Efficient jamming attack against MIMO transceiver

In this paper, we consider the optimal design of linear space codes for an indoor multiple input multiple output (MIMO) visible light communication (VLC) system with maximum-likelihood (ML) detection, where we assume that the full knowledge of channel state information (CSI) is available at both the transmitter and the receiver. For such a system with on-off-keying (OOK) modulation, an optimal closed-form linear space code that maximizes the minimum Euclidean distance for ML detection is attained for the scenario with two transmitters and multiple receivers. Our results reveal the fact that when the channel is good, the optimal code has either a diagonal or an anti-diagonal structure, and when the channel is bad, the optimal code is to diagonalize the channel matrix first, then, optimally distribute the total transmission power into each eigen-subchannel, and then, follow a repetition coding (RC) scheme. Computer simulations verify both the accuracy and the validity of our theoretical analysis, and show that our newly proposed linear space code provides significant error performance gains over currently available space modlution (SM) and RC in literature without CSI at the transmitter.

This paper addresses the design of optimal and near-optimal detectors for a practical interference channel scenario where the transmitters employ discrete modulation schemes. The conventional detectors, which either ignore the interference or successively detect then cancel the interference, typically assume that the desired signal and/or the interference are Gaussian. This paper proposes detectors that explicitly take into account the modulation formats of both the desired signal and the interference. The optimal maximum-likelihood (ML) detector that minimizes the probability of detection error for a given set of modulation schemes is derived first. A joint minimum-distance detector (MD) is then presented as a low-complexity approximation of the optimal ML detector. It is demonstrated by analysis and by simulation that the proposed detectors can significantly outperform their conventional counterparts. In particular, while the interference-ignorant and the successive interference cancellation detectors are both prone to error floors, the proposed optimal ML and joint MD detectors are not.

In this paper, we consider a cognitive multi-hop relay secondary user (SU)\nsystem sharing the spectrum with some primary users (PU). The transmit power as\nwell as the hop selection of the cognitive relays can be dynamically adapted\naccording to the local (and causal) knowledge of the instantaneous channel\nstate information (CSI) in the multi-hop SU system. We shall determine a low\ncomplexity, decentralized algorithm to maximize the average end-to-end\nthroughput of the SU system with dynamic spatial reuse. The problem is\nchallenging due to the decentralized requirement as well as the causality\nconstraint on the knowledge of CSI. Furthermore, the problem belongs to the\nclass of stochastic Network Utility Maximization (NUM) problems which is quite\nchallenging. We exploit the time-scale difference between the PU activity and\nthe CSI fluctuations and decompose the problem into a master problem and\nsubproblems. We derive an asymptotically optimal low complexity solution using\ndivide-and-conquer and illustrate that significant performance gain can be\nobtained through dynamic hop selection and power control. The worst case\ncomplexity and memory requirement of the proposed algorithm is O(M^2) and\nO(M^3) respectively, where $M$ is the number of SUs.\n

For multi-input multi-output (MIMO) systems, the detection performance can be severely deteriorated by the channel state information (CSI) uncertainties. In this paper, we propose a learnable robust MIMO detector by taking the statistics of CSI imperfection into account. Specifically, we first formulate a robust maximum likelihood (ML) detection problem and then develop an alternating direction method of multipli-ers (ADMM) based solution, which involves the calculations of closed-form expressions in each iteration. Furthermore, a model-driven neural network is established by unfolding the derived ADMM algorithm whose penalty parameters are learned via offline training. Simulation results demonstrate that the proposed network can considerably outperform the conventional mismatched ML detector and even approach the optimal robust ML detector with only 5 layers.

Interference mitigation by applying Joint Processing (JP) in cooperative cellular networks boasts cell-edge performance compared to non-cooperative systems. However, downlink JP requires knowledge of Channel State Information (CSI) at all collaborating Base Stations (BSs), where interference received at the User Equipments (UEs) is markedly affected by the level of CSI quality influenced amongst others by the method of distributing CSI to the BSs. In this contribution, we compare CSI Distribution over the Air (CD-A) with CSI Distribution over the Backhaul (CD-B) considering a Frequency Devision Duplex (FDD) system. For CD-A, CSI is directly fed back from the UEs to the collaborating BSs via the uplink channel (assuming link adaptation is performed according to one master BS to which a UE has been assigned). Adopting CD-B, CSI is foremost transmitted only to the master BS and then forwarded to the other BSs using backhaul connections. This method typically introduces additional latency due to routing issues. In this contribution, we show that CD-A outperforms CD-B in the largest part of the cooperation area, even when pedestrian user velocities and small backhaul latencies are considered.

