Performance Analysis of MIMO-NOMA Systems with Randomly Deployed Users

The Non-Orthogonal Multiple Access (NOMA) is considered as a technique that satisfies increasing demands of spectrum efficiency and system capacity in future 5G networks since the technique serves numerous number of users in one resource block with multiple power levels. The application or execution of multiple input multiple output (MIMO) to NOMA system helps in building up the performance gains of NOMA. Here an effectual communication deep neural network (CDNN) that addresses problem of power allocation in MIMO NOMA system is designed and used which helps to integrate the deep learning technique with the MIMO NOMA system in order to provide an improved sum data rate and energy efficiency of the system. Improved sum data rate and energy efficiency is realized further using extensive simulations and it is then compared to realise the performance of the neural network. The results then shows that the CDNN integrated MIMO NOMA system shows the best performance and also the algorithm is efficient for the systems with both mobile and stationary users.

This paper focuses on the multi‐pair amplify‐and‐forward massive multiple‐input multiple‐output (MIMO) relay enabled two‐tier heterogeneous cellular networks in terms of aggregate spectral efficiency (SE), where the macro cells are overlaid with dense small cells. Specially, the maximum ratio combining/maximal ratio transmission and zero‐forcing processing schemes are employed at massive MIMO relay, respectively. The macro base stations located in the center of the cells are equipped with massive MIMO antenna array, while the randomly distributed macro users (MUs), small cell base stations, and small cell users are equipped with single antenna. The positions of MUs, small cell users and small cell base stations are modelled as independent Poisson point processes. For such two‐tier heterogeneous cellular networks, to obtain the aggregate SE in target cell, we first formulate the end‐to‐end signal‐to‐interference noise ratios for a communication pair as well as the expression of power amplification factor at target macro base stations so that the total spectrum efficiency per cell is formulated. Then, by employing stochastic geometry, the closed‐form expressions of the power amplification factor and the expectation of the reciprocal of the equivalent end‐to‐end signal‐to‐interference noise ratios are achieved. With these results, we derive the lower bounds of SEs for maximum ratio combining/maximal ratio transmission and zero‐forcing schemes. The derivations display a clear perspective about the impact of network parameters on the system‐level performance, which includes macro cell radius, small cell radius, macro cell guard radius, the mean number of MUs per cell and so on. To achieve optimal (or suboptimal) system performance, we find that the radius of small cell should be less than the guard radius. For the guard radius, there exists an upper bound, only under which the increase of the guard radius would contribute to the improvement of SE. At the same time, our results show that with given network parameters, especially the guard radius and the small cell radius, there exists a unique greatest lower bound in terms of macro cell radius. When the macro cell radius is less than the greatest lower bound, the aggregate SE is increasing with the macro cell radius. On the contrary, the achievable aggregate SE saturates to the corresponding constant, approximately. In addition, the greatest lower bound of the macro cell radius is the function of the guard radius and the small cell radius. Copyright © 2016 John Wiley & Sons, Ltd.

Multiple transmit and receive antennas (MIMO) have been used in OFDM systems for capacity improvement. In practice, channel state information has to be estimated for diversity combining or space-time decoding. Previous work on channel estimation assumes that MIMO channels are independent and identically distributed (i.i.d.). In certain propagation environments, there exists spatial correlation among channels corresponding to different pairs of transmit and receive antennas. The spatial correlations can he exploited to improve channel estimation. In this paper, we develop a minimum mean-square-error (MMSE) channel estimator for MIMO-OFDM systems that can make full use of the spatial correlation. We also design optimum training sequences that minimize the channel estimation error. When MIMO channels are i.i.d., the training sequences for different transmit antennas are orthogonal and with equal power. However, when MIMO channels are spatially correlated, the power allocation for training sequences can be further optimized. Our simulation results show that the proposed MMSE estimator can exploit spatial and frequency correlations of MIMO channels in OFDM systems and therefore has good performance.

A good understanding of the wireless channel, its key physical parameters and the modeling issues, lays the foundation for the rest of the book. This is the goal of this chapter. A defining characteristic of the mobile wireless channel is the variations of the channel strength over time and over frequency. The variations can be roughly divided into two types (Figure 2.1): Large-scale fading , due to path loss of signal as a function of distance and shadowing by large objects such as buildings and hills. This occurs as the mobile moves through a distance of the order of the cell size, and is typically frequency independent. Small-scale fading , due to the constructive and destructive interference of the multiple signal paths between the transmitter and receiver. This occurs at the spatial scale of the order of the carrier wavelength, and is frequency dependent. We will talk about both types of fading in this chapter, but with more emphasis on the latter. Large-scale fading is more relevant to issues such as cell-site planning. Small-scale multipath fading is more relevant to the design of reliable and efficient communication systems – the focus of this book. We start with the physical modeling of the wireless channel in terms of electromagnetic waves. We then derive an input/output linear time-varying model for the channel, and define some important physical parameters. Finally, we introduce a few statistical models of the channel variation over time and over frequency.

