Implementation of Adaptive Modulation for Broadband Wireless Access Networks using Cognitive Radio Approaches Sami H. O. Salih*, Ali Al-Refai**, Mamoun Suliman*, Abbas Mohammed** *
Sudan University of Science and Technology, Khartoum, Sudan ** Blekinge Institute of Technology, Karlskrona, Sweden
[email protected],
[email protected],
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[email protected] Abstract – Broadband Wireless Access (BWA) has become the best way to meet escalating business demand for rapid Internet connection and integrated "triple play" services. In addition, not only for topographic but also for technological limitations, alternative wireless solutions have been found. These systems are designed based on Cognitive Radio (CR) approaches, which can adjust its operation according to the environment and technical variations. This tracking feature allows the communication system to deliver the Best Ever, compare to Best Effort, services to the users. In this paper, an implementation of a cognitive engine for adaptive modulation and coding (AMC) is presented. This engine will track the radio channel variations in terms of SNR and be able to select a suitable modulation order among predefined Modulation and Coding Schemes (MCS) to maintain the specified BER by the user requirements. Keywords: Cognitive Engine (CE); Adaptive Modulation and Coding (AMC); Software Defined Radio (SDR); Cognitive Radio (CR); Broadband Wireless Access (BWA)
I.
INTRODUCTION
In traditional communication systems, the transmission is designed for the "worst case" channel scenario, thus coping with the channel variations due to the long term and short term fading or the Doppler effect and still delivering an error rate below the specified limit. Adaptive transmission schemes, instead, are designed to track the instantaneous channel quality and adapting channel throughput to the actual channel state. The tracking feature may relate to the signal strength (RSSI, IT or, SNR), the amount of traffic produced (Congestion, Erlang), the mobility profile of the Customer Premises Equipment (CPE), or a combination of two or more features [1, 2]. These techniques are taking the advantage of the time-varying nature of the wireless channel to vary the transmitted power level, symbol rate, coding scheme, constellation size, or any combination of these parameters, with the purpose to provide specific BER and hence improving the link average spectral efficiency measured by bits/s /Hz. The concept of adaptive modulation was wildly addressed, in [3, 4] the positive impact of using Adaptive Modulation and Coding (AMC) was demonstrated in terms of Quality of Service (QoS) metrics. In [5] a cognitive approach proposed using Fuzzy logic and in [9,10] neuralnetworks algorithm was used, however when considering the limited number of Modulation and Coding Schemes (MCS) profiles used Artificial Intelligent (AI) technique may inefficiently consume the system resources.
Furthermore, different simulation platforms were developed to consider specific parts of the Cognitive Radio (CR) system, each of which simulates a part of the CR system, so it’s not possible to aggregate different simulation platforms to investigate the performance of an entire CR system. Most of the developed test beds use Field Programmable Gate Array (FPGA) near radios (i.e. Antenna, Power Amplifier, and Frequency Band Converters) and general purpose devices to control the system [6, 7]. However, splitting the CR platform adds more complexity to the test bed since a high-speed connection is required between the two parts. None of the published papers, up to the best of the author’s knowledge, address the issue of implementing a unified test bed simulating the behavior of the AMC function working for the access layer in BWA systems. Indeed, the Cognitive Engine (CE) function implemented here has two unique features; it’s fully backward compatible with the Software Defined Radio (SDR) platform supporting any modulation order [1], and it’s developed on modular approach, making it forward compatible with future MCSs. This paper is structured as follows: In section II, an overview of the cognitive engine is discussed with relation to the previously implemented SDR function for AMC [1]. Design steps and the performance analysis of the CE design and its behavior on real world environment is demonstrated in sections III and VI, respectively. Finally, conclusions are drawn in section V. II.
