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(,8x2  0C"?I-10  0JQ F /&2)  D3G(2   0x;Q  #& D2G(2 ! 0*Q  (, O 4G/ WiMax (2  " 0 7Q Y"(, JADSL(2 # 0L I+;2 \Collaborative Networks(2t $ 0{Q :Y1 Collaborative Networks The idea of antennas collaborating originated from MIMO (multiple input, multiple output) systems. Multiple antennas under independent fading paths cooperated to increase multiplexing gain. However, due to the limited space on a mobile device, multiple antennas often can t be realised. A distributed version, where multiple users each with an antenna cooperate to increase data throughput collectively is known as a collaborative network. B 2 ffff: % 3 A2 & 0hQ  "  Weisi Guo - 1st Year PhD Student@!(2  ff ff ff/ ' 0<ݨ2g+1> _Coded Cooperation There are many cooperation strategies, all of them can implement coding to decrease errors. A distributed turbo coding scheme amongst multiple users is an area of research. How each code performs under different channel conditions and varying fading speeds is very crucial. Another area of investigation is superimposing codes and determining the optimum amount of coded cooperation to maximize instantaneous capacity. It has been found that 100% coded cooperation is not always the best. There exists a trade off between coding and multiplexing gain for different channel conditions.F` 2 ff_ff: ( 3 A2W= ) 0; +Gv1 Power Control Power allocation amongst a collaborative network is important for practical energy efficiency and channel capacity. Centralised power control, and distributed power control schemes are considered for varying knowledge of channel state information (CSI). How to estimate CSI effectively and how this impacts capacity is investigated. It was found that CSI is critical to uplink transmitter collaboration, whereas optimal power control is critical to downlink receiver cooperation. Further investigation optimized results using different modulation methods that account for capacity, as opposed to standard Gaussian input assumptions. This leads to a mercury filling scheme, where power is not always most allocated to the best uplink channel node. d 2 Pffffffrff: * 3 A%%: - 3 A&#$/f / s *A ? ?$*^0& f 0 s *A ? ?[/$<21  2 0; 0}I001 [Mercury Filling (BPSK): 2 3 0hP %}L34& n*Water Filling Power Allocation (Gaussian):+ 2+ 5 0T; ?GY1F TConclusions and Future Work The project started April 2007 as an EPSRC collaboration project between Cambridge University and Newcastle University. I plan to derive a distributed mercury power control mechanism that maximizes channel capacity for practical modulation methods. The project will also look at various convolution and turbo coding schemes in a variety of channel conditions. 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None of them deliver one service the customers want: ultra high speed static data transfer. People are on the move only 10% of their time, and with growing high quality media on demand, bandwidth is in short supply. This project intends to create a fixed wireless high data rate collaborative network. R 2 ff'ffff RB  s *Dff $$RB  s *Dff#0  0LL /+1v J Data Speed 2   6/L & !5# M User Mobility 2x2  0C"?) <$&vx2  0C"? %)8x2  0fC"?'5,Kx2  0C"? (,8x2  0C"?I-10  0JQ F /&2)  D3G(2   0x;Q  #& D2G(2 ! 0*Q  (, O 4G/ WiMax (2  " 0 7Q Y"(, JADSL(2 # 0L I+;2 \Collaborative Networks(2t $ 0{Q :Y1 Collaborative Networks The idea of antennas collaborating originated from MIMO (multiple input, multiple output) systems. Multiple antennas under independent fading paths cooperated to increase multiplexing gain. However, due to the limited space on a mobile device, multiple antennas often can t be realised. A distributed version, where multiple users each with an antenna cooperate to increase data throughput collectively is known as a collaborative network. B 2 ffff: % 3 A2 & 0hQ  "  Weisi Guo - 1st Year PhD Student@!(2  ff ff ff ' 0<ݨ2g+18 9Coded Cooperation There are many cooperation strategies, all of them can implement coding to decrease errors. Cooperative codes can offer extra diversity in s  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~+@      !"