We employ a resource block (RB1)-based compression method using neural networks for the channel coefficients under the specification of the third generation partnership project (3GPP). An autoencoder is trained to compress/decompress the channel coefficients, and the same compressor/decompressor is used for all RBs. Because the proposed method is RBbased, it universally applies to various combinations of resource configurations allowed by 3GPP. It is essential to compress the channel coefficients because they are stored in a buffer that takes a large memory when a large bandwidth is allocated. The buffer provides the channel coefficients to different blocks of the baseband modem. We investigate the compression considering two formats for the complex channel coefficients, Cartesian and polar. For each case, we train and test a separate autoencoder to maximize the compression performance. We reduce the buffer size by about 3 times without losing the performance by more than 0.1 dB, and the proposed algorithm is reliably applicable to both new radio (NR) and long-term evolution (LTE).1A set of 12 consecutive subcarriers

NPU Scheduler Tiling memory transactions between internal memories and external memories Communicating with AP Host and other processing units

A practical rate-matching system for constructing rate-compatible polar codes is proposed. The proposed polar code circular buffer rate-matching is suitable for transmissions on communication channels that support hybrid automatic repeat request communications, as well as for flexible resource-element rate-matching on single transmission channels. Our proposed circular buffer rate matching scheme also incorporates a bit-mapping scheme for transmission on bit-interleaved coded modulation (BICM) channels using higher order modulations. An interleaver is derived from a puncturing order obtained with a low complexity progressive puncturing search algorithm on a base code of short length, and has the flexibility to achieve any desired rate at the desired code length, through puncturing or repetition. The rate-matching scheme is implied by a two-stage polarization, for transmission at any desired code length, code rate, and modulation order, and is shown to achieve the symmetric capacity of BICM channels. Numerical results on AWGN and fast fading channels show that the rate-matched polar codes have a competitive performance when compared with the spatially-coupled quasi-cyclic LDPC codes or LTE turbo codes, while having similar rate-dematching storage and computational complexities.

Polar codes are the latest breakthrough in coding theory, as they are the first family of codes with explicit construction that provably achieve the symmetric capacity of binary-input discrete memoryless channels. Polar encoding and successive cancellation decoding have the complexities of N log N , for code length N. Although, the complexity bound of N log N is asymptotically favorable, we report in this work methods to further reduce the encoding and decoding complexities of polar coding. The crux is to relax the polarization of certain bit-channels without performance degradation. We consider schemes for relaxing the polarization of both very good and very bad bit-channels, in the process of channel polarization. Relaxed polar codes are proved to preserve the capacity achieving property of polar codes. Analytical bounds on the asymptotic and finite-length complexity reduction attainable by relaxed polarization are derived. For binary erasure channels, we show that the computation complexity can be reduced by a factor of six, while preserving the rate and error performance. We also show that relaxed polar codes can be decoded with significantly reduced latency. For additive white Gaussian noise channels with medium code lengths, we show that relaxed polar codes can have lower error probabilities than conventional polar codes, while having reduced encoding and decoding computation complexities.

We consider the problem of polar coding for transmission over m-user multiple access channels. In the proposed scheme, all users encode their messages using a polar encoder, while a multiuser successive cancellation decoder is deployed at the receiver. The encoding is done separately across the users and is independent of the target achievable rate. For the code construction, the positions of information bits and frozen bits for each of the users are decided jointly. This is done by treating the polar transformations across all the m users as a single polar transformation with a certain polarization base. We characterize the resolution of achievable rates on the dominant face of the uniform rate region in terms of the number of users m and the length of the polarization base L. In particular, we prove that for any target rate on the dominant face, there exists an achievable rate, also on the dominant face, within the distance at most (m-1)√m/L from the target rate. We then prove that the proposed L MAC polar coding scheme achieves the whole uniform rate region with fine enough resolution by changing the decoding order in the multiuser successive cancellation decoder, as L and the code block length N grow large. The encoding and decoding complexities are O(N log N) and the asymptotic block error probability of O(2-N0.5-ϵ) is guaranteed. Examples of achievable rates for the 3-user multiple access channel are provided.

This paper explores the blind decoding of control channels for obtaining other user identities in 3GPP specification, such as high-speed packet access and long-term evolution. The reliable decoding of control channels with user identities is crucial to mitigate inter-cell interference as well as multi-user interference. This paper exploits a method of user identity filtering followed by a method of user identity detection based on the traffic persistency, which is common to all standards. Hence, the proposed methods are applicable to all the standards regulated by 3GPP specification. In particular, this paper analyzes the proposed other user identity detection algorithm under the random coding. Simulation results show that the proposed method is reliable even at low SNRs and is also aligned with the analysis.

