To obviate the need for full Channel State Information (CSI) feedback in a Downlink (DL) Multiple Input Multiple Output (MIMO), 5G New Radio (5GNR) employs codebook-based limited feedback techniques. These techniques require the User Equipment (UE) to choose the best possible precoder for the downlink transmission from the set of codebook entries, and the corresponding Precoding Matrix Indicator (PMI) is sent as feedback to the Base Station. Various methods have been proposed in the literature to select the best possible PMI. The Exhaustive Search (ES) method that examines all possible entries of the codebook is the conventional method. Our paper presents a low-complexity PMI selection method that leverages the spatial characteristics of the codebook entries. In this method, the UE approximates the transmit beam to estimate the coarse Angle of Departure (AOD), which is then mapped to PMI using a novel PMI-to-angle mapping lookup table. The size of the codebook is then reduced using this coarse estimate and the final PMI is obtained by searching the reduced codebook. Through this proposed method the number of codebook entries to be searched is significantly reduced, and simulations demonstrate that the proposed algorithm reduces the search complexity by about 94%, even while maintaining the same performance as the conventional exhaustive search scheme.

MassiveMIMO (mMIMO) enables users with different requirements to get connected to the same base station (BS) on the same set of resources. In the uplink of Multi-user massive MIMO (MU-mMIMO), while such heterogeneous users are served, decoupling facilitates the use of user-specific detection schemes. In this paper, we propose a low-complexity linear decoupling scheme called Sequential Decoupler (SD), which aids in the parallel detection of each user's data stream. The proposed algorithm shows significant complexity reduction. Simulations reveal that the complexity of the proposed scheme is only 0.15% of the conventional Singular Value Decomposition (SVD) based decoupling and is about 47% of the pseudo-inverse based decoupling schemes when 80 users with two antennas each are served by the BS. Also, the proposed scheme is scalable when new users are added to the system and requires fewer operations than computing the decoupler all over again. Further numerical analyses indicate that the proposed scheme achieves significant complexity reduction without any degradation in performance and is a promising low-complex alternative to the existing decoupling schemes.

The limited bandwidth of the Fronthaul (FH) network is one of the main challenges faced by the 5G Open Radio Access Networks (O-RAN). The FH network connects the O-RAN Distributed Unit (O-DU) to one or more O-RAN Radio Units (O-RU). In Frequency Division Duplexing (FDD) Massive Multiple-Input and Multiple-Output (MIMO) systems, the FH load increases in proportion to the number of antennas during the transmission of Channel State Information - Reference Signal (CSI-RS) for downlink channel sounding. However, the O-RAN functional split scales the FH capacity with the number of MIMO layers. This paper proposes an alternative to O-RAN functional split (7-2x) for CSI-RS in Downlink Channel Sounding systems. The proposed split distributes the signal processing functions for CSI-RS transmission into two stages. The first stage (CSI-RS base sequence mapping) is implemented in the O-DU, and the second stage (superimposing orthogonal weights) is implemented in the O-RU. Our results indicate that the proposed split significantly reduces the fronthaul CSI-RS transportation load while enabling much simpler processing at O-DU and O-RU compared to the existing O-RAN split. Additionally, the paper proposes a computationally efficient way to implement Least Squares (LS) channel estimation on the User Equipment (UE) side, again by exploiting the structure of the CSI-RS pilots.

Sixth-generation (6G) technology is expected to enhance satellite connectivity for autonomous vehicles, ensuring ubiquitous and uninterrupted service delivery even during unexpected disruptions or failures. To realize this ambitious objective, it is necessary to establish a vehicle-to-satellite (V2S) communication network characterized by ultra-reliability, low-latency and high data rate. Since autonomous vehicles are often battery-powered, the energy constraint poses a significant challenge in achieving this objective. Therefore, it is necessary to investigate non-terrestrial networks (NTN) channel models and comprehensively analyze the requisite link margin. Popular satellite communication channel models see shadowing as a two-state Markov process based on heavy shadowing (bad state) and light or no shadowing (good state) and transition between each state. This paper proposes a novel combined state encompassing the likelihood of both good and bad states. Our analysis with the combined state suggests that a sufficiently lower link margin is required to achieve 90% or higher link availability, compared to the link margin required for bad state. A combined state benchmarked system shall lead to optimized power consumption, extending the battery life of the autonomous vehicle and reducing its energy constraints. In the link-level simulations, signal-to-noise ratio (SNR) requirement to incur less than 10−3 bit error rate (BER) for the proposed combined state has been obtained. Interestingly, for the given BER, the SNR required for bad state is much higher compared to the combined state.

