillment Constraint for RT service
Since the RT UE k has the QoS requirements of the maximum delay
(ii) QoS Fulfillment Constraint for NRT service
Since the NRT UE k has the QoS requirements of the minimum transmission rate, its transmission rate, Rk, should be larger than or equal to R .k* We then have
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*, NRT
k k
R R k , (3.3)
where ΨNRT is the set of NRT UEs. Note that, since BE UE only has the QoS requirement of BER and the BER requirement can be easily fulfilled by setting its transmission power, we do not consider the QoS fulfillment constraint for BE service here.
(iii) Sub-channel Allocation Constraint
In the macro-femto system, a sub-channel can be allocated to at most one UE at each sub-frame, since HeNB is only equipped with one transmit antenna.
Thus, the sub-channels allocation constraint is given by
1 ,
The total power allocation for downlink data transmission at HeNB should have a limitation. Let P* be the maximum transmission power. We then have the power budget constraint as
The sub-channel n is regarded as available if the detected RSSn is less than or equal to the threshold of RSS, RSSth. Hence, the cognitive sub-channel availability constraint can be expressed by
, 0, if , , , .
k n
n l th
b RSS RSS k n l (3.6)
(vi) HUE QoS Satisfaction Constraint
In order to avoid violating the QoS requirements when UE is urgent, each UE should transmit some bits at each frame. Since HeNB is mainly established to serve HUEs, the QoS requirements of HUEs must be guaranteed first. Let γk
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be the minimum transmission bits of UE k at the current frame. We then have the HUE QoS satisfaction constraint by
1 , resource management problem of HeNB can be formulated to an optimal problem as follows,
It is complicated to find an optimal solution for the optimal problem of (3.8) by exhaustive search. Therefore, we propose the cognitive priority-based resource management (CPRM) scheme to heuristically find a sub-optimal allocation solution, B*, in (3.8) to maximize the system throughput and satisfy the QoS requirements of UEs.
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3.2 Cognitive Priority-based Resource Management Scheme
The cognitive priority-based resource management (CPRM) scheme can sense the available sub-channels from the environment, assign a suitable priority value to each UE, and then allocate the radio resource to UEs according to their priority values.
Figure 3.1 shows the HeNB with the CPRM scheme. The CPRM scheme is in charge of sub-channel allocation, modulation assignment, and power allocation for HeNB. It contains three algorithms: cognitive channel determination (CCD) algorithm, priority and minimum bits determination (PBD) algorithm, and priority-based resource allocation (PRA) algorithm.
The input of the CCD algorithm is the sensing information, which is the RSS detected on each sub-channel at the last frame. According to the sensing information, the CCD algorithm determines the number of sub-frames required for HeNB to sense sub-channels and the set of available sub-channels for HeNB. The inputs of the PBD algorithm are users’ QoS fulfillment information and queue information. Based on these information, the PBD algorithm assigns a service priority value and the minimum transmission bits to each UE. Note that, the outputs from the CCD and PBD algorithms are the input of the PRA algorithm. The inputs of the PRA algorithm are the channel information, the QoS fulfillment information, the queue information, the set of available sub-channels, UEs’ priority values, and their minimum transmission bits. According to these inputs, the PRA algorithm can allocate the suitable sub-channel, modulation order, and transmission power to UEs. Details are given in the following.
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Figure 3.1: The HeNB with the CPRM scheme.
3.2.1 Cognitive Channel Determination Algorithm
The cognitive channel determination (CCD) algorithm determines the minimum number of sub-frames required for HeNB to sense sub-channels, and finds the available sub-channels for HeNB to avoid interfering with MeNB and its neighboring HeNBs. Let Ln be the minimum required number of sub-frames for HeNB to sense
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deviation on sub-channel n in false alarm problem, R is the sensing deviation on n sub-channel n in mis-detection problem, and Q(a) is the complementary distribution function of the standard Gaussian and is given by
2
respectively. By adding (3.11) and (3.12), we have
1 * 1 *
On the other hand, if the sub-channel n satisfies the cognitive sub-channel availability constraint of the system constraint (iii), it would be regarded as the available sub-channel. Therefore, the set of available sub-channels for HeNB, denoted by Φ, is given by
n RSSn RSSth and Ln L, 1 n N
. (3.15)
3.2.2 Priority and Minimum Bits Determination Algorithm
The priority and minimum bits determination (PBD) algorithm assigns the priority values to UEs according to their packet delay or transmission rate. It also determines the minimum transmission bits at the current frame to UEs based on the
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QoS fulfillment information and queue information.
