Cross-Layer Exploration of Link Adaptation in Wireless LANs with TCP Traffic Sofie Pollin∗ , Bruno Bougard∗† , Gregory Lenoir, Liesbet Van der Perre, Bart Van Poucke, Francky Catthoor§ and Ingrid Moerman+ IMEC Kapeldreef 75 B-3001 Leuven Belgium email:{pollins,bougardb,lenoir,vdperre,vanpouck,catthoor}@imec.be
[email protected] Abstract— Wireless communication systems enable new and exciting applications, but also impose significant technical challenges to achieve the required performance at minimal energy consumption. Traditionally, those systems are designed and optimized for operation in one typical situation, taking into account some worst case considerations and a functional layer partitioning which simplifies the design to a great extent. To enable a broad range of applications in a highly dynamic wireless environment, with optimal performance in each situation, this worst case partitioning of the system is not a good strategy. In this paper we show that a physical layer mode, optimal from a physical-centric point of view, results in a bad performance when used with a real protocol stack on top of it. We show that this effect is strengthened when the system is designed on the edge of what is possible for its energy budget. A new cross-layer design methodology is the only way to achieve the best possible end user performance versus energy and deserves extensive further research.
I. I NTRODUCTION The main challenge for future wireless communication systems is to achieve the required performance for a broad range of applications at minimal energy consumption. The dynamic propagation conditions result in a significant and varying speed mismatch between wired and wireless networks. This can cause severe congestion problems which result in a performance degradation that cannot be tolerated by future demanding multimedia applications. However, next to the propagation and environment conditions, also the data rate requirements of future applications are highly dynamic. Hence, traditional systems, who are designed and optimized for operation in one typical situation, which is only valid a small percentage of the time, most of the time can not achieve the optimal performance with minimal energy consumption. ∗ Also
Ph.D. student at K.U.Leuven, E.E. Dept., ESAT/INSYS by the Flemish Fund for Scientific Research (FWO) § Also Professor at K.U.Leuven, E.E. Dept., ESAT/INSYS + Also Professor at U.Gent, E.E. Dept., INTEC/IBCN † Granted
Techniques are being developed that adapt to the current propagation conditions, and select operating points to achieve a maximum performance or minimum energy depending on the current propagation conditions [1], [2]. Other techniques follow the idea to provide the ’just required’ performance with the minimum energy considering the propagation conditions [3], [4]. However, those algorithms are designed on a per layer basis, taking into account some worstcase considerations (e.g. concerning a target average data rate) which simplifies the design to a great extent. As this worst-case conditions are only valid a small percentage of the time, the global energy or performance optimality of the system is jeopardized. Moreover, previous work has shown [5], [6] that the capacity improvement provided by advanced physical layers can collapse by using inadequate protocols. This motivates the recent popularity of cross-layer optimization research, aiming at a better match between the physical layer and the protocols and between protocols themselves. Removing these worst-case bounds between the layers and adapting at run-time to achieve, across all layers, a globally optimized working point, is a very challenging problem though. Indeed, all complex cross-layer interactions should be taken into account to enable a stable operation without these worst-case bounds. New techniques are needed to enable this efficiently at run-time, without a too large cost penalty that decreases the overall gain. In [7] a novel approach is proposed to carry out this complex problem by doing as much as possible of the work at design-time, and capturing all information needed at run-time in scenarios. In this paper we have continued from this approach, and we examine in particular the cross-layer effect of certain local optimization decisions on the end-user performance and total energy consumption. Indeed, the real performance metrics are those quantifying the quality of service provided by the complete communication
Proceedings Symposium IEEE Benelux Chapter on Communications and Vehicular Technology, 2003, Eindhoven
