Channel-Aware Random Access in the Presence of Channel Estimation Errors

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.

In the pursuit of ever increasing demand of higher data rates, modern wireless communication systems are resorting to tighter frequency reuse patterns thereby leading to interference-limited systems. Recently proposed interference-aware receiver [1] is a low complexity and efficient receiver design for interference mitigation in cellular systems. However this advanced receiver necessitates the channel knowledge of both the desired and interfering signals which may not be available in a practical system. In this paper, we investigate the performance of interference-aware receiver under imperfect channel state information (CSI). We consider uncorrelated Rayleigh flat fading channels and model the estimation error as independent complex Gaussian random variables. We derive upper bound on the pair wise error probability (PEP) and show that the interference-aware receiver is characterized by full diversity in the presence of channel estimation errors. We compare the performance of interference-aware receiver with that of MMSE receiver and show that the degradation in the performance of MMSE receiver is much more pronounced than that of interference-aware receiver under imperfect CSI.

We consider Multiple-input multiple-output (MIMO) wiretap channel with one transmitter, one receiver, one cooperative jammer, and one eavesdropper, where each node equips with multiple antennas. Eigenbeam-space division multiplexing (E-SDM) and cooperative jamming are known as the techniques improving secrecy rate. E-SDM enables the transmitter and the legitimate receiver to communicate with each other in parallel using channel state information (CSI) known to them to maximize the legitimate receiver's mutual information. On the other hand, cooperative jamming is to jam only the eavesdropper to degrade eavesdropper's mutual information. However, co-channel interference and residual interference from the cooperative jammer are caused by channel estimation error, which degrades legitimate receiver's mutual information. How these interference influence on the secrecy rate has not been clarified. In this paper, we derive lower and upper bounds on secrecy rate using legitimate receiver's mutual information in the presence of channel estimation error and eavesdropper's mutual information who uses minimum mean square error (MMSE) receiver. We analyze the effect of co-channel interference and residual interference from the cooperative jammer on the secrecy rate.

Physical layer (PHY) security is recently regarded as a promising technique to improve the security performance of wireless communication networks. Current developments in PHY security are often based on the assumption of perfect channel state information (CSI). In this paper, both security and reliability performance for the downlink cloud radio access network with optimal remote radio heads (RRHs) node selection are investigated in a practical scenario by considering channel estimation (CE) errors. In particular, a three-phase transmission scheme is proposed and the linear minimum mean-square error (MMSE) estimation method is utilized to obtain the CSI. Based on the CSI estimates and the statistics of CE errors, the outage probability and intercept probability are derived in closed-form expression to evaluate the security and reliability performance, respectively. In addition, two possible cases (with or without intercepting signals from baseband unit) are considered for the eavesdropper. It is found that the suggested optimal RRHs selection scheme outperforms the nonselection scheme, and that the increasing number of RRHs can lower the outage probability as well as the intercept probability. It is also shown that there exists an optimal training number to minimize the sum of the outage probability and intercept probability. Finally, simulation results are provided to corroborate our proposed studies.

The advantages envisioned from using large antenna arrays have made massive multiple- input multiple-output systems (also known as massive MIMO) a promising technology for future wireless standards. Despite the advantages that massive MIMO systems provide, increasing the number of antennas introduces new technical challenges that need to be resolved. In particular, symbol detection is one of the key challenges in massive MIMO. Obtaining accurate channel state information (CSI) for the extremely large number of chan- nels involved is a difficult task and consumes significant resources. Therefore for Massive MIMO systems coherent detectors must be able to cope with highly imperfect CSI. More importantly, non-coherent schemes which do not rely on CSI for symbol detection become very attractive. Expectation propagation (EP) has been recently proposed as a low complexity algo- rithm for symbol detection in massive MIMO systems , where its performance is evaluated on the premise that perfect channel state information (CSI) is available at the receiver. However, in practical systems, exact CSI is not available due to a variety of reasons in- cluding channel estimation errors, quantization errors and aging. In this work we study the performance of EP in the presence of imperfect CSI due to channel estimation er- rors and show that in this case the EP detector experiences significant performance loss. Moreover, the EP detector shows a higher sensitivity to channel estimation errors in the high signal-to-noise ratio (SNR) regions where the rate of its performance improvement decreases. We investigate this behavior of the EP detector and propose a Modified EP detector for colored noise which utilizes the correlation matrix of the channel estimation error. Simulation results verify that the modified algorithm is robust against imperfect CSI and its performance is significantly improved over the EP algorithm, particularly in the higher SNR regions, and that for the modified detector, the slope of the symbol error rate (SER) vs. SNR plots are similar to the case of perfect CSI. Next, an algorithm based on expectation propagation is proposed for noncoherent symbol detection in large-scale SIMO systems. It is verified through simulation that in terms of SER, the proposed detector outperforms the pilotbased coherent MMSE detector for blocks as small as two symbols. This makes the proposed detector suitable for fast fading channels with very short coherence times. In addition, the SER performance of this detec- tor converges to that of the optimum ML receiver when the size