A design of rate-compatible polar codes suitable for HARQ communications is proposed in this paper. An important feature of the proposed design is that the puncturing order is chosen with low complexity on a base code of short length, which is then further polarized to the desired length. A practical rate-matching system that has the flexibility to choose any desired rate through puncturing or repetition while preserving the polarization is suggested. The proposed rate-matching system is combined with channel interleaving and a bit-mapping procedure that preserves the polarization of the rate-compatible polar code family over bit-interleaved coded modulation systems. Simulation results on AWGN and fast fading channels with different modulation orders show the robustness of the proposed rate-compatible polar code in both Chase combining and incremental redundancy HARQ communications.

Arıkan's polar codes are proven to be capacity-achieving error correcting codes while having explicit constructions. They are characterized to have encoding and decoding complexities of l log l, for code length l. In this work, we construct another family of capacity-achieving codes that have even lower encoding and decoding complexities, by relaxing the channel polarizations for certain bit-channels. We consider schemes for relaxing the polarization of both sufficiently good and sufficiently bad bit-channels, in the process of channel polarization. We prove that, similar to conventional polar codes, relaxed polar codes also achieve the capacity of binary memoryless symmetric channels. We analyze the complexity reductions achievable by relaxed polarization for asymptotic and finite-length codes, both numerically and analytically. We show that relaxed polar codes can have better bit error probabilities than conventional polar codes, while having reduced encoding and decoding complexities.

In Long-Term Evolution (LTE) downlink control channel, a large number of blind decoding attempts are made, while the number of valid codewords is limited. The blind decoding results are then verified using a 16-bit cyclic redundancy check (CRC). However, even with the 16-bit CRC, the false alarm (FA) rate of such blind decoding is inevitably high. This paper investigates the problem of pruning of blind decoding results for reduction of the FA rate. To the best of our knowledge, the approach using a soft correlation metric (SCM) shows the best FA reduction performance among existing schemes. However, following the Bayes principle, we propose novel likelihood-based pruning that provides systematic balancing between the FA rate and the miss (MS) rate. Moreover, the simulation results show that the signal-to-noise ratio (SNR) gain of our proposed scheme is unbounded, with respect to the SCM-based scheme, in the independent and identically distributed (i.i.d.) Rayleigh fading channel. Moreover, the proposed scheme is shown to be less complex than the existing scheme. Finally, it is proved that, as SNR increases, the proposed approach has the decision error probability that approaches the minimum value yielding near-optimal contraction of Voronoi regions for pruning of blind decoding results.

This paper considers the problem of unknown boosting factor estimation for Long Term Evolution (LTE) downlink control channel, whose signal can be boosted or deboosted for power control. The boosting factor needs be estimated and utilized to deploy advanced receiver algorithms. We first show that an iterative algorithm can be used to find the solution for this estimation problem. However, the use of iterative algorithms poses numerous modem implementation challenges. For this reason, we investigate non-iterative estimation of the boosting factor and propose a novel approach based on joint estimation of the boosting factor and modulation symbols. The main idea of the proposed scheme is to utilize various techniques such as bias reduction and approximate dimension reduction and improve the estimation performance of this baseline method. The simulation results show that the proposed non-iterative algorithm can achieve near optimal performance close to that of the maximum likelihood (ML) solution for a wide range of signal-to-noise ratio (SNR).

Iterative detection and decoding (IDD) relies on passing useful extrinsic information between the detector and the decoder. Due to the sub-optimality of practical detector and/or decoder, the direct output LLRs from the detector or the decoder may not provide sufficient gains to each other. Proper scaling of the extrinsic LLRs based on certain optimality criteria may improve the performance of the IDD receiver. However, finding optimal scaling function for IDD receiver in general is still an open problem. In this paper, we investigate LLR scaling of the detector and the decoder output based on maximization of generalized mutual information.

This paper considers the algebraic construction and performance of non-binary spatially-coupled low density parity check (LDPC) codes. A replicate-and-mask approach is presented to construct finite-length algebraic quasi-cyclic (QC) spatially-coupled (SC) LDPC codes. Numerical results show the superiority of non-binary algebraic SC QC LDPC codes over the corresponding random non-binary (block and SC) LDPC codes. In this paper, it is demonstrated that the threshold saturation phenomenon, previously demonstrated for binary SC LDPC codes, also holds for non-binary SC LDPC codes over the binary-input AWGN channel with BPSK modulation.

