Adaptive Communication using Softcomputing

Adaptive Communication using Softcomputing Techniques 1,2,4

Atta-ur-Rahman

3

Ijaz Mansoor Qureshi

5

Muhammad Hammad Salam

4

Muhammad Zeeshan Muzaffar

1

Barani Institute of Information Technology, PMAS Arid Agriculture University Rawalpindi, Pakistan 2 Institute of Signals, Systems and Soft-computing (ISSS), Islamabad, Pakistan 3 Department of Electrical Engineering, Air University, Islamabad, Pakistan 4 School of Engineering & Applied Sciences (SEAS), ISRA University, Islamabad, Pakistan 5 School of Information Technology (SIT), The University of Lahore, Blue Area, Islamabad, Pakistan [email protected] [email protected] [email protected] [email protected] Abstract— Adaptive communication has gained attention of almost every recent communication system because of its rate enhancement and context aware features. In this concept, different transmission parameters like transmit power, forward error correcting (FEC) code rate and modulation scheme are adaptively chosen according to the channel state information. Consequently, that set of transmission parameters is chosen that maximizes the channel capacity as well as fulfills the power and bit error rate constraints. Finding the optimum value of the said parameters is a highly non-linear problem with huge search space for solution. In this paper, we have investigated Ant Colony Optimization (ACO) in conjunction with a fuzzy rule base system (SA-FRBS) for adaptive coding, modulation and power in an orthogonal frequency division multiplexing environment. Proposed scheme is compared with Simulated Annealing and FRBS (SA-FRBS) assisted adaptive coding modulation and power scheme as well as with the fixed power scheme. Superiority of proposed scheme is shown by the simulations. Keywords-component; ACO; Simulated Annealing; OFDM; FRBS; BER; Adaptive Modulation and Coding

I.

INTRODUCTION

Application of evolutionary and soft-computing algorithms for solution of complex problems in engineering is an emerging area of research nowadays. Nature and biologically inspired algorithms like Ant Colony Optimization (ACO), Bee Colony Optimization (BCO), Particle Swarm Optimization and Genetic Algorithms are given a potential importance in many areas of research presently. Orthogonal Frequency Division Multiplexing (OFDM) systems has gained attention of almost every present and future communication systems due to promising data rates and less vulnerability of frequency selectivity and intersymbol-interference (ISI) problem. In OFDM systems each sub-channel has different type of channel conditions so using same set of transmission parameters like code rate, modulation scheme and transmit power would not be equally suitable for all sub-channels. For example, a sub-channel with good conditions can support a high code rate and high modulation order. In contrast, a subchannel with poor conditions would need a relatively low code rate and small modulation symbol. Similarly the need of transmit power may vary from one sub-channel to another. This situation demands adaptive parameter selection rather than the fixed ones.

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A Genetic Algorithm (GA) based adaptive resource allocation scheme was proposed by Reddy [1], to increase the user data rate where water-filling principle was used as a fitness function. Moreover, it was shown that chromosome length helps to achieve optimum power requirement. A subchannel allocation based on bidding model and auction algorithm proposed by [2], where throughput was sustained but user data rates were compromised. Moreover, auction algorithm use to allocate subchannel to the appropriate most user who require that subchannel. A novel efficient resource allocation algorithm for multiuser OFDM system using a joint allocation method and root finding algorithm to achieve good performance even with low signal to noise ratio (SNR) was proposed by [3]. Another interesting paper with adaptive resource allocation based on modified GA and particle swarm optimization (PSO) for multiuser OFDM system was proposed by [4]. GA has been modified by using a fractional generation gap. It converges faster than the original one and it was found that PSO performs better than GA. An approach akin to the previous one, ant colony optimization (ACO) evolutionary technique for subcarrier allocation in OFDMA-based wireless system was proposed by [5]. Technique was capable of finding one optimal solution in a short period of time. Adaptive subcarrier and power allocation with fairness for multi-user space-time block-coded OFDM system was investigated in contrast to Greedy algorithm as well as water-filling principle [6]. An optimization problem for power constraints and use GA to maximize the sum capacity of OFDM system with the total power constraint was investigated in [7]. Also it was shown that GA is better than conventional methods. A scheme for resource allocation in downlink MIMOOFDMA with proportional fairness where dominant Eigen channels obtained from MIMO state matrix are used to formulate the scheme with low complexity in [8], scheme provides much better capacity gain than static allocation method. A PSO based Adaptive multicarrier cooperative communication technique which utilizes the subcarrier in deep fade using a relay node in order to improve the bandwidth efficiency [9] where centralized and distributed versions of PSO were investigated. A low complexity subcarrier and power allocation technique based upon GA to maximize the sum of user data rates in MIMO-OFDMA system was proposed in [10]. Another GA based efficient real-time subcarrier and bit

