[Paper Review] A PAPR Reduction Method Based on Artificial Bee Colony Algorithm for OFDM Signals

Yajun Wang, Wen Chen+1 more|arXiv (Cornell University)|Mar 13, 2020

PAPR reduction in OFDM24 references105 citations

TL;DR

The paper proposes a modified artificial bee colony (ABC) algorithm to optimize partial transmit sequences (PTS) for OFDM, achieving lower PAPR with reduced computational complexity compared to prior PTS methods.

ABSTRACT

One of the major drawbacks of orthogonal frequency division multiplexing (OFDM) signals is the high peak to average power ratio (PAPR) of the transmitted signal. Many PAPR reduction techniques have been proposed in the literature, among which, partial transmit sequence (PTS) technique has been taken considerable investigation.However, PTS technique requires an exhaustive search over all combinations of allowed phase factors, whose complexity increases exponentially with the number of sub-blocks. In this paper, a newly suboptimal method based on modified artificial bee colony (ABC-PTS) algorithm is proposed to search the better combination of phase factors. The ABC-PTS algorithm can significantly reduce the computational complexity for larger PTS subblocks and offers lower PAPR at the same time. Simulation results show that the ABC-PTS algorithm is an efficient method to achieve significant PAPR reduction.

Motivation & Objective

• Motivate reduction of PAPR in OFDM systems due to high peak-to-average power ratio.
• Propose a suboptimal phase-optimization method using a modified ABC algorithm for PTS.
• Reduce computational complexity for large PTS subblocks while improving PAPR performance.

Proposed method

• Formulate PAPR minimization as choosing a phase vector b from {e^{j2πℓ/W}}^{M}, with M sub-blocks.
• Introduce ABC-PTS: modify ABC to handle discrete phase factors {±1} or {±1, ±j} for b.
• Represent a solution as b_i = [b_{i1},…,b_{iM}] and define fitness as 1/(1+f(b_i)).
• Employed and onlooker bees generate neighbor phase candidates using equations (11)-(13) and discrete mappings (12)-(13).
• Abandoned sources are replaced by scouts via equation (15); track best solution over iterations.

Experimental results

Research questions

• RQ1 Can ABC-based search efficiently find near-optimal phase factors for PTS in OFDM?
• RQ2 Does ABC-PTS reduce PAPR significantly with lower computational complexity than OPTS and other heuristics?
• RQ3 How do ABC-PTS parameters (population size S, limit, iterations K) affect PAPR performance and complexity?
• RQ4 How does ABC-PTS compare to IPTS, GD, RS, PSO-PTS, and MDGA in PAPR reduction for large M sub-blocks?

Key findings

• ABC-PTS achieves lower PAPR than several suboptimal methods at similar or lower computational cost.
• For M=16 sub-blocks and W=2, ABC-PTS with K=40 attains about 6.65–6.75 dB PAPR at CCDF=10^{-3}, while IPTS is 7.95 dB and OPTS reaches 6.45 dB.
• ABC-PTS reduces complexity to SK compared to OPTS, which is W^{M-1}, yielding substantial savings at similar performance levels.
• With S=30 and S=40, ABC-PTS achieves 6.8 dB and 6.7 dB respectively, vs. OPTS 6.45 dB, with only 2.75% and 3.66% of OPTS complexity.
• ABC-PTS performance improves relative to PSO-PTS and MDGA as iterations grow, and 0.1 dB improvement between K=30 and K=60 is observed.
• The algorithm is easy to tune due to only three control parameters (S, Limit, K).