In the presence of co-channel interference in cellular networks, interference mitigation by detecting the desired signal jointly with the interference promises considerable gain over the conventional way of handling the interference as colored Gaussian. Even though such interference-aware detection can improve the performance, it requires some information on the interference. In particular, the modulation format of the interference has to be classified to this end, when it is not signaled by the network explicitly. This paper investigates interference modulation classification methods for interference-aware joint detection. We propose an iterative interference modulation classification algorithm that utilizes the decoded information of the desired signal in order to cancel the desired signal from the received signal. After the cancellation, the remaining signal can be treated as interference plus noise so that we can classify the modulation format of the interference at reduced complexity with small performance loss due to decoding errors.

This paper proposes a way of applying interference- aware interference mitigation algorithms to device- to-device (D2D) communications in cellular networks for system throughput improvement. One of main purposes for using D2D communications is to offload throughput passing through a base station in cellular networks. In this sense, interference management between cellular and D2D signals is inevitable for both cellular and D2D mobile stations (MSs). Recently, interference-aware interference mitigation algorithms have been proposed in a theoretical aspect as well as a practical aspect. These algorithms operate based on interference information given at a MS, which can be obtained via network assistance or blind estimation. This paper explains how interference- aware algorithms can be applied to D2D communications in cellular networks. Moreover, this paper analyzes the throughput performance by deriving the rate upper-bounds. Simulation results demonstrate that the system throughput can be significantly improved while not losing the performance of a cellular MS too much.

This paper proposes an interference-aware interference cancellation (IAIC) algorithm that effectively mitigates inter-cell interference. In modern cellular networks, a serving signal could be disrupted due to interfering signals transmitted from neighbor cells in proximity to the serving cell. In order to overcome the disturbance of interfering signals, the recent 3GPP standard specification has explored a way of using network assistance, called network-assisted interference cancellation and suppression (NAICS). IAIC is in the same research direction of NAICS such that decoding an interfering signal as well as a serving signal is enabled via network assistance. Hence, IAIC is able to decode both signals as in a multiple access channel (MAC). However, a serving signal is only of concern while all decoded signals are of concern in a MAC. Using this characteristic, IAIC can be implemented as a practical solution. This paper demonstrates that the performance of IAIC is superior as well as requiring less complexity.

Optimal iterative log‐MAP decoding of turbo codes requires accurate knowledge of the operating signal‐to‐noise ratio (SNR). However, the SNR information, available at practical decoders for bit‐interleaved coded modulation systems, such as the third generation partnership project high‐speed packet access and long‐term evolution wireless cellular systems, may be inaccurate. In this study, two decoder architectures for improved turbo decoding in the presence of SNR mismatch are proposed. The SNR‐mismatch aware turbo decoder selects the decoder which is estimated to have the best performance at the current mismatch, according to the test criterion. The SNR‐mismatch compensated turbo decoder provides a more accurate estimation of the noise variance and concurrently scales the channel and the decoder log‐likelihood ratios (LLRs) to continue decoding. Two different methods are proposed to find the optimal scaling factors online, one on the symbol level and the other on the bit level. This study shows that online LLR scaling, without prior knowledge about the noise mismatch statistics, can result in near‐optimal turbo decoding regardless of the initial SNR mismatch.

A scheme for concatenating the recently invented polar codes with non-binary MDS codes, as Reed-Solomon codes, is considered. By concatenating binary polar codes with interleaved Reed-Solomon codes, we prove that the proposed concatenation scheme captures the capacity-achieving property of polar codes, while having a significantly better error-decay rate. We show that for any ε > 0, and total frame length N, the parameters of the scheme can be set such that the frame error probability is less than 2-N1-ε, while the scheme is still capacity achieving. This improves upon 2-N0.5-ε, the frame error probability of Arikan's polar codes. The proposed concatenated polar codes and Arikan's polar codes are also compared for transmission over channels with erasure bursts. We provide a sufficient condition on the length of erasure burst which guarantees failure of the polar decoder. On the other hand, it is shown that the parameters of the concatenated polar code can be set in such a way that the capacity-achieving properties of polar codes are preserved. We also propose decoding algorithms for concatenated polar codes, which significantly improve the error-rate performance at finite block lengths while preserving the low decoding complexity.