ADAPTIVE COMMUNICATION SYSTEM DESIGN
A. Design Scope For the time being, most BWA communication systems support variety of MCSs and allows for the schemes (also called profile) to change on a burst-by-burst basis per link, depending on channel conditions. Current systems contain separate hardware channel for each MCS. The more intelligent approach is to design a reconfigurable platform in hardware to support a wide range of channel specifications (for instance, WiMAX uses BPSK, QPSK, 16QAM, and 64QAM, or even higher constellations size in future systems) based on SDR, and then design a CE to determine which profile (or scheme) to load and operate. The AMC module is responsible for such mechanism, as shown in Figure 1, a higher data rate can be achieved by 64QAM in WiMAX when the channel conditions is relatively good. On the other hand, when the channel quality degrades, applying higher constellation will lead to dramatically increased the BER. Thus, a lower modulation order must be used to deliver reasonable Quality of Service (QoS) to the users.
On the half-duplex systems, a feedback channel is required to allow the mobile terminal update the base station with the downlink Channel Quality Indicator (CQI). For the uplink, the base station can estimate the channel quality based on the received signal quality. In contrast, full-duplex systems, which is the case here, where the transmitter and receiver circuits are presented on both communication ends, the receiver part can perform the downlink channel quality measurement and pass the CQI to the transmitter locally, so there is no need for channel quality feedback, hence saving channel resources.
As Figure 2 represents when there is a good AWGN channel conditions in the communications link at a particular time higher modulation order can be used to improve the channel efficiency while maintaining an acceptable BER. Figure 3 shows the relation between SNR and the BER for various modulations order. From the graphs in Figure 3, in order to maintain a certain level of BER (transmission quality) for a given SNR (channel condition), a suitable modulation order have to be chosen to deliver the highest possible transmission quality.
Figure 3. BER vs. SNR Lookup Table Figure 1. Adaptive Modulation and Coding used in WiMAX [5]
B. SDR Platform The BWA model is developed based on the IEEE 802.16e standard documents using SDR approach and it was successfully tested in [1] and the references thereon cited therein. A single function that can give different modulation order from BPSK to M-QAM (M= 2n, where n = 2, 4, 6, …) is implemented in Matlab. The function is called with the modulation order and the Signal-to-Noise Ratio (SNR) in dB as input, and then it plots the associated constellation and calculates the Bit Error Rate (BER) of the transmitter.
Both functionality and performance of the AMC module can be compared with the hardware model in [2]. C. Cognitive Engine Functionality The functionality of the CE is to continuously monitor the environment, by sensing selected cognitive features, refer to predefined polices, perform its logic to pick up the suitable configuration profile, and then automatically direct the SDR to load and execute the appropriate profile. Figure 4 shows the block diagram and the signal flow in the CE procedure.
Figure 4. Cognitive Mechanism [3] Figure 2. AMC Constellation Diagrams for BPSK, QPSK, 16QAM, and 64QAM
III.
COGNITIVE ENGINE DESIGN
A. Design Environment In this paper, Matlab functions are implemented to simulate the behavior of CE for the AMC element of the BWA systems. The implemented functions are fully compatible with a previous function implemented to simulate the SDR platform for AMC [1].
BER. Hence, a logical comparison is made to select a suitable modulation order. By doing so, the maximum limit of BER is imposed by the user’s desired BER as shown in Figure 7 (B, C, and D).
B. Cognitive Engine flowchart
Figure 7. CE Functionality with Different User Desired BER
Figure 5. AMC Cognitive Engine Flowchart
This module is designed to work with the AMC function; together they perform the Cognitive AMC processes as shown in Figure 6.
Figure 6. Cognitive Radio Module
Depending on application, the user (or the application layer protocol) has to provide the desired BER for the transmission; CE will perform its procedure to keep the BER below the targeted level. A lookup table is built representing the relation between SNR and BER as shown in Figure 3 to reflect the channel condition given in SNR to its vis-à-vis
C. Performance Analysis In Figure 7.3(A), the handoff scenario is simulated. In such scenario, SNR measure at the mobile terminal decrease from its peak near the first base station to the dip value in the boundary to the peak again near the second. Thus, SNR is generated between 0 to 25 dB. The user then provide the desired BER limit 10-1, 10-2, and 10-3 for Figure 7.3 (B, C, and D) respectively. The CE function then refers to the lookup table and switch between the modulation orders to keep BER bellow the required level. In each graph the function determine the percentage of each modulation order used. The summation is usually below the 100% because there are some packets not transmitted because SNR decreased to a level not possible to achieve the required BER even by the lower constellation defined (i.e. BPSK). IV.