Frequency Hopping(FH) is an effective way to ensure resilience to jamming and eavesdropping. We consider an ultra-fast FH system where a contiguous set of subcarriers of an OFDM block which hold data are altered periodically based on a random code pattern. Since only a small set of subcarriers of the OFDM symbol are used in any one hop, receiver synchronization for timing and frequency is difficult since the time-domain auto-correlation properties degrade. In this paper, we compare existing preamble and processing methods using auto-correlation for synchronization. We then propose a new preamble with a two-step improved timing estimate. Simulation and results are presented which show the effectiveness of the proposed preamble and processing scheme, especially at low Signal-to-Noise Ratios as evident from the plots where a 3-dB improvement in the probability of detection at -6 dB SNR is seen.

The Demodulation Reference Signal (DM-RS) pilot pattern in 5G NR uses Orthogonal Cover Codes (OCC) and frequency orthogonal pilot positions to enable channel estimation of different users or spatially multiplexed layers of the same user. These OCCs utilize Walsh-Hadamard codes in a pilot-on-pilot arrangement to introduce code orthogonality. Our work reveals how the choice of the OCC and frequency position of the pilot sequence can improve the channel estimate quality. The key contributor to this improvement is an implicit pattern in the OCC structure which derives from the theory of Orthogonal Variable Spreading Factor codes. With the help of an appropriate denoising and interpolation scheme, the proposed estimation scheme is shown to significantly reduce the Mean Square Error of the channel estimates for various DM-RS configurations.

High-Speed Vehicle-to-Everything (V2X) communications is an important aspect of 5G NR technology that requires Ultra-Reliable Low-Latency Communication (URLLC) under highly dispersive channel conditions. In this context, Orthogonal Time Frequency and Space (OTFS) modulation is known to achieve time and frequency diversity, especially in time-dispersive channels with large Doppler frequencies. Literature suggests that Multi-Carrier Code Division Multiple Access (MC-CDMA) can also achieve high diversity gain due to spreading in frequency and time. In this paper, we consider two MC-CDMA schemes, one in which the symbols are spread only along frequency (MC-CDMA-FS) and one where the spread sequence is mapped over both time and frequency (MC-CDMA- TFS). Also, two different precoders based on Walsh-Hadamard (WH) and Zadoff-Chu (ZC) sequences are considered for both of these schemes. We present a comparative performance study of OTFS with MC-CDMA-FS and MC-CDMA-TFS using both the precoders for a single user case, in doubly dispersive channels. Our simulation results show that MC-CDMA-TFS and MC-CDMA-FS schemes with ZC precoding resembles OTFS and even performs better in channels with larger delay and Doppler spreads in this framework.

Overloaded Multi-user MIMO (MU-MIMO) links are prone to interference due to several factors. To mitigate this interference many authors have proposed interference aware receivers in MU-MIMO scenario. Max-Log MAP (MLM) interference modulation classification before joint data detection in 1 × 2 overloaded MU-MIMO systems is optimal, but has a very high complexity. LORD [1] based interference modulation classification followed by data detection MU-MIMO receiver is optimal and is also computationally efficient. However, LORD based detection scheme fails if the receiver has one antenna. We propose a low complexity Quasi MLM interference modulation classification followed by joint data detection for 1×2 overloaded MU-MIMO systems which results in optimal bit-error rate performance.