allocation for multiuser OFDM transmission technique was proposed in which overall transmit power was minimized under user constraint [11]. A subcarrier-chunk based technique in which resource allocation problem for the downlink of Orthogonal Frequency Division Multiple Access (OFDMA) wireless systems was proposed in [12]. The scheme dramatically reduces the complexity and fairness among users’ data rates is very satisfactory despite the loss with respect to the unconstrained case where the only target is the maximization of the sum data rate. In [13] the authors proposed a Fuzzy Rule Based System (FRBS) for adaptive coding and modulation in OFDM systems where quadrature amplitude modulation (QAM) and convolutional codes were used as forward error correction (FEC) codes and modulation schemes respectively. In [14], same authors proposed FRBS for adaptive coding and modulation where Product codes were used as FEC. In both of these papers, power was kept constant while code rate and modulation was adaptive. In [15], same authors used GA and Water-filling principle in conjunction with FRBS for adaptive coding, modulation and power in OFDM systems, where GA was used to adapt the power. It was found that GA assisted adaptive power case performs better than waterfilling principle in terms of channel capacity. In [16], authors investigated differential evolution (DE) algorithm with FRBS for adaptive coding, modulation and power. A rate enhancement scheme for OFDM based HYPERLAN was proposed in [17] where GA was used. In [18], authors proposed adaptive coding and modulation scheme using machine learning framework that helps in predicting the modulation and code rate based upon past observations and CSI. In [19], authors proposed an outer loop link adaptation for bit interleaved coded modulation BICM-OFDM using adaptive kernel regression for learning. In [20], a data driven approach to link adaptation is used where machine learning classifier is used to select the modulation and code rate. In this paper ant colony optimization algorithm with Fuzzy Rule Base System (ACO-FRBS) is proposed for adaptive coding, modulation and power in OFDM system for rate enhancement according to the individual subchannel CSI. The remainder of this paper is organized as follows. In section 2, system model is introduced. Performance of coded modulation is presented in section 3. Section 4 formulates a constrained optimization problem. In section 5 a brief introduction to FRBS is given. Section 6 contains a brief introduction of ACO; Section 7 contains the performance comparison of the scheme, while section 8 concludes the paper. II.

SYSTEM MODEL

In this paper, standard OFDM channel is considered with N number of subcarriers. It is assumed that complete channel state information (CSI) is known at receiver. The frequency domain representation of system is given by;

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rk = h k . p k .x k + z k ; k = 1, 2,......, N

(1)

where rk , hk , p k , x k and z k denote received signal, channel coefficient, transmit amplitude, transmit symbol and the Gaussian noise of subcarrier k = 1, 2,......, N , respectively. The overall transmit power of the system is

Ptotal = ∑ k =1 p k N

and the noise distribution is complex

Gaussian with zero mean and unit variance. It is assumed that signal transmitted on the kth subcarrier is propagated over Rayleigh flat fade channel and each subcarrier faces a different amount of fading independent of each other. The proposed adaptation model is given in Fig-1. Subchannel estimates provided by the PHY layer receiver and quality of service demands per subcarriers is given to the adaptation block, which in return provides the updated parameter set. These new parameters are sent to the PHY layer transmitter using a feedback channel.

OFDM PHY Transmitter

OFDM Channel

PHY layer Receiver

Quality of Service (QoS) Demand/ Subcarrier

Sub-channel Estimates

Feedback Channel

New Modulation Code rate Power

Link Adaptation using evolutionary algorithms and Fuzzy Rule Based System

Figure 1. Brief diagram of proposed System

III.

CODED MODULATION

In this section performance of standard modulation and codes being used in IEEE 802.11n/g/b are analyzed in terms of bit error rate (BER) and SNR. For experimentation the sequence of operations is carried out in same way as given in fig-2. Following is the detail of each component. The initial set of experiments is carried over an additive white Gaussian Noise (AWGN) channel. The sequence of simulations is given in fig-2 A. Coding Scheme The codes used as adapting coding parameters are nonrecursive convolutional codes with code rates taken from the set C with constraint length 7. Set C is given below; C = {1/ 4,1/ 3,1/ 2, 2 / 3,3 / 4} (3) B. Modulation Scheme In this paper we have utilized Quadrature Amplitude Modulation (M-QAM) for adapting the modulation paramter, with rectangular constellation. The modulation symbols are taken from the following set. Set M is given by; M = {2,4,8,16,32,64,128} (4)

2013 International Conference of Soft Computing and Pattern Recognition (SoCPaR)

Bit loading

FEC Encoder

V.

