Game theoretic study of protecting MIMO transmissions against smart attacks

Orbital Angular Momentum (OAM) has been considered as a new dimension in wireless transmission. In Multiple Input Multiple Output (MIMO) system, OAM can also be utilized as the new Degrees of Freedom (DoF) to manipulate the beamforming instead of the traditional Channel State Information (CSI) feedback, so that the computation complexity of the beamforming dramatically decreases, as well as the capacity of the transmission is maintained approaching the upper bound of the MIMO system. For example, this beamforming manipulation with OAM is extremely efficient in the Line of Sight (LoS) MIMO system. In this paper, the mathematical model of the DoF in beamforming manipulation in MIMO with OAM is established. The computation complexity is evaluated, and compared in LoS environment within MIMO, MIMO-OAM, and OAM. The quantitatively simulation confirms the fact that MIMO-OAM has the best trade-off between the capacity and the computation complexity, which gives a consolidated evidence that the MIMOOAM scheme can be adopted as an efficient beamforming scheme in LoS backhaul links between the base station and the self backhaul small cell station, or Near Field Communications (NFC) directly between mobile terminals. Due to these promising applications, MIMO-OAM may become one of the candidate technologies in Beyond 5G (B5G) mobile communications.

Environmental issues and the need to reduce energy consumption for lowering operating costs have pushed power efficiency to become one of the major issues of current research in the field of wireless networks. This paper addresses a number of multiple input multiple output (MIMO) precoding and scheduling techniques across the PHY and MAC layers that can operate under a reduced link budget and collectively improve the transmit power efficiency of a base station, while maintaining the same levels of service. Different MIMO transmission and precoding schemes proposed for LTE, achieving varying degrees of multiuser diversity in both the time, frequency as well as the space domain, are examined. Several fairness-aware resource allocation algorithms are applied to the considered MIMO schemes and a detailed analysis of the tradeoffs between power efficiency and quality of service is presented. This paper explicitly examines the performance of a system serving real-time, VoIP traffic under different traffic loading conditions and transmit power levels. It is demonstrated that by use of efficient scheduling and resource allocation techniques significant savings in terms of consumed energy can be achieved, without compromising QoS.

In recent years, several approaches have been proposed aiming the optimal joint design of finite impulse response (FIR) multiple-input multiple-output (MIMO) transmitter and receiver. Assuming that the channel input is power constrained and that the channel model is a known FIR MIMO system, it is possible to design high performance transceivers in the presence of additive noise. Unfortunately, most optimal transceivers available so far, do not have fast implementation and simple design approaches. This work proposes a minimum mean square error (MMSE) design approach where constraints are incorporated into the structure of the FIR MIMO transmitter, in such a way that the resulting optimal MIMO transmitter, in the MMSE sense, has inherent fast algorithm implementation. In comparison with the general jointly optimized transceivers recently proposed, the new approach allows substantial reduction in the computational complexity at expense of mild performance degradation.

This chapter is attempted to provide a survey of the advanced concepts and related issues involved in Multiple Input Multiple Output (MIMO) systems. MIMO system technology has been considered as a really significant foundation on which to build the next and future generations of wireless networks. The chapter addresses advanced MIMO techniques such as polarization diversity and antenna selection. We gradually provide an overview of the MIMO features from basic to more advanced topics. The first sections of this chapter start by introducing the key aspects of theMIMO theory. TheMIMO systemmodel is first presented in a genericway. Then, we proceed to describe diversity schemes used in MIMO systems. MIMO technology could exploit several diversity techniques beyond the spatial diversity. These techniques essentially cover frequency diversity, time diversity and polarization diversity. We further provide the reader with a geometrically based models for MIMO systems. The virtue of this channel modeling is to adopt realisticmethods for modeling the spatio-temporal channel statistics from a physical wave-propagation viewpoint. Two classes for MIMO channel modeling will be described. These models involve the Geometry-based Stochastic ChannelModels (GSCM) and the Stochastic channel models. Besides the listedMIMO channel models already described, we derive and discuss capacity formulas for transmission over MIMO systems. The achieved MIMO capacities highlight the potential of spatial diversity for improving the spectral efficiency of MIMO channels. When Channel State Information (CSI) is available at both ends of the transmission link, the MIMO system capacity is optimally derived by using adaptive power allocation based on water-filling technique. The chapter continues by examining the combining techniques for multiple antenna systems. Combining techniques are motivated for MIMO systems since they enable the signal to noise ratio (SNR) maximization at the combiner output. The fundamental combing techniques are the Maximal Ratio Combining (MRC), the Selection Combining (SC) and the Equal Gain Combining(EGC). Once the combining techniques are analyzed, the reader is introduced to the beamforming processing as an optimal strategy for combining. The use of multiple antennas significantly improves the channel spectral efficiency. Nevertheless, this induces higher system complexity of the communication system and the communication system performance is effected due to correlation between antennas that need to be deployed at the same terminal. As such, the antenna selection algorithm for MIMO systems is presented. To elaborate on this point, we introduce Space time coding techniques for MIMO systems and we evaluate by simulation the performance of the communication system. Next, we emphasis on multi polarization techniques for MIMO systems. As a background, we presume that the reader has a thorough understanding of antenna theory. We recall the basic antenna theory and concepts that are used throughout the rest of the chapter. We rigorously introduce the 3D channel model over the Non-Line of Sight (NLOS) propagation channel for MIMO system with polarized antennas. We treat the depolarization phenomena and we study its effect on MIMO system capacity. The last section of the chapter provides a scenario for collaborative sensor nodes performing distributed MIMO system model which is devoted to sensor node localization in Wireless Sensor Networks. The localization algorithm is based on beamforming processing and was tested by simulation. Our chapter provides the reader by simulation examples for almost all the topics that have been treated for MIMO system development and key issues affecting achieved performance.

