Spectral Efficiency Gain Research Articles

1.06 Tbit/s space division multiplexing self-homodyne coherent transmission with high spectral efficiency probabilistic shaping multidimensional modulation

We propose a probabilistic shaping multidimensional (MD) modulation format for self-homodyne coherent transmission systems. The redundancy of the MD signal is separated and transmitted together with the optical pilot tone (PT). The 4D/8D/12D modulation is constructed at the transmitter based on the amplitude translation (AT)-set partitioning (SP) principle and the probabilistic amplitude shaping (PAS) structure. Moreover, the hard-decision (HD) and soft-decision (SD) decoders for MD signals are introduced. We implement 20 GBaud 4D/8D/12D probabilistic shaping (PS) 16384-level quadrature amplitude modulation (QAM) transmission over 22.5 km uncoupled multicore fiber to analyze the performance. The experimental results show that it is possible for the MD signal to provide spectral efficiency (SE) gain up to 3.31 bit/s/Hz/4D-sym. The maximum SE transmitted is up to 57.29 bit/s/Hz, i.e., 17.5 bit/s/Hz/4D-sym, with a bit rate of 1.06 Tbit/s.

Channel estimation for reconfigurable intelligent surface‐assisted mmWave using multidimensional Runge Kutta orthogonal matching pursuit algorithm in MIMO systems

SummaryIn the context of Reconfigurable Intelligent Surface (RIS) assisted millimeter wave communications within the Multiple Input Multiple Output (MIMO) system, Channel Estimation (CE) poses a significant challenge due to signal sparsity and blockages between transmitters and receivers. Consequently, communication faces difficulties without a direct line‐of‐sight path, leading to increased pilot overhead. To address these obstacles, this research introduces a channel estimation approach for RIS‐aided millimeter wave communication employing the Multidimensional Runge Kutta Orthogonal Matching Pursuit (MRKOMP) algorithm in MIMO systems. The proposed method aims to enhance network coverage and channel capacity for efficient data transmission. By utilizing the multiscale cumulative residual distribution entropy function, the method establishes an indirect path when the line‐of‐sight is obstructed. Furthermore, the multidimensional orthogonal matching pursuit methodology calculates sparsity levels and facilitates sparse recovery. Optimization of the weight parameter through the Runge Kutta optimizer effectively eliminates sparsity, resulting in improved spectral efficiency and RIS gain. Simulation results demonstrate a significant 99.9% enhancement in network capacity and spectral efficiency compared to existing methods.

Analysis and Optimization of Hardware Impaired FD Rician-Faded mMIMO Systems

We consider a full duplex (FD) multi-cell massive multi-input-multi-output (mMIMO) network, wherein FD mMIMO base-stations (BSs) serve multiple FD user equipments (UEs) on the same time-frequency resource. We derive closed-form uplink and downlink spectral efficiency (SE) expressions by considering i) radio-frequency impairments at the BSs and the UEs; ii) dynamic-resolution analog-to-digital converter/digital-to-analog converter (ADC/DAC) architecture at the BSs and low-resolution ADC/DAC at the UEs; and iii) spatially-correlated Rician channels. We optimize the non-convex global energy efficiency metric for this FD system by using the minorization-maximization (MM) framework, which transforms the optimization into a sequence of surrogate concave problems. We construct a novel surrogate function to exploit the MM framework. We numerically characterize the effect of FD interference on Rayleigh- and Rician-faded mMIMO networks, and show the higher immunity of the latter to the FD interference. We also show the SE gain provided by the dynamic ADC/DAC architecture which allows us to judiciously choose ADC/DAC resolution, while reducing the power consumption.

