The inter-cell interference component represents the sum of the WCDMA waveforms of all the users who do not communicate with the NodeB cell under.

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W-CDMA (WCDMA; Wideband Code Division Multiple Access), along with UMTS-FDD, UTRA-FDD, or IMT CDMA Direct Spread is an air interface.

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W-CDMA (WCDMA; Wideband Code Division Multiple Access), along with UMTS-FDD, UTRA-FDD, or IMT CDMA Direct Spread is an air interface.

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Introduction. The LTE Toolbox can be used to generate standard compliant W-CDMA/HSPA/HSPA+ uplink and downlink complex baseband waveforms.

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In practice, in a system such as WCDMA this waveform would be an OVSF code combined with the scrambling code. Figure 6. 7 (b) illustrates the procedures.

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MUO wideband code division multiple access (WCDMA) terminals are projected to advanced waveform terminals are likely to be slow to roll out, even with the.

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The WCDMA system is part of the UMTS. It is developed by the 3G Partnership Program, which is composed of evolved core cellular networks.

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Peak to average power ratio, 24 Gaussian waveform, 66, GMSK, 48 modified 8PSK, 53, OFDM waveform, 66 WCDMA waveform, 59 Peak value,.

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The WCDMA system is part of the UMTS. It is developed by the 3G Partnership Program, which is composed of evolved core cellular networks.

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Abstract: The mobile user objective system (MUOS) uses WCDMA as a basis for its waveform definition. This paper deals with the quasi-analytic method of.