In this work, we apply the idea of random phase rotation of the modulated symbols to a system with interfering transmitters and a Maximum-likelihood (ML) receiver. Expressions for Bit Error Rate (BER) for a given channel realization and the average Block Error Rate (BLER) for an error correcting code are derived for the Hard-Decision based ML receiver (HDML). The derived expressions are verified through simulation results and it is observed that at high signal to noise ratio (SNR), when the channel is quasi-static (block fading), random phase rotation provides a gain in BLER when compared to the case without random phase rotation. Also it is observed that the BLER for a system with random phase rotation approaches the BLER for the case of no co-channel interference. The gain due to random phase rotation is also verified for an LTE framework with Soft-Decision based ML receiver (SD-ML).

In Uplink Coordinated multipoint (Uplink CoMP) schemes as in LTE [1], multiple base-stations (EnodeBs) receive signals from the same user equipment (UE). Combining this information appropriately using the backhaul is known to yield high gains, especially when the interference avoidance is used across the EnodeBs in CoMP. We consider a load-balanced, dense, small-cell deployment where a UE may be connected not to the strongest EnodeB in order to ensure nearly equal loading across different small cells. In such situations, the benefit of uplink co-operation between the EnodeBs will be very significant since some of the co-operating EnodeBs may have a more stronger signal copy from the UE, than the desired EnodeB. Further, we consider the universal-reuse between the small cells so that the spectrum utilization is maximized (i.e.no interference avoidance), and non-linear (joint) detectors are used at the EnodeBs to handle the strong interferences. We propose a novel joint log- likelihood ratio (LLR) computation, vector quantization and combining approach for such a very strong interference regime. Not only does this quantized sharing approach reduce the bit rate required on the backhaul between the different EnodeBs, but also provides a performance within 0.3dB of the unquantized LLR sharing performance with only 1.2 to 2.4 bits per LLR.

In soft frequency reuse (SFR) when the user is classified as a cell-center user based on signal-to-interference-plus-noise-ratio in a sub-band, the user retains its sub-band. On the other hand, if the user is classified as a cell-edge user, a new sub-band is allocated. We analyze the impact of correlation between the cell-center sub-band and the cell-edge sub-band for a user when the SFR technique is used in a cellular orthogonal frequency division multiple access (OFDMA) system. The coverage probability (CP) and the average rate are derived for the following two cases: 1) when the sub-bands are independent and 2) when the sub-bands are completely correlated. We show that correlation significantly decreases the edge CP and the average rate of the SFR technique, and as the power control factor increases, the impact of correlation decreases. Fractional frequency reuse (FFR) and SFR techniques are compared and it is shown that the impact of correlation on the FFR is significantly lower. Furthermore, it is also shown that the FFR provides better edge CP and average rate when compared with the SFR in single-input single-output (SISO) networks, thereby suggesting that the FFR should be preferred over SFR in cellular SISO OFDMA systems. However, for single-input multiple-output networks, the SFR provides a better average rate when compared with the FFR, especially when the sub-band correlation is not significant. Finally, the impact of log-normal shadowing has been carefully studied.

Time-domain channel shortening filters (CSFs) are used in orthogonal frequency division multiplexing (OFDM) systems to increase the spectral efficiency. We consider transmit-side CSF to reduce the cyclic prefix (CP) length required for the OFDM symbol. We first construct a simple CSF scheme which, however, has a poorer spectral efficiency (SE) when compared to a system with adequate CP length (which we refer to as a full-CP system). We then propose a modified CSF (MCSF) structure that exploits the null-space of an under-determined system of equations, and provides independent equivalent channels to the receiver. The SE of the MCSF is shown to be generally higher than the full-CP system when the chosen CP length is significantly smaller than the channel delay spread. Simulated BLER performance for turbo-coded multi-antenna OFDM systems reveals that the MCSF can provide up to 2 dB gain in${{\bf E}_{\bf b}}/{{\bf N}_{\bf 0}}$when compared with the full-CP system for the same transmit information rate.