Deep learning (DL)-based channel state information (CSI) feedback improves the capacity and energy efficiency of massive multiple-input multiple-output (MIMO) systems in frequency division duplexing mode. However, multiple neural networks with different lengths of feedback overhead are required by time-varying bandwidth resources. The storage space required at the user equipment (UE) and the base station (BS) for these models increases linearly with the number of models. In this paper, we propose a DL-based changeable-rate framework with novel quantization scheme to improve the efficiency and feasibility of CSI feedback systems. This framework can reutilize all the network layers to achieve overhead-changeable CSI feedback to optimize the storage efficiency at the UE and the BS sides. Designed quantizer in this framework can avoid the normalization and gradient problems faced by traditional quantization schemes. Specifically, we propose two DL-based changeable-rate CSI feedback networks CH- CsiNetPro and CH- DualNetSph by introducing a feedback overhead control unit. Then, a pluggable quantization block (PQB) is developed to further improve the encoding efficiency of CSI feedback in an end-to-end way. Compared with existing CSI feedback methods, the proposed framework saves the storage space by about 50% with changeable-rate scheme and improves the encoding efficiency with the quantization module.

Pair-wise error probability (PEP) is analyzed in the presence of channel estimation error (CEE) for orthogonal frequency division multiplexing (OFDM) in a quasi-static Rayleigh fading channel. Subcarriers in OFDM are grouped in an equi-spaced manner with the number of subcarriers in a group equal to the number of channel taps. One group is dedicated for training and the rest are for data transmission. A linear minimum mean square error (LMMSE) based channel estimation for coherent detection is considered in this paper. This enables the signal dependent CEE to be uncorrelated to the data. Since ML decoding employed with perfect CSI becomes suboptimal in the presence of CEE, an optimal ML decoding is derived. It is observed from our training-based PEP expression that the CEE does not reduce the diversity order but contributes to a loss of coding gain. Moreover, a code design criterion is established. To reduce the loss of coding gain, an optimal training scheme is developed, based on the PEP expression. Loss of performance due to an imperfect channel estimate is quantified in terms of bounds on bit error probability (BEP) for high SNR. Our analytical findings are corroborated by simulation examples.

Aiming at the problem of high complexity and low feedback accuracy of existing channel state information (CSI) feedback algorithms for frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, this paper proposes a CSI compression feedback algorithm based on deep learning (DL), which is suitable for single-user and multi-user scenarios in massive MIMO systems. This algorithm considers the spatial correlation of massive MIMO channel and uses bidirectional long short-term memory (Bi-LSTM) and bidirectional convolutional long short-term memory (Bi-ConvLSTM) network to decompress and recover the CSI for single-user and multi-user, respectively. The proposed DL-based CSI feedback network is trained offline by massive MIMO channel data and could learn the structural characteristics of the massive MIMO channel by fully exploiting the channel information in the training samples. The simulation results show that compared with several classical CSI compression feedback algorithms, the proposed CSI compression feedback algorithm has lower computational complexity, higher feedback accuracy, and better system performance in massive MIMO systems.

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 this paper, we propose a cross-attention and self-attention Transformer-based network (CASA-TransNet) for channel state information (CSI) feedback in dual-polarized massive multiple-input multiple-output (MIMO) systems. In dualpolarized massive MIMO systems, the channels in the horizontal and vertical polarization directions have a high correlation, which can be exploited for joint compression and reconstruction of the dual-polarized channels to improve the CSI feedback performance. The proposed CASA-TransNet employs a twostream cross-attention Transformer architecture and extracts the correlation between horizontal and vertical polarization CSI via cross-stream exchange of the query embeddings. Furthermore, a self-attention Transformer is utilized to model long-term dependencies of the dual-polarized CSI. Simulation results show that the proposed CASA-TransNet outperforms other deep learningbased methods, achieving better CSI reconstruction performance even at high compression ratios. Compared to state-of-the-art schemes, CASA-TransNet achieves a performance gain of $3.47 \sim 5.74 \mathrm{~dB}$.

This letter considers the effect of channel estimation errors on the performance of space-time coded (STC) systems with transmit and receive antenna selection over quasi-static flat fading channels. By performing pairwise error probability analysis and presenting numerical examples, we show that the diversity order achieved with perfect channel state information (CSI) is still achievable with imperfect CSI used both at the antenna selection and the space-time decoding processes. We note that our results apply to general STC systems with both transmit and/or receive antenna selection based on largest received powers which can be estimated by any channel estimator.