There are two kinds of fading models for large and small scale fading in the propagation process of electromagnetic waves, and this paper analyzes the root-mean-square delay expansion and Les K factor of two kinds of small scale fading characteristics under line-of-sight and non-line-of-sight scenarios channels by combining the statistical method with the fitting. A channel model library is established, and multiple large-scale fading models are used to compare the residual sum of squares and the goodness of fit to analyze the large-scale fading characteristics in HF signal transmission. Combined with the performance of the characteristics existing in HF signal transmission, we propose a wireless channel multipath identification algorithm based on D-S evidence theory, analyze the algorithm operation flow, and test the algorithm for multi-channel scene identification. The CDF of the rms delay extensions under the line-of-sight and non-line-of-sight scene channels can be fitted well with shape parameters of 0.727 and 0.272 and scale parameters of 655.33 and 1173.83, respectively. The CDFs of the Rice K factors for the 2 scenarios fit the normal distribution well, with standard deviations less than 3 dB. The wireless channel multipath identification algorithm based on D-S evidence theory can improve the wireless signal transmission identification and optimize the frequency response of wireless signal transmission.

Base station (BS) locations are usually modelled using one of two extremes: at one end is a deterministic, hexagonal, location model; while at the other is a random deployment following a Poisson point process (PPP). However, real-world networks follow neither extreme; as such, in this paper, we focus on a modified perturbed hexagonal lattice model that, in terms of regularity, lies in between the PPP and the perfect hexagonal lattice models. In our modified perturbed hexagonal lattice, the location of all interfering BSs, except for the serving BS under consideration, are perturbed. We provide a simple and tight upper bound on the average total interference in an interference-limited reuse-1 network. The bound is presented in the form of a polynomial in the distance from the serving BS and another polynomial in the normalized perturbation. The presented formulation is useful in obtaining simple analytical expressions for various network parameters such as SIR and/or coverage probability. As an added benefit, the formulations here quantify the loss in the coverage probability in moving from the perfect lattice model to a random BS deployment. We use simulations to illustrate the accuracy of the theory developed.

We develop a new class of event detection algorithms in Wireless Sensor Networks where the sensors are randomly deployed spatially. We formulate the detection problem as a binary hypothesis testing problem and design the optimal decision rules for two scenarios, namely the Poisson Point Process and Binomial Point Process random deployments. To calculate the intractable marginal likelihood density, we develop three types of series expansion methods which are based on an Askey-orthogonal polynomials. In addition, we develop a novel framework to provide guidance on which series expansion is most suitable (i.e., most accurate) to use for different system parameters. Extensive Monte Carlo simulations are carried out to illustrate the benefits of this framework as well as the quality of the series expansion methods, and the impacts that different parameters have on detection performance via the Receiver Operating Curves (ROC).

This article investigates the achievable spectral efficiency (SE) of millimeter wave massive multiple-input multiple-output systems with three different array topologies deployed at the base station (BS), namely uniform rectangular array (URA), uniform linear array (ULA) or uniform circular array (UCA). To be practical, both small scale fading and large scale path loss, and shadowing effects are considered in the analysis. A 3D channel model taking both the azimuth and elevation domains into account is adopted for the analysis of the URA and UCA topologies. Considering randomly distributed users in the cell, we firstly deduce the distribution functions of the distances between the users and the BS, and the azimuth and elevation angles of departure, respectively. Based on the derived probability distribution, tight approximations on the achievable SE with maximum ration transmission under the three antenna array topologies are derived. Analytical results show that under all the three topologies, the total achievable SE increases with the number of BS antennas, but decreases with the radius of cell and decay exponent. Moreover, it converges to a saturated value in the high SNR regime and a large inter-element spacing. It is also found that the UCA configuration has a superior achievable SE than the URA and ULA cases, and thus is the best choice for mmWave massive MIMO systems in terms of the achievable SE.

An analytical space-time fading correlation for the multi-input-multi-output (MIMO) channel, as a function of antenna spacing, angle spread, and Doppler spread, is derived and is verified with deterministic simulations. We investigate the performance of the uplink spatial fading correlation of a MIMO channel. We consider four uniform circular array (UCA) elements at both mobile station and base station. The angle of arrival and the angle of departure at the base station and the mobile station are distributed by truncated Gaussian and uniform probability distribution function(s) respectively. For large values of N, the simulation and analytical results agree. The results show that spatial separation between antenna elements can be minimized at the base station by employing two or more antennas at the mobile station