Intelligent Analog Beam Selection and Beamspace Channel Tracking in THz Massive MIMO With Lens Antenna Array

Beamspace multiple-input-multiple-output (MIMO) as a green technology can efficiently substitute for the conventional massive MIMO, provided that the beamspace channel is acquired precisely. The prior efforts in this area of study, especially the learning-driven ones, however, indicate remarkable performance losses owing to a lack of generalization. In this paper, we propose a modified non-linear auto-regressive exogenous (NARX) model for tracking and predicting the beamspace channel over the sequences of time. Benefiting from bounded generalization error, fast convergence, limited prediction variance, and negligible performance loss, the proposed scheme achieves up to 15% spectral efficiency (SE) gain over its counterparts. We further improve this performance by means of an ensemble learning technique for simultaneously training multiple NARX modules in parallel, thus leading to a 23% SE gain. Relying on the predicted beamspace channel, we propose a beamspace analog beam selection technique through fine-tuning the architecture of a pre-trained off-the-shelf GoogleNet, which brings up to 21% SE gain over similar baselines. With the aid of an ensemble learning technique, it is further indicated numerically that up to 34% SE improvement can be achieved, as compared to the counterparts.

Joint Uplink Power Control, Downlink Beamforming, and Mode Selection for Secrecy Cell-Free Massive MIMO With Network-Assisted Full Duplexing

In this article, we investigate a secrecy cell-free massive multiple-input multiple-output system with network-assisted full duplexing for both noncolluding eavesdropper (Eve) and colluding Eve cases, where the access points (APs) are designed to serve downlink users and uplink users simultaneously and flexibly in the presence of Eves. To confirm the secrecy transmission, we consider the problem of jointly optimizing duplex mode selection and secrecy transceivers to maximize the overall secrecy spectral efficiency, where the information signals at APs are injected with artificial noise (AN) to prevent interception of information by Eves. Considering that downlink secure beamforming, uplink transmission power, and uplink receivers are tightly coupled in both the objective function and the constraints, we propose a two-loop strategy to solve the optimization problem. In particular, we propose a quantum-inspired tabu search algorithm to find a better duplex mode in the outer loop for each iteration. Then, for the inner loop, we propose a successive convex approximation based iterative algorithm to optimize the transceivers and AN by fixing the duplex mode selection vectors obtained from the outer loop. To address the proposed inner loop design subproblem, we resolve the subproblem by a series of nonconvex-convex approximate methods, such as the equivalent transformation and the iterative concave-convex procedure. The simulation results show that the proposed solution can achieve a higher secrecy spectral efficiency gain than the fixed-mode duplex scheme and simple greedy duplex mode selection schemes and achieve secrecy spectral efficiency performance similar to that of the optimal exhaustive search solution for both noncolluding Eve and colluding Eve cases.

Resource Management for Multiplexing eMBB and URLLC Services Over RIS-Aided THz Communication

Integrating the multitude of emerging internet of things (IoT) applications with diverse requirements in beyond fifth generation (B5G) networks necessitates the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services. However, bandwidth limited and congested sub-6GHz bands are incapable of fulfilling this coexistence. In this paper, we consider a reconfigurable intelligent surface (RIS)-aided wideband terahertz (THz) communication system to this end. In specific, we formulate a resource management problem, aiming at jointly optimizing the reflection coefficient of the RIS elements and the transmit power of the base station, as well as the wideband THz resource block allocation. To solve this problem, we adopt a supervised learning approach relying on optimization, deep learning and ensemble learning methods. Simulation results show that for an RIS of size <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$11\times 11$ </tex-math></inline-formula> , up to 49% spectral efficiency gain is achieved for the eMBB service compared to the counterparts, while ensuring the reliability and latency requirements of the URLLC service. Further, the ensemble learning model can perform real-time resource management at the expense of up to 1% performance loss, compared to the optimization approach.

Direct Conversion of Hybrid Precoding and Combining From Full Array Architecture to Subarray Architecture for mmWave MIMO Systems