stack to the application, while the only effective energy consciousness indicator is the actual energy that is drained from the battery. Traditional approaches partially fail when the scope is raised to the user level, using intermediate performance and energy metrics. Knowing these cross-layer interactions, enables working in a globally optimized working point, instead of a sub-optimal one resulting from a number of local optimizations in each layer of the protocol stack. During operation, the system can then be reconfigured or adapted locally, based on knowledge of the impact of this adaptation on the global performance. The remainder of this paper is structured as follows. In Section II previous work on local optimization techniques for wireless networks is discussed. These works are then compared with cross-layer designs, taking into account more realistic assumptions about the scenario. The approach we use for a cross-layer exploration is presented in Section III. Applying the approach to enhance the Link Adaptation techniques for OFDM WLAN systems, results in the figures presented in Section IV. II. R ELATED W ORK Adapting to a time-varying channel to maximize the average throughput (or correctly delivered bits per second) for a given transmission power constraint is a well-understood problem. A variety of schemes to set the physical layer control knobs have been developed for a broad range of scenarios, optimizing physical layer throughput or energy consumption [3], [8], [9], [10]. A control knob is a tunable system parameter, e.g. modulation, code rate and output power in the case of traditional OFDM WLAN systems. These schemes are based on the assumption that data is generated at a constant and known rate, or that there is always data to transmit. However, for most applications, this rate varies in time. Link Adaptation techniques that ignore these dynamics in application data rate, and are hence mostly designed for the worst case, can be very inefficient in their use of power and bandwidth. Recently, Link Adaptation schemes have been enhanced to adapt the link data rate to the channel condition, targeting an average link rate based on the current traffic requirements [11], [12]. It is shown that, by taking into account this limited cross-layer information, a large energy gain can be obtained for a fixed packet rate, taking a small penalty in transmission delay into account. Moreover, these Link Adaptation techniques are still mainly focusing on the physical layer, and do not take the inefficiencies of, and cross-layer interactions with, realistic higher layer protocols into account. Next to these local optimization techniques focusing at one algorithm of one part of the communication protocol stack, many initiatives aim at developing cross-layer optimization frameworks, especially for WLAN and wireless ad hoc networks [13]. Most of them focus on the interaction between the Data Link
Control (DLC) protocol (consisting of the Medium Access Control and Logical Link protocols) on the one hand, and the physical, network and transport layers on the other hand. In [14] the physical-layer mode selection for power efficient transmission is derived by jointly considering the physical and MAC layer, which has a high impact on the total power consumption. Barret et al. show that the interaction between routing and MAC protocols for ad hoc networks is significant, and should be taken into account when making design time decisions [15]. The interactions between the MAC 802.11 and TCP have also been studied extensively, and many optimizations are proposed [5], [16]. These works hence show that a cross-layer design and optimization is imperative to meet future performance and energy requirements. A cross-layer study of Link Adaptation, taking into account 802.11 MAC and TCP interactions, has however not been studied yet. III. C ROSS -L AYER E XPLORATION A PPROACH In this paper, we aim at combining and enhancing previous approaches to study a cross-layer optimization of Link Adaptation for a specific scenario. Run-time adaptation is based on a set of control knobs, which can be set to adapt to the current requirements and environment constraints. Doing the optimization cross-layer, taking into account all interactions, is a complex non-linear problem and difficult to do at run-time. A framework, capable of doing complex optimizations efficiently at run-time has been considered in [7]. The approach is based on the idea to shift as much as possible of the work to the design-time phase. For a set of propagation conditions, cross-layer optimized working points are derived. As a result, the optimization at run-time consists of a simple database look-up, depending on the current propagation conditions and scenario. Hence, the performance and energy gain of the optimization do not collapse by the algorithm complexity and cost at runtime. To enable the extraction of optimized points at design-time, good scenarios, metrics and channel and protocol models are needed though. A. System under Scope The main goal of this research is to examine the impact of the control knobs at each level of the protocol stack on the energy and per f ormance. More specifically, we consider an OFDM WLAN system. This is to provide a more general framework to derive optimized Link Adaptation schemes taking into account all cross-layer interactions, leading inherently to a more global optimum. The knobs considered here are traditional Link Adaptation configuration knobs, applicable to OFDM based WLAN, which can be set as listed in Table I. For the output power, only two values are considered, as this is sufficient to show the effects of cross-layer interactions. To evaluate the performance of each combination of configuration knobs, a realistic scenario should be considered. From a network point of view, the main use of WLAN is to extend the Internet with a wireless
Fig. 1.