#$%&'()*,./0123456789:L?ABCDEFGH-KMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxz{|}~Root EntrydO)`G3JPicturesrCurrent User /SummaryInformation(TRPowerPoint Document(DocumentSummaryInformation84DArial00LL'Ԗ0Ԗ B9.  @n?" dd@  @@``  F2Fb$~SI1T7u.b$v? ijtݥ.b$qaDX{u/1&b$y>^hf+'69^ Wb$*Ru<\6vFK]b$ag.%w=mPIwrSR9qCw$ Z6c{i찱vؿ?Ю46uBbni CP,|11vydԆ5V~i(5&?󽑃C3[ Y>_݃7f0Ϝ7TĄ̐* .{a,9"{kr zr%g,_=fbKbO)!7> -u80:~ ($Dy- [u~,OL2Z4({[] R߅]gns뱙o#o6_K2qnMmt:~|?|8i$x[h\Upιmrf7n&BI>*$F54M汵T૥>@ JğCBSf9&$fɞ3g93sܫW||:ؠ Δ;ưqT(t&!- A V>BAhY.q!cr\߽׿f§ʴO= 1/`g+s2ghIuLjO:)/xۭY~1yj>=ƧkbP,N(xr"ſ's@}9o^8q.\G7CLa:ڕwLL:7)Ha^% #?M9s8/&fE9jz:є7A TPNȍت@ НP 6` d3Dua-0yÔ>d ]SHkx+ٹup۳4ٺn>pe?ASİ?gTLQ[+Q\37p?֍S2Y.Pd}bIq5a@1#fPt(EJ=Y1G(Zy\oRlv{12H1vCKFx Owr`R\-LOd^ռ /So!dZ=j{%Mv٧eYds5?%=Լ~ x}!yl*'Wc砤΄7 ȱ ދHcM7zޝnkrƾL=m I/iCjдP><: #!ԉFlmn?\C10(.$gPR h7Rc6O)dlyrE |ϖ=#!b@F2BϘxUro=X٥ 94 Zfj#\ϕD~K[ڹtZunOɭTƜYĉ..bB/WWH{LOxwgCvog{\,K5N2o{J,+WF/y}`:0;bi/߹|*ϾX MUr|?[s?:NDfk.mk|G|ҚliȬmse֐ܓ_=4Q!\ΤGMgj+>x?ΨarLF MZR(FX1(4J   Equation Equation.30,Microsoft Equation 3.0Equation ՜.+,0    |CustomUniversity of Cambridge ArialDefault DesignMicrosoft Equation 3.0 Collaborative Wireless Networks  Fonts UsedDesign TemplateEmbedded OLE Servers Slide Titles_ߴ wg209wg209low fading channels, but not in fast fading channels.: 2 ]fffff: ( 3 A@3W > ) 0; +Gv1 Power Control Power allocation amongst a collaborative network is important for practical energy efficiency and channel capacity. Centralised power control, and distributed power control schemes are considered for varying knowledge of channel state information (CSI). How to estimate CSI effectively and how this impacts capacity is investigated. It was found that CSI is critical to uplink transmitter collaboration, whereas optimal power control is critical to downlink receiver cooperation. Further investigation optimized results using different modulation methods that account for capacity, as opposed to standard Gaussian input assumptions. This leads to a mercury filling scheme, where power is not always most allocated to the best uplink channel node. d 2 Pffffffrff: * 3 A%%: - 3 A&#$/f / s *A ? ?$*^0& f 0 s *A ? ?[/$<21  2 0; 0}I001 [Mercury Filling (BPSK): 2 3 0hP %}L34& n*Water Filling Power Allocation (Gaussian):+ 2+ 5 0T; ?GY1F TConclusions and Future Work The project started April 2007 as an EPSRC collaboration project between Cambridge University and Newcastle University. I plan to derive a distributed mercury power control mechanism that maximizes channel capacity for practical modulation methods. The project will also look at various convolution and turbo coding schemes in a variety of channel conditions. On the aspect of relay strategies, game theory and biological collaborative communications will be looked into.4 2 ff 6 0DP )HC2II l$http://www.cl.cam.ac.uk/research/DTG%(2%X ; 0,\&'* < 0 6P m'i2 |Purple bar denotes the amount of noise a node s channel has. $? 2<X = 0O#\&'1$X > 0 \&'O#f ? 0$;  m'3$ The better the channel (less noise), the more power it should be allocated and the more it should cooperate with other weaker channels. This is for Gaussian inputs in a MIMO system. $ 2X B 0h-\&'J. C < 6; *\&'g- :9 D 0L r(m'y3. }/In a constellation modulated input system: an offset (mercury filling) is performed before power allocation (water filling). This takes into account the capacity limit of a channel. Performance is similar at low SNR, but can give opposite power allocations at high SNR as a channel reaches capacity. $0 2-X E 0')\&'*` G c $A ?? 8&+1: ` H c $A ?? ;&1>  K 0lj8)9 z6Slow Fading Cooperative Coding (Diversity order of 2):7 27 L 0<)<= t0Fast Fading Cooperative Coding (Diversity of 1):1 21H  0nF! ? 3380___PPT10.CSM"xKl[Epv% M՘(BJF!MZH=8TqOJIZT(PPˁR 8^8 ў*NUUqK13_y: xy3;3;]ˁC `n;G[VJ`v`[8WPTp_]3'?) YQï:ǯ^E;M9H-ҭO#4O` F˨fpSV`1|~tL,$|Qs ӄtB9ݧcEO(^Gq:_v%4BOBg}xuЅ`m0`@#z]4~L&]б O]ʧsR1SvlTPx0~¥Ywabe~k]A|$v刞9z?8.1a8) %@2I*d৆A]?O3{_,$iU}cwQ% )EA9JYl8aµyX+}gPD^7q/Fa=' ^7du+ ~);9\0/f~H+T%?U `޶_f6|:IG6'ͤQGwo6gυK̸Z+qP|Y1sqPrl  >PIwrSR9qCw$ Z6c{i찱vؿ?