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Self-Organizing Terminal Architecture for Cognitive Radio Networks
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AbstractCognitive radio sensing the spectrum to fully utilize radio spectrum has been considered as a key technology toward future wireless communications. We generalize this concept toward communication and networking environment sensing to leverage co-existing systems/networks in addition to opportunity transmission like original cognitive radio, to create the self-organized cognitive radio networking architecture for terminal devices to fully utilize spectrum and co-existing systems/networks. We use an example to demonstrate the advantages through cooperating cognitive radio networks.
I. INTRODUCTION UE to the diverse application scenarios such as different data rates and different propagation distances, a good
number of international wireless communication standards have been widely deployed in past years, in addition to 3G and popular legacy 2G systems. Multiple standards may co-exist such as well-known Bluetooth and WiFi at global available 2.4G Hz ISM band, which results in co-existence standard like Bluetooth 2.0 and IEEE 802.15.2. With more applications into attention, Universal Mobile Access (UMA) to combine both GSM/GPRS at 1.8-2G Hz band and Wireless LAN at 2.4G Hz bands first introduces an international effort to allow multiple-standard multiple-band system into realistic ubiquitous wireless applications, while software defined radio (SDR) is considered as a mean to facilitate such a concept. Since the pioneer research on cognitive radio by J. Mitola [2-3] and FCCs regulations to facilitate cognitive radio, it has been considered as one key technology for future wireless communications and ubiquitous networking to fully utilize spectrum efficiency. In other words, instead of developing a universal wireless communication system governing all kinds of applications that requires tremendous and revolution efforts in establishing infrastructure and replacement of billions mobile terminals, an intelligent terminal device who can learn communication environments (available frequency spectrum, available infrastructure and/or systems at license
This research was supported in-part by the National Science Council and
National Telecommunication Program under the contracts NSC 96-2219-E-002-008 and NSC 95-2923-I-002-001-MY2.
Kwang-Cheng Chen, Ling-Hung Kung, and David Shiung are with the Institute of Communication Engineering, National Taiwan University, Taipei, Taiwan 106 e-mail: [email protected]
Ramjee Prasad is with the Center for TeleInFrastruktur, Aalborg University, Aalborg, Denmark. e-mail: [email protected].
Shihi Chen is with the Institute for Information Industry, email: [email protected].
band or unlicensed band, etc.) and can adapt communication to meet own networking purpose of quality of services (delay, jitter, cost, etc.) shall be desirable for future wireless communications. In this paper, we would like to propose complete terminal device architecture to realize cognitive radio networking and general convergence of Internet [1] applications, to connect various consumer devices, such as PC, mobile phone, game station, TV, etc. under various systems.
To distinguish between software radio and cognitive radio, we adopt a simple concept by defining software radio to adjust system parameters over a processor-based platform (that is usually facilitated by digital signal processor(s) to execute physical layer transmission receiver functions), so that one platform can serve multiple system specifications. A cognitive radio shall be able to sense the communication environments, including spectrum sensing, so that the device is able to self-organize appropriate communication and networking functions through re-configurable communication/network processor(s). By this point of view, we consider the entire communication/networking as multiple-standard systems co-existing in time, frequency, and spatial (geographical location or distance) domains. It is a generalization from traditional cognitive radio definition that the secondary system is allowed by leveraging idle radio resources (in time and/or frequency domain) of the primary user system. We use a popular scenario to demonstrate advantages of such cognitive radio networking architecture.
II. RATE-DISTANCE NATURE OF WIRELESS COMMUNICATIONS Assuming a primary communication system (or pair(s) of
transmitter and receiver) is functioning; a cognitive radio that is the secondary user for the spectrum explores channel status and seeks possibility to utilize such spectrum for communication. The channel can be commonly modeled as an Elliot-Gilbert channel [6,14] with two possible states: existence of primary user(s) (a state not allowing any secondary user to transmit), and non-existence of primary user (a state allowing secondary user(s) to transmit). In addition to many exciting research earlier, such as [7-8], we would like to exploit a critical and practical nature in wireless and thus cognitive radio systems. The rate-distance relationship, which has not been drawn a lot of attention but is critical in state-of-the-art wireless communication systems. Let us illustrate this observation from a realistic IEEE 802.11 a/g OFDM PHY and MAC [4] as Figure 1. Due to the received power level, the system will automatically adjust PHY transmission rate accordingly, and
Self-Organizing Terminal Architecture for Cognitive Radio Networks
Kwang-Cheng Chen, Ling-Hung Kung, David Shiung, Ramjee Prasad, Shihi Chen
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thus throughput via MAC. It is common in state-of-the-art wireless communication systems.