The topology considered for the end-to-end performance modeling.
last hop. To that extent, we consider a system as depicted in Figure 1 for the influence of the physical layer control knobs on the end-to-end performance. As TCP represents 90% of current Internet traffic, it consists of 10 TCP sources connected to a single WLAN user through an Access Point (AP). Each TCP source delivers 5MB to the WLAN destination, per packet of 500 Bytes. Linking WLAN access networks to the Internet can lead to severe congestion and sometimes, to poor interactions with TCP [17]. As Link Adaptation has an impact on the throughput of the wireless link, it severely impacts the TCP performance. In fact, Link Adaptation can be seen as a means to handle congestion in wireless networks [18]. We do this exploration assuming a static frequency selective fading channel state, hence applicable for slow fading channels which is a requirement for effective Link Adaptation. We assume advanced channel-coding techniques like turbo-coding are used to cope with the channel variations at very small timescales [19]. The frequency selective fading corresponds to the first channel state as considered in [20]. Next to this frequency selective fading, the average pathloss is modeled as a function of distance as in [22], [23], [24]. A pathloss of 81dB corresponds here to a distance of 10m, which is realistic for indoor propagation conditions. B. Two-level Modeling The cross-layer interdependencies of the wireless link are highly non-linear. The throughput depends namely on the packet service rate of the wireless link, but also on the packet arrival rate which is very dynamic when Internet traffic is considered [25]. The service rate of the link depends on the link bit rate, but also on the Packet Error Rate (PER). Through the DLC Automatic
knob modulation code rate PT x (dBm)
range BPSK, QPSK, 16QAM, 64QAM 1/2, 2/3, 3/4 20, 18
TABLE I T HE LOW LEVEL CONFIGURATION knobs CONSIDERED .
Repeat reQuest (ARQ) mechanism, this PER translates in an increased delay or, non-linearly, in a packet loss, depending on the number of retransmissions. The packet arrival rate is dynamic resulting from the TCP congestion avoidance algorithm. This congestion avoidance reacts to packet losses on the link following an Additive Increase, Multiplicative Decrease (AIMD) law. Time-outs which occur if the Round Trip Time (RTT) becomes too large, are handled accordingly. To capture all non-linear effects, the performance of each combination of physical layer knobs and the cross-layer interactions can be measured mainly through system profiling using event-driven simulation (using ns − 2 to that purpose [26]). On the other hand, the protocol simulator requires information about the radio-link: the Bit Error Rate (BER), the physical layer data rate and energy. Yet, those data are dependent on the physical layer knobs. Those dependencies are also non-linear, and they are usually identified through time-driven Monte-Carlo simulations carried out on functional models mimicking the channel, the radio chain and digital signal processing behavior. Those models are behavioral, time-driven models. One can easily understand that combining both event-driven and time-driven models would lead to unacceptable simulation times. A workaround is to
fit, on the results of the physical layer simulations, empirical behavioral relations that relate the minimum input required by the protocol to the values of the knobs considered. Those mathematical relations can be included into the protocol simulator without simulation time penalty. We speak about a 2-level modeling approach where the time-driven physical layer models constitute the first level and the event-driven protocol simulation the second [20]. The minimum input required is the physical layer rate, the energy to send and to receive a unit of data, and the error rate of each packet. C. Energy and Performance Metrics Having a realistic scenario and good models to capture all cross-layer interactions makes it possible to capture the performance of each configuration in a set of metrics. For this cross-layer exploration, the metrics considered are throughput, or correctly received bits per second and energy per data unit. These can be determined at physical layer, or taking into account the overhead of the MAC protocol or on top of the transport layer. From a physical-centric point of view, the throughput can be determined based on physical layer blocks as T = f (Nmod , Rc ) × (1 − BlER),
(1)
where Nmod denotes the modulation, Rc the code rate, BlER the Block Error Rate (Blocks because turbo coding is used). This formula assumed that no protocol overhead exists, and that an infinite number of per block retransmissions are possible when a block failed. We call this a physical-centric approach as this formula can be determined based on information available at physical layer only. It is a bound to what can ideally be delivered to the higher layer, if that higher layer would exploit the physical layer as efficiently as possible. Similarly, the energy per unit can be calculated taking into account the transmission power and the system implementation as Tup