Ю46uBbni CP,|11vydԆ5V~i(5&?󽑃C3[ Y>_݃7f0Ϝ7TĄ̐* .{a,9"{kr zr%g,_=fbKbO)!7> -u80:~ ($Dy- [u~,OL2Z4({[] R߅]gns뱙o#o6_K2qnMmt:~|?|8i$x[h\Upιmrf7n&BI>*$F54M汵T૥>@ JğCBSf9&$fɞ3g93sܫW||:ؠ Δ;ưqT(t&!- A V>BAhY.q!cr\߽׿f§ʴO= 1/`g+s2ghIuLjO:)/xۭY~1yj>=ƧkbP,N(xr"ſ's@}9o^8q.\G7CLa:ڕwLL:7)Ha^% #?M9s8/&fE9jz:є7A TPNȍت@ НP 6` d3Dua-0yÔ>d ]SHkx+ٹup۳4ٺn>pe?ASİ?gTLQ[+Q\37p?֍S2Y.Pd}bIq5a@1#fPt(EJ=Y1G(Zy\oRlv{12H1vCKFx Owr`R\-LOd^ռ /So!dZ=j{%Mv٧eYds5?%=Լ~ x}!yl*'Wc砤΄7 ȱ ދHcM7zޝnkrƾL=m I/iCjдP><: #!ԉFlmn?\C10(.$gPR h7Rc6O)dlyrE |ϖ=#!b@F2BϘxUro=X٥ 94 Zfj#\ϕD~K[ڹtZunOɭTƜYĉ..bB/WWH{LOxwgCvog{\,K5N2o{J,+WF/y}`:0;bi/߹|*ϾX MUr|?[s?:NDfk.mk|G|ҚliȬmse֐ܓ_=4Q!\ΤGMgj+>x?ΨaxWMlG~3;6z84BpTU R19!qBAU%B@PEQS@H E@ؼ73 ZS"|of5/h~ ;0T@cIpKrahUla!l4.}bhC`'^~'5a8kyϫ}^ٙ_Ӈ/ I&Ϗ0 tzμ{,pjqOg T?>wc30IQ~ugHKz߼OmO P,lՃ@P AtPN|ؾx>/b3pW Sڧ80tDn<7OtMr#L=.O;a_Ƃ'T]I;_Sa&6ZRGSUL=q kg5/YBF-zlҧ*a{s X sBU Xp/C.ƨ^q5;Z{=?o 5bB ;GQw=?Zqzln&w!z}uqR\[Twd+H#/ls?._j3S";ߏ B|owc?.Qxɞ!=~\ /٣=e[ʞxxe3tf2q(a-N);1QFڒG3Jmֈ!q2Sa﫰i[88>_4)]Ŗ+E~nuZږWL-@]E;}d@xn;qFa`>ݖg|S=d ,!G^b`@eJNfc0}x{ t+K%I)@AR?6fs4 > c3Y|O :Jq;Pp;7 k inÖkIAI0l `޻^VVV xU/AlFiqhippPW j+VMQ"W 7GnvYȬ6޼߼yo}ϧ/04f4tLr0 KmQSxwh:搦/Ƒv ;թ[";nv-ӄ2u:CgvT`ؤ},a1E> s,*::.bGIޛ 9I`HM/oj:Φx&KSX8 Wtpl ^.2jWgqe H|]yd2ݠӘY+tk-D:jRv Gep3^ M ŞO^>&0U:Y8*e X̟<@\JJY9DgO*w&MPN.(Jr(Xra : `ZX= 1(4J   Equation Equation.30,Microsoft Equation 3.0Equation Equation.30,Microsoft Equation 3.0Equation Equation.30,Microsoft Equation 3.0 Equation Equation.30,Microsoft Equation 3.0/ 0LDArialȃ0ȃ0``'0" B9.  @n?" dd@  @@``  M6Mb$~SI1T7u.b$v? ijtݥ.b$qaDX{u/1&b$y>^hf+'69^ Wb$*Ru<\6vFK]b$ag.%w=mݸ2$G -iפRxWb$be:}Y력Ux&Ll 0AA fff@ff3ʚ; H8ʚ;g42d2d0pB%pP<4dddd,A10`& 80___PPT10 ppCollaborative Wireless Networks  H $*0 )) 0L$)(  : 7 3 A r<&4J:   3 A   C `A0identifier1University of Cambridgec3x  c $Pk.     0$ l%2  p,Computer Laboratory Digital Technology Group-(2-    0T$  u)  HWireless Communications Today Wireless communications today has evolved into two complimentary sectors: a mobile cellular network and a static home network. None of them deliver one service the customers want: ultra high speed static data transfer. People are on the move only 10% of their time, and with growing high quality media on demand, bandwidth is in short supply. This project intends to create a fixed wireless high data rate collaborative network. R 2 ff'ffff RB  s *Dff $$RB  s *Dff#0  0LL /+1v J Data Speed 2   6/L & !5# M User Mobility 2x2  0C"?) <$&vx2  0C"? %)8x2  0fC"?'5,Kx2  0C"? (,8x2  0C"?I-10  0JQ F /&2)  D3G(2   0x;Q  #& D2G(2 ! 0*Q  (, O 4G/ WiMax (2  " 0 7Q Y"(, JADSL(2 # 0L I+;2 \Collaborative Networks(2t $ 0{Q :Y1 Collaborative Networks The idea of antennas collaborating originated from MIMO (multiple input, multiple output) systems. Multiple antennas under independent fading paths cooperated to increase multiplexing gain. However, due to the limited space on a mobile device, multiple antennas often can t be realised. A distributed version, where multiple users each with an antenna cooperate to increase data throughput collectively is known as a collaborative network. B 2 ffff: % 3 A2 & 0hQ  "  Weisi Guo - 1st Year PhD Student@!(2  ff ff ff ' 0<ݨ2g+18 9Coded Cooperation There are many cooperation strategies, all of them can implement coding to decrease errors. Cooperative codes can offer extra diversity in slow fading channels, but not in fast fading channels.: 2 ]fffff: ( 3 A@3W > ) 0; +Gv1 Power Control Power allocation amongst a collaborative network is important for practical energy efficiency and channel capacity. Centralised power control, and distributed power control schemes are considered for varying knowledge of channel state information (CSI). How to estimate CSI effectively and how this impacts capacity is investigated. It was found that CSI is critical to uplink transmitter collaboration, whereas optimal power control is critical to downlink receiver cooperation. Further investigation optimized results using different modulation methods that account for capacity, as opposed to standard Gaussian input assumptions. This leads to a mercury filling scheme, where power is not always most allocated to the best uplink channel node. d 2 Pffffffrff: * 3 A%%: - 3 A&#$/f / s *A ? ?