Hybrid precoding and combining for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems with subarray (SA) architecture is a promising technology for 6G because of their low complexity, cost, and power consumption compared to the full array (FA) architecture. This paper proposes an iterative algorithm for designing hybrid precoding and combining for the SA architecture. It is called direct conversion of iterative hybrid precoding and combining from FA to SA (DCIFS). The proposed algorithm involves an iterative process that begins by designing a hybrid precoding and combining matrix for the FA structure and then converts it into an SA matrix by setting certain entries to zero while achieving better performance. It does not depend on the antenna array geometry, unlike other techniques such as the orthogonal matching pursuit (OMP) hybrid precoding and combining approach. We investigate the proposed algorithm with two scenarios. In the first scenario, we use the proposed DCIFS scheme only at the base station (BS) and the iterative FA hybrid scheme at the mobile station (MS), whereas in the second, we use the proposed DCIFS scheme at both the BS and the MS. Simulation results demonstrate that the proposed design approach achieves a spectral efficiency comparable to that of the FA hybrid design counterpart, especially for a large system, while maintaining low complexity. For example, when SNR= 0 dB and the number of transmitted streams ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N_s</i> ) = 2, the proposed algorithm provides about 1.5 bps/Hz spectral efficiency gain compared to the OMP hybrid design for the first scenario. Moreover, when the number of iterations is low and the number of BS antenna and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N_s</i> is high, the proposed approach outperforms the conventional SA hybrid design in terms of spectral efficiency with the same hardware complexity.

User Subgrouping and Power Control for Multicast Massive MIMO Over Spatially Correlated Channels

Massive multiple-input-multiple-output (MIMO) is unquestionably a key enabler of the fifth-generation (5G) technology for mobile systems, enabling to meet the high requirements of upcoming mobile broadband services. Physical-layer multicasting refers to a technique for simultaneously serving multiple users, demanding for the same service and sharing the same radio resources, with a single transmission. Massive MIMO systems with multicast communications have been so far studied under the ideal assumption of uncorrelated Rayleigh fading channels. In this work, we consider a practical multicast massive MIMO system over spatially correlated Rayleigh fading channels, investigating the impact of the spatial channel correlation on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">favorable propagation</i> , hence on the performance. We propose a subgrouping strategy for the multicast users based on their channel correlation matrices’ similarities. The proposed subgrouping approach capitalizes on the spatial correlation to enhance the quality of the channel estimation, and thereby the effectiveness of the precoding. Moreover, we devise a max-min fairness (MMF) power allocation strategy that makes the spectral efficiency (SE) among different multicast subgroups uniform. Lastly, we propose a novel power allocation for uplink (UL) pilot transmission to maximize the SE among the users within the same multicast subgroup. Simulation results show a significant SE gain provided by our user subgrouping and power allocation strategies. Importantly, we show how spatial channel correlation can be exploited to enhance multicast massive MIMO communications.

UE Grouping Algorithms to Maximize Frequency Sharing in Hybrid IBFD Networks

Demonstrated Spectral Efficiency (SE) gain by In Band Full Duplex (IBFD) makes it an attractive radio technology. Owing to inherent complexities in IBFD, Hybrid IBFD Cellular Network (HICN) is opted in practice because it limits IBFD capability only to Base Station and continues with legacy Half Duplex (HD) User Equipments (UEs). Since sharing frequencies within UE groups maximizes sum-SE in a HICN, we formulate a grouped UEs maximization problem for achieving maximum frequency sharing. Unlike heuristic search methods developed in literature, in this paper, we build a mathematical framework for UE grouping. Three grouping algorithms are analytically derived in closed-forms with varying performance and time complexity trade-offs. The optimal algorithm provides benchmarking on these performance measures. Using structural advantages of a HICN, we derive reduced search-optimal algorithm that reduces time complexity while guaranteeing optimal grouping, whereas near-optimal algorithm achieves close-to-optimal grouping with substantially reduced time complexity for real-time cellular systems. We further introduce the concept of ineligible UEs, which uniformly reduces time complexity of all three algorithms without impacting their grouping performance. Extensive simulations reveal that the proposed near-optimal algorithm achieved a median sum-SE of 93.5% of theoretically doubling maximum over legacy HD systems, thus emerging as a prospective solution.