The paper describes a channel simulator especially suited for simulating transmissions over satellites. The increasingly large instantaneous bandwidths of satellite transmission systems make it challenging to perform a digital channel simulation in the lab using standard equipment, as dynamic range and bandwidth are linked and the available data converter technology and feasible clock rates for digital processing impose a certain limit, which is typically lower than the feasible bandwidth of an analogue transponder on a high operating frequency. The paper describes the architecture of a digital channel simulator, which allows combining of multiple signal processor cards for parallel processing to a signal bandwidth which exceeds the bandwidth of a single card. By cascading up to seven processor boards the channel simulator can simulate a bandwidth of up to 700 MHz. In case the transponder bandwidth to be simulated is less than the full bandwidth of the simulator, the total bandwidth can be divided up into up to three sub-bands which operate independently The propagation effects and effects of the satellite transponder itself are simulated for each sub-band. To test the behaviour of the system under interference conditions, Arbitrary Waveform Generators allow insertion of multiple interference signals with configurable bandwidths and sampling rates. A shaped noise source allows the user to simulate the presence of in-band and out-of-band interferers in each sub-band. Phase noise is implemented by a multiplication with ejphi(t) for each sub-band. Both a memoryless non-linearity is simulated and an output filter can be configured for each sub-band. Additive white Gaussian noise may be added with variable noise density. Large scale and small scale fading can be simulated by either streaming complex fading multiplication factors from hard disk or using integrated channel models. For automated setups, the channel simulator can be fully remote controlled.

UAV(Unmanned aerial vehicles)는 현재 많은 분야에서 관심을 받고 있으며 독특한 장점 덕분에 기존 무선 네트워크를 보조하기 위한 대안으로서 떠오르고 있다. UAV의 가장 큰 장점은 3차원 공간의 높은 자유도(Degree of freedom, DoF)를 적극적으로 활용할 수 있다는 점이다. 구체적으로, 3차원 공간에서 UAV의 높은 고도는 LoS(Line of sight)가 지배적인 환경을 형성하므로 무선 네트워크에 넓은 커버리지(Coverage)를 제공한다. 또한, 고정적인 배치나 한정적인 이동만이 가능한 기존 기지국이나 릴레이(Relay)와 달리 UAV는 수평이나 수직의 이동이 자유롭다. UAV의 3차원 공간 위에서 자유로운 궤적은 기존 셀룰러 네트워크의 성능을 위해 최적화되어 셀룰러 네트워크에 큰 이득을 제공한다. 본 논문에서는 위의 이점들을 살려 UAV를 기존 무선 네트워크와 결합하기 위한 연구들, 즉 UAV 채널 모델링과 UAV 궤적 최적화를 살펴보도록 한다. 첫 번째로, UAV 채널 모델링을 크게 대규모 페이딩(Large scale fading)과 소규모 페이딩(Small scale fading) 채널로 나누어 정리하고 세부적인 부분들로 현재 UAV 채널에 관한 연구 동향을 파악하도록 한다. 두 번째로, UAV의 3D에서 높은 자유도를 활용하기 위한 UAV 궤적 최적화 문제를 일반적인 수식으로 나타내본다. 더불어, UAV 궤적 최적화에 관한 최신 연구들로 일반적인 수식에 대한 구체적인 이해를 돕고 동시에 전반적인 연구 동향을 살펴본다. 마지막으로 UAV 기반 셀룰러 네트워크의 중요한 연구 주제들을 살펴보는 것으로 후속적으로 이루어질 연구를 논의하며 UAV 기반 네트워크의 잠재력을 짚어보며 마무리한다.

We consider an energy harvesting (EH) relay-assisted, two hop, cooperative communication system. For it, we analytically characterize EH relay outage probability when the source device transmissions undergo small scale fading as well as large scale fading. In the analysis, we develop simple yet insightful upper bound for the EH relay outage probability. Furthermore, we investigate asymptotic outage probability of EH relay. We validate our analytical results with Monte-Carlo simulations. Assuming other system parameters fixed, our results show that the EH relay outage probability decreases with the increase in the harvesting power.

Massive multiple-input multiple-output (MIMO), a technique that can greatly increase spectral efficiency (SE) of cellular networks, has attracted significant interests in recent years. One of the major limitations of massive MIMO systems is pilot contamination, which will deteriorate the SE. The superimposed pilot-based scheme has been proved to be a viable method for pilot contamination reduction. However, it cannot break through another limitation of massive MIMO, i.e., spatial correlation. In addition, it will also lead to interference between the pilot and user data since they are imposed together. In this paper, we try to tackle these two issues, which will be described as follows. Firstly, a column-wise asymptotically orthogonal matrix, named as pseudo-channel matrix, is developed by orthogonalization of received signal. To recover the information about the large-scale fading (LSF) coefficients, the pseudo-channel matrix is truncated according to the cardinality of adjacent users set (CAUS). By this means, spatial correlation can be mitigated effectively. Secondly, robust independent component analysis (RobustICA) is used to reduce the interference caused by user data, and as a result the system performance can be further improved. Numerical simulation results demonstrate the effectiveness of the proposed method.

Capacity maximizing in massive MIMO with linear precoding for SSF and LSF channel with perfect CSI

Wireless communication technology profoundly affects each person's life; channel model has become one of the research hot spots in the trade union in the wireless communication system. This article firstly expounds three kinds of fading channels: large-scale fading, small scale fading, and SISO channel model in wireless communication system, then simulates the impact of distances to the path loss in different channel models through MATLAB, and lastly concludes the full text.