$*^0& f 0 s *A ? ?[/$<21  2 0; 0}I001 [Mercury Filling (BPSK): 2 3 0hP %}L34& n*Water Filling Power Allocation (Gaussian):+ 2+ 5 0T; ?GY1F TConclusions and Future Work The project started April 2007 as an EPSRC collaboration project between Cambridge University and Newcastle University. I plan to derive a distributed mercury power control mechanism that maximizes channel capacity for practical modulation methods. The project will also look at various convolution and turbo coding schemes in a variety of channel conditions. On the aspect of relay strategies, game theory and biological collaborative communications will be looked into.4 2 ff 6 0DP )HC2II l$http://www.cl.cam.ac.uk/research/DTG%(2%X ; 0,\&'* < 0 6P m'i2 |Purple bar denotes the amount of noise a node s channel has. $? 2<X = 0O#\&'1$X > 0 \&'O#f ? 0$;  m'3$ The better the channel (less noise), the more power it should be allocated and the more it should cooperate with other weaker channels. This is for Gaussian inputs in a MIMO system. $ 2X B 0h-\&'J. C < 6; *\&'g- :9 D 0L r(m'y3. }/In a constellation modulated input system: an offset (mercury filling) is performed before power allocation (water filling). This takes into account the capacity limit of a channel. Performance is similar at low SNR, but can give opposite power allocations at high SNR as a channel reaches capacity. $0 2-X E 0')\&'*` G c $A ?? 8?'1: ` H c $A ?? ;?'2=  K 0lj8)9 z6Slow Fading Cooperative Coding (Diversity order of 2):7 27 L 0<)<= t0Fast Fading Cooperative Coding (Diversity of 1):1 21H  0nF! ? 3380___PPT10.CSM"xKl[Epv% M՘(BJF!MZH=8TqOJIZT(PPˁR 8^8 ў*NUUqK13_y: xy3;3;]ˁC `n;G[VJ`v`[8WPTp_]3'?) YQï:ǯ^E;M9H-ҭO#4O` F˨fpSV`1|~tL,$|Qs ӄtB9ݧcEO(^Gq:_v%4BOBg}xuЅ`m0`@#z]4~L&]б O]ʧsR1SvlTPx0~¥Ywabe~k]A|$v刞9z?8.1a8) %@2I*d৆A]?O3{_,$iU}cwQ% )EA9JYl8aµyX+}gPD^7q/Fa=' ^7du+ ~);9\0/f~H+T%?U `޶_f6|:IG6'ͤQGwo6gυK̸Z+qP|Y1sqPrl  >PIwrSR9qCw$ Z6c{i찱vؿ?Ю46uBbni CP,|11vydԆ5V~i(5&?󽑃C3[ Y>_݃7f0Ϝ7TĄ̐* .{a,9"{kr zr%g,_=fbKbO)!7> -u80:~ ($Dy- [u~,OL2Z4({[] R߅]gns뱙o#o6_K2qnMmt:~|?|8i$x[h\Upιmrf7n&BI>*$F54M汵T૥>@ JğCBSf9&$fɞ3g93sܫW||:ؠ Δ;ưqT(t&!- A V>BAhY.q!cr\߽׿f§ʴO= 1/`g+s2ghIuLjO:)/xۭY~1yj>=ƧkbP,N(xr"ſ's@}9o^8q.\G7CLa:ڕwLL:7)Ha^% #?M9s8/&fE9jz:є7A TPNȍت@ НP 6` d3Dua-0yÔ>d ]SHkx+ٹup۳4ٺn>pe?ASİ?gTLQ[+Q\37p?֍S2Y.Pd}bIq5a@1#fPt(EJ=Y1G(Zy\oRlv{12H1vCKFx Owr`R\-LOd^ռ /So!dZ=j{%Mv٧eYds5?%=Լ~ x}!yl*'Wc砤΄7 ȱ ދHcM7zޝnkrƾL=m I/iCjдP><: #!ԉFlmn?\C10(.$gPR h7Rc6O)dlyrE |ϖ=#!b@F2BϘxUro=X٥ 94 Zfj#\ϕD~K[ڹtZunOɭTƜYĉ..bB/WWH{LOxwgCvog{\,K5N2o{J,+WF/y}`:0;bi/߹|*ϾX MUr|?[s?:NDfk.mk|G|ҚliȬmse֐ܓ_=4Q!\ΤGMgj+>x?ΨaxWMlG~3;6z84BpTU R19!qBAU%B@PEQS@H E@ؼ73 ZS"|of5/h~ ;0T@cIpKrahUla!l4.}bhC`'^~'5a8kyϫ}^ٙ_Ӈ/ I&Ϗ0 tzμ{,pjqOg T?>wc30IQ~ugHKz߼OmO P,lՃ@P AtPN|ؾx>/b3pW Sڧ80tDn<7OtMr#L=.O;a_Ƃ'T]I;_Sa&6ZRGSUL=q kg5/YBF-zlҧ*a{s X sBU Xp/C.ƨ^q5;Z{=?o 5bB ;GQw=?Zqzln&w!z}uqR\[Twd+H#/ls?._j3S";ߏ B|owc?.Qxɞ!=~\ /٣=e[ʞxxe3tf2q(a-N);1QFڒG3Jmֈ!q2Sa﫰i[88>_4)]Ŗ+E~nuZږWL-@]E;}d@xn;qFa`>ݖg|S=d ,!G^b`@eJNfc0}x{ t+K%I)@AR?6fs4 > c3Y|O :Jq;Pp;7 k inÖkIAI0l `޻^VVV xU/AlFiqhippPW j+VMQ"W 7GnvYȬ6޼߼yo}ϧ/04f4tLr0 KmQSxwh:搦/Ƒv ;թ[";nv-ӄ2u:CgvT`ؤ},a1E> s,*::.bGIޛ 9I`HM/oj:Φx&KSX8 Wtpl ^.2jWgqe H|]yd2ݠӘY+tk-D:jRv Gep3^ M ŞO^>&0U:Y8*e X̟<@\JJY9DgO*w&MPN.(Jr(C o  1+m 1(4J   Equation Equation.30,Microsoft Equation 3.0Equation Equation.30,Microsoft Equation 3.0Equation Equation.30,Microsoft Equation 3.0 Equation Equation.30,Microsoft Equation 3.0/ 0LDArialȃ0ȃ0``'0" B9.  @n?" dd@  @@``  M6Mb$~SI1T7u.b$v? ijtݥ.b$qaDX{u/1&b$y>^hf+'69^ Wb$*Ru<\6vFK]b$ag.