The key challenge in a broadcast system is receiver heterogeneity, where the weakest receiver typically constrains the entire system performance. Traditionally, this arises from heterogeneous channel SNRs at different receivers. In Multiple-Input Multiple-Output (MIMO) systems, a further heterogeneity is caused by different antenna numbers across receivers. We propose a compressive broadcast framework to address both types of heterogeneity. By layering compressive sensing (CS) over MIMO transmissions, our framework ensures a received source quality commensurate with the channel SNR and the MIMO channel dimension. Compared with a conventional framework, our framework achieves more smooth rate increase with channel SNR and much higher performance for multi-antenna receivers.

This paper investigates the issue of high complexity of digital predistortion for the nonlinear distortion introduced due to crosstalk in addition to the nonlinear distortion generated by a power amplifier (PA) in 2 × 2 MIMO transmitters. This paper proposes a less complex, novel model for digital predistortion (DPD) for linearizing multiple-input multiple-output (MIMO) transmitters. This model is constructed by taking account of both linear and nonlinear crosstalks, along with the fact that the nonlinearity generated due to crosstalk would be lesser than the nonlinearity already available due to PA. For proof-of-concept, it is shown for two different scenarios that proposed model provides similar linearization performance as compared to 2 × 2 PH model with less number of coefficients.

A channel state information (CSI) feedback algorithm based on scalar quantization is proposed for a multiple-input multiple-output (MIMO) transmission. The performance of the proposed algorithm is evaluated using the effective transmission rate, which incorporates the effect of the CSI feedback overhead on transmission time. Besides that, the power consumed due to the computational complexity of the CSI feedback algorithm is taken into account together with the transmit power when evaluating the energy efficiency. We compare our proposed algorithm to other typical CSI feedback algorithms under correlated channel conditions. Simulation results show that the proposed algorithm enables practical MIMO transmission to achieve an relative transmission rate at 95% of the ideal rate.

In conventional ways of fast responding and high performing model predictive control using Field Programmable Gate Arrays (FPGA), the approaches developed are applicable only to Single Input Single Output System (SISO). Many real time systems are Multiple Input Multiple Output (MIMO) systems and loop interaction is a major concern and has to be reduced in control of MIMO systems. Also, direct digital control with ability to solve the Quadratic Programming (QP) problem online, which can be applied to complex systems with fast response time and/or embedded applications and with limited computational resource for supervisory control approach, is required in many complex industrial MIMO systems. A novel direct digital control of Model predictive controller with FPGA for a MIMO system which considers and reduces loop interaction and which can also be implemented with very less investment is proposed here. The system was modeled from first principles and then simulated in Labview environment for MIMO system. Implementation of the system and response was verified for a stirred tank system. The proposed system has good performance in terms of reduction in loop interaction which is a major concern in implementation of controller for MIMO systems. Also, direct digital control of MIMO system instead of supervisory control was accomplished.

In this paper, the effective capacity of a multiple-input multiple-output (MIMO) system in two different cases with receive antenna selection (RAS) and transmit antenna selection (TAS) schemes is investigated. A closed-form solution for the maximum constant arrival rate of this network with statistical delay quality of service (QoS) constraint is extracted in the quasi-static fading channel. This study is conducted in two different cases. When channel state information (CSI) is not available at the MIMO transmitter, implementation of TAS is difficult. Therefore, RAS scheme is employed and one antenna with the maximum instantaneous signal to noise ratio is chosen at the receiver. On the other hand, when CSI is available at the transmitter, TAS scheme is executed. In this case, one antenna is selected at the transmitter. Moreover, an optimal power-control policy is applied to the selected antenna and the effective capacity of the MIMO system is derived. Finally, this optimal power adaptation and the effective capacity are investigated in two asymptotic cases with the loose and strict QoS requirements. In particular, we show that in the TAS scheme with the loose QoS restriction, the effective capacity converges to the ergodic capacity. Then, an exact closed-form solution is obtained for the ergodic capacity of the channel here.