If we consider the propagation distance between transmitter and receiver to have correspondence received power, we may create a new model for such a feature of wireless communications, and we may call it as rate-distance feature of wireless communications. We may further consider such a distance as a measure of signal received power, rather than Euclidean distance nor propagation distance, to characterize propagation factors for networking operation like [17]. Consequently, distance measure D means any possible location point with received signal power as propagation Euclidean distance D under certain long-term fading. Figure 2(a) illustrates rate-distance feature and the system having two transmission rates as an example. Figure 2(b) shows maximum allowable interference caused by the secondary user(s) to primary users system at the origin. It may be generally considered that lower rate transmission is more vulnerable to such interference. Consequently, as Figure 2(c) shows, a secondary user transmission rate/power can be scheduled without affecting primary user(s). Therefore, in case a cognitive radio senses possible opportunity to transmit, its transmission rate (and thus power) is determined by the following rate-distance conditions: (a) Channel capacity in fading channel in terms of
rate-power allocation (b) Interference level by co-existing operating system(s) (c) Maximal tolerable interference to active primary
system user(s) (d) Effective distance relationship among primary and
secondary user devices
Figure 2 actually depicts the worst case scenario of cognitive
radio communication, and more rate-distance nature can be leveraged such as Figure 3. The base station and mobile station in the primary system are communicating. Due to their effective distance, the low-rate is selected. Near the boundary of the cell (according to base station), there are two cognitive radio devices wishing to establish communication under the low-level of interference from primary system. As the Figure showing, high-rate communication might be possible between these two cognitive radios without affecting primary system, and the interference from active primary system nodes to cognitive radios can be tolerated.
As a matter of fact, multiuser detection (MUD) can be applied here to alleviate co-channel interference for cognitive radios, as cognitive radios know communication status of primary system users. From initial synchronization to user identification, all can be jointly determined [21-22]. It is not limited to CDMA communications. In [18], it is shown that OFDM communications can utilize MUD to cancel co-channel interference, without precise knowing primary users. Based on the development of adaptive modulation [11-13], we may summarize a mathematical condition to determine rate-power for secondary cognitive radios.
III. DEVICE ARCHITECTURE AND COGNITIVE RADIO DESIGN Figure 4 depicts the device architecture of our proposed
self-organized cognitive radio, which consists of several major functional blocks: n Cognitive Radio: It recognizes wireless communication
environments and co-existing systems/networks. n Software-Defined Radio: Based on the decision of
self-coordinator, SDR configures to appropriate transceiver parameters for communication of mobile device. [24] provided an example to fully program SDR for OFDM to CDMA.
n Re-configurable MAC: Self-coordinator also determines best possible routing among available systems/networks, and re-configurable MAC adjusts to proper subroutines in a universal access protocol machine.
n Network-layer procedures: Self-organized coordinator instructs right network layer functions such as radio resource allocation, mobility management, etc. to complete wireless network operation.
n Self-organized communication/networking coordinator: The brain of terminal device determines (1) decent access
BaseStation
MobileStation
PrimaryCommunicationLink
SecondaryCommunicationLink
High-rate region forprimary communication
Low-rate region forprimary communication
High-rate region forsecondary communication
Low-rate region forsecondary communicationwithout affecting primaryhigh-rate communication
Figure 3: Rate-Distance Nature of Co-existing Primary/Secondary
Communications.
ReceivedPower
(a)
Distance betweenTransmitter & Receiver
ReceivedPower
(b)
Distance betweenTransmitter & Receiver
Channel Capacity
High Transmission RateLow Transmission Rate
Maximum AllowableInterference to Primary System
Distance betweenTransmitter & Receiver
Channel Capacity
Maximum Allowable Interference to Priamry System
Secondary User Transmission Power
ReceivedPower
(c)
Figure 2: ate-Distance Feature of Cognitive Radio.
n IEEE 802.11a/g OFDM transmission with RF factorsn Payload=1000 bytes; DATA rate =ACK raten IEEE 802 100 nsec delay spread fading channel
Figure 1: Received Power versus IEEE 802.11a/g OFDM PHY Data Rate (and thus MAC Throughput)
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network route based on cognitive radio information (2) configuration of proper hardware and software of cognitive radio (3) maintenance of users communication need for terminal device.
n RF: It might consist of several sub-band RFs to cover right frequency range, with capability of adjustable RF filtering to fit selected system parameters.