%w=mݸ2$G -iפRxWb$be:}Y력Ux&Ll 0AA fff@ff3ʚ; H8ʚ;g42d2d0pB%pP<4dddd,A10`& 80___PPT10 ppCollaborative Wireless Networks  H $*0 )) 0L$)(  : 7 3 A r<&4J:   3 A   C `A0identifier1University of Cambridgec3x  c $Pk.     0$ l%2  p,Computer Laboratory Digital Technology Group-(2-    0T$  u)  HWireless Communications Today Wireless communications today has evolved into two complimentary sectors: a mobile cellular network and a static home network. None of them deliver one service the customers want: ultra high speed static data transfer. People are on the move only 10% of their time, and with growing high quality media on demand, bandwidth is in short supply. This project intends to create a fixed wireless high data rate collaborative network. R 2 ff'ffff RB  s *Dff $$RB  s *Dff#0  0LL /+1v J Data Speed 2   6/L & !5# M User Mobility 2x2  0C"?) <$&vx2  0C"? %)8x2  0fC"?'5,Kx2  0C"? (,8x2  0C"?I-10  0JQ F /&2)  D3G(2   0x;Q  #& D2G(2 ! 0*Q  (, O 4G/ WiMax (2  " 0 7Q Y"(, JADSL(2 # 0L I+;2 \Collaborative Networks(2t $ 0{Q :Y1 Collaborative Networks The idea of antennas collaborating originated from MIMO (multiple input, multiple output) systems. Multiple antennas under independent fading paths cooperated to increase multiplexing gain. However, due to the limited space on a mobile device, multiple antennas often can t be realised. A distributed version, where multiple users each with an antenna cooperate to increase data throughput collectively is known as a collaborative network. B 2 ffff: % 3 A2 & 0hQ  "  Weisi Guo - 1st Year PhD Student@!(2  ff ff ff ' 0<ݨ2g+18 9Coded Cooperation There are many cooperation strategies, all of them can implement coding to decrease errors. Cooperative codes can offer extra diversity in slow fading channels, but not in fast fading channels.: 2 ]fffff: ( 3 A@3W > ) 0; +Gv1 Power Control Power allocation amongst a collaborative network is important for practical energy efficiency and channel capacity. Centralised power control, and distributed power control schemes are considered for varying knowledge of channel state information (CSI). How to estimate CSI effectively and how this impacts capacity is investigated. It was found that CSI is critical to uplink transmitter collaboration, whereas optimal power control is critical to downlink receiver cooperation. Further investigation optimized results using different modulation methods that account for capacity, as opposed to standard Gaussian input assumptions. This leads to a mercury filling scheme, where power is not always most allocated to the best uplink channel node. d 2 Pffffffrff: * 3 A%%: - 3 A&#$/f / s *A ? ?$*^0& f 0 s *A ? ?[/$<21  2 0; 0}I001 [Mercury Filling (BPSK): 2 3 0hP %}L34& n*Water Filling Power Allocation (Gaussian):+ 2+ 5 0T; ?GY1F TConclusions and Future Work The project started April 2007 as an EPSRC collaboration project between Cambridge University and Newcastle University. I plan to derive a distributed mercury power control mechanism that maximizes channel capacity for practical modulation methods. The project will also look at various convolution and turbo coding schemes in a variety of channel conditions. On the aspect of relay strategies, game theory and biological collaborative communications will be looked into.4 2 ff 6 0DP )HC2II l$http://www.cl.cam.ac.uk/research/DTG%(2%X ; 0,\&'* < 0 6P m'i2 |Purple bar denotes the amount of noise a node s channel has. $? 2<X = 0O#\&'1$X > 0 \&'O#f ? 0$;  m'3$ The better the channel (less noise), the more power it should be allocated and the more it should cooperate with other weaker channels. This is for Gaussian inputs in a MIMO system. $ 2X B 0h-\&'J. C < 6; *\&'g- :9 D 0L r(m'y3. }/In a constellation modulated input system: an offset (mercury filling) is performed before power allocation (water filling). This takes into account the capacity limit of a channel. Performance is similar at low SNR, but can give opposite power allocations at high SNR as a channel reaches capacity. $0 2-X E 0')\&'*` G c $A ?? 8?'1: ` H c $A ??f;?'2S>  K 0lj8)9 z6Slow Fading Cooperative Coding (Diversity order of 2):7 27 L 0v<)= t0Fast Fading Cooperative Coding (Diversity of 1):1 21H  0nF! ? 3380___PPT10.CSM"xKl[Epv% M՘(BJF!MZH=8TqOJIZT(PPˁR 8^8 ў*NUUqK13_y: xy3;3;]ˁC `n;G[VJ`v`[8WPTp_]3'?) YQï:ǯ^E;M9H-ҭO#4O` F˨fpSV`1|~tL,$|Qs ӄtB9ݧcEO(^Gq:_v%4BOBg}xuЅ`m0`@#z]4~L&]б O]ʧsR1SvlTPx0~¥Ywabe~k]A|$v刞9z?8.1a8) %@2I*d৆A]?O3{_,$iU}cwQ% )EA9JYl8aµyX+}gPD^7q/Fa=' ^7du+ ~);9\0/f~H+T%?U `޶_f6|:IG6'ͤQGwo6gυK̸Z+qP|Y1sqPrl  >PIwrSR9qCw$ Z6c{i찱vؿ?