QAM Modulator

In this section FRBS is designed for optimum selection MCP per subcarrier based upon received SNR and QoS. The steps involved in creation of FRBS are described below.

AWGN Channel Bit Receiving

FEC Decoder

FUZZY RULE BASE SYSTEM

QAM Demodulator

Figure 2. Brief diagram of simulations

The total number of MCPs can be given by; (5) P = CxM = {(c i , m j ); ∀c i ∈ C , ∀m j ∈ M } Then graph for each MCP is obtained and some of these graphs are depicted in fig-3 using the sequence of operations shown in fig-2.

A. Data Acquisition Facts from the graphs are obtained by drawing straight horizontal lines on the graphs on certain BER values. Then the points of intersection of these lines and the curves (a modulation code pair) are noted and according SNR value is noted. This is shown in fig-4.

Figure 4. Obtaining facts from graphs

Figure 3. BER comparison of different QAM using rate 1/2 code

IV. RATE OPTIMIZATION In order to maximize the data rate for the overall OFDM system, following constrained optimization problem is considered.

max RTotal =

1 N

N

∑R

k

k =1

s.t, BER k ≤ BER QoS k

(6)

and N

PTotal = ∑ p k < PT k =1

where R k = (log 2 (M ))k rk is the bit rate of kth subcarrier which is product of code rate rk and number of modulation bits/symbol (log 2 (M )) k . PT is the total transmit power and

BERQoS k is target BER that depends upon a specific quality of service (QoS) request or application requirement over ith subcarrier, while N is total number of subcarriers in OFDM system.

B. Rule Formulation Rules for every pair are obtained by the appropriate fuzzy set used. That is by putting complete pair in input/output set and a rule generated for each pair. C. Elimination of Conflicting Rule The rules having same IF part but different THEN parts are known as conflicting rules. This appears when more than one modulation code pair (MCP) are available for given specification. D. Fuzzy Rule Base Creation Using the Lookup table in above phase, Fuzzy Rule Base is created using Fuzzy Logic Toolbox in MATLAB. The rule format can be given as below; {IF ( x 1 is L1 and x 2 is Q7) THEN y is P2} Following are the components of FRBS. •

Fuzzy Sets There are two input variables namely received SNR and QoS. There is one output variable for modulation code pair MCP. There are thirty-one sets (L0 to L30) for first input variable named SNR and sixteen sets (Q1 to Q16) for second input variable QoS; there are twenty-five sets in output variable MCP.

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•

Inference Engine Standard Mamdani Inference Engine (MIE) is used that will infer which input pair will be mapped on to which output point.

VI.

ADAPTIVE POWER APPROACHES

energy; the slow cooling gives them more chances of finding configurations with lower internal energy than the initial one. SA has a large number of applications in bioinformatics, engineering and other disciplines. In this technique we have to choose next state based upon fitness criteria. SA also uses the same fitness function shown in fig-5 and described in Equ. (8). In order to find the optimum power vector, the basic flat power vector (initial guess) is passed through the OFDM system. Once the state is known, optimum vector is found that gives the highest throughput.

A. Ant Colony Optimization (ACO) Ant colony optimization [21] is a nature inspired algorithm that is based on the behavior of ant colony searching for food in parallel. In ACO approach, several artificial ants perform a sequence of operation iteratively. In each iteration, several ants search in parallel for good solution in the solution space. Ants that hit the better solution than before are allowed to leave behind a pheromone trail for others to follow. While an ant traces a single path (or vector), an element is selected probabilistically depending upon two factors. • Pheromone concentration • Desirability function (fitness function) which is problem specific greedy heuristic to aid in search for good solution

The fitness function in our case is FRBS, which determines the throughput against any selected power vector. This is shown in fig-5 while mathematically, it can be written as; 1 N R = ∑ Rk N k =1 1 N 1 N (8) = ∑ (log 2 (M ))k rk = ∑ (MCP ) k N k =1 N k =1 1 N = ∑ FRBS ( p k α k ,QoS k ) N k =1 In this equation the final p’s are determined by ACO, with the help of that FRBS selects the optimum modulation code pairs, that eventually results in an enhanced bit rate.