FD Cell-Free Massive MIMO Systems With Downlink Pilots: Analysis and Optimization

We consider a full duplex (FD) massive multiple-input multiple-output (mMIMO) cell-free (CF) system, where a large number of multi-antenna FD access points (APs) jointly serve multiple FD user equipments (UEs). The APs transmit precoded downlink pilots for the UEs to estimate the effective downlink channel. For this system, we derive closed-form uplink and downlink spectral efficiency (SE) expressions by considering i) radio-frequency (RF) impairments at the APs and UEs; ii) dynamic resolution analog-to-digital converter/digital-to-analog converter (ADC/DAC) architecture at the APs and low-resolution ADC/DACs at the UEs; and iii) spatially-correlated Rician channels. We then maximize the non-convex global energy efficiency metric by using the block minorization-maximization technique, which decomposes the main optimization into multiple convex surrogate sub-problems. We analytically show that the SE gain obtained with downlink training is limited for practical CF systems with RF and ADC/DAC impairments, Rician channel and pilot contamination. We also extensively investigate the impact of FD interferences on the downlink training gain.

Self-Interference Cancelation-Based Workload-Driven Duplex-Model Selection in Machine-Type Communication Networks

The machine-type communication (MTC) enables a broad range of applications from mission-critical services to massive deployment of autonomous devices in the Internet of Things (IoT) networks. To release more spectrum resources for facilitating the explosive traffic of MTC in ultradense Cellular-IoT (CIoT) networks, full-duplex (FD) technology has been considered as a candidate mechanic due to its respective advantages in terms of the spectral efficiency (SE). Compared with the traditional half-duplex (HD) technology, the FD technology can (in theory) attain twice the SE gain. Nevertheless, the performance of FD technology is severely limited by both the self-interference (SI) and the mutual interference (MI) in the presence of multiple users. What is more serious is that when FD technology is used in a multiuser communication system, the system performance is very sensitive to the service load of the entire network, and it may even appear fragile in an ultrahigh load environment. In this article, we will delve into investigating the performance of the FD technique in the CIoT networks by considering the impacts of a variety of aspects, including the SI cancelation capability (SICC) of the FD-mode devices, the network’s workload, as well as the devices’ distribution density (DDD), the purpose of which is to maximize both the SE and the sum throughput (ST) of the network by optimizing those critical parameters. It is shown that the FD mode is capable of improving the ST of the CIoT networks in either the low-traffic-volume or low-device-density regime, provided that the devices’ SICC could be up to 100dB. At the same time, research results show that further increasing both the devices’ density and the traffic load, even without compromising the FD devices’ SICC, will still help improve the performance superiority of FD technology. Numerical results show that by implementing an appropriate workload-driven mode-selection scheme, we can sufficiently exploit the FD/HD gain according to the instantaneous radio frequency environment.

Grouping Length Permutation Encapsulated Packets to Improve Spectral Efficiency

This letter proposes a novel transport-layer encapsulation scheme, which integrates the permutation-based transmission and the packet grouping to improve spectral efficiency. For a group of packets, each data unit (DU) is divided into three portions: permutation-conveyed DU (PCDU), grouping-conveyed DU (GCDU), and conventionally conveyed DU (CCDU). The PCDU is conveyed through the selection among various lengths for GCDU plus CCDU. The GCDU is used for forming sub-groups so that, in each sub-group, the GCDU is encapsulated into the first packet only while removed from the other packets. Unlike CCDUs, the delivery of PCDUs and removed GCDUs does not consume any communication resource. To promote the improvement of spectral efficiency, an algorithm based on the binary search tree is proposed for the GCDU design in each sub-group, and a lower bound on the number of bits in the removed GCDUs is achieved in closed-form. Illustrative numerical results substantiate the spectral efficiency gain obtained by our permutation-based scheme in comparison with the conventional transport-layer encapsulation.