Ю46uBbni CP,|11vydԆ5V~i(5&?󽑃C3[ Y>_݃7f0Ϝ7TĄ̐* .{a,9"{kr zr%g,_=fbKbO)!7> -u80:~ ($Dy- [u~,OL2Z4({[] R߅]gns뱙o#o6_K2qnMmt:~|?|8i$x[h\Upιmrf7n&BI>*$F54M汵T૥>@ JğCBSf9&$fɞ3g93sܫW||:ؠ Δ;ưqT(t&!- A V>BAhY.q!cr\߽׿f§ʴO= 1/`g+s2ghIuLjO:)/xۭY~1yj>=ƧkbP,N(xr"ſ's@}9o^8q.\G7CLa:ڕwLL:7)Ha^% #?M9s8/&fE9jz:є7A TPNȍت@ НP 6` d3Dua-0yÔ>d ]SHkx+ٹup۳4ٺn>pe?ASİ?gTLQ[+Q\37p?֍S2Y.Pd}bIq5a@1#fPt(EJ=Y1G(Zy\oRlv{12H1vCKFx Owr`R\-LOd^ռ /So!dZ=j{%Mv٧eYds5?%=Լ~ x}!yl*'Wc砤΄7 ȱ ދHcM7zޝnkrƾL=m I/iCjдP><: #!ԉFlmn?\C10(.$gPR h7Rc6O)dlyrE |ϖ=#!b@F2BϘxUro=X٥ 94 Zfj#\ϕD~K[ڹtZunOɭTƜYĉ..bB/WWH{LOxwgCvog{\,K5N2o{J,+WF/y}`:0;bi/߹|*ϾX MUr|?[s?:NDfk.mk|G|ҚliȬmse֐ܓ_=4Q!\ΤGMgj+>x?ΨaxWMlG~3;6z84BpTU R19!qBAU%B@PEQS@H E@ؼ73 ZS"|of5/h~ ;0T@cIpKrahUla!l4.}bhC`'^~'5a8kyϫ}^ٙ_Ӈ/ I&Ϗ0 tzμ{,pjqOg T?>wc30IQ~ugHKz߼OmO P,lՃ@P AtPN|ؾx>/b3pW Sڧ80tDn<7OtMr#L=.O;a_Ƃ'T]I;_Sa&6ZRGSUL=q kg5/YBF-zlҧ*a{s X sBU Xp/C.ƨ^q5;Z{=?o 5bB ;GQw=?Zqzln&w!z}uqR\[Twd+H#/ls?._j3S";ߏ B|owc?.Qxɞ!=~\ /٣=e[ʞxxe3tf2q(a-N);1QFڒG3Jmֈ!q2Sa﫰i[88>_4)]Ŗ+E~nuZږWL-@]E;}d@xn;qFa`>ݖg|S=d ,!G^b`@eJNfc0}x{ t+K%I)@AR?6fs4 > c3Y|O :Jq;Pp;7 k inÖkIAI0l `޻^VVV xU/AlFiqhippPW j+VMQ"W 7GnvYȬ6޼߼yo}ϧ/04f4tLr0 KmQSxwh:搦/Ƒv ;թ[";nv-ӄ2u:CgvT`ؤ},a1E> s,*::.bGIޛ 9I`HM/oj:Φx&KSX8 Wtpl ^.2jWgqe H|]yd2ݠӘY+tk-D:jRv Gep3^ M ŞO^>&0U:Y8*e X̟<@\JJY9DgO*w&MPN.(Jr(b @ !># 1(4J   Equation Equation.30,Microsoft Equation 3.0Equation Equation.30,Microsoft Equation 3.0Equation Equation.30,Microsoft Equation 3.0 Equation Equation.30,Microsoft Equation 3.0/ 0LDArialȃ0ȃ0``'0" B9.  @n?" dd@  @@``  M6Mb$~SI1T7u.b$v? ijtݥ.b$qaDX{u/1&b$y>^hf+'69^ Wb$*Ru<\6vFK]b$ag.%w=mݸ2$G -iפRxWb$be:}Y력Ux&Ll 0AA fff@ff3ʚ; H8ʚ;g4!d!d0^pB%pP<4dddd,A10`& 80___PPT10 ppCollaborative Wireless Networks  H $*0 )) 0L$)(  : 7 3 A r<&4J:   3 A   C `A0identifier1University of Cambridgec3x  c $Pk.     0$ l%2  p,Computer Laboratory Digital Technology Group-(2-    0T$  u)  HWireless Communications Today Wireless communications today has evolved into two complimentary sectors: a mobile cellular network and a static home network. None of them deliver one service the customers want: ultra high speed static data transfer. People are on the move only 10% of their time, and with growing high quality media on demand, bandwidth is in short supply. This project intends to create a fixed wireless high data rate collaborative network. R 2 ff'ffff RB  s *Dff $$RB  s *Dff#0  0LL /+1v J Data Speed 2   6/L & !5# M User Mobility 2x2  0C"?) <$&vx2  0C"? %)8x2  0fC"?'5,Kx2  0C"? (,8x2  0C"?I-10  0JQ F /&2)  D3G(2   0x;Q  #& D2G(2 ! 0*Q  (, O 4G/ WiMax (2  " 0 7Q Y"(, JADSL(2 # 0L I+;2 \Collaborative Networks(2t $ 0{Q :Y1 Collaborative Networks The idea of antennas collaborating originated from MIMO (multiple input, multiple output) systems. Multiple antennas under independent fading paths cooperated to increase multiplexing gain. However, due to the limited space on a mobile device, multiple antennas often can t be realised. A distributed version, where multiple users each with an antenna cooperate to increase data throughput collectively is known as a collaborative network. B 2 ffff: % 3 A2 & 0hQ  "  Weisi Guo - 1st Year PhD Student@!(2  ff ff ff ' 0<ݨ2g+18 9Coded Cooperation There are many cooperation strategies, all of them can implement coding to decrease errors. Cooperative codes can offer extra diversity in slow fading channels, but not in fast fading channels.: 2 ]fffff: ( 3 A@3W > ) 0; +Gv1 Power Control Power allocation amongst a collaborative network is important for practical energy efficiency and channel capacity. Centralised power control, and distributed power control schemes are considered for varying knowledge of channel state information (CSI). How to estimate CSI effectively and how this impacts capacity is investigated. It was found that CSI is critical to uplink transmitter collaboration, whereas optimal power control is critical to downlink receiver cooperation. Further investigation optimized results using different modulation methods that account for capacity, as opposed to standard Gaussian input assumptions. This leads to a mercury filling scheme, where power is not always most allocated to the best uplink channel node. d 2 Pffffffrff: * 3 A%%: - 3 A&#$/f / s *A ? ?