=
Power
=
E
f (Nmod , Rc ) 1 − BlER f (PT x , system)
= Tup × Power
(2) (3) (4)
where the only penalty is an ideal per block retransmission when the transmission failed. This physical layer point of view does not take into account the overhead introduced by the Data Link Control (DLC) protocol, which is huge if IEEE 802.11 is used. When considering the Distributed Coordination Function (DCF) mode, for each data packet sent, a set of control messages (Request To Send (RTS), Clear To Send (CTS) and ACKnowledgement (ACK)) need to be sent. These messages are sent using a different constellation order (BPSK, QPSK and 16QAM with code rate 1/2 are possible in the standard [27]). Moreover, time penalties exist, e.g.
due to the collision avoidance mechanism in the DCF mode and the required Short Interval Frame Space (SIFS) and Distributed Interval Frame Space (DIFS) periods, in which the channel capacity cannot be exploited. A last penalty exists in the physical layer synchronization sequence, and the Physical Layer Control Packet (PLCP) header before each packet. In this header the information about the chosen working point can be communicated. Adding these overheads and taking into account a packet size of 500 bytes, it is possible to formulate approximating formulas to calculate the average use f ul throughput and energy per use f ul data. Those have the form of the physical layer centric formulas (Formulas 1 and 2), a part from the overhead added, and hence assume a linear behavior. They assume that data is always available to send and that an infinite number of retransmissions is carried out if necessary. Moreover, they assume that the control messages are error free. The overheads are considered in terms of equivalent OFDM symbols, which represent 4µ s. They give the time spent on transmitting useful data versus the total time spent in a frame. Similarly, for the energy, the overhead is the time up to transmit useful data versus the total time up. Indeed, as we assume the same system configuration is used for sending the control messages, the energy penalty can be calculated as an increase in the Tup term. The difference with the penalty term for the throughput is that the average waiting time is not taken into account here. Indeed, the Power Amplifier can be shut down. T
=
f (Nmod , Rc ) × (1 − PER) × IFS +
Tup
=
f (Nmod , Rc ) × 1 − PER
Packtsize+header mod×Rc
PER
(5)
Packetsize mod×Rc Packetsize+header + PLCP + mod Ctrl×R mod×Rc ctrl ctrl
(6) + PLCP + mod Ctrl×R Packetsize mod×Rc Packetsize
= [1 − (1 − BlER) Blocksize ]
ctrl
ctrl
(7)
PER is the Packet Error Rate, IFS is the equivalent number of OFDM symbols for the average waiting time (i.e. SIFS and DIFS periods and the average backoff period), PLCP are 13 equivalent OFDM symbols for the PLCP header, and modctrl and Rctrl are the modulation and code rate for the aggregate control messages Ctrl. The PER can be calculated easily knowing the BlER as the BlER process follows a Poisson distribution (7). Taking into account the interaction with TCP is complex, and should be done using event-driven protocol simulation. Indeed, the TCP behavior determines if there is data to send or not at the AP, and hence influences the maximum throughput that can be achieved. Through the TCP congestion avoidance, this depends on the radio constellation chosen, resulting in a larger delay or PER. Taking into account all this interactions can be done easily using
the extended protocol simulator built. The metrics are now determined taking into account all possible overhead penalties, as all of them are modeled in the simulator. To that extent, the results obtained for these simulations give the most complete view on the system characteristics. IV. S IMULATION R ESULTS In this section, we first profile the performance and energy consumption of each possible working point (all modulation and code rate combinations), for an output power of 20dBm (Table I). This is done using the models and metrics as discussed in the previous section. We show what the different optimal configurations are, if determined at each layer of the stack considered. The impact of the distance is also shown. The difference between the local and the cross-layer optimization is the largest at large distances. This is because at large distances, we are working on the ’edge’ of what is possible for a certain output power or energy consumption. However, to design systems that achieve the best possible performance for a certain energy consumption, working on the ’edge’ is imperative. Indeed, we show that by lowering the output power to 18dBm, the energy consumption is decreased, but the penalty of doing the optimization locally increases significantly.