The cognitive radio along with interaction of self-organized coordinator and SDR can be summarized in Figure 5. We temporarily do not consider the circuit reuse situation under this scheme, in this paper.
It is well known that cognitive radio is centered around
spectrum sensing [7-8]. However, as Figure 5 shows, there are a lot more information needed to practically make a good sensing, not only in spectrum, but also in some networking functions, as a generalized sensing (or cognition). We categorize such spectrum/network sensing features into: n RF Signal Processing includes (carrier) frequency, signal
bandwidth, signal strength (RSSI), SINR estimation n BB Pre-detection Signal Processing includes, symbol rate,
carrier and timing, pilot signal, channel fading n BB Post-detection Processing (some might be done
pre-detection stage) includes system/user identification, modulation parameters, FEC type and rate, MIMO parameters, transmission power control
Networking Processing information includes multiple access protocol or MAC, radio resource allocation (such as time slot, sub-carrier, code, allocation), ARQ & traffic pattern (ABR, CBR, or VBR), routing or mobility information. The purpose for above list is to execute spectrum sensing, identification of co-existing systems/networks, and then operation of such co-existing systems/networks. [11,19,22-23] are some
examples to facilitate partial functions in the list. The cognitive radio cycle to show working flow is therefore summarized as Figure 6. [3,7] had developed cognitive cycle concepts. Since we generalize to cognitive radio network along with rate-distance concept, it primarily distinguishes new novel features here. Cognitive radio functions not only sensing spectrum and fitting spectrum resource, but also sensing networking environment and adapting into cognitive routing in the network level.
IV. RE-CONFIGURABLE MAC Medium access control (MAC) of wireless networking for
mobile/ubiquitous computing has been another fundamental element in addition to radio transceiver. [20] described some fundamental challenges for wireless networks in fading channels and principles to resolve them. After a series of efforts, a unified MAC algorithm is presented in [16] to execute most well-known access protocols, via leveraging the concept that multiple access conducts either carrier sensing (generalized as collision avoidance) or collision detection, to form CATE and CRTE (collision avoidance/resolution tree structures to generally represent all protocols) [27].
Re-configurable MAC Algorithm RP_1 if(access method = blocked) {
allow new arrivals during previous cycle DN;} if (memoryless_after_lost is set) { have all noted in DN call CATE(type_CATE;)} else{ unmarked nodes in DN call CATE(type_CATE); associate marked nodes in DN to group number #(original group number -g);} unmarked nodes in CN call CRTE(type_CRTE); associate marked nodes in CN to group number # (original group numbers -g); if (report grouping result is set) { all nodes report the grouping result back;} set g=1; // start to process each group RP_2 if(access method=free){ nodes with new arrival packets during the processing of group #(g-1) TX(g);} nodes in group #g TX(g); process group #g with GP(gp_scheme); if (there is no transmission){ g++; if (G is set ){ // G is the maximum TE size
Radio/WirelessMedium
RF Analysis BB-PHYAnalysis
System/UserAnalysis
NetworkAnalysis
Channel State &Rate-Distance
Self-OrganizedCoordinator &
SpectrumUtilizationDecision
RadioResource
Re-configurableMAC
SDR
AD/DA &Filtering
RF
Packets/Traffic
Figure 6: Self-Organized Cognitive Radio Cycle
RF AD BB-DSP MAC & NetworkFeatures
FrequencyBandwidthRSSISINR Estimation
Symbol RateCarrier & TimingPilot SignalChannel Fading
System/User IdentificationModulation ParametersFEC Type & RateMIMO ParametersPower Control
Multiple Access ProtocolRadio Resource AllocationARQ & Traffic PatternRouting/Mobility Information
Sensing/Cognitionof Cognitive Radio
Self-OrganizedCoordinator
Decision of SpectrumOptimization & Utilization
RF AD/DA &Filtering BB-SDRRe-configurable
MACNetwork
Functions
Packets
Assisted Information for MUD
Channel State &Rate-Distance
Figure 5: Self-Organized Cognitive Radio Hardware Design
CognitiveRadio
SoftwareDefinedRadioADC
DAC
Re-configurableMAC & Networking Protocol
RF-1 RF-N
Radio Resource AllocationBandwidth/Channel Allocation
Handoff/RoamingRouting, QoS, Security
Coordinatorof Self-
OrganizedCommunication& Networking
.
