Structure-based water-filling algorithm in multipath MIMO channels

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

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.

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.

Next generation wireless communication systems use massive Multi Input Multi Output (m-MIMO) antenna arrays for their enhanced beamforming capabilities. Providing accurate Channel State Information (CSI) is vital for optimizing m-MIMO communication systems. The complexity of channel reconstruction grows exponentially with the number of antennas, causing traditional methods to become increasingly complicated. Machine-learning techniques can be a useful alternative for channel reconstruction using partial CSI feedback. This paper presents the results of a simulation study built using the MATLAB 5G Toolbox and a neural network trained using the simulated data. The simulator emulates a 5G channel to generate its path delays and gains, and the realistic CSI feedback. This data was used to train and test a neural network to estimate the dominant path gains and delays. The models showed promising results while operating on limited CSI data.

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.

Optimizing beamforming in IoT-WET: A symbiotic-based approach under imperfect channel state information

Efficient jamming attack against MIMO transceiver

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 investigate joint source channel coding (JSCC) for wireless image transmission over multipath fading channels. Inspired by recent works on deep learning based JSCC and model-based learning methods, we combine an autoencoder with orthogonal frequency division multiplexing (OFDM) to cope with multipath fading. The proposed encoder and decoder use convolutional neural networks (CNNs) and directly map the source images to complex-valued baseband samples for OFDM transmission. The multipath channel and OFDM are represented by non-trainable (deterministic) but differentiable layers so that the system can be trained end-to-end. Furthermore, our JSCC decoder further incorporates explicit channel estimation, equalization, and additional subnets to enhance the performance. The proposed method exhibits 2.5 – 4 dB SNR gain for the equivalent image quality compared to conventional schemes that employ state-of-the-art but separate source and channel coding such as Better Portable Graphics (BPG) and Low-Density Parity-Check (LDPC) schemes. The performance further improves when the system incorporates the channel state information (CSI) feedback. The proposed scheme is robust against OFDM signal clipping and parameter mismatch for the channel model used in training and evaluation.

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.

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.

The knowledge of channel state information (CSI) at the transmitter (TX), which in case of time division duplex (TDD) systems can be easily obtained due to radio channel reciprocity, can dramatically increase the spectral efficiency of a multiple-input multiple-output (MIMO) system. This paper presents a robust link adaptation method for TDD systems employing MIMO-OFDM channel eigenmode based signalling. We propose a rather simple logarithm-free, bit and power loading algorithm which requires low signalling overhead. Proposed method maintains constant frame error rate (FER) by controlling the instantaneous transmitted power in such a way that the average SNR at the equalizer output is kept constant. The scheme takes into account channel estimation errors at TX side by partial compensation of the SNR estimation errors. By using a simple scalar channel encoder, for an unbalanced MIMO system with larger number of transmit than receive antennas, the achieved spectral efficiency at low and medium SNR is significantly higher than the outage MIMO capacity with unknown CSI at the TX. The simulation results show that for a constant FER, the throughput degradation compared to the universally accepted Hughes-Hartogs algorithm is negligible.

In high‐mobility scenarios, the time variation of mobile radio channels leads to a loss of orthogonality among subcarriers in orthogonal frequency division multiplexing (OFDM) systems, resulting in intercarrier interference (ICI) and performance deterioration. Conventional channel estimation schemes are usually based on pilot tones, which are distributed in each OFDM symbol to estimate the channel variation. Hence, the channel estimator itself suffers from ICI. In this study, a new estimation scheme, which does not suffer from ICI, is proposed to estimate the channel variation within OFDM symbols. The main idea is to zero‐pad (ZP) the OFDM symbol in the time domain. Then, in the middle of the ZP interval, an impulse signal is inserted as a pilot sample, which is used to estimate the channel at the pilot signal in the OFDM symbol. Finally, a linear model is used to estimate the channel variation over an OFDM symbol. Additionally, we derive the mean squared error (MSE) of the proposed estimation technique under the constraint that the channel varies linearly within OFDM symbols. Simulation results show that our scheme can achieve a substantial improvement in the bit error rate (BER) performance of OFDM, in spite of the OFDM symbol length being increased. Moreover, in many cases, the new scheme can achieve the same BER performance as the perfect knowledge of channel state information (CSI). Theoretical analysis and numerical simulations show that our scheme achieves excellent performance with much lower computational complexity.

In frequency division duplex mode of massive multiple-input multiple-output systems, the downlink channel state information (CSI) must be sent to the base station (BS) through a feedback link. However, transmitting CSI to the BS is costly due to the bandwidth limitation of the feedback link. Deep learning (DL) has recently achieved remarkable success in CSI feedback. Realizing high-performance and low-complexity CSI feedback is a challenge in DL-based communication. We develop a DL-based CSI feedback network in this study to complete the feedback of CSI effectively. However, this network cannot be effectively applied to the mobile terminal due to its excessive number of parameters and high computational complexity. Therefore, we further propose a new lightweight CSI feedback network based on the developed network. Simulation results show that the proposed CSI network maintains a few parameters and parameter complexity while exhibiting better reconstruction performance than existing works. These findings suggest the feasibility and potential of the proposed techniques.