A novel approach for beamforming based on adaptive combinations of vector projections

Novo Algoritmo com Restrição de Norma l0 para Conformação de Feixe e Seleção de Antenas

“…w Isso justifica a inclusão da restrição de norma 0 em (12), a qual leva em conta o número de cadeias de RF disponíveis no sistema. Além disso, a maximização de Γ é substituída pela minimização da potência instantânea dos sinais interferentes adicionado ao ruído, visto que o vetor ( ) n z está prontamente disponível para o sistema [17], [18], [21], [22]. Contudo, apenas a minimização da interferência não garante a maximização de .…”

“…onde 2 μ denota o passo de adaptação da restrição de projeção adaptativa. Assim, substituindo ( 22) e ( 23) em (21), o processo de atualização de ( ) n w é reescrito como segue:…”

5G Mobile Communication, Challenges: A Survey

“…Also, in cellular application it is used to reduce interference. Some unresolved issues [24][25][26]in this stream are Pilot Contamination, Millimeter wave hybrid beamforming, Channel correlation of beamforming array antennas for millimeter waves, Scarcity of beams. The challenge in beamforming is silicon technology required for complex processing as 5G puts on ultra-low latency.…”

Stochastic Modeling of the KCLMS Algorithm in the Context of Adaptive Beamforming

“…Mobile communication systems have used adaptive beamforming techniques to dynamically adjust the radiation pattern of an antenna array, aiming to reduce interferences and strengthen the signal-of-interest (SOI); as a result, an improved signal-to-interference-plus-noise ratio (SINR) is achieved at the array output [1,2], thus increasing both spectral and energy efficiency of the system [3]. Among the beamforming algorithms (e.g., [4][5][6][7][8][9] and references therein), the constrained least-mean-square (CLMS) [4] has been widely used, due to its low computational complexity and dependence only on knowledge of the angle-of-arrival (AOA) of the SOI [10]. However, this algorithm shows poor convergence characteristics as the number of antennas increases [7], limiting its use in modern massive MIMO (multiple-input multiple-output) applications (such as in 5G networks [11,12]).…”

“…Considering its practical applicability and the improved performance achieved, a theoretical study of the KCLMS algorithm through a stochastic model can be useful to gain a deeper understanding of the algorithm behavior [as an alternative to the use of extensive Monte Carlo (MC) simulations]. Such a model aims to describe the algorithm behavior under different operating conditions, allowing to obtain fair performance comparisons, to establish cause-and-effect relationships, and to identify anomalous algorithm behavior [6,9,[15][16][17][18]. Nevertheless, despite the inherent importance, the stochastic modeling of the KCLMS algorithm has not been discussed so far in the literature.…”