PHY
Data Link& MAC
Network
..
Applications & Services
Figure 4: Self-Organized Cognitive Radio Device Architecture
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if(g>G){ goto RP_1;} else{goto RP_2;}} else{goto RP_2;}} elseif(there is transmission){ if(access method=freee){ nodes in group #(g+1) to group #(g+t) DN;} //t is the duration of the transmission If(the transmission is a success){ the successful node removes the transmitted pracket from buffer; if (completeness is set){ g++; if(g>G){goto RP_1;} else{goto RP_2;}} else{ if memoryless_after_lost is set){ mark the loser in CN;} else{mark the loser in CN and DN;} current cycle ends and goto RP_1;}} else{ collided nodes CN; if(completeness is set){ g++; if(g>G){goto RP_1;} else{goto RP_2;}} else{ if(memoryless_after_lost is set){ mark the loser in CN;} else{mark the loser in CN and DN;} current cycle ends and goto RP_1;}}}
ALOHA
with Random backoff
Basic Q-ary CRA
p-persistent CSMA
CSMA/CA
GRAP
Slot time One transmission + One feedback
One transmission + One feedback
One Propagation Delay
Defined in Spec.
One propagation delay
Access method
Free Free Free Free Blocked
Completeness
No No No No Yes
Memory-less after loss
No Yes No Yes No
Report grouping result
No No No No Yes
Group process scheme
Two way handshaking
Two way handshaking
Two way handshaking
Four way handshaking
Polling
Type of CATE
None None Geometric.CATE
BEB.CATE
Uniform.CATE
Type of CRTE
Geometric.CRTE or BEB.CRTE
Q-ary CRA.CRTE
Geometric.CRTE
BEB.CRTE
Uniform.CRTE
Table 1: Re-configuration of MAC parameters
V. COOPERATIVE NETWORKING Following above architecture, self-organized coordinator
schedules right networking functions in routing to control QoS, and decides appropriate configuration of MAC, software radio communication parameters, and RF parameters. Typical approach [10, 26] toward self-organized wireless communications looks into topology control of the entire possible networks/systems, and optimizes based on different criterion. UMA even considers the update of infrastructure. Toward a practical realization, we consider the problem from a different angle, that is, a terminal determines routing based solely on information available to radio access networks. The radio access network can be a part of digital cellular like UTRAN, an access point of Wireless LANs connected to Internet, or a base station (or a subscriber station in mesh network) in WiMAX. We therefore generally assume users from K systems that are operating within certain geographical area, and devices can access all operating frequency bands. Traditionally one mobile device capable of operating in one system cannot operate in another system, and resources within these K systems may not be evenly distributed in that some of the systems may be crowded whereas others may have no or little traffic. Through cognitive radio, we shall leverage possible cooperation among these systems to improve the individual and overall performance. The primary challenge is to determine right cooperation among various combinations of systems to enhance performance or QoS. Please note that the users may want cost as a performance measure in practical applications. Without loss of generality, we consider a circuit switching (CS) network (such as 2G/3G cellular) with n1 users and a packet switching (PS) network (such as WiFi) with n2 users. These N = n1 + n2 users to operate cognitive mode between such systems, we want to demonstrate effective routing to enhance overall cognitive radio network performance as the following figure. The packet loss is due to collision with retransmission, and the number of users in the network is also assumed to be relatively steady.
1n
2n
Figure 7: Queuing Model of Cooperative Access Networks Access Network 1: CS network, modeled as a multi-server
queue with N1 servers. The service rates of N1 servers are all
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deterministic and equal to 1. A certain user is served by only one server and no more than N1 users can be admitted into this network.
Access Network 2: PS network, modeled as a single-server queue with a multiple access device in front of the server to decide which user has the right to access media. The service rate of this server is deterministic with rate 2, and the multiple access scheme is assumed to be slotted ALOHA with retransmission probability q.