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The rest of the paper is organized as follows: Section 2 presents the signal model where sufficient statistics and an equivalent matrix representation are derived. For sampling at the receiver, we assume that out-of-band noise is first suppressed by an ideal low-pass filter LPF with bandwidth W , which has the same bandwidth as the transmitted signal. However, the assumption of perfect synchronization between users is not realistic, especially for a cellular CDMA uplink. In order to tackle this problem, we have proposed an effective approximation algorithm based on sphere-decoding approach to find the approximate capacity for large MIMO system with finite constellation input in [ 34 ]. In MIMO channels with finite constellation input, a similar problem occurs when the input alphabet set or the number of antennas is too large, e. If the input signal vector d k of each user follows a zero mean complex Gaussian distribution with unit input power constraint, i. However, we use it in the numerical results section Section 3. The capacity limit for a CDMA system with symbol-asynchronous transmission the symbol epochs of the signal are not aligned at the receiver has also been studied in [ 12 — 15 ]. Any coding scheme which achieves the capacity of the channel with input d and output r t can also be employed to the channel with input d and decoding based on output z instead of r t. This is particularly helpful in an overloaded CDMA system [ 8 , 21 ], where the number of users exceed the spreading factor.{/INSERTKEYS}{/PARAGRAPH} There have been several studies trying to deal with the continuous-time asynchronous CDMA system setup. However, the approach and framework can be extended or transferred to other wireless standards. The problem of optimal decoding d is similar to the detection problem in [ 27 ], Proposition 3. Accordingly, the optimal decision 3 can be made using the following decision variables. A matrix canonical form is useful to characterize the capacity from a sufficient statistic. The proof for Proposition 1 is given in Appendix 3. Let us define R 1 , R 2 ,…, R K as the maximum number of bits that can be reliably transmitted from user 1, user 2, …, user K per block of N symbols. The capacity analysis for a real cellular network with continuous-time waveform, time-variant-spreading, asynchronous CDMA is difficult due to the following reasons. Next, for a time-variant spreading CDMA system, the approach based on the asymptotic properties of a Toeplitz form [ 23 ], which is crucial for the capacity analysis with ISI channel [ 12 , 24 ], cannot be employed since the variation of spreading sequence destroys the Toeplitz structure of the equivalent channel matrix. The sampling capacity is then characterized by 4. In this figure, both the achievable sum-rates achieved by sufficient statistic from Sections 3. The equivalent channel channel H k is given by. Moreover, the resulting equivalent channel in 5 corresponds to a traditional discrete-time MIMO multiple-access channel MAC , which are used in various research literature. Then the sufficient statistics can be expressed as. The capacity with sampling at the receiver is also provided to exemplify the performance loss due to a typical post-processing at the receiver. The proof for Proposition 2 is given in Appendix 4. An equivalent discrete-time channel model is derived based on sufficient statistics for optimal decoding of the transmitted messages. Since the matched filtering at the receiver yields a sufficient statistic, the uplink WCDMA capacity achieved by any other receiver structures is upper bounded by the capacity achieved by the sufficient statistic using matched filtering. In this section, we provide a deeper analysis on this observation by characterizing the asymptotic behavior of the sum-capacity. A matrix representation of channel model is provided for which the equivalent additional noise is shown to be a Gaussian distributed random vector. In Section 3 , the capacity analysis is provided considering finite constellations and Gaussian-distributed input signals. This corresponds to a real cellular CDMA network with long scrambling codes, in which the effective spreading sequence will vary between symbols. Various realistic assumptions are incorporated into the problem, which make the study valuable for performance assessment of real cellular networks to identify potentials for performance improvements in practical receiver designs. These losses are due to the finite time limit of our sampling window as the Nyquist sampling theorem states that a infinite sample sequence is required to be able to perfectly recover a finite energy and band-limited signal [ 26 ], Theorem 8. Therewith, we investigate the capacity loss due to sampling, which is a traditional discretization approach in practical systems. Since the Gaussian-distributed input is the optimal input for a given mean power constraint, 10 serves as an outer bound for the capacity region with a practically motivated input, i. Then, we show that d can be uniquely decoded, i. Regarding the capacity upper bounds in Sections 3. The capacity regions are then characterized using the equivalent channel considering both finite constellation and Gaussian distributed input signals. A deeper analysis on this asymptotic behavior will be given in the next section. The capacity employing sampling is also investigated in this section. Although being introduced more than 50 years ago, CDMA is still largely employed and developed nowadays due to its various advantages such as enabling universal frequency reuse, improving handover performance by soft-handover, and mitigating the effects of interference and fading. A more theoretical approach on CDMA capacity analysis has been pursued in [ 9 — 11 ] by modeling the spreading sequences with random sequences. However, most of them focus on other performance metrics than capacity e. Those results are particularly useful for spreading sequence design in a real WCDMA cellular network. The left-hand side sub-figure presents the sum- and individual capacities for Gaussian and 4-QAM input signals. The proof of Lemma 1 is given in Appendix 2. On the left-hand side, the solid lines represent the capacities with Gaussian input and the dotted lines represent the capacities with 4-QAM input. Accordingly, a necessary condition to simultaneously achieve the individual capacities with a finite constellation input signal, which takes the signal constellation structure into account, is derived. The results are mainly based on the following