$*^0& f 0 s *A ? ?[/$<21  2 0; 0}I001 [Mercury Filling (BPSK): 2 3 0hP %}L34& n*Water Filling Power Allocation (Gaussian):+ 2+ 5 0T; ?GY1F TConclusions and Future Work The project started April 2007 as an EPSRC collaboration project between Cambridge University and Newcastle University. I plan to derive a distributed mercury power control mechanism that maximizes channel capacity for practical modulation methods. The project will also look at various convolution and turbo coding schemes in a variety of channel conditions. On the aspect of relay strategies, game theory and biological collaborative communications will be looked into.4 2 ff 6 0DP )HC2II l$http://www.cl.cam.ac.uk/research/DTG%(2%X ; 0,\&'* < 0 6P m'i2 |Purple bar denotes the amount of noise a node s channel has. $? 2<X = 0O#\&'1$X > 0 \&'O#f ? 0$;  m'3$ The better the channel (less noise), the more power it should be allocated and the more it should cooperate with other weaker channels. This is for Gaussian inputs in a MIMO system. $ 2X B 0h-\&'J. C < 6; *\&'g- :9 D 0L r(m'y3. }/In a constellation modulated input system: an offset (mercury filling) is performed before power allocation (water filling). This takes into account the capacity limit of a channel. Performance is similar at low SNR, but can give opposite power allocations at high SNR as a channel reaches capacity. $0 2-X E 0')\&'*` G c $A ??8'1>; ` H c $A ??;'1>  K 0ljG9)g: z6Slow Fading Cooperative Coding (Diversity order of 2):7 27 L 0<)= t0Fast Fading Cooperative Coding (Diversity of 1):1 21H  0nF! ? 3380___PPT10.CSM"xKl[Epv% M՘(BJF!MZH=8TqOJIZT(PPˁR 8^8 ў*NUUqK13_y: xy3;3;]ˁC `n;G[VJ`v`[8WPTp_]3'?) YQï:ǯ^E;M9H-ҭO#4O` F˨fpSV`1|~tL,$|Qs ӄtB9ݧcEO(^Gq:_v%4BOBg}xuЅ`m0`@#z]4~L&]б O]ʧsR1SvlTPx0~¥Ywabe~k]A|$v刞9z?8.1a8) %@2I*d৆A]?O3{_,$iU}cwQ% )EA9JYl8aµyX+}gPD^7q/Fa=' ^7du+ ~);9\0/f~H+T%?U `޶_f6|:IG6'ͤQGwo6gυK̸Z+qP|Y1sqPrl  >PIwrSR9qCw$ Z6c{i찱vؿ?Ю46uBbni CP,|11vydԆ5V~i(5&?󽑃C3[ Y>_݃7f0Ϝ7TĄ̐* .{a,9"{kr zr%g,_=fbKbO)!7> -u80:~ ($Dy- [u~,OL2Z4({[] R߅]gns뱙o#o6_K2qnMmt:~|?|8i$x[h\Upιmrf7n&BI>*$F54M汵T૥>@ JğCBSf9&$fɞ3g93sܫW||:ؠ Δ;ưqT(t&!- A V>BAhY.q!cr\߽׿f§ʴO= 1/`g+s2ghIuLjO:)/xۭY~1yj>=ƧkbP,N(xr"ſ's@}9o^8q.\G7CLa:ڕwLL:7)Ha^% #?M9s8/&fE9jz:є7A TPNȍت@ НP 6` d3Dua-0yÔ>d ]SHkx+ٹup۳4ٺn>pe?ASİ?gTLQ[+Q\37p?֍S2Y.Pd}bIq5a@1#fPt(EJ=Y1G(Zy\oRlv{12H1vCKFx Owr`R\-LOd^ռ /So!dZ=j{%Mv٧eYds5?%=Լ~ x}!yl*'Wc砤΄7 ȱ ދHcM7zޝnkrƾL=m I/iCjдP><: #!ԉFlmn?\C10(.$gPR h7Rc6O)dlyrE |ϖ=#!b@F2BϘxUro=X٥ 94 Zfj#\ϕD~K[ڹtZunOɭTƜYĉ..bB/WWH{LOxwgCvog{\,K5N2o{J,+WF/y}`:0;bi/߹|*ϾX MUr|?[s?:NDfk.mk|G|ҚliȬmse֐ܓ_=4Q!\ΤGMgj+>x?ΨaxWMlG~3;6z84BpTU R19!qBAU%B@PEQS@H E@ؼ73 ZS"|of5/h~ ;0T@cIpKrahUla!l4.}bhC`'^~'5a8kyϫ}^ٙ_Ӈ/ I&Ϗ0 tzμ{,pjqOg T?>wc30IQ~ugHKz߼OmO P,lՃ@P AtPN|ؾx>/b3pW Sڧ80tDn<7OtMr#L=.O;a_Ƃ'T]I;_Sa&6ZRGSUL=q kg5/YBF-zlҧ*a{s X sBU Xp/C.ƨ^q5;Z{=?o 5bB ;GQw=?Zqzln&w!z}uqR\[Twd+H#/ls?._j3S";ߏ B|owc?.Qxɞ!=~\ /٣=e[ʞxxe3tf2q(a-N);1QFڒG3Jmֈ!q2Sa﫰i[88>_4)]Ŗ+E~nuZږWL-@]E;}d@xn;qFa`>ݖg|S=d ,!G^b`@eJNfc0}x{ t+K%I)@AR?6fs4 > c3Y|O :Jq;Pp;7 k inÖkIAI0l `޻^VVV xU/AlFiqhippPW j+VMQ"W 7GnvYȬ6޼߼yo}ϧ/04f4tLr0 KmQSxwh:搦/Ƒv ;թ[";nv-ӄ2u:CgvT`ؤ},a1E> s,*::.bGIޛ 9I`HM/oj:Φx&KSX8 Wtpl ^.2jWgqe H|]yd2ݠӘY+tk-D:jRv Gep3^ M ŞO^>&0U:Y8*e X̟<@\JJY9DgO*w&MPN.(Jr(3$, W[ ae$g 1(4J   Equation Equation.30,Microsoft Equation 3.0Equation Equation.30,Microsoft Equation 3.0Equation Equation.30,Microsoft Equation 3.0 Equation Equation.30,Microsoft Equation 3.0/ 0LDArial00LL'Ԗ0Ԗ" B9.  @n?" dd@  @@``  M6Mb$~SI1T7u.b$v? ijtݥ.b$qaDX{u/1&b$y>^hf+'69^ Wb$*Ru<\6vFK]b$ag.%w=mݸ2$G -iפRxWb$be:}Y력Ux&Ll 0AA fff@ff3ʚ; H8ʚ;g4!d!d0^pB%pP<4ddddA10L& 80___PPT10 ppCollaborative Wireless Networks  H "xKl[Epv% M՘(BJF!MZH=8TqOJIZT(PPˁR 8^8 ў*NUUqK13_y: xy3;3;]ˁC `n;G[VJ`v`[8WPTp_]3'?) 