Detecção de sinais em sistemas OFDM operando em canais que variam rapidamente no tempo

Carvalho, Laísa; Neto, Raimundo Sampaio

doi:10.14209/sbrt.2019.1570558259

Cited by 1 publication

(6 citation statements)

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“…where q kk = √ MW k h k , which is defined using (2-10). The downlink expression characterized by (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21) allows to perform a simplified equalization due to the presence of a diagonal matrix. Note the similarity of (2-21) with (2-9).…”

Section: Downlink Configurationmentioning

confidence: 99%

“…The channel matrix is composed of impulse responses that vary with each column of the matrix. Hence, the elements do not repeat along the diagonals of the matrix [17]. The channel matrix H(i) is defined as…”

Section: Ofdma In Time-varying Linear Environmentsmentioning

confidence: 99%

“…whereñ i is the normalized noise component. Substituting (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18) and (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) in (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16), we obtain r = E out Diag(q)W Mw + σñ. (4)(5)(6)(7)(8)(9)(10)(11)…”

Section: Ofdma In Non-linear Channelsmentioning

confidence: 99%

“…where MAI represents the multiple access interference caused by the remaining active users. The time-invariant linear channels are a particular case of (3-17) whereH The equation (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17) shows that in time-varying linear channels the CS-CDMA systems do not remove completely the MAI.…”

Section: Cs-cdma With Zero Forcing Equalizationmentioning

confidence: 99%

“…m , all k), the expression (2-28) can be rewritten as r (m)ZP (i) =H (0) m z i + n(i) i = 0, 1, ..., B − 1, (4-29)where z i is computed in(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23).By collecting B consecutive received signals, a (N + L) × B observation matrix is formed, such that R m = [r m (0) ... r m (B − 1)] =H (0) m Z + N, (4-30) where Z = K k=1 d k c T k and N = [n(0) ... n(B − 1)]. Applying the non-linear transformation represented in Figure 4.1, we obtain R m = [r m (0) ... r m (B − 1)] =H (0) m P + N, (4-31) where the N × B matrix P is the non-linear transformation of the N × B matrix Z, such that P = f (Z) = [w 1 ... w B ], (4-32) where w b = [w b,1 ... w b,N ] T with b = 1, .., B and represents the column vectors of the matrix P. The components w b,i with i = 1, .., N are computed as…”

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confidence: 99%

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Orthogonal Multiple Access Schemes in Linear and Non-Linear Channels

HERNANDEZ¹

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Wireless communications are one of the pillars of the development of the new generations of mobile communications. Each generation has used some multiple access technique to take advantage of the channel's resources. This dissertation presents an analysis of two orthogonal multiplexing techniques. Both techniques implement block transmission, where one is combined with the Code Division Multiple Access (CDMA) approach, while the other uses the multicarrier transmission technique Orthogonal Frequency Division Multiplexing (OFDM). The performance and spectral occupation of both techniques and their advantages are analyzed. Analytical expressions for the Power Spectral Density of the signals were obtained, which allowed establishing comparisons between both methods. The study of these multiplexing techniques is carried out in different propagation channels to evaluate the behavior of both systems in general. The three types of channels evaluated in this work are linear and time-invariant, linear and time-variant and, finally, non-linear and time-invariant. Each type of channel was modeled in matrix form for both systems independently. The simulations consider the Zero Forcing and Minimum Mean Squared Error equalizers, assuming a known channel.

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