B. Simulated Annealing Simulated annealing (SA) is a global optimization algorithm in which the concepts of statistical mechanics and combinatorial optimization are combined. It was developed by Kirkpatrick et al. in 1983[22]. It is very famous for finding global optimum in very large search spaces. Its name originates from the metallurgy process annealing, a technique involving heating and controlled cooling of a material to increase the size of its crystals and reduce their defects. The heat causes the atoms to become unstuck from their initial positions (a local minimum of the internal energy) and wander randomly through states of higher

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Quality of Service Vector Q

α Transmit Power Vector (P)

2 1

α

2 2

α N2

(MCP) 1

(MCP) 2

1 N

N

∑r

i

i =1

Throughput

Fuzzifier& De-Fuzzifier Standard triangular fuzzifier is used with AND as MIN and OR as MAX. Standard Center Average Defuzzifier (CAD) is used for defuzzification.

Fuzzy Rule Base System (FRBS)

•

(MCP) N

Figure 5. Fitness function Block

VII. RESULTS In this section proposed scheme is compared with other schemes. Table-1 contains the parameters used to perform the simulations. Table-1 Simulation Parameters Sr. 1 2 3 4

Parameter Number of Subcarriers N=Number of Ants Fitness Function for ACO and SA ACO and SA iterations Channel considered for simulation

5 6 7 8

Channel Coefficients range Quality of Service (QoS) Adaptive Criterion Parameters being adapted

Value 1024 Fuzzy Rule Base System Fig-5 50 IEEE 802.11n indoor channel (WIFI) [0.1-0.4] 10e-2,10e-3,10e-4 and 10e-5 SA-FRBS, ACO-FRBS Code rate, Modulation and power

In fig-6, performance of ACO-FRBS assisted adaptive coding modulation and power (ACMP) scheme is investigated for various target bit error rates that is 10e-2 (low) to 10e-5 (high). At 25dB throughput approaches 5.5bits/s/Hz, and as target BER is becoming stringent the throughput is being compromised such that at very high value of QoS it approaches to 3.5bits/s/Hz. In fig-7 to fig-10, proposed scheme is compared with SA-FRBS based ACMP [23] and FRBS assisted ACM [13] scheme with fixed power case for different target BERs. In fig-7, for SNR range of (0dB to 20dB) ACO-FRBS assisted scheme significantly performs better than SA-FRBS and

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fixed power schemes, in terms of throughput. While, for SNR greater than 20dB ACO-FRBS and SA-FRBS perform is nearly same but still it is far better than the fixed transmit power case. In this case target BER was 10e-2. In fig-8, results are almost similar that of fig-8 as far as SNR range is till 20dB, however, for SNR>20dB SA-FRBS is relatively better than ACO-FRBS scheme, and at 30dB this difference approaches 0.5bits/s/Hz. In fig-9, results are quite interesting, for SNR below or equal 20dB, ACO-FRBS is far better than SA-FRBS and fixed power scenario, but for SNR values between 23dB to 27dB SA-FRBS becomes up and for SNR values above 27dB both approaches have same performance. In fig-10, however, ACO-FRBS assisted ACMP outperforms compared to SA-FRBS assisted ACMP and fixed power case with only adaptive coding and modulation for all cases of SNR except in the vicinity of 25dB SNR both schemes become close in terms of throughput. Particularly, in this case, performance of SA-FRBS becomes equal to the fixed power case at an SNR value of 15dB and below 5dB but for rest of the values of SNR, SA-FRBS outperforms compared to fixed power case.

Figure 8. Comparison of proposed schemes with QoS=10e-4 per subcarrier

Figure 9. Comparison of proposed schemes with QoS=10e-4 per subcarrier

Figure 6. Comparison of proposed scheme with QoS=10e-2 per subcarrier

Figure 10. Comparison of proposed scheme for different target BER

VIII. CONCLUSIONS

Figure 7. Comparison of proposed scheme with QoS=10e-3 per subcarrier

In this paper Ant Colony Optimization with a Fuzzy Rule Based System (ACO-FRBS) is proposed for adaptive coding, modulation and power (ACMP) in OFDM systems.

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Also the proposed scheme is compared with Simulated Annealing and fuzzy rule based system (SA-FRBS) assisted ACMP scheme. From the simulation results it is deduced that ACO-FRBS scheme performs significantly better than SA-FRBS and fixed power schemes in terms of throughput for SNR values below 20dB. For above 20dB SNR values, both schemes perform somewhat identically, except some minor fluctuation in some cases. However, it was apparent from the simulations that adaptive power based schemes outperform compared to fixed power scheme. Performance of the proposed scheme has been investigated over IEEE 802.11n (WIFI) environment for IEEE standard indoor channel. Simulation results show the viability of the proposed scheme and its significance in terms of rate enhancement compared to its fixed power variant. Performance is measured for different quality of service demands (target BER) per subcarrier in OFDM system.

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ACKNOWLEDGEMENTS

[15]

This research work was partially supported by Barani Institute of Information Technology (BIIT), Rawalpindi, Pakistan.

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