With the rapid proliferation of broadband wireless services, it is of paramount importance to understand how fast data can be sent through a wireless local area network (WLAN). Thanks to a large body of research following the seminal work of Bianchi, WLAN throughput under saturated traffic condition has been well understood. By contrast, prior investigations on throughput performance under unsaturated traffic condition was largely based on phenomenological observations, which lead to a common misconception that WLAN can support a traffic load as high as saturation throughput, if not higher, under nonsaturation condition. In this paper, we show through rigorous analysis that this misconception may result in unacceptable quality of service: mean packet delay and delay jitter may approach infinity even when the traffic load is far below the saturation throughput. Hence, saturation throughput is not a sound measure of WLAN capacity under nonsaturation condition. To bridge the gap, we define safe-bounded-mean-delay (SBMD) throughput and safe-bounded-delay-jitter (SBDJ) throughput that reflect the actual network capacity users can enjoy when they require finite mean delay and delay jitter, respectively. Our earlier work proved that in a WLAN with multi-packet reception (MPR) capability, saturation throughput scales superlinearly with the MPR capability of the network. This paper extends the investigation to the nonsaturation case and shows that superlinear scaling also holds for SBMD and SBDJ throughputs. Our results here complete the demonstration of MPR as a powerful capacity-enhancement technique for WLAN under both saturation and nonsaturation conditions.

This paper revisits the study of wireless carrier-sense multiple access (CSMA) protocols enabled with multi-packet reception (MPR) capabilities. This study employs a new paradigm in the literature of random access based on multi-objective and financial portfolio optimization tools. Under this new optimization framework, each packet transmission is regarded not only as a network resource, but also as a financial asset with different values of return and risk (or variance of the return). The objective of this network-financial optimization is to find the transmission policy that simultaneously optimizes network metrics (such as throughput and efficient power consumption), as well as economic metrics (such as fairness, return and risk). Two transmission models are considered for performance evaluation: a Bernoulli transmission model that facilitates analytic derivations, and a Markov model that considers the backlog states of the network and that facilitates dynamic stability analysis. This work is focused on the characterization of the boundary (envelope) or the Pareto optimal frontier of different types of trade-off performance region. These regions include the conventional throughput and stability regions, as well as new trade-off regions such as sum-throughput vs. fairness, sum-throughput vs. power consumption, and return vs. risk. Fairness is evaluated by means of the Gini-index, which is used in the field of economics to measure population income inequality. Transmit power is directly linked to the global transmission attempt rate. In scenarios with weak MPR capabilities, the system has problems in achieving simultaneously good values of fairness and high values of sum-throughput. This is because of an underlying non-convex throughput region which is typical of protocols dominated by unresolvable collisions. On the contrary, in scenarios with strong MPR capabilities, good fairness, higher energy consumption efficiency, and high sum-throughput performances can be simultaneously achieved. Carrier-sensing is shown to improve the convexity of the throughput region in scenarios with weak MPR, thereby achieving a better trade-off between metrics, including return and risk. However, the effects of carrier-sensing are shown to disappear in scenarios with strong MPR capabilities or with underlying convex throughput regions. The combination of MPR with carrier-sensing tools helps in reducing risk in the network and to fight issues of wireless random access such as the hidden/exposed terminal problems.KeywordsS-ALOHARandom accessMulti-objective portfolio optimizationPareto optimal trade-off curve

This paper compares the performance of a MIMO system applying a Mismatched Maximum Likelihood Detector (MMLD) with that applying a Generalized Likelihood Ratio Detector (GLRD) in the presence of channel estimation errors. Two sources of error are considered: errors due to noise and errors due to time variations induced by Doppler effects. The MMLD relies on the available channel estimate whereas the GLRD combines the channel estimate with a block of received data and uses a joint channel-symbol estimation approach to solve for the transmitted symbols. The GLRD is shown to outperform the MMLD whenever errors in channel estimation are incurred and the performance gap depends on the amount of error. Furthermore, the GLRD provides a unified receiver structure which can also be applied to differentially encoded MIMO systems and to situation where no channel estimate is available as in blind MIMO. The improved performance of the GLRD comes with increased computational complexity. This problem is addressed through search efficient algorithms based on the branch-estimate-bound optimization framework. We analyze the performance of the GLRD and provide a tight upper bound on the BER of the system at high SNR. Simulation results are shown for a 2 × 2 MIMO system.

Cooperation is an effective technique to increase the diversity in wireless relay networks and hence improve the Bit Error Rate (BER). In this paper, a scheme combining turbo coding and cooperative relay techniques, comprising one source, one destination and a single relay is proposed and studied. The aim of this study is to investigate the bit error rate (BER) performance in cooperative turbo coded systems equipped with an omni-directional antenna element at the transmission and the reception in the presence of channel estimation errors. Herein, the Amplify-and-Forward (AF) cooperation protocol is adopted at the relay level. Furthermore, a single hop scenario is considered, and the imperfect channel state information (CSI) is assumed to be present at the source-destination link. Two turbo codes, are generated at the source using the same polynomial, with overall rates of 1/2 and 1/3, respectively, the 1/2-rated one being generated from the latter using puncturing. Before iterative decoding, the destination performs a maximum ratio combing (MRC) of the signals emanating from the source and the relay transmitters. According to the authors 'knowledge, the investigation of the effect of channel estimation errors on the performance of such systems has not yet been reported.