We consider two types of traffic in services: CBR and ABR (representing voice and data), while arrival rates of the same service type are assumed to be equal. CBR: Deterministic arrival rate c, number of users in the PS network Nc, delay bound c. ABR: Poisson arrival rate with mean a, number of users in the PS network Na. We measure the average delay in the wireless end. The overall performance is to calculate the average delay of all users:
1
1[ ]N
ii
E D DN =
=
and compare whether this value is smaller with cooperation. The individual performance is to calculate the average delay for users within one specific network:
,1
1[ ] 1, 2kn
k i kik
E D D kn =
= =
We further embed cost model into our analysis. For a user to use system k, she/he needs to pay Pk dollars to access it, k = 1, 2. We can calculate the average price of all users
1
1[ ]N
ii
E P PN =
=
The cognitive radio shall route traffic based on the selection criterion: small enough delay to satisfy delay bound for CBR service and to lower average price. We then derive mathematical equations for average delay of a single user.
(i) CBR via AN1: For services with arrival rate c < 1, this is a D / D / 1 queue, and the delay would be a constant
DC,1 = 1 / 1 (ii) ABR via AN1: For services with arrival rate a < 1, this is modeled as a M / D / 1 queue and the average delay would be
1,1
1 1
1[ ]2( )
aA
a
E D l mm m l
= +-
(iii) CBR via AN2: Assume in AN2 there are Nc 1 other CBR users with the same arrival rate. There is no ABR user. The average delay can be found in [1] to be:
,, 2
2 ,
11 1[ ]2
s cC
s c
p qE D
p qm - +
= +
where q is the retransmission probability and ps is the probability of successful transmission. For Nc = 1, ps,c = 1, while for Nc > 1, ps,c = (1 c / 2)(Nc 1).
(iv) ABR via AN2: Assume in AN2 there are Na 1 other ABR users with the same arrival rate. There is no CBR user. The average delay can be seen from [26] to be:
,,2
2 ,
11 1[ ]2
s aA
s a
p qE D
p qm - +
= +
where q is the retransmission probability and ps is the probability of successful transmission. For Na = 1, ps,a = 1, while for Na > 1, ps,a = exp(-a(Na 1) ). We evaluate the following scenario with the relationship between different rates as a = c = 0.11 = 0.022. The average delay for one CBR or ABR service is shown in Fig. 8 and the unit slot time for delay is equal to 1 / 2. We also assume that P1 > P2.
If we set the delay bound c to be 8 time slots, then AN2 can take up to 15 CBR services while not maintaining appropriate quality. Say n1 = n2 = 8, N1 = 10, we then have 1n = 1, 2n = 15, and
4 .1 6 w ith o u t c o o p e ra tio n[ ]
7 .3 4 w ith c o o p e ra tio nE D =
2
3 .3 1 w ith o u t c o o p e ra tio n[ ]
7 .4 9 w ith co o p eratio nE D =
1 2 1 21 1 1 15[ ] [ ]2 2 16 16
E P P P P P E P = + > + =
In this case, some of the old CBR users in AN1 would switch to AN2 due lower price and acceptable delay. For ABR users, since there is no delay limit, users in AN1 would switch to AN2 until the PS network can no longer support such that its throughput starts to decrease. In the above numerical example, the effectiveness of cooperative self-organized (by mobile device only) cognitive radio networking is consequently successfully verified.
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2 4 6 8 10 12 14 160
2
4
6
8
10
12
14
Number of users in the network
Ave
rage
del
ayCBR delay in scenario 1
AN1 AnaSim w/o bufSim w/ buf
2 4 6 8 10 12 14 160
2
4
6
8
10
12
14
Number of users in the network
Ave
rage
del
ay
ABR delay in scenario 1
AN1 AnaSim w/o bufSim w/ buf
Figure 8 Performance of Self-organized Cognitive Radio in Cooperative Access Networks
VI. CONCLUSION We demonstrated a practical architecture design of
self-organized cognitive radio based on the newly found rate-distance nature, to meet future wireless communication need, which consists of precise/novel cognitive radio structure, corresponding cognitive cycle, re-configurable MAC design, and finally self-organized coordinator to determine appropriate routing traffic to enhance overall utilization and efficiency of wireless networks. Such cognitive radio achieves not only spectrum efficiency but also more importantly the networking efficiency of entire wireless networks in picture, which facilitate the cognitive radio networks to support better networking efficiency at a given bandwidth.
REFERENCES [1] T. Ojanpera, Convergence Transforms Internet, Wireless Personal
Communications, vol. 37, no. 3-4, May 2006. [2] J. Mitola III, G. Q. Maguire, Cognitive Radio: Making Software Radios
More Personal, IEEE Personal Communications, August 1999. [3] J. Mitola III, Cognitive Radio Architecture, Wiley, 2006. [4] IEEE 802.11 MAC and PHY Specifications, 2003. [5] F. K. Jondral, Software-Defined Radio Basics and Evolution to
Cognitive Radio, EURASIP Journal on Wireless Communications and Networking, PP.275-283, 2005.