Point BPSK, 1/2 BPSK, 2/3 BPSK, 3/4 QPSK, 1/2 QPSK, 2/3 QPSK, 3/4 16QAM, 1/2 16QAM, 2/3 16QAM, 3/4 64QAM, 1/2 64QAM, 2/3
Phy-centric bps E/bit 8.68 5.41 11.58 4.09 13.02 3.64 17.36 2.76 23.15 2.10 26.05 1.88 34.73 1.43 46.29 1.10 51.74 1.00 45.33 1.14 – –
MAC overhead bps E/bit 5.32 6.06 6.35 4.66 6.79 4.19 8.28 3.25 9.48 2.55 9.96 2.32 11.48 1.85 12.53 1.50 11.90 1.51 – – – –
TCP simulation bps E/bit 4.15 9.19 4.92 7.43 5.23 6.85 6.09 5.66 6.90 4.77 7.20 4.49 7.93 3.89 8.56 3.45 8.10 3.52 – – – –
TABLE II T HE NORMALIZED Energy per bit (/1.198e − 07J) VERSUS NORMALIZED throughput (/691kbps) FOR ALL TRADITIONAL WORKING POINTS , DISTANCE 5m AND PT x = 20dBm. T HIS ANALYSIS CAN BE DONE AT PHY,
Point BPSK, 1/2 BPSK, 2/3 BPSK, 3/4 QPSK, 1/2 QPSK, 2/3
Phy-centric bps E/bit 8.68 5.41 11.46 4.13 8.08 5.87 14.76 3.25 – –
MAC OR TRANSPORT LEVEL .
MAC overhead bps E/bit 5.32 5.74 5.59 4.92 – – 1 24.25 – –
TCP simulation bps E/bit 3.35 10.09 3.38 9.22 – – – – – –
TABLE III T HE NORMALIZED Energy per bit VERSUS throughput FOR ALL TRADITIONAL WORKING POINTS , DISTANCE 50 M AND PT x = 20dBm. T HIS ANALYSIS CAN BE DONE AT PHY, MAC OR TRANSPORT LEVEL .