lemma. In particular, we characterize the capacity region when the input signal is fixed to finite constellations, e. Figure 1 illustrates an implementation to obtain the sufficient statistic from the continuous-time received signal. Therefore, the channel capacity is preserved when the continuous-time output r t is replaced by the sufficient statistic z. Moreover, in practice, time-variant-spreading sequences based on Gold or Kasami codes [ 1 — 3 ] are often used rather than time-invariant or random spreading sequences. In this study, we assume a tapped-delay line channel model 2 with L multi-paths [ 27 ] Chap. The asymptotic capacity when the SNR goes to infinity is analyzed and discussed in Section 4. Given the capacity bounds measured directly at the receive antenna of a real system, we can now assess the capacity loss due to a specific post-processing at the receiver. In [ 15 ], the spectral efficiency of an asynchronous CDMA system has been considered while neglecting the ISI by assuming a large spreading factor. Therewith, the sufficient statistic is based on the first term of 3 , which can be rewritten as. In [ 13 , 14 ], the authors studied user and sum capacities of a symbol-asynchronous CDMA system but with chip-synchronous transmission the timing of the chip epochs are aligned assumption, which made the analysis tractable using a discrete-time model. {PARAGRAPH}{INSERTKEYS}This paper investigates the capacity limit of an uplink WCDMA system considering a continuous-time waveform signal. In addition, we use fixed user delays which are randomly drawn within a symbol time once at the beginning of simulations, i. Note that in the real cellular networks, since the sampling window is finite, perfect reconstruction of a band-limited signal is not guaranteed even if the sampling rate is equal to Nyquist rate [ 26 ], Chap. As a result, the sampled received signal at time t n is given by. Thus, we can focus on the capacity of the equivalent discrete-time channel in 5 , which is given by the capacity region of a discrete memoryless MAC [ 32 ]. This implies that the multi-path channel may help the equivalent channel matrix H to achieve full-rank. Since a sufficient statistic for decoding the transmitted messages preserves the capacity of the system, the capacity of a continuous-time channel can be computed using a sufficient statistic [ 25 ], Chap. Finally, Section 5 concludes the paper. Firstly, we start from the asymptotic sum-capacity of a simple K -user MAC, where each user transmits only one data stream. However, the results in Fig. In addition, the architecture of WCDMA systems still has room for improvement, especially at the uplink receiver side base station [ 4 , 5 ]. Accordingly, the Gaussian capacity offers a capacity outer bound for the real WCDMA cellular networks using finite constellation input. As expected, the capacity region enlarges with increasing SNR. First, an equivalent discrete-time signal model is complicated to be expressed due to the asynchronization between symbols and chips. To this end, we first derive the sufficient condition , which holds for all kinds of input signals including signals based on finite and infinite constellations. The specific details about the algorithm can be found in [ 34 ]. Various realistic assumptions are included into the system such as: continuous-time waveform-transmitted signal and time-variant spreading and an asynchronous multi-code CDMA system with ISI over frequency-selective channels. Since the second term of 3 does not depend on the received signal r t , we can drop it. The algorithm to compute the entropy is out of the scope of this work. Moreover, we analyze the asymptotic capacity when the signal-to-noise ratio goes to infinity. Recalling that z is a sufficient statistic for optimal i. The performance assessment of such networks is of significant importance. It is worth noting that although the signal model is constructed based on a WCDMA system, the approach and framework can be extended or transferred to other wireless standards. Then the received signal is given by. Motivated by the fact that most existing research on multiuser CDMA capacity have focused on theoretical analysis with simplified system assumptions, in this work, we present a framework for capacity analysis of a WCDMA system with more realistic assumptions, which make the framework and results more valuable for the performance assessment of real cellular networks. The conditions to simultaneously achieve the individual capacities are derived, which reveal the impacts of signature waveform space, channel frequency selectivity and signal constellation on the system performance. The capacity region of the uplink WCDMA channel is then characterized by the closure of the convex hull of the union of all achievable rate vectors R 1 , R 2 ,…, R K satisfying [ 32 ], [ 25 ], Chapter We now characterize the uplink WCDMA capacity region considering two specific input signals: finite constellation with uniformly distributed input and Gaussian-distributed input. Typically, the capacity region of a channel with finite constellation input is numerically characterized via Monte Carlo simulation because a closed-form expression does not exist. It is shown that the received signal passing through a bank of matched filters, where the received signal is matched to the delayed versions of the signature waveforms, results in a sufficient statistic for decoding d based on r t. The assessment of such impact on the capacity is also considered in this work. Next, once again, we motivate our study from a practical perspective by focusing on the finite constellation input signal. Moreover, as the SNR tends to infinity, the capacity region converges to the corresponding source entropy outer bound i. Additionally, we provide the capacity region when the input signal follows a Gaussian distribution, which is the optimal input distribution for additive Gaussian noise channels. Thus, by appropriately choosing the signature waveforms and matched fingers, which yield a full-rank equivalent channel matrix H , the transmitted symbols, d 1 ,…, d K , can be perfectly i. As expected, the sum-capacity achieved by the sufficient statistic is an upper bound for the sum-rates achieved by systems employing sampling. Then, the transmitted signal for user k can be expressed as. In [ 6 — 8 ], the optimal spreading sequences and capacity limits for synchronous CDMA have been studied with a discrete-time signal model. By following the similar steps as in [ 31 ], the matrix representation of the equivalent channel can be obtained from 4 as.