The accurate performance evaluation of differential amplitude and phase-shift keying (DAPSK) with postdetection equal gain combining (EGC) over generalized fading channels is of great theoretical interest and practical importance. In this paper, by using a decision variable-based moment generating function approach, exact error probability results for DAPSK over generalized Rician and Nakagami fading channels are derived, taking into account the effects of all the system and fading channel parameters. Several maximum likelihood (ML)-based detectors that do not require channel state information (CSI) are proposed for generalized Rician fading channels, and an exact bit error probability (BEP) union bound for the ML detection is derived. Assuming CSI, a performance upper bound for DAPSK with EGC is also presented. Simulation and numerical results show when both detectors have no CSI, the conventional DAPSK detector may perform closely to (though worse than) the ML detector. However, the EGC receiver with CSI performs substantially better than the ML detector without CSI, and very closely to the coherent APSK detector.

Multiple Input Multiple Output(MIMO) has been in much importance in recent past because of high capacity gain over a single antenna system. In this article, analysis over the capacity of the MIMO channel systems with spatial channel with modified precoder and decoder has been considered when the channel state information (CSI) is considered partial. Due to delay in acquiring transmitted information at the receiver end, the time selective fading wireless channel often induces incomplete or partial CSI. The dynamic CSI model has also been implemented consisting channel mean and covariance which leads to extracting of channel estimates and error covariance which then further applied with the modified precoder and decoder utilizing both the parameters indicating the CSI quality since these are the functions of temporal correlation factor, and based on this, the model covers data from perfect to statistical CSI, either partially or fully blind. It is found that in case of partial and imperfect CSI, the capacity depends on the statistical properties of the error in the CSI which has been manipulated according to the precoder and decoder conditions. Based on the knowledge of statistical distribution of the deviations in CSI knowledge, a new approach which maximizes the capacity of spatial channel model with modified precoder and decoder has been tried. The interference then interactively reduced by employing the iterative channel estimation and data detection approach, where by utilizing the detected symbols from the previous iteration, multiuser/MIMO channel estimation and symbol detection is improved.

In full-duplex orthogonal frequency-division multiplexing systems with IQ imbalances, a frequency-domain least-squares (FD-LS) channel estimator is proposed to estimate both source-to-destination (intended) and destination-to-destination (self-interference) channels. Subsequently, an optimal closed-form pilot matrix is derived to minimize the sum of mean square errors (sum-MSE) of the proposed FD-LS channel estimator. Then, an improved FD-LS estimator is presented and proved to further improve the performance of the FD-LS by exploiting the time-domain property of the channel. In the presence of channel estimation error, an optimal low-complexity maximum likelihood (ML) detector is developed by using eigenvalue and singular value decompositions and whitening the residual self-interference plus noise. Simulation results show that given the proposed FD-LS estimator, the optimal pilot matrix performs much better than those nonsingular pilot matrices with larger conditional numbers (CN). To be specific, the former achieves about 10-dB signal-to-noise ratio (SNR) gain over the latter with CN = 10, the improved FD-LS channel estimator provides about 6-dB SNR gain over the FD-LS estimator at a fixed bit error rate (BER), and the proposed whitening-filter ML detector performs at least 0.6 dB better than the conventional ML detector at a given BER of $10^{-3}$ in the medium-and high-SNR regions.

Orthogonal space-time block codes (OSTBCs) have attracted much attention owing to their simple code construction, maximal diversity gain, and low maximum-likelihood (ML) detection complexity when channel state information (CSI) is available at the receiver. This paper addresses the problem of ML OSTBC detection with unknown CSI. Focusing on the binary and quaternary PSK constellations, we show that blind ML OSTBC detection can be simplified to a Boolean quadratic program (BQP). From an optimization viewpoint the BQP is still a computationally hard problem, and we propose two alternatives for dealing with this inherent complexity. First, we consider the semidefinite relaxation (SDR) approach, which leads to a suboptimal, but accurate, blind ML detection algorithm with an affordable worst-case computational cost. We also consider the sphere decoding approach, which leads to an exact blind ML detection algorithm that remains computationally expensive in the worst case, but generally incurs a reasonable average computational cost. For the two algorithms, we study implementation methods that can significantly reduce the computational complexity. Simulation results indicate that the two blind ML detection algorithms are competitive, in that the bit error performance of the two algorithms is almost the same and is noticeably better than that of some other existing blind detectors. Moreover, numerical studies show that the SDR algorithm provides better complexity performance than the sphere decoder in the worst-case sense, and vice versa in the average sense.

The multi-hop layered interference network is investigated with delayed knowledge of channel state information (CSI) at all nodes. It is demonstrated how multi-hopping can be utilized to increase the achievable degrees of freedom (DoF). In particular, for the K-user 2K-hop interference network, a multi-phase transmission scheme is proposed which systematically exploits the layered structure of the network and delayed CSI to achieve DoF values which scale with K. As such, this result provides the first example of a network with distributed transmitters and delayed CSI whose DoF scales with the number of users, although sub-linearly.