SYSTEM IMPLEMENTATION
A. System Limitations Adaptive modulation is more suitable for a two way communication system (Full Duplex) since the adaptive coefficients have to be synchronized in order to allow channel measurements and signaling to take place. A typical communications system consist of three main parts: a transmitter that send the data (information), the receiver that process and retrieves the information, and the channel which is the physical media between the transmitter and the receiver. The CE part on the receiver circuit evaluates the received packets to estimate the CQI, and then adapt the SDR on the transmitter circuit according to a predefined logic. The unified test bed assumes that both SDR and CR parts are implemented on the same hardware platform and at both communication ends as shown in Figure 8; this hypothesis gives the CE functions a direct
accessibility to all parameters within the transceiver which, with high probability, have the same values on the other end under the same channel conditions. Thus, no signaling channel is needed in parallel to the traffic channel, thus saving the radio spectrum channel resources. However, an internal signal flow is required in order to keep the transceiver harmonized (i.e. use the same modulation and demodulation order).
Figure 8. Relation Between SDR and CE in a CR Transeiver
The encoding process was not taken into account when designing this function, when used; it will significantly enhance the throughput. From the SDR function the effect of encoding is not on the scope. From the cognitive engine perspective, the improvement made by encoding is directly reflected on the BER; therefore, it’s not a cognitive engine matter. B. Real world Implementation of the AMC CE successfully tracks the SNR and selects the suitable modulation order in order to maintain the desired BER specified by the application used. As in Figure 7 A, the scattered SNR levels are generated to test the CE in the worst case scenario. However, for real world implementations, a distance based slow-varying SNR model is adopted to analyze the performance of the CE comparing to the AMC developed using hardware approaches. Fortunately, this slow-varying assumption allows the receiver part of the communication system to anticipate the channel conditions for the next transmission interval in terms of CQI. Since this knowledge can only be gained from projections using past CQI, so AMC based systems can operate more efficiently under this assumption. When multipath Fading or Doppler effects take place because of the multipath signals or the relatively high speed mobility of the CPE, the communication system needs more time to estimate the SNR level for the next transmission interval. However, when the transmitted packets are coherent (e.g. VoIP) the system has to tolerate some errors to reduce delay and jitter. This inaccuracy is crucial for AMC system implementation, since a poor system performance will result if the channel estimate is obsolete and/or inaccurate at the time of transmission. For instance, a handoff scenario is assumed here to verify the system performance in real world, which demonstrates a realized scenario with a channel variation from good, bad,
then again good SNR level. This scenario just affects the channel condition faced by the CE, and has nothing to do with the main CE mechanism. Obviously, the functionality of the CE will remain the same as in Figure 7; however, in this scenario the simulation environment tends to behave as in real world operation. C. Performance Analysis of the AMC Cognitive Engine Table 1 represents the percentage of packets transmitted using the modulation order selected by the CE function when the user specified the desired BER and according to the instantaneous channel conditions. The "Low SNR" column gives the percentage of the packets that didn't transmit because of the low SNR level; this means that there is no modulation order sufficient to maintain the desired BER for the given channel condition. In this scenario the channel condition represented in SNR follows a handover procedure with SNR varying gradually according to the effect of the long-term fading while the mobile CPE moves to another cell area. CE maintains the desired BER in the varying channel condition by altering the modulation order. TABLE I. Modulation Order (%)
10 -1 10 -2 10 -3
PERCENTAGE OF MODULATION ORDER USED BPSK
QPSK
16QAM
64QAM
38
27
18
17
Low SNR 0
17
22
17
7
37
11
20
18
2
49
Average system efficiency can be calculated for the designed function using the method specified in [8]. The auther in [8] simulate a transmission of 36 bit as leaset common of multiples. For instance, 72 symbols from BPSK1/2 or 8 symbols from 64_QAM3/4 are both equal to 36 bits.
where N represents the percentage of the MCS used, and the efficiency coefficients for each modulation order are taken from Table II. TABLE II.
