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D2D Communication

Pratik Gaikwad Abhishek Datar

Motivation

A new paradigm to enhance performance in cellular networks

Technology used in LTE-A systems.

Spectrum efficiency improved by underlay D2D communication

Tradeoff: Interference – Mitigated used complex resource allocation methods

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Background In D2D communication, RA deals with allocating system resources in an efficient

and economic manner.

Increased number of wide range applications and connected devices

Proximity services-D2D discovery,D2D communication.

Advantages :• High spectral efficiency• Energy efficient.• Resource reuse• Low delay.

Disadvantages :• Interference prone

Problem Definition

Simulation of D2D communication Underlaying cellular Networks to find reuse candidates and calculating throughput

I. We worked on resource allocation problem to improve the throughput while guaranteeing QoS.

We focus on how to find the reuse candidates and access rate.

Throughput for the system with increasing cluster radius is also studied.

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Simulation of D2D system

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Results

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r(m)

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With Fading

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Description/Analysis

Number of D2D users=100

Number of cellular users=100

Number of reuse candidates=100

Assumption is made that all are reuse candidates.

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Performance Comparison

The access rate drop significantly with fading

D2D can achieve higher access rate without fading

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Result

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Proposed Method D2D resource allocation model is considered.

First the reuse candidates are calculated from the available set of D2D users.

The constraint includes D2D pair using resource from a single cellular user.

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Mathematically the problem can be formulated as

Here ,

cn is the number of bits assigned to nth carrier

We assume that the adaptive modulator allows cn to take values in the set D={ 0,1,....M}

M is the maximum number of bits/OFDM symbol that can be transmitted by each sub carrier.

f(cn) is the required received power in a sub carrier for reliable reception of c information bits per symbol when channel gain is equal to unity.

αn the magnitude of channel gain of the nth sub carrier

As the power needed to transmit a certain number of bits in a subcarrier is independent of the numbers of bits allocated to other subcarriers, it turns out that a greedy approach is optimal.  A Greedy algorithm assigns bits to the subcarriers one bit at a time, and in each assignment, the subcarrier that requires the least additional power is selected. The bit allocation process will be completed when all R bits are assigned and the required data rate is achieved.

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Algorithm Basic structure of the algorithm

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The initialization stage computes, for each subcarrier, the additional power needed to transmit an additional bit.

For each bit assignment iteration, the subcarrier that needs the minimum additional power is assigned one more bit, and the new additional power for that subcarrier is updated.

After iterations, the final bit assignment gives the optimal bit allocation for each subcarrier.

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Results

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Subchannel Indice

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Analysis of Result

Variable parameters in the code are• Number of sub carriers• Required Bit rate

Rayleigh fading channel with Rician absolute is being used

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Performance Analysis Using static time division multiple access or frequency division multiple access

using multi access channel scheme, user is allocated a time slot or frequency to apply OFDM with adaptive modulation.

For performance analysis, we have considered three other static sub carrier allocation methods

OFDM-TDMA: User is assigned a predetermined TDMA time slot and can use all the subcarriers within that time slot exclusively.

OFDM-FDMA: User is assigned a predetermined band of subcarriers and can only use those subcarriers exclusively in every OFDM symbol.

OFDM Interleaved-FDMA: this is the same as OFDMFDMA except that subcarriers assigned to a user are interlaced with other users’ subcarriers in the frequency

domain.

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Performance Analysis To ensure a fair comparison, we use optimal a single user bit allocation (OBA) for

the single user on assigned sub carriers.

For each BER requirement, we compute f(c) for all c ϵ D and then use our algorithm to calculate the subcarrier allocation for the MAO case.

For all other static subcarrier allocation schemes, the allocations are independent

of the BER. Once the subcarrier allocation is fixed, we apply the optimal bit and power allocation algorithm.

The final average power per bit divided by the noise power spectral density level gives the average bit SNR.

We repeat this procedure for different BER values, and the results obtained.

We find that our approach has at least 3–4 dB advantage over all other schemes.

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Related Works One of the main requirements to modulation technique is the ability to combat ISI.

There are many methods proposed to combat ISI.

[1] Y. Chen, K. B. Letaief, and J. C.-I. Chuang, “Soft-output equalization and TCM for wireless personal communication systems,” IEEE J. Select. Areas Commun., vol. 16, pp. 1679–1690, Dec. 1998.

[2] W. C. Lo and K. B. Letaief, “Adaptive equalization and interference cancellation

for wireless communications systems,” IEEE Trans. Commun., vol. 47, pp. 538–545, Apr. 1999.

OFDM one of the most promising solutions

[3] L. J. Cimini, Jr. and N. R. Sollenberger, “OFDM with diversity and coding for high-bit-rate mobile data applications,” Mobile Multimedia Commun., vol. 1, pp. 247–254, 1997.

[4] C. Y. Wong, R. S. Cheng, K. B. Letaief and R. Murch, "Multiuser OFDM with Adaptive Subcarrier, Bit, and Power Allocation", IEEE Journal on Selected Areas In Communications, Vol. 17, No. 10, OCTOBER 1999

[5] Y. Zhang, and K. B. Letaief, “Multiuser Adaptive Subcarrier-and-Bit Allocation With Adaptive Cell Selection for OFDM Systems”, IEEE Transaction on wireless communications, Vol. 3, No. 5, Sep 2004

Related Works The problem of optical power allocation has been studied in

[6] B. S. Krongold, K. Ramchandran, and D. L. Jones, “Computationally efficient optimal power allocation algorithm for multicarrier communication systems,” in Proc. IEEE Int. Conf. Communications (ICC’98), Atlanta, GA, pp. 1018–1022.

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