Throughput Maximization for Wireless Powered Buffer-Aided Successive Relaying Networks

In this paper, we consider a wireless powered cooperative network, where two energy-constrained buffer-aided relays harvest energy from the source to assist the information delivery to the destination. To improve the spectral efficiency, a wireless powered successive relaying (WPSR) protocol is proposed to allow both relays to receive the information from the source and forward the buffered data to the destination, taking into consideration of the inter-relay-interference (IRI). We first reveal the impact of data buffers and energy storages at the relays by the achievable throughput comparison of the WPSR scheduling schemes with and without considering data buffers and energy storages. Our analysis validates the great potential of the buffer-and-forward data transmission and the harvest-store-use energy management strategies. Then, the network throughput is maximized via adaptive time and power allocation subject to the stability constraints of data buffers and energy buffers, and an adaptive wireless powered buffer-aided successive relaying (WPBSR) scheduling scheme is proposed. The proposed scheme approaches the optimal throughput of the wireless powered successive relaying network with bounded delay and finite length of data and energy buffers. Numerical results validate the analysis and noticeable spectral efficiency gain.

Cluster-Based Multi-Carrier Hybrid Beamforming for Massive Device Terahertz Communications

We propose a cluster-based multi-carrier beam division multiple access (MC-BDMA) scheme to enable massive device terahertz (THz) communications with the dynamic-subarray hybrid beamforming (HBF) architecture. This scheme is motivated by a limitation of conventional HBF architectures, i.e., the number of users cannot exceed the number of radio frequency (RF) chains. By exploiting the unique properties of THz channels, such as high distance-and-frequency dependence, high sparsity, and small angular spread, we propose multiple users in the same cluster to be supported by a single RF chain with distance-aware multi-carrier modulation. Moreover, we propose different clusters to be divided by BDMA that is accomplished through HBF design. In our proposed scheme, we first design a novel antenna selection and subarray partition algorithm for the dynamic-subarray HBF architecture to compensate for performance loss caused by diverse channels among users. We then design an algorithm for multi-carrier HBF which maximizes the achievable throughput and eliminates the inter-beam interference. Numerical results show that our proposed cluster-based MC-BDMA scheme with the dynamic-subarray HBF architecture provides an almost 100% spectral efficiency gain and a 60% energy efficiency gain over the conventional non-cluster HBF scheme in massive device communications.

Phase‐index correlation delay shift keying modulation

High-data-rate phase-orthogonality correlation-delay-shift-keying (HPO-CDSK) transmits 2-bits orthogonal signal in parallel and achieve higher spectral efficiency in the category of non-coherent detection chaotic systems. However, the spectral efficiency gain mainly relies on the sacrifice of energy efficiency. The study of HPO-CDSK is extended and phase-index CDSK (PI-CDSK) is proposed. By virtue of index modulation (IM) technology in PI-CDSK, according to the index bit, the orthogonal phase is used to distinguish the active carrier which bears the information bit. Since two bits (including index bit and information bit) are borne in one carrier, compared with HPO-CDSK, PI-CDSK can achieve double energy efficiency gain. Moreover, PI-CDSK can decrease noise interference and eliminate the intra-signal interference (ISI) component in demodulation. Results show that, with the same energy efficiency, PI-CDSK has better bit-error rate (BER) performance than that of CDSK, DCSK and their improved schemes.

An energy efficient resource allocation and transmit antenna selection scheme in mm‐wave using massive multiple‐input multiple‐output technology

SummaryThe massive multiple‐input multiple‐output (MIMO) improves the reliability of transmission and capacity of the channel. Resource allocation (RA) and transmit antenna selection (TAS) can minimize the complexity in implementation and hardware costs. In this research, both the RA and the TAS of wireless communication in millimeter‐wave (mm‐wave) with massive MIMO technology are considered. Two different solutions are developed for this research such as the deep learning method for efficient resource allocation process and optimization algorithm for TAS process. Here, the RA process is done with the help of attention‐based capsule auto‐encoder (ACAE) architecture which allocates the radio resources like power, space, time and frequency to all the available users in the system. Further, battle royale optimization (BRO) algorithm is utilized to select an efficient antenna from multiple antennas at BS. This optimization algorithm optimally selects an efficient antenna so that, user equipment (UEs) can create high quality links and achieves a reduced power consumption rate of the whole architecture. The overall system performance depends on the selection of optimal antenna which in terms enhances spectral efficiency (SE), energy efficiency (EE), reliability, and diversity gain of MIMO technology. In this way, both RA and optimal antenna selection schemes are performed to maximize the overall performance of wireless communication with massive MIMO technology for 5G wireless communication applications. The implementation of the proposed methodology is evaluated on MATLAB. Finally, the efficiency of the developed method is improved with respect to the capacity, EE, and SE.