A. Impact of the Distance First, for a range of distances, the energy and throughput is determined for each of the three layers considered, taking into account a fixed PT x of 20dBm. In Tables II and III the results can be seen for the different working points, for a distance of 5m and 50m respectively. At each layer, the point corresponding to the highest throughput also consumes the least energy. This is because a larger average throughput results in a smaller time needed to send a packet (duty cycle). This reduces the (Power Amplifier) PA duty cycle which is the main contributor to the power consumption. Indeed, changing the constellation order does not result in a reduction of the effective PA energy consumption [21]. The values obtained for the energy and throughput at each layer differ significantly (Figure 2). This is rather obvious, as at each layer, more overhead is taken into account. However, for each distance, the optimal working points at the physical and at the TCP layer differ (Tables II and III). In Table IV, the optimal working points for each distance at each layer are shown. It should be noted that the decisions differ significantly, as the local and global decision never result in the same constellation chosen. In Figure 3 the impact of taking a local decision at the physical layer on the global energy and throughput can be seen. For the smaller distances, the impact is approximately 5%, which is not dramatic. For the large distances however, the impact is significant. Indeed, the physical layer optimal point results in a very bad TCP performance and no significant throughput can be noted. Hence,
when working on the edge of the performance space of systems, it is dangerous to make local decisions based on some simplified assumptions about the interactions of the higher protocol layers. B. Impact of the Output Power In the previous section we have shown that when working on the edge of what is possible for a certain distance and output power, it is dangerous to optimize the performance based on local assumptions. Link Adaptation techniques are however designed to work on this edge, and deliver the performance just required by the user at minimal energy consumption, by lowering the output power if possible. To that extent, we do the previous experiment, for a PT x of 18dBm. The results for a distance of 5m are listed in Table IV-B. As shown in Tables VI and VII, this indeed results in a significant reduction of the energy, while the throughput remains unchanged (Table VII).
Distance 5 10 20 30 40 50
Phy-centric Mod. Code 16QAM 3/4 16QAM 3/4 16QAM 2/3 16QAM 1/2 QPSK 2/3 QPSK 1/2
TCP simulation Mod. Code 16QAM 2/3 16QAM 2/3 16QAM 1/2 QPSK 3/4 QPSK 1/2 BPSK 2/3
TABLE IV T HE OPTIMAL POINTS AT EACH LAYER ( PHYSICAL OR TCP), FOR EACH DISTANCE AND FOR PT x = 20dBm.
(a) Normalized energy (/1.198e − 07J) of the best working point at each layer.
(b) Normalized throughput (/691kbps) of the best working point at each layer. Fig. 2. Comparison of the energy and throughput values of the metrics measured at each layer of the protocol stack, for PT x = 20dBm. For each layer, the working point with the largest throughput and minimal energy is chosen. As shown in Table IV, the optimal points differ at each layer.
(a) Normalized energy (/1.198e − 07J) of the physical layer optimal points evaluated on top of TCP.
(b) Normalized throughput (/691kbps) of the physical layer optimal points evaluated on top of TCP.
Fig. 3. Comparison of the energy and throughput of the physical layer optimal points, compared to the cross-layer optimal points, for PT x = 20dBm. This comparison is done at TCP or cross-layer level, showing hence the total loss that is encountered by doing the optimization locally.
Point BPSK, 1/2 BPSK, 2/3 BPSK, 3/4 QPSK, 1/2 QPSK, 2/3 QPSK, 3/4 16QAM, 1/2 16QAM, 2/3 16QAM, 3/4 64QAM, 1/2
Phy-centric bps E/bit 3.22 5.19 4.29 3.93 4.83 3.51 6.44 2.67 8.59 2.04 9.66 1.83 12.88 1.41 17.17 1.10 19.19 1.00 16.70 1.15
TCP simulation bps E/bit 1.54 8.82 1.83 7.15 1.94 6.60 2.25 5.46 2.56 4.62 2.67 4.35 2.94 3.78 3.17 3.37 3.00 3.45 – –
TABLE V T HE NORMALIZED Energy per bit (/8.56e − 08J) VERSUS NORMALIZED throughput (/1863kbps) FOR ALL TRADITIONAL WORKING POINTS , DISTANCE CAN BE DONE AT PHY,
Point 16QAM, 1/2 16QAM, 2/3 16QAM, 3/4
5m, PT x OF 18dBm. T HIS ANALYSIS MAC OR TRANSPORT LEVEL .