It has been generally believed that the multiple-input multiple-output channel capacity grows linearly with the size of antenna arrays. In terms of degrees of freedom, linear transmit and receive arrays of length L in a scattering environment of total angular spread \Ω\ asymptotically have \Ω\L degrees of freedom. In this paper, it is claimed that the linear increase in degrees of freedom may not be attained when scattered electromagnetic fields in the underlying scattering environment are statistically correlated. After introducing a model of correlated scattering, which is referred to as the colored scattering model, we derive a capacity upper bound, assuming that the channel is known perfectly at the receiver and in distribution at the transmitter. Unlike the uncorrelated case, the prelog factor of the capacity, i.e., the number of degrees of freedom, in the colored scattering channel is asymptotically limited by \Ω\·min{L, 1/ ΓI} where Γ is a parameter determining the extent of correlation. In other words, for very large arrays in the colored scattering environment, degrees of freedom can get saturated to an intrinsic limit rather than increasing linearly with the array size.

To deal with a time-varying nature of the wireless channel, the most modern wireless systems use link adaptation in which the transmission rate is adjusted according to the current fading status to ensure reliable communication. For example, the current long-term evolution (LTE) standards require the user equipment (UE) to report channel quality indicator (CQI) to the base station (BS) to determine the data rate of a transmission (code) block. Such link adaptation effectively eliminates fading-induced outage and provides ergodic throughput in the long run. When there is delay in CQI feedback, however, such knowledge of the channel status is inevitably outdated, and outage becomes problematic again. In this paper, we characterize a future channel in which the reported CQI is actually used as a conditional random variable given current channel observation. The dependence of the future channel to the current observation is captured by channel correlation. By considering an outage event as a failed transmission, we can compute probability of successful transmission for each value of transmission rate to determine the best transmission rate which maximizes the expected throughput. If ergodicity holds for a channel process, then such expected throughput becomes close to an empirical throughput in the long run.

This paper proposes a symbol-level combining (SLC) scheme for hybrid automatic-repeat-request (HARQ), being used with an interference-aware successive decoding (IASD) algorithm [1]. Recently, it is revealed that the capacity in an interference channel over point-to-point codes can be achieved by combining two schemes: one is jointly decoding an interfering signal with a serving signal and the other is treating it as noise. Due to its high computational complexity, joint decoding can be practically replaced with successive decoding as suggested in [1]. However, when HARQ is enabled, it has been not well defined how interference should be handled at each transmission. Since interference is changed at each transmission, it is not helpful to store the information of interfering signals. Instead, the proposed scheme employes the decoded information of interfering signals in order to convert an interference channel to a point-to-point channel. The proposed scheme only requires a fixed size of memory and does not increase the detector complexity of IASD with respect to the number of retransmission. Simulation results demonstrate the superiority of the proposed scheme to the optimal SLC scheme at the conventional receiver.

In this paper, we consider a hybrid automatic repeat request (HARQ) system with bit-interleaved coded modulation over wireless channels. At higher-order modulations, bit-level combining of log-likelihood ratios (LLRs) is sub-optimal compared to symbol-level maximal ratio combining (MRC). Since symbol level combining is not always feasible, we propose novel bit-level LLR HARQ combining techniques that deploy a modified Turbo principle at the receiver. Our proposed combining methods make use of the information available from previous transmissions to improve the performance of both detection and decoding at the current transmission. We analyze the proposed combining schemes using modified information transfer charts. We show that significant coding and throughput gains can be achieved using our proposed HARQ combining schemes with minimal extra memory requirements.

The paper establishes the optimal generalized degrees of freedom (GDOF) of 3-user M × N multiple-input multiple-output (MIMO) Gaussian interference channel (GIC) in which each transmitter has M antennas and each receiver has N antennas. A constraint of 2M ≤ N is imposed so that random coding with message-splitting achieves the optimal GDOF. Unlike symmetric case, two cross channels to unintended receivers from each transmitter can have different strengths, and hence, well known Han-Kobayashi common-private message splitting would not achieve the optimal GDOF. Instead, splitting each user's message into three parts is shown to achieve the optimal GDOF as well as O(1) capacity approximation.

