Low-complexity Suboptimal Detectors Research Articles

Over-the-air beamforming with reconfigurable intelligent surfaces

Reconfigurable intelligent surface (RIS)-empowered communication is a revolutionary technology that enables to manipulate wireless propagation environment via smartly controllable low-cost reflecting surfaces. However, in order to outperform conventional communication systems, an RIS-aided system with solely passive reflection requires an extremely large surface. To meet this challenge, the concept of active RIS, which performs simultaneous amplification and reflection on the incident signal at the expense of additional power consumption, has been recently introduced. In this paper, deploying an active RIS, we propose a novel beamforming concept, over-the-air beamforming, for RIS-aided multi-user multiple-input single-output (MISO) transmission schemes without requiring any pre/post signal processing hardware designs at the transmitter and receiver sides. In the proposed over-the-air beamforming-based transmission scheme, the reflection coefficients of the active RIS elements are customized to maximize the sum-rate gain. To tackle this issue, first, a non-convex quadratically constrained quadratic programming (QCQP) problem is formulated. Then, using semidefinite relaxation (SDR) approach, this optimization problem is converted to a convex feasibility problem, which is efficiently solved using the CVX optimization toolbox. Moreover, taking inspiration from this beamforming technique, a novel high-rate receive index modulation (IM) scheme with a low-complexity sub-optimal detector is developed. Through comprehensive simulation results, the sum-rate and bit error rate (BER) performance of the proposed designs are investigated.

Cooperative Abnormality Detection via Diffusive Molecular Communications.

In this paper, we consider abnormality detection via diffusive molecular communications (MCs) for a network consisting of several sensors and a fusion center (FC). If a sensor detects an abnormality, it injects a number of molecules into the medium which is proportional to its sensing output. Two transmission schemes for releasing molecules into the medium are considered. In the first scheme, each sensor releases a different type of molecule (DTM), whereas in the second scheme, all sensors release the same type of molecule (STM). The molecules released by the sensors propagate through the MC channel and some may reach the FC where the final decision regarding whether or not an abnormality has occurred is made. We derive the optimal decision rules for both DTM and STM. However, the optimal detectors entail high computational complexity as log-likelihood ratios (LLRs) have to be computed. To overcome this issue, we show that the optimal decision rule for STM can be transformed into an equivalent low-complexity decision rule. Since a similar transformation is not possible for DTM, we propose simple low-complexity sub-optimal detectors based on different approximations of the LLR. The proposed low-complexity detectors are more suitable for practical MC systems than the original complex optimal decision rule, particularly when the FC is a nano-machine with limited computational capabilities. Furthermore, we analyze the performance of the proposed detectors in terms of their false alarm and missed detection probabilities. Simulation results verify our analytical derivations and reveal interesting insights regarding the tradeoff between complexity and performance of the proposed detectors and the considered DTM and STM schemes.

Error Probability Analysis and Applications of Amplitude-Coherent Detection in Flat Rayleigh Fading Channels

This paper presents the detector design and symbol error rate (SER) analysis of $M$ -ary amplitude shift keying over multipath fading channels using amplitude-coherent detection (ACD). The optimum detector is derived using the maximum likelihood criterion, and then it is used to derive two efficient low-complexity suboptimal detectors. The probability distribution function of the decision variables and the SER of the two suboptimal detectors are expressed using simple closed-form analytical formulas when single receiving antenna is used. The SER with receiver diversity is obtained using Monte Carlo simulation. The obtained analytical and simulation results reveal that ACD can provide reliable SER as compared with noncoherent detection. Moreover, we present an efficient blind channel estimation algorithm using ACD and hybrid modulation frame structures.

A Low-Complexity Near-ML Differential Spatial Modulation Detector

Differential spatial modulation (DSM) is a newly-emerging differential scheme tailored to the spatial modulation technique, which selects only one among a group of antennas for transmission at any time instant. DSM, however, gives rise to prohibitive search complexity when the number of transmit antennas is large. In this letter, a low-complexity suboptimal detector is proposed for DSM. It is designed based on the maximum-likelihood criterion but takes more candidates for the antenna activation orders into account. The detection is performed in two steps: the first step is to confine the number of candidates for the modulated symbols to a small portion by exploiting the symmetry of the signal constellation; the second step is to select the most likely modulated symbols from the output of the first step according to the determined antenna activation order via a Viterbi-like algorithm. Analyses and simulations show that the proposed detector achieves near-optimal performance yet largely reduces the search complexity.

Lattice Reduction-Based Approximate MAP Detection with Bit-Wise Combining and Integer Perturbed List Generation

For iterative detection and decoding (IDD) in multiple-input multiple-output (MIMO) systems, the log-likelihood ratio (LLR) of each coded bit can be found by an optimal bit-wise maximum a posteriori probability (MAP) detector. However, since this MAP detector requires a prohibitively high computational complexity, low-complexity suboptimal detectors are desirable. In this paper, lattice reduction (LR)-based MIMO detection is investigated to derive a low-complexity detector that can achieve near MAP performance for IDD. In order to approximate LLR values incorporating the extrinsic information provided by a soft-input soft-output (SISO) decoder, bit-wise LR-based minimum mean square error (MMSE) filters are derived. Furthermore, in order to minimize the performance degradation due to quantization (or rounding) errors in the LR-based detection, a low-complexity integer perturbed list generation method is proposed, where no tree search is used by taking advantage of a near orthogonal channel basis obtained by LR. Through a complexity analysis and simulations, it is shown that the proposed approach achieves near optimal performance, while the complexity is comparable with that of the MMSE soft cancellation method, which is known to be computationally efficient. As a bit-wise detector, a parallel implementation of the proposed method would be straightforward, which lowers the detection delay.

Performance Analysis of Two-User Cooperative Multiple Access Systems With DF Relaying and Superposition Modulation

In this paper, a two-user cooperative multiple access (CMA) system is considered. Using the superposition modulation scheme, we propose a low-complexity suboptimal detector at the destination to achieve the performance close to that of the optimal maximum-likelihood sequence detector (MLSD). For independent and non-identically distributed (i.n.d.) Rayleigh-faded channels, we derive error decision regions and present the exact bit-error rate (BER) expression in an integral form, which is validated by simulation results. We also approximate the decision regions and obtain the BER bound in closed form. Another efficient transmission scheme, i.e., the time-division broadcast (TDBC) protocol operating in three time slots, is also considered and analyzed. BER results show that the superposition modulation outperforms the others and can achieve both full diversity order and high coding gain.

User Selection Criteria for Multiuser Systems With Optimal and Suboptimal LR Based Detectors

In this correspondence, we investigate user selection criteria for various multiple input multiple output (MIMO) detectors to exploit the multiuser diversity. Different user selection criteria are derived for various MIMO detectors, including the maximum likelihood (ML) detector and low complexity suboptimal detectors. It is shown that the user selection criterion plays a crucial role in exploiting both multiuser and receive (or spatial) diversity. We also show that the ML and even some low complexity suboptimal detectors (based on the lattice reduction (LR)) can achieve a full multiuser and receive diversity when the user selection criterion is properly chosen.