First, we introduce the time-to-expiration (TTE) parameter to represent the degree of urgency of UE [42]. The TTE value for UE k is denoted by Vk in unit of violate its delay requirement and its HOL packet will be dropped after the next frames.
On the other hand, for NRT UE k, Vk is derived from its minimum transmission rates;
that is If Vk < 0, the NRT UE k is regarded as urgent, since it already violates its minimum transmission rate requirement.
We have the following two priority assignment rules:
(i) HUE has a higher service priority than MUE.
(ii) RT UE has a higher priority value than NRT and BE UEs, while urgent NRT UE has a higher priority value than un-urgent RT UE.
Accordingly, the service priority of UE k, denoted by uk is designed as follows,
,
where ∆uk is the priority value of each service type, and is given by
*.
24 because the maximum value of ∆uk is 2. For the same service type, the smaller the Vk
is, the larger the ∆uk. The ∆uk of RT UE k is set to be , which is always larger than that of un-urgent NRT UE, since the maximum value of ∆uk of un-urgent NRT UE k is 1. The ∆uk of urgent NRT UE k is set to be μ, which is between the minimum value of ∆uk for urgent RT UE k (1.5) and the maximum value of ∆uk for un-urgent RT UE k (1.3). Therefore, the priority assignment rule (ii) is satisfied. On the other hand, the ∆uk of BE UE k is set to be 0, since the BE service is the background traffic. By the design of ∆uk, the QoS fulfillment constraints (i) and (ii) can be satisfied.
The minimum number of transmission bits allocated to UE k at the current frame to avoid violating QoS requirements, γk, is given as packet should be completely transmitted at the current frame. Because Vk of NRT UE k may be less than 1, the denominator of γk is given by max{1,Vk}. Since BE HUE is
1 1
k 1
V
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the subscribed UE of HeNB, it should also get the service. We consider that the BE HUE should obtain at least one resource unit. Thus, the γk of BE HUE k is set to be
3Ne,which is the average transmission bits at one resource unit. Moreover, the value of γk of BE MUE k is set to be 0, since it is not the subscribed UE of HeNB.
3.2.3 Priority-based Resource Allocation Algorithm
The PRA algorithm allocates sub-channel, modulation order, and transmission power to UEs with the maximum priority value.
Let Ψc be the set of backlogged UEs with the maximum priority value, where
1max and 0, 1
.c k uk k Kuk Bk k K
(3.20)
In order to achieve high system throughput and save the transmission power, the optimal pair of UE k* and sub-channel n* is selected based on the maximum SINR by
* * power budget constraint. Based on the achievable SINR between UE k* and HeNB on sub-channel n*, denoted by k**, system constraint (ii), the modulation order with **,
k
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k the PRA algorithm will allocate the next sub-frame of sub-channel n* to UE k* until k* 0 or the sub-channel n* has no void sub-frame.
Once the resource allocated to UE k* is completed, the CPRM scheme sets its priority value to be 0 and looks for another best pair of (k*, n*). If the sub-channel n* has no void sub-frame for data transmission, it is removed from Φ. Note that, in order to satisfy the sub-channel allocation constraint of the system constraint (i), one sub-channel is allocated to one UE at each sub-frame. If there is still the residual resource after all UEs are served (uk ≤ 0, 1 ≤ k ≤ K), the PRA algorithm sets γk = Bk, 1
≤ k ≤ K, and redo (3.21) to allocate the residual resource to the backlogged UE with the maximum SINR. The procedure of the PRA algorithm is repeated until all radio resource is allocated to the selected UEs or no backlogged UEs exists.
3.2.4 Summary of The CPRM Scheme
Based on the CCD, PBD, and PRA algorithms, the CPRM scheme can find a sub-optimal solution for (3.8) to maximize the system throughput of HeNB, and guarantees the QoS requirements of its serving UEs. The CPRM scheme can be summarized as the following five steps.
Step 1. Initialize the CPRM scheme. Set Ln of all sub-channels by (3.14) in the CCD algorithm. Also, set uk and γk of all serving UEs by (3.17) and (3.19),
27 respectively, in the PBD algorithm.
Step 2. Find the set of the available sub-channels, Φ, by (3.15) in the CCD algorithm.
Only when |Φ| > 0 and l ≤ L, the CPRM scheme would go to step 3.
Only when |Φ| > 0 and l ≤ L, the CPRM scheme would go to step 3.