This paper discusses evaluation of the capacity region for interference networks with point-to-point (p2p) codes. Such capacity region has recently been characterized as union of several sub-regions each of which has distinctive operational characteristics. Detailed evaluation of this region, therefore, can be accomplished in a very simple manner by acknowledging such characteristics, which, in turn, provides an insight for a simple implementation scenario. Generalized message assignment is considered in this paper, and it is shown to provide strictly larger achievable rates than what traditional message assignment does when a receiver with joint decoding capability is used.

A scheme for concatenating the recently invented polar codes with interleaved block codes is considered. By concatenating binary polar codes with interleaved Reed-Solomon codes, we prove that the proposed concatenation scheme captures the capacity-achieving property of polar codes, while having a significantly better error-decay rate. We show that for any ε > 0, and total frame length N, the parameters of the scheme can be set such that the frame error probability is less than 2-N 1-ε, while the scheme is still capacity achieving. This improves upon 2-N 0.5-ε, the frame error probability of Arikan's polar codes. We also propose decoding algorithms for concatenated polar codes, which significantly improve the error-rate performance at finite block lengths while preserving the low decoding complexity.

We consider bit interleaved coded modulation (BICM) receiver performance improvement based on the concept of generalized mutual information (GMI). Increasing achievable rates of BICM receiver with GMI maximization by proper scaling of the log likelihood ratio (LLR) is investigated. While it has been shown in the literature that look-up table based LLR scaling functions matched to each specific transmission scenario may provide close to optimal solutions, this method is difficult to adapt to time-varying channel conditions. To solve this problem, an online adaptive scaling factor searching algorithm is developed. Uniform scaling factors are applied to LLRs from different bit channels of each data frame by maximizing an approximate GMI that characterizes the transmission conditions of current data frame. Numerical analysis on effective achievable rates as well as link level simulation of realistic mobile transmission scenarios indicate that the proposed method is simple yet effective.

The co-channel interference (CCI) has become a major throughput-limiting factor for cell-edge users in the modern cellular systems such as the Long Term Evolution (LTE) network. Spatial interference whitening (IW) is an effective low-complexity linear method to suppress colored spatial interference in a MIMO setup. However, for CCIs using Alamouti's type of SFBC (space-frequency block coding) transmission, the conventional per-subcarrier IW fails to exploit the SFBC structure and thus is suboptimal. To address that, we propose an optimal method called enhanced interference whitening (eIW). eIW works on a properly combined augmented MIMO model for two subcarriers involved in the same SFBC encoding block and thus can fully exploit the interference structure. Simulations show that if the interfering cell uses SFBC significant performance gains can be achieved by eIW no matter whether or not SFBC is used for the serving cell.

This comment corrects a few errors found in the derivation of the maximum likelihood estimate of differential phase in the paper, "A Technique for Orthogonal Frequency Division Multiplexing Frequency Offset Correction." We show that the problem of differential phase estimation can be considered as an estimation problem in the presence of nuisance parameters, which does not satisfy strong ancillarity. The approach in the above paper to solve this problem can be understood as conditioning for elimination of nuisance parameters but without taking proper steps. After making corrections on the proof, it is demonstrated that the estimator in the above paper is inherently suboptimal and thus prior knowledge on the nuisance parameters, if available, can be utilized to further improve the estimation performance.

This paper proposes a codeword-based iterative detection and decoding (IDD) algorithm for multiple-input multiple-output (MIMO) systems. In the proposed algorithm, multiple streams in a codeword are jointly detected at the rank-reduced (RR) maximum a posteriori (MAP) receiver and inter-stream interference is mitigated with successive interference cancelation (SIC). Thus, the algorithm is abbreviated to RR-MAP-SIC. Recent wireless standards such as Long-Term Evolution require the system to encode data bits per codeword, not per stream. As a result, conventional SIC algorithms could lose joint information among streams in a codeword because different streams of the same codeword are treated as interference. Instead, the proposed RR-MAP-SIC minimizes the loss of joint information by using the rank-reduced MAP detector. In addition, this paper compares the detector complexity of RR-MAP-SIC and investigates how the probability of symbol error is changed in terms of the covariance of the residual interference. As the number of iterations increases, the covariance is reduced so that the error events also decrease. Lastly, the extrinsic information transfer (EXIT) chart is used to analyze the performance of RR-MAP-SIC. Simulation results demonstrate the superiority of RR-MAP-SIC over the conventional algorithm and numerically verify the EXIT chart analysis.

