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Theoretical and experimental studies of multiple-input/multiple-output (MIMO) radio channels are presented. A simple stochastic MIMO model channel has been developed. This model uses the correlation matrices at the mobile station (MS) and base station (BS) so that results of the numerous single-input/multiple-output studies that have been published...
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... and where the symmetrical map- ping matrix results from the standard Cholesky factorization of the matrix provided that is nonsin- gular [28]. Subsequently, the generation of the simulated MIMO channel matrix can be deduced from the vector . Note that the correlation matrices and the Doppler spectrum cannot be chosen independently, as they are connected through the PAS at the MS [21]. The pin hole [4] or keyhole [22] effect has been raised in previous work. This effect would occur when scattering regions surrounding both transmit and receive ends are separated by a screen with a small hole in the middle. In this case, the received signal between the antenna elements at both ends of the MIMO radio link are uncorrelated, but still would have low rank. Although data were collected in variety of environments, the pin hole or keyhole effect was not observed. Neither can the model reproduce this effect. This is explained by the fact that in [22] the transfer channel matrix is described as a dyad with one degree of freedom, where each channel coefficient writes as the product of two independent zero-mean complex Gaussian random variables. It is, therefore, not possible to claim that a linear generation process as described in (9) can account for the product of random variables. III. M EASUREMENT C AMPAIGNS The objectives of the measurement campaigns described in this section are to collect the data required to 1) validate the MIMO radio channel model and 2) estimate the model parameters that characterize different environments. Two measurement setups are considered. They differ in the motion of the Tx and the antenna array topology of the Tx, as illustrated in Fig. 2 and described in [23] and [24]. In both setups, the Tx is at the MS and the stationary Rx is located at the BS. These two setups provide measurement results with different correlation properties of the MIMO channel for small antenna spacings of the order of 0.5 or 1.5 . The BS consists of four parallel Rx channels. The sounding signal is a MSK-modulated linear shift register sequence of a length of 127 chips, clocked at a chip rate of 4.096 Mcps. At the Rx, the channel sounding is performed within a window of 14.6 s, with a sampling res- olution of 122 ns (1/2 chip period) to obtain an estimate of the complex impulse response (IR). The NB information is subse- quently extracted by averaging the complex delayed signal com- ponents. A more thorough description of the stand-alone testbed (i.e., Rx and Tx) is documented in [25]. 1) First Measurement Setup: A standard vertically polarized dipole antenna (Fig. 3) is mounted on a rotating bar and describes a circumferential distance of 20 where is the wavelength. The operating frequency is 1.71 GHz. A syn- thesized antenna array with a separation of 0.5 between the antenna elements is considered in the post-processing analysis. A uniform linear antenna array with four elements polarized at 45 spaced by 0.45 is used at the BS. 2) Second Measurement Setup: An interleaved antenna array with four vertically polarized sleeve dipoles elements mounted at the MS is moved along a linear slide over a distance of 11.8 as shown in Fig. 4. Such an antenna arrangement is described in detail in [24] and is used to reduce the mutual coupling between the elements, thus, preserving the omnidi- rectionality of the radiation pattern of the antenna elements. After post processing, a linear array is derived from it with a spacing of 0.4 . The Tx uses a 1-to-4 switch with a switch interval of 50 s between each element of the antenna array, implementing a pseudo parallel transmission within 200 s. Note, the operating frequency is 2.05 GHz (UMTS band) for this setup. At the BS, a uniform linear array with four vertically polarized sleeve dipoles elements with a spacing of 1.5 is employed. The MS consists of two trolleys where one trolley contains all the electronic hardware of the Tx and the other, later referred to as the “satellite,” is equipped with the linear slide carrying the Tx antenna array. The two trolleys are connected by 10-m coaxial and signal cables. The purpose of using two trolleys is to reduce the effect of reflections from metallic surfaces. The satellite trolley is made of wood and the metallic part of the linear slide is shielded by microwave absorbers as shown in Fig. 4. Channel data were collected in two types of environments: picocell and microcell. Here the term picocell and microcell refer to indoor-to-indoor and indoor-to-outdoor environment, respectively. The measurement campaigns are undertaken in several buildings at two main locations: the campus of Aalborg University and the Aalborg International Airport. Table I summarizes the environments. For each environment (A–G), several MS locations are selected to provide a set of measurements where both line-of-sight (LOS) and non-LOS (NLOS) scenarios are present. Moreover, several BS locations are selected within the same environment in addition to the MS locations, as illustrated in Fig. 5, in order to increase the reachness of the data set. A total of 107 paths are investigated within these seven environments. The first measurement setup is used to investigate 15 paths in a microcell environment, i.e., environment A in Table I. The MS is positioned in different locations inside a building while the BS is mounted on a crane and elevated above roof top level (i.e., 9 m) to provide direct LOS to the building. The antenna is located 300 m away from the building. The second setup is used to investigate 92 paths for both microcell and picocell environments, i.e., environment B and C to G, respectively, as shown in Table I. The distance between the BS and the MS is 31 to 36 m for microcell B, with the BS located outside. The stochastic model relies on the wide sense stationary (WSS) condition, meaning that as a rule of thumb the scatterers should be 10 times the distance travelled by the MS away from it, which is equivalent to about 20 m in the present case. This condition can be questionable when considering practical measurements. Indeed, when measurements were undertaken in small rooms, the travelled distance of the MS was very similar to the distance of the MS to the wall, hence possibly not fulfilling the WSS condition. These restrictions need to be considered when discussing the validity of the simulated ...