EFFECIENCIES OF MCSS
Modulation BPSK QPSK 16QAM 64QAM
Efficiency 0.5 1.5 3 4.5
The average efficiency of the transmission using the CE function is shown in Table III when the user specified the desired BER and assuming slow varying channel condition. Table III shows the direct relationship between the desired BER and average efficiency. Another important observation is that the average efficiency is always better using low modulation orders (BPSK or QPSK) in most times. Obviously, using higher modulation orders (i.e. 16QAM or 64QAM) is better in terms of spectral efficiency. However, due to their susceptibility to errors, it is not
recommended to utilize higher modulation orders for the entire transmission when the CPE faces low SNR. TABLE III.
AVERAGE EFFICIENCY OF THE AMC USING CE
BER 10 -1 10 -2 10 -3
Average Efficiency 1.9 1.24 0.985
V.
CONCLUSIONS
Following the ever changing communication protocols is one of the major expenditures for the networks operators. However, reconfigurable platforms will contribute to minimize this cost. Moreover, CR can offer more than just providing the same hardware functionalities in software. To discover what CR can offer beyond that, simulations test beds may trigger some thoughts. One of the main pros of the CR, that is enabling system engineers to focus on applications of interest rather than low-level details, which reduce design effort, provides higher reliability, and allows easy deployment on different target platforms. In this paper an implementation of the CE performed as a module to work with a wide range of MCS profiles in AMC based communication system for BWA based on CR approaches is presented. A unified test bench for AMC function using CR approaches is performed using computer simulation, the compatibility between the SDR and the CE part and its benefit to the average efficiency is verified. Both functionality and system performance are verified with the legacy hardware approach.
REFERENCES [1]
Sami Salih, and Mamoun Suliman, “Implementation of adaptive modulation and coding techniques using Matlab”, IEEE, ELMAR, 2011. [2] IEEE 802.16-2006: “IEEE Standard for Local and Metropolitan Area Networks - Part 16: Air Interface for Fixed Broadband Wireless Access Systems”. [3] Dania Marabissi and others, “Efficient Adaptive Modulation and Coding techniques for WiMAX systems”, ICC 2008 proceedings. [4] Otuski, Nobuaki; Yoshizawa, Shingo; Miyanaga, Yoshikazu, “A New Adaptive Modulation and Coding Applied on a 400-Mbps OFDM Wireless Communication System”, 2006. ISCIT '06. [5] Hazem Shatila, Mohamed Khedr, Jeffrey H. Reed, “Adaptive Modulation and Coding for WiMAX Systems with Vague Channel State Information using Cognitive Radio ”, SPECTS, 2010. [6] Kaushik R. Chowdhury and Tommaso Melodia, “Platforms and Testbeds for Experimental Evaluation of Cognitive Ad Hoc Networks”, IEEE Communication Magazine, Vol. 48, No. 9, September 2010. [7] Paul D. Sutton, “Iris: An Architecture for Cognitive Radio Networking Testbeds”, IEEE Communication Magazine, Vol. 48, No. 9, September, 2010. [8] Mohammad Reza, Hassan Taheri, “Mobile WiMAX Capacity Estimation in Various Conditions”, 18th Iranian Conference on Electrical Engineering (ICEE), 2010. [9] J. Lotze, S.A. Fahmy, J. Noguera and L.E. Doyle, “A Model-Based Approach to Cognitive Radio Design”, IEEE Selected Areas in Communications, Vol. 29, No. 2, pp. 455-468, February 2011. [10] Zhenyu Zhang, Xiaoyao Xie, “Intelligent cognitive radio: Research on learning and evaluation of CR based on Neural Network”, ICICT 2007. [11] Colson, N., and others, “Autonomous Decision Making Process for the Dynamic Reconfiguration of Cognitive Radios”, Computer Communications and Networks (ICCCN), 2008.