Optimal iterative log-MAP (LM) decoding of turbo codes requires accurate signal to noise ratio (SNR) information. In practice, there is SNR mismatch priori to decoding due to inaccurate SNR estimation. Although max-log-MAP turbo decoding avoids the detrimental effect of SNR mismatch, its performance is inferior to LM decoding at accurate SNR estimation. In this paper, we propose two architectures for improved turbo decoding in presence of SNR mismatch. The first architecture called “SNR-Mismatch Aware Turbo (SMAT) Decoder” selects the decoder with the best performance at any SNR mismatch. The second architecture called “SNR-Mismatch Compensated Turbo (SMCT) Decoder” performs accurate SNR-mismatch estimation and compensates for the mismatch while decoding. We provide symbol-based as well as bit-level LLR histogram-based approaches for SNR mismatch estimation. We show that the proposed SMCT decoder has near-optimal performance regardless of the initial SNR mismatch. We demonstrate the effectiveness of the proposed turbo decoding architectures by Monte Carlo simulations.

Third Generation Partnership Project Long Term Evolution (3GPP LTE) provides potential of higher spectral efficiency by using multi-user multiple-input and multiple-output (MU-MIMO) system. Because of the coarse knowledge of channel state information at the transmitter (CSIT) under current standard, co-scheduled user equipments (UEs) may suffer large residual multi-user interference in MU-MIMO. In this paper, we investigate performances of various types of receivers in the cases where residual interference is not negligible. Interference ignoring receiver is expected to perform poorly in this scenario, and hence, we consider interference-aware receivers. Interference rejection combiner (IRC) and joint Max-Log-MAP receiver are considered as interference-aware receivers. In order to perform joint detection of the target and interference layers, UE needs to know interference modulation which current standard does not provide. Because of this, we consider modulation-estimation-based joint receiver as our advanced downlink MU-MIMO receiver. Performances of aforementioned receivers are investigated in several practically relevant cases. It is shown that modulation-estimation-based joint receiver can significantly outperform IRC or the joint receiver with assumption of fixed interference modulation.

In this paper, we investigate performances of simple transmission strategies. We first consider two user SISO Gaussian symmetric interference channel (IC) for which Etkin, Tse and Wang proposed a scheme (ETW scheme) which achieves one bit gap to the capacity. We compare performance of point-to-point (p2p) codes with that of the ETW scheme in practical range of transmitter power. It turns out that p2p coding scheme performs better or as nearly good as the ETW scheme. Next, we consider K user SISO Gaussian symmetric IC. We define interference regimes for K user SISO Gaussian symmetric IC and provide closed-form characterization of the symmetric rate achieved by the p2p scheme and the ETW scheme. Using this characterization, we evaluate performances of simple strategies with K=3, and show the similar trend to two user case.

This paper deals with the problem of classification of digital modulation. In particular, we develop and propose a practical modulation classification scheme based on the likelihood of observations. While ML classification is well known and shows the optimal performance, its computational complexity prevents it from being easily implemented in hardware. On the contrary, our proposed scheme has low computational complexity and near optimal classification performance. Moreover, this scheme is designed to perform in fast fading channels. It is shown that our proposed classifier takes advantage of the channel variation without loosing near optimality.

This paper proposes a codeword-based iterative detecting and decoding (IDD) algorithm using a rank- reduced maximum likelihood (ML) detector with pre- whitened interference over multiple-input multiple- output (MIMO) channels. This iterative algorithm operates on the principle of successive interference cancelation (SIC) where all the layers per codeword are decoded together at each iteration. The recent wireless standard requires to encode data bits per codeword, not per layer at multiple antennas. Thus, SIC algorithms based on layer-separating detectors could lose joint information between layers in a single codeword. Instead, the proposed algorithm minimizes this loss by using rank-reduced ML detectors over soft feedback. Simulations are performed on a space-time bit-interleaved coded modulation over Rayleigh fading channels, and demonstrate the proposed SIC algorithm is superior to comparable iterative and non-iterative IDD algorithms.

Blind adaptive I/Q imbalance compensation has become more popular during the last decade due to its simplicity and need for no training data. In existing work, it has been pointed out that a statistical property of a signal, which is referred to as the properness condition, can be used for blind I/Q imbalance compensation. In this letter, the equi-absolute variance condition as well as the properness condition are used to propose two blind adaptive I/Q imbalance compensation algorithms based on the well-known LMS and RLS adaptation algorithms. The performances of the proposed algorithms are evaluated through simulations, and it is shown that the proposed algorithms provide nice convergence behaviors and high image rejections.