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Citations
... The noise power at the user receivers is set to σ n = −110 dBm. Path loss and fading channel models are taken from [36]. The correlation between the users' channels is set to t = 0.5. ...
... 3: From the given w k , compute R wc (t) by using problem (36). If the value of R wc (t) converges, the iteration is complete. ...
This paper investigates a multi-antenna, multi-input multi-output (MIMO) dual-functional radar and communication (DFRC) system platform. The system simultaneously detects radar targets and communicates with downlink cellular users. However, the modulated information within the transmitted waveforms may be susceptible to eavesdropping. To ensure the security of information transmission, we introduce non-orthogonal multiple access (NOMA) technology to enhance the security performance of the MIMO-DFRC platform. Initially, we consider a scenario where the channel state information (CSI) of the radar target (eavesdropper) is perfectly known. Using fractional programming (FP) and semidefinite relaxation (SDR) techniques, we maximize the system’s total secrecy rate under the requirements for radar detection performance, communication rate, and system energy, thereby ensuring the security of the system. In the case where the CSI of the radar target (eavesdropper) is unavailable, we propose a robust secure beamforming optimization model. The channel model is represented as a bounded uncertainty set, and by jointly applying first-order Taylor expansion and the S-procedure, we transform the original problem into a tractable one characterized by linear matrix inequalities (LMIs). Numerical results validate the effectiveness and robustness of the proposed approach.
... Therefore, in this evaluation, the simulation uses fixed channel correlation to focus on the effect of the array degree of freedom and amplifier nonlinear distortion. We use the Kronecker model [30] as a model that allows arbitrary channel correlations to be set. This evaluation assumed a three-stream MIMO transmission, and the evaluation was conducted in an environment where the channels are low correlated (correlation coefficient ρ = 0) and where the channels are relatively highly correlated (correlation coefficient ρ = 0.5). ...
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-4
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... the vectors a ST li = a S (φ ST li , ϑ ST li ) and a CT mi = a C (φ CT mi , ϑ CT mi ) are used to denote the array responses at the l-th sensing AP and the m-th communication AP as seen from the i-th target location, respectively, with ∥a ST li ∥ 2 = N S and ∥a CT mi ∥ 2 = N C , and α mil ∼ CN 0, σ 2 mil contains, on the one hand, the square root of the channel propagation gain and the random phase shift of this propagation path and, on the other hand, the bi-static radar cross-section of the ith target as observed from the m-th communication AP and the l-th sensing AP. Based on the classical Kronecker model described by Kermoal et al. in [34], the NLOS component can be expressed as ...
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... , e jπ(N {rx,tx} −1) sin(θ) ] T is the array steering vector for an angle of arrival/departure θ and g {rx,tx} is a Laplacian power density, whose standard deviation describes the angular spread σ {rx,tx} AS of the propagation cluster at the BS (σ tx AS = 2 • ) and MT j (σ rx AS = 35 • ) side [31]. The overall channel covariance matrix per MT j is constructed as C δj = C tx δj ⊗ C rx δj due to the assumption of independent scattering in the vicinity of transmitter and receiver, see, e.g., [32]. In the case of MTs equipped with a single antenna, C δj degenerates to the transmit-side covariance matrix C tx δj . ...
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... To solve the complexity curse of SCMs, simpler models [47,48] are usually adopted to enable computationally feasible channel estimation, where one of the most often applied model is the Saleh-Valenzuila channel model (SV model, [48]). We name these simpler models as computational channel models (CCMs). ...
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... As shown in Figure 3(a), when the spatial correlation coefficients at the Tx (Rx) are assumed to be independent of the Rx (Tx), the correlation matrix in (8) is considered to be seperatable and written as the Kronecker product of the correlation matrices at the Tx and Rx [27], i.e., (9), respectively, Then, Kronecker model [26], [27] is expressed as ...
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... That is, empirical validation refers to the process to properly fit and describe measurements with theoretical models previously proposed in the literature. This is the approach we consider here for empirical validation, and the one followed in other works as[21]. ...
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... The channel from the RIS to the k-th user is denoted by r k ∈ C N with r k ∼ N C (0, C r,k ). The channel from the BS to the RIS is assumed to follow the Kronecker channel model with non-zero mean [26], given by ...
Rate splitting multiple access (RSMA) and reconfigurable intelligent surface (RIS) are two prospective technologies for improving the spectral and energy efficiency in future wireless communication systems. In this work, we investigate a rate splitting (RS) technique for an RIS-aided system in the presence of only statistical channel knowledge. We propose an algorithm with a quasi closed-form solution based only on the second-order channel statistics, which reduces the design complexity of the system as it does not require estimation of the channel state information (CSI) and optimisation of the precoding filters and phase shifts of the RIS in every channel coherence interval.