Phy-centric 20dBm 18dBm 2.00 1.41 1.54 1.10 1.40 1.00
TCP simulation 20dBm 18dBm 5.44 3.78 4.83 3.37 4.93 3.45
TABLE VI A COMPARISON OF THE energy per bit FOR BOTH OUTPUT POWER VALUES CONSIDERED . T HE energy IS NORMALIZED TO 8.56e − 08J.
Point 16QAM, 1/2 16QAM, 2/3 16QAM, 3/4
Phy-centric 20dBm 18dBm 4.38 4.38 5.83 5.84 6.52 6.52
TCP simulation 20dBm 18dBm 1 1 1.08 1.08 1.02 1.02
Fig. 4. Comparison of the throughput (/406kbps) measured at each layer of the protocol stack, for PT x = 18dBm. For each layer, the working point with the largest throughput is chosen.
of view, performs suboptimal taking into account protocol layers on top of it. The effect is the most dramatic when working on the edge of the performance space of the system. This is however the case when designing systems that tend to be the most energy efficient. A cross-layer design is hence the only way to achieve the best possible end user performance versus energy and deserves extensive further research. To enable this crosslayer design, we have developed a framework to explore the global impact of local optimization techniques.
TABLE VII A COMPARISON OF THE throughput FOR BOTH OUTPUT POWER VALUES CONSIDERED . T HE throughput IS NORMALIZED TO 5.48mbps.
R EFERENCES [1] Neelesh B. Mehta and A.J. Goldsmith, Throughput Analysis of Link Adaptaion in Interference-Limited Cellular Systems, IEEE VTC ’01, pp. 2660-2664, 2001. [2] Neelesh B. Mehta and A.J. Goldsmith, Performance Analysus
The problem occurs however at larger distances. In Figure 4 the evolution of the optimal throughput at physical and cross-layer point of view is shown. From both points of view, the throughput decreases significantly. However, the optimal points a physical or crosslayer point of view differ significantly. This is shown in Figure 5, where the difference between the cross-layer performance of the physical layer optimal points is very sub-optimal compared to the global optimal points. At a distance of 10m, the difference is already 20%, and for larger distances the physical layer optimal point results in a very bad TCP cross-layer performance (more than a factor 100 smaller throughput). V. C ONCLUSION To meet emerging application requirements, particularly when energy is a limited resource, a cross-layer design and tuning of algorithms is needed to reach global optimal solutions. We demonstrate that traditional Link Adaptation schemes which do not take the MAC and TCP characteristics into account cannot achieve this. We show that traditional Link Adaptation schemes are usually too optimistic about the higher protocol layers and draw hence incomplete conclusions. A physical layer mode that is optimal from a physical layer centric point
of Link Adaptation in Wireless Data Networks, IEEE Global Telecommunications Conference (Globecom), 2000. [3] A. J. Goldsmith and S.-G. Chua, Variable-rate variable-power MQAM for fading channels, IEEE Trans. on Communication, vol.45, pp. 1218-1230, Oct 1997. [4] F. Tang, L. Deneire, M. Engels, A general optimal switching scheme for link adaptation, in Proc. IEEE VTC Fall’01, Oct 2001. [5] S. Xu, T. Saadawi, Does the IEEE 802.11 MAC protocol work well in multihop wireless ad hoc networks?, IEEE Comm. Magazine, 2001. [6] S. Toumpis, A. J. Goldsmith, Performance, optimization and cross-layer design of media access protocols for wireless ad hoc networks, IEEE ICC, pp. 2234-2240, 2003. [7] B. Bougard, G. Lenoir, W. Eberle, F. Catthoor A New Approach to Dynamically Trade-off Performance and Energy Consumption in Wireless Communication Systems, IEEE SIPS, 2003. [8] K. Balachandran, S. R. Kadaba, S. Nanda Channel quality estimation and rate adaptation for cellular mobile radio, IEEE J. Select. Areas Commun., vol. 17, no. 7, pp. 1244-56, July 1999. [9] S. Nanda, K. Balachandran, S. Kumar, Adaptation techniques in wireless packet data services, IEEE Commun. Mag., vol. 38, no. 1, pp. 54-64, Jan. 2000.