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L     0$L l%2  p,Computer Laboratory Digital Technology Group-(2-    0dL  u)  Wireless Communications Today Wireless communications today has evolved into two complimentary sectors: a mobile cellular network and a static home network. None of them deliver one service the customers want: ultra high speed static data transfer. People are on the move only 10% of their time, and with growing high quality media on demand, bandwidth is in short supply. This project intends to create a fixed wireless high data rate collaborative network and tackle the cooperative issues to optimize instantaneous throughput.R 2 ff(ffff RB  s *Dff $$RB  s *Dff#0  0L /+1v J Data Speed 2   6|L & !5# M User Mobility 2x2  0C"?) <$&vx2  0C"? %)8x2  0fC"?'5,Kx2  0C"? (,8x2  0C"?I-10  0L F /&2)  D3G(2   0L  #& D2G(2 ! 0L  (, O 4G/ WiMax (2  " 0LL Y"(, JADSL(2 # 0L I+;2 \Collaborative Networks(2t $ 0hP :Y1 Collaborative Networks The idea of antennas collaborating originated from MIMO (multiple input, multiple output) systems. Multiple antennas under independent fading paths cooperated to increase multiplexing gain. However, due to the limited space on a mobile device, multiple antennas often can t be realised. A distributed version, where multiple users each with an antenna cooperate to increase data throughput collectively is known as a collaborative network. B 2 ffff: % 3 Ad2 & 0P  "  Weisi Guo - 1st Year PhD Student@!(2  ff ff ff ' 0P 2g+18 9Coded Cooperation There are many cooperation strategies, all of them can implement coding to decrease errors. Cooperative codes can offer extra diversity in slow fading channels, but not in fast fading channels.: 2 ]fffff: ( 3 A@3W > ) 07P +Gv1 Power Control Power allocation amongst a collaborative network is important for practical energy efficiency and channel capacity. Centralised power control, and distributed power control schemes are considered for varying knowledge of channel state information (CSI). How to estimate CSI effectively and how this impacts capacity is investigated. It was found that CSI is critical to uplink transmitter collaboration, whereas optimal power control is critical to downlink receiver cooperation. Further investigation optimized results using different modulation methods that account for capacity, as opposed to standard Gaussian input assumptions. This leads to a mercury filling scheme, where power is not always most allocated to the best uplink channel node. d 2 Pffffffrff: * 3 A%%: - 3 A&#$/f / s *A ? ?$*^0& f 0 s *A ? ?[/$<21  2 0fP 0}I001 [Mercury Filling (BPSK): 2 3 0P %}L34& n*Water Filling Power Allocation (Gaussian):+ 2+ 5 0|P ?GY1F TConclusions and Future Work The project started April 2007 as an EPSRC collaboration project between Cambridge University and Newcastle University. I plan to derive a distributed mercury power control mechanism that maximizes channel capacity for practical modulation methods. The project will also look at various convolution and turbo coding schemes in a variety of channel conditions. On the aspect of relay strategies, game theory and biological collaborative communications will be looked into.4 2 ff 6 0dPP )HC2II l$http://www.cl.cam.ac.uk/research/DTG%(2%X ; 0,\&'* < 0lYP m'i2 |Purple bar denotes the amount of noise a node s channel has. $? 2<X = 0O#\&'1$X > 0 \&'O#f ? 0lP  m'3$ The better the channel (less noise), the more power it should be allocated and the more it should cooperate with other weaker channels. This is for Gaussian inputs in a MIMO system. $ 2X B 0h-\&'J. C </P *\&'g- :9 D 0@SP r(m'y3. }/In a constellation modulated input system: an offset (mercury filling) is performed before power allocation (water filling). This takes into account the capacity limit of a channel. Performance is similar at low SNR, but can give opposite power allocations at high SNR as a channel reaches capacity. $0 2-X E 0')\&'*` G c $A ??8'1>; ` H c $A ??;'1>  K 01P G9)g: z6Slow Fading Cooperative Coding (Diversity order of 2):7 27 L 04P <)= t0Fast Fading Cooperative Coding (Diversity of 1):1 21H  0nF! ? 3380___PPT10.CSMrS}Ǵ 1