Visible light communication (VLC) has become increasingly popular and has sparked a wide interest from various research areas. In order to fully realize the potential of VLC and to provide seamless connectivity to users, the underlying channel model of the system must be carefully understood. However, in a practical environment which considers specific geometrical configurations of the network and user behavior effects, the optical channel can often become too complex to be modelled mathematically. In this work, we apply deep learning (DL) to design an effective signal detection scheme for an indoor VLC communication system. With the aid of DL, our system is able to learn directly from the transmitted and received symbols and can reliably detect the original transmitted signals during the real time implementation with only a limited instantaneous knowledge of the channel. The simulation results confirm that our model offers very close performance to the optimal maximum likelihood (ML) detection with perfect channel state information (CSI). We also consider specific indoor environments (e.g., using a hotspot model) to confirm the robustness of the learning based schemes in different indoor scenarios.

This paper considers a scenario in which multiple device-to-device (D2D) users can reuse the uplink resources of a cellular network to transmit directly to their corresponding receivers. The aggregated interference from the D2D users is limited by applying a threshold on the allowable interference in the base station. The problem is solved under two types of constraints, namely, the peak interference and average interference constraints. In the former, we assume that full channel state information (CSI) is available at the base station, and we optimize the allowable transmit power for the D2D users so that the number of coexisting D2D communications is maximized. We further define a quality-of-service constraint for the D2D users. In practice, however, it is difficult to have complete CSI at the base station as it imposes heavy signaling overhead. Therefore, in the latter scenario, we assume that no knowledge about the location of D2D users and their CSI are available at the base station. This approach does not impose any signaling overhead. Our results show that even with no CSI knowledge, we are able to improve the system performance in terms of throughput by allowing coexisting D2D communications while satisfying the cellular user's constraints.

We have considered a dual-hop wireless network consisting of a decode-and-forward (DF) relay in presence of an eavesdropper. Relay can correctly decode and retransmit only if the received signal-to-noise ratio (SNR) meets a particular threshold. The eavesdropper can tap signals both from the relay and the source. The performance of the maximal ratio combining (MRC) and selection combining (SC) diversity at the eavesdropper is investigated. Whether knowledge of channel state information (CSI) at the transmitter is available or not is considered for the performance evaluation in secrecy outage probability and ergodic secrecy rate. Asymptotic analyses of performances are provided. We show that direct link has a significant impact on system secrecy. It is found that secrecy performances are the best if the relay is always able to decode correctly. We also found that knowledge of CSI helps to achieve better secrecy at lower rate. It is observed that unbalance created due to constraint on either of the source-relay link average SNR or relay-destination link average SNR does not yield similar secrecy outage performance but both can limit the secrecy outage performance. On the contrary, it is found that only source-relay link quality can limit the ergodic secrecy rate performance.

Using outdated channel state information (CSI) in an opportunistic relaying system (ORS) leads to wrong selection of the best relay, which substantially deteriorates its performance. In this paper, therefore, we propose a robust cooperative scheme coined opportunistic space-time coding (OSTC) to deal with the outdated CSI. A predefined number (i.e., $N$ ) of relays, instead of a single relay in ORS, are opportunistically selected from $K$ cooperating relays. At the selected relays, $N$ -dimensional orthogonal space-time coding is employed to encode the regenerated signals in a distributed manner. Then, $N$ branches coded signals are simultaneously transmitted from the relays to the destination, followed by a simple maximum-likelihood decoding at the receiver. To evaluate its performance, the closed-form expressions of outage probability and ergodic capacity are derived, together with an asymptotic analysis that clarifies the achievable diversity. Analytical and numerical results reveal that a full diversity of $K$ is reaped by the proposed scheme when the knowledge of CSI is perfect. In the presence of outdated CSI, where the diversity of ORS degrades to one, the diversity of $N$ can be kept. Moreover, OSTC’s capacity is remarkably higher than that of the existing schemes based on orthogonal transmission. From the perspective of multiplexing-diversity tradeoff, the proposed scheme is the best cooperative solution until now.

Recent advances in information theory show that employing multiple antennas at both sides of a wireless channel promises enormous capacity potential. With perfect knowledge of channel state information (CSI) at the transmitter, eigen-beamforming is the optimal coding scheme to exploit this potential. However, in nonstationary wireless environments, high complexity on multiple-input-multiple-output (MIMO) channel tracking and large amounts of CSI feedback render such an approach impractical. By exploiting the wireless multipath channel structure characterized by the path delays and the path directions-of-departure/arrival, we propose a new space-time transmit scheme which employs a structure-based water-filling algorithm.