(a) Normalized energy (/8.56e−08J) of the physical layer optimal points evaluated on top of TCP.
(b) Normalized throughput (/1863kbps) of the physical layer optimal points evaluated on top of TCP.
Fig. 5. Comparison of the energy and throughput of the physical layer optimal points, compared to the cross-layer optimal points, for PT x = 18dBm. This comparison is done at TCP or cross-layer level.
[10] C. Kose, D. L. Goeckel On power adaptation in adaptive signaling systems, IEEE Trans. Commun., vol. 48, no. 11, pp. 169-73, Nov. 2000. [11] C. Schurgers, Energy-aware Wireless Communications, PhD thesis, University of California Los Angeles, 2002. [12] E. Uysal-Biyikoglu, A. El Gamal, B. Prabhakar, Adaptive Tranmission of Variable-Rate Data over a Fading Channel for Energy-Efficiency, IEEE Globecom, Vol. 1, pp. 97-101, 2002. [13] A. J. Goldsmith, S. B. Wicker, Design challenges for energy-
Proc. of IEEE VTC 1999, pp. 2308-2312, 1999. [23] T. Schwengler, et al., Propagation Models at 5.8 GHz - Path Loss and Building Penetration, Proc. of IEEE Ratio and Wireless Conference, pp. 119-124, 2000. [24] J. Medbo, J. Berg, Simple and Accurate Pathe Loss Modeling at Corridors, Proc. of IEEE VTC, pp. 30-36, 2000. [25] T. Hagiwara, H. Majima, T. Matsuda, M. Yamamoto, Impact of round trip delay self-similarity on TCP performance, Proc. of Comp. Comm. and Networks Conf., pp. 166171, Oct. 2001.
constrained ad hoc wireless networks, IEEE Wireless Commu-
[26] ns-2 network simulator, Obtain via http://www.isi.edu/nsnam.
nications, August 2002.
[27] The Institute of Electrical and Electronics Engineers, Inc. IEEE
[14] J. Zhao, Z. Guo, W. Zhu, Power efficiency in IEEE 802.11a WLAN with cross-layer adaptation, IEEE ICC, Vol. 3, pp. 20302034, 2003. [15] C. L. Barret, M. Drozda, A. Marathe, M. V. Marathe, Characterizing the interaction between routing and MAC protocols in ad-hoc networks, MOBIHOC, pp. 137-144, 2002. [16] R. Jiang, Vikram Gupta, C. V. Ravishankar, Interactions between TCP and the IEEE 802.11 MAC protocol, Proc. Of DISCEX, 2003. [17] M. Garcia, J. Choque, L. Sanchez, L. Munoz, An experimental study of Snoop TCP performance over the IEEE 802,11b WLAN, Wireless Personal Communications, 2002. The 5th Internations Symposium on, Vol. 3, pp. 1068-1072, Oct. 2002. [18] D. C. ONeil, Adaptive Congestion Control for Wireless Networks Using TCP, IEEE ICC, Vol. 1, pp. 82 -86, 2003. [19] B. Bougard et al., A scalable 8.7-nJ/bit 75.6-Mb/s Parallel Concatenated Convolutional (Turbo-) Codec, Proceedings of the IEEE International Solid State Circuit Conference (ISSCC ’03), Feb. 2003. [20] B. Bougard et al., Modeling of Energy-Scalable Wireless Local Area Network Transcievers, submitted to ICC 2004. [21] B. Come et al., Impact of front-end non-idealities on bit error rate performance of WLAN-OFDM transceivers, Proceedings of IEEE RAWCON, 2000. [22] J. Kivinen, et al., Wideband Indoor Radio Channel Measurements with Direction of Arrival Extimations in the 5GHz Band,
Std 802.11 Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications, 1999 edition.