We consider the decoding of LDPC codes in presence of non-Gaussian noise, especially a set of ¿-mixture models. For each of these models, the optimal LLRs are presented. We study the performance degradation due to the use of incorrect LLR in presence of a given noise model. Without modifying the existing LDPC decoder, we propose robust initial LLR which require minimum knowledge about the underlying noise model and are computationally less complex. Since BER simulations are computationally heavy, we use density evolution to compare the thresholds of different LLRs.

Cochannel interference occurs when two or more signals overlap in frequency and are present concurrently. Unlike in spread-spectrum multiple-access systems where the different users necessarily share the same channel, cochannel interference is a severe hindrance to frequency- and time-division multiple-access communications, and is typically minimized by interference rejection/suppression techniques. Rather than using interference suppression, we are interested in the joint estimation of the information-bearing narrow-band cochannel signals. Novel joint estimators are proposed that employ a single-input demodulator with oversampling to compensate for timing uncertainties. Assuming finite impulse-response channel characteristics, maximum likelihood (ML) and maximum a posteriori (MAP) criteria are used to derive cochannel detectors of varying complexities and degrees of performance. In particular, a (suboptimal) two-stage joint MAP symbol detector (JMAPSD) is introduced that has a lower complexity than the single-stage estimators while accruing only a marginal loss in error-rate performance at high signal-to-interference ratios. Assuming only reliable estimates of the primary and secondary signal powers, a blind adaptive JMAPSD algorithm for a priori unknown channels is also derived. The performance of these nonlinear joint estimation algorithms is studied through example computer simulations for two cochannel sources.

Dual-mode adaptive algorithms with rapid convergence properties are presented for the equalization of frequency selective fading channels and the recovery of time-division multiple access (TDMA) mobile radio signals. The dual-mode structure consists of an auxiliary adaptive filter that estimates the channel during the training cycle. The converged filter weights are used to initialize a parallel bank of filters that are adapted blindly during the data cycle. When the symbol timing is known, this filter bank generates error residuals that are used to perform approximate maximum a posteriori symbol detection (MAPSD) and provide reliable decisions of the transmitted signal. For channels with timing jitter, joint estimation of the channel parameters and the symbol timing using an extended Kalman filter algorithm is proposed. Various methods are described to reduce the computational complexity of the MAP detector, usually at the cost of some performance degradation. Also, a blind MAPSD algorithm for combining signals from spatially diverse receivers is derived. This diversity MAPSD (DMAPSD) algorithm, which can be easily modified for the dual-mode TDMA application, maintains a global set of MAP metrics even while blindly tracking the individual spatial channels using local error estimates. The performance of these single-channel and diversity MAPSD dual-mode algorithms are studied via computer simulations for various channel models, including a mobile radio channel simulator for the IS-54 digital cellular TDMA standard.

A new blind equalization algorithm based on a suboptimum Bayesian symbol-by-symbol detector is presented. It is first shown that the maximum a posteriori (MAP) sequence probabilities can be approximated using the innovations likelihoods generated by a parallel bank of Kalman filters. These filters generate a set of channel estimates conditioned on the possible symbol subsequences contributing to the intersymbol interference. The conditional estimates and MAP symbol metrics are then combined using a suboptimum Bayesian formula. Two methods are considered to reduce the computational complexity of the algorithm. First, the technique of reduced-state sequence estimation is adopted to reduce the number of symbol subsequences considered in the channel estimation process and hence the number of parallel filters required. Second, it is shown that the Kalman filters can be replaced by simpler least-mean-square (LMS) adaptive filters. A computational complexity analysis of the LMS Bayesian equalizer demonstrates that its implementation in parallel programmable digital signal processing devices is feasible at 16 kbps. The performance of the resulting algorithms is evaluated through bit-error-rate simulations, which are compared to the performance bounds of the maximum-likelihood sequence estimator. It is shown that the Kalman filter and LMS-based algorithms achieve blind start-up and rapid convergence (typically within 200 iterations) for both BPSK and QPSK modulation formats.>