[6] N. Devroye, P. Mitran, V. Tarokh, Limits on Communications in a Cognitive Radio Channel, IEEE Communications Magazine, pp.44-49, June 2006.
[7] S. Haykin, Cognitive Radio: Brain-Empowered Wireless Communications, IEEE Journal on Selected Areas in Communications, pp.201-220, No. 2, Vol. 23, Feb. 2005.
[8] S. M. Mishra, A. Sahai, R.W. Broderson, Cooperative Sensing among
Cognitive Radios, Proc. IEEE International Conference on Communications, 2006.
[9] A. Scaglione, D. Goeckei, J.N. Laneman, Cooperative Communications in Mobile Ad Hoc Networks, IEEE Signal Processing Magazine, pp.18-29, Sep. 2006.
[10] M. Cardei, J. Wu, S. Yang, Topology Control in Ad Hoc Wireless Networks Using Cooperative Communication, IEEE Trans. On Mobile Computing, vol. 5, no. 6, pp.711-724, June 2006.
[11] A.J. Goldsmith, The Capacity of Downlink Fading Channels with Variable Rate and Power, IEEE Trans. On Vehicular Technology, vol. 46, no. 3, pp.569-580, Aug. 1997.
[12] A.J. Goldsmith, S-G Chua, Variable-Rate Variable Power MQAM for Fading Channels, IEEE Trans. On Communications, vol. 45, no. 10, pp.1218-1230, Oct. 1997.
[13] ------, Adaptive Coded Modulation for Fading Channels, IEEE Trans. On Communications, vol. 46, no. 5, pp.595-602, May 1998.
[14] H.S. Wang, N. Moayeri, Finite-State Markov Channel A Useful Model for Radio Communication Channels, vol. 44, no. 1, Feb.
[15] M.S. Hsieh, K.C. Chen, A Novel Interference Identification, Proc. IEEE VTC, 2002.
[16] C.M. Teng, K.C. Chen, A Unified Algorithm for Wireless MAC Protocols, Proc. IEEE VTC, 2002.
[17] Chih-Cheng Tseng, Hsuan-Tsang Chen, Kwang-Cheng Chen, Characterizing The Wireless Ad Hoc Networks by Using The Distance Distributions, Proc. IST Mobile & Wireless Communications Summit 2006, Mykonos, Greece, June, 2006.
[18] C.S. Ni, K.C. Chen, Co-Channel Interference Suppression for Coded OFDM Systems over Frequency Selective Fading Channels, Proc. IEEE VTC Fall, 2004.
[19] W.C. Wu, K.C. Chen, Identification of Synchronous CDMA Multiuser Detection Using Preprocessing, IEEE Journal on Selected Areas in Communications special issue on Signal Processing in Wireless Communications, Dec. 1998.
[20] K.C. Chen, "Medium Access Control of Wireless Local Area Networks for Mobile Computing", IEEE Networks, pp.50-64, September, 1994.
[21] C.M. Chang, K.C. Chen, Joint Timing and Carrier Phase Estimation of DS/CDMA Multi-user Communications, special issue on Global Spread Spectrum Communications, IEEE Journal on Selected Areas in Communications, Jan. 2000.
[22] ------, Joint linear user identification, timing, phase, and amplitude estimation in DS/CDMA communications, IEEE Communications Letters, April, 2000.
[23] Alam, M.; Prasad, Ramjee; Farserotu, J.; Quality of Service Among IP-Based Heterogeneous Networks, vol. 8, no. 6, IEEE Personal Communications, 2001.
[24] K.C. Chen, S.T. Wu, A Programmable Architecture for OFDM-CDMA, IEEE Communications Magazine feature subject on Software and DSP in Radio, pp. 76-82, Nov. 1999.
[25] Y. Yang and T. Yum, Delay distribution of slotted ALOHA and CSMA, in IEEE Transactions on Communications, Nov. 2003.
[26] C. Bettstetter, et al., Self-Organization in Communication Networks: Overview and State-of-the-Art, WWRF White Paper, 2005.
[27] Y.K. Sun, K.C. Chen, D.C. Twu, Generalized Tree Multiple Access Protocol for Wireless Communications, Proc. IEEE PIMRC, Helsinki, 1997. University Science, 1989