In order to decrease system implementation cost of fiber-optic relaying systems for multiple-input multiple output (MIMO) mobile transmissions, we have proposed a novel relaying scheme using spatial modulation (SM) technology. The proposed scheme uses much fewer radio over fiber (RoF) links than the conventional fiber-optic relaying schemes. We studied the performance of the proposed scheme through computer simulations and confirmed that the proposed scheme can achieve much better transmission performance than the conventional ones. Computer simulations show that the optimum number of RoF links of the proposed scheme is around 2 or 3 for 4×4 MIMO transmissions, and around 3 or 4 for 8×8 MIMO transmissions.

An approach to predict the throughput of multiple-input multiple-output (MIMO) transmission systems in radio network planning tools is proposed in this paper. Two different measurement campaigns have been evaluated. At first the Shannon MIMO transmission capacity is calculated based on MIMO channel sounder measurements. At second LTE throughput measurements in a field trial network with specific network configurations have been investigated. Both campaigns were located in urban areas and a 2×2 MIMO system with cross-polarised antennas was used. The MIMO capacity gain is derived from both investigations and it is shown that it is correlated with the difference between the free space path loss and the experienced path loss. This measure is the basis of the suggested approach. It includes the length as well as the land usage of the radio path and it requires only input data which is generally available from macro cell propagation models.

We propose a precoder design to minimize the codeword error rate of multiple-input multiple-output (MIMO) systems in the presence of channel estimation errors. Our proposed scheme only requires knowledge of the second-order statistics of the channel, noise and the estimation errors; an estimate of the instantaneous channel state information (CSI) is not required. Compared to the instantaneous CSI requirement, our assumption is more practical since obtaining an accurate CSI is challenging if the channel fluctuates rapidly, while the channel statistics are likely to remain unchanged for a much longer period. Furthermore, the feedback overhead from the receiver to the transmitter is greatly reduced. When compared to the case without transmit preceding, our proposed design achieves a significant improvement in error performance: (i) for a real 2 × 2 MIMO system with 4-PAM modulation and at codeword error rate of 104, our proposed precoder design achieves coding gains of up to 6 dB and (ii) for a real 4 × 4 MIMO system with BPSK modulation and at codeword error rate of 104, coding gains of up to 6.5 dB can be achieved.

Consider a multiple input multiple output (MIMO) system with pulse amplitude modulation (PAM), where each transmit antenna is allowed to choose a different transmission delay. (We refer to this system as a MIMO system with asynchronous PAM.) Such MIMO systems have been studied before for increasing diversity and avoiding unintentional beamforming. We quantify the capacity/outage capacity improvement due to asynchronism and the corresponding spectral leakage (if any) for MIMO point-to-point and MIMO broadcast channels. We show that under commonly used transmitter spectral mask constraints, the channel has unbounded degrees of freedom. Synchronous PAM exploits only a finite number of these degrees of freedom, and asynchronism helps to improve capacity. For most cases of interest, two distinct shifts attain most of the capacity improvement. These gains are robust to interference from adjacent carriers. Analysis of the spectrum of the asynchronous PAM suggests that optimal water-filling based schemes are useful for improving capacity only in directional links due to possible spectral leakage, while uniform power allocation leads to capacity improvement without any extra spectral leakage.

An adaptive digital predistortion (PD) technique is proposed for linearization of power amplifiers (PAs) in multiple-input multiple-output (MIMO) transmitters. Instead of constructing a separate feedback path at each PA, we propose a PD technique based on a combined feedback, which adds all the PAs' outputs to form a single feedback. We develop an algorithm for finding the multiple PA characteristics jointly from the combined feedback. The PD parameters are calculated from the identified PA characteristics. Although the proposed method requires more computations for PA identification than the conventional method, its hardware implementation is much simpler and can overcome the crosstalk problem of a radio frequency (RF) selector, which is necessary for multiple feedback selection. The simulation results show that, despite its simple structure, the linearization performance of the proposed method is almost the same as the separate multiple feedback method.

In multiple-input multiple-output (MIMO) transmission systems, the channel state information (CSI) at the transmitter can be used to add linear precoding to the transmitted signals in order to improve the performance and the reliability of the transmission system. This paper investigates how to properly join precoded closed-loop MIMO systems and nonbinary low density parity check (NB-LDPC). Theqelements in the Galois field, GF(q), are directly mapped toqtransmit symbol vectors. This allows NB-LDPC codes to perfectly fit with a MIMO precoding scheme, unlike binary LDPC codes. The new transmission model is detailed and studied for several linear precoders and various designed LDPC codes. We show that NB-LDPC codes are particularly well suited to be jointly used with precoding schemes based on the maximization of the minimum Euclidean distance (max-dmin) criterion. These results are theoretically supported by extrinsic information transfer (EXIT) analysis and are confirmed by numerical simulations.