Impact of fractional bandwidth on the bit error rate of a beamforming system

Waheed, Owais Talaat; Shabra, Ayman; Elfadel, Ibrahim Abe M.

doi:10.1109/mwscas.2016.7869995

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…SADDE algorithm contains following six steps: Initialize a starting M p population with individuals { X i : i = 1, 2, ⋯, M p }. Evaluate the objective function for the parameter vector. Mutate the parameter trial vectors: The operation is performed by the arithmetical combination of individuals. For each parameter vector X i of the parent generation, a trial vector V i is generated according to the following equation:

\begin{matrix} lefttrue \\ {((), {normalV}_{i}^{g + 1})}_{j} = {((), {normalX}_{i}^{g})}_{j} + α_{i}^{g} \cdot [{((), {normalX}_{best}^{g})}_{j} - {((), {normalX}_{i}^{g})}_{j}] + β_{i}^{g} \cdot [{((), {normalX}_{m}^{g})}_{j} - {((), {normalX}_{n}^{g})}_{j}], \\ m, n \in [1, M_{p}], m \neq n, \end{matrix}

where

α_{i}^{g}

and

β_{i}^{g}

are the scaling factors, which are adjusted by equations

α_{i}^{g + 1} = ({, \begin{array}{c} α_{l} + {italicrand}_{1}^{*} α_{u}, italicif \begin{matrix} center \end{matrix} {rand}_{2} < 0.1 \\ α_{i}^{g}, \begin{matrix} center \end{matrix} italicotherwise \end{array}),

β_{i}^{g + 1} = ({, \begin{array}{c} β_{l} + {italicrand}_{3}^{*} β_{u}, \begin{matrix} center \end{matrix} italicif \begin{matrix} center \end{matrix} {rand}_{4} < 0.1 \\ β_{i}^{g}, \begin{matrix} center \end{matrix} italicotherwise & ... \end{array})

…”

Section: Evolution Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of self‐adaptive dynamic differential evolution and particle swarm optimization for smart antennas in wireless communication

Chiu

Tong

Cheng

2019

Int J Communication

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In this paper, ultrawide band (UWB) communication systems with eight transmitting and receiving ring antenna arrays are implemented to test the bit error rate and capacity performance. By using the ray-tracing technique to compute any given indoor wireless environment, the impulse response of the system can be calculated. The synthesized beamforming problem can be reformulated into a multiobjective optimization problem. Self-adaptive dynamic differential evolution (SADDE) and particle swarm optimization (PSO) are used to find the excitation current and the feed line length of each antenna to form the appropriate beam pattern. This pattern can then reduce the bit error rate and increase the channel capacity and receiving energy. Numerical results show that the fitness value and the convergence speed by the SADDE are better than those by the PSO. Moreover, the SADDE had better results for both line-ofsight and nonline-of-sight cases. In other words, compared with PSO, SADDE has improved more effectively the main beam radiation energy and reduced the multipath interference.

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\begin{matrix} lefttrue \\ {((), {normalV}_{i}^{g + 1})}_{j} = {((), {normalX}_{i}^{g})}_{j} + α_{i}^{g} \cdot [{((), {normalX}_{best}^{g})}_{j} - {((), {normalX}_{i}^{g})}_{j}] + β_{i}^{g} \cdot [{((), {normalX}_{m}^{g})}_{j} - {((), {normalX}_{n}^{g})}_{j}], \\ m, n \in [1, M_{p}], m \neq n, \end{matrix}

where

α_{i}^{g}

and

β_{i}^{g}

are the scaling factors, which are adjusted by equations

α_{i}^{g + 1} = ({, \begin{array}{c} α_{l} + {italicrand}_{1}^{*} α_{u}, italicif \begin{matrix} center \end{matrix} {rand}_{2} < 0.1 \\ α_{i}^{g}, \begin{matrix} center \end{matrix} italicotherwise \end{array}),

β_{i}^{g + 1} = ({, \begin{array}{c} β_{l} + {italicrand}_{3}^{*} β_{u}, \begin{matrix} center \end{matrix} italicif \begin{matrix} center \end{matrix} {rand}_{4} < 0.1 \\ β_{i}^{g}, \begin{matrix} center \end{matrix} italicotherwise & ... \end{array})

…”

Section: Evolution Algorithmsmentioning

confidence: 99%

“…In various types of systems presented, fixed multiple beams and fully adaptive smart antennas are commonly used. With the assistance of the smart antenna system, a beamforming can be developed to reduce the bit error rate and increased the capacity in the wireless communication system …”

Section: Introductionmentioning

confidence: 99%

Comparison of self‐adaptive dynamic differential evolution and particle swarm optimization for smart antennas in wireless communication

Chiu

Tong

Cheng

2019

Int J Communication

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“…While conceptually not novel, the terahertz domain still poses specific challenges. Aiming at large relative bandwidths within each communication channel, simple phase-shifting typologies reach their limit and must be replaced with true time delays [9]. Synchronization and delay compensation of all local oscillator (LO) and baseband (BB) signals become detrimental to achieving proper phase steering.…”

Section: Introductionmentioning

confidence: 99%

“…While the targeted data rates necessitate a true-time delay architecture, the used MMIC has an LO steering phase shifter. This deliberate choice is a trade-off between chip size, design complexity, and influence on the phased array [9].…”

Section: Introductionmentioning

confidence: 99%

Wideband BPSK Transmitter Phased Array Operating at 246 GHz With ±30° Steering Range

Hebeler,

Steinweg,

Ellinger

et al. 2024

IEEE Access

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This paper presents the construction and measurement of a phased array BPSK transmitter system operating at 246 GHz with tested data rates of up to 32 Gbit s =1 . The system is built using custom monolithic microwave integrated circuit (MMIC) and a novel packaging solution. The construction showcases the flexibility in the chosen approach, paving a path for future systems. It is the first TX array with significant beam steering capabilities tested above a pure antenna pattern stage above 200 GHz. The system is tested for realistic usage scenarios. Bit-error-rate (BER) measurements are used to showcase real-world usability. BER over angle and BER over steering angle are measured to verify the operation of the transmitter.

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