Multilevel Pulse Position Modulation With Level Trimming for Electromagnetic Nanocommunications in the Terahertz Band

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Nanomachines</i> are submicrometer-scale devices led by nanotechnology that can perform simple sensing, local actuation, limited data processing, storage, and communication tasks in the terahertz (THz) band, that is, from 0.1-10 THz. Electromagnetic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nanocommunication</i> among nanomachines results in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nanonetwork</i> which could breakthrough promising applications in multiple domains such as software-defined metamaterials, in-body communication, and on-chip communication. This study adopts a modulation scheme for nanomachine communication based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multilevel</i> pulse position modulation (ML-PPM). The multilevel scheme uses several orthogonal codes and is combined with PPM to generate the final transmit signal consisting of several multilevels. In this paper, we propose a more advanced scheme called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">level trimming</i> to further boost the data rates of the ML-PPM scheme. Employing level-trimming, we transmit a fewer number of levels than required in ML-PPM, which will results in an spectral efficiency gain at the nanoreceiver. The simulation results reveal that the link capacity of the proposed scheme can be increased more than twofold using the level-trimming approach while the error rate performance remains better than the conventional ML-PPM. Moreover, the computational complexity of transceivers is reduced with the transmission of fewer levels, which is a significant necessity in nanocommunication. Also, although level-trimming causes artificial errors, it also improves the decoding performance by reducing the number of levels. We believe that the potential impact of this study will open doors for further investigations on various possible modulation formats for THz nanocommunication.

Globally Optimal Spectrum- and Energy-Efficient Beamforming for Rate Splitting Multiple Access

Rate splitting multiple access (RSMA) is a promising non-orthogonal transmission strategy for next-generation wireless networks. It has been shown to outperform existing multiple access schemes in terms of spectral and energy efficiency when suboptimal beamforming schemes are employed. In this work, we fill the gap between suboptimal and truly optimal beamforming schemes and conclusively establish the superior spectral and energy efficiency of RSMA. To this end, we propose a successive incumbent transcending (SIT) branch and bound (BB) algorithm to find globally optimal beamforming solutions that maximize the weighted sum rate or energy efficiency of RSMA in Gaussian multiple-input single-output (MISO) broadcast channels. Numerical results show that RSMA exhibits an explicit globally optimal spectral and energy efficiency gain over conventional multi-user linear precoding (MU-LP) and power-domain non-orthogonal multiple access (NOMA). Compared to existing globally optimal beamforming algorithms for MU-LP, the proposed SIT BB not only improves the numerical stability but also achieves faster convergence. Moreover, for the first time, we show that the spectral/energy efficiency of RSMA achieved by suboptimal beamforming schemes (including weighted minimum mean squared error (WMMSE) and successive convex approximation) almost coincides with the corresponding globally optimal performance, making it a valid choice for performance comparisons. The globally optimal results provided in this work are imperative to the ongoing research on RSMA as they serve as benchmarks for existing suboptimal beamforming strategies and those to be developed in multi-antenna broadcast channels.

FlexDRAN: Flexible Centralization in Disaggregated Radio Access Networks

Radio Access Network (RAN) disaggregation allows operators to mix-and-match multivendor components and bring RAN services from one end to the other. Despite this goal, issues of resource misuse or performance undershoot may arise because of inflexible RAN function deployment and uncoordinated decision-making across different network segments. To address these issues, this paper considers full flexibility in the synthesis of end-to-end RAN services from a set of disaggregated and uncoordinated components. In particular, five design factors are jointly considered to maximize the overall network spectral efficiency: (1) User association, (2) Remote radio unit clustering, (3) RAN functional split, (4) Fronthaul network routing, and (5) Baseband unit placement. To efficiently deal with the formulated problem, we propose a two-level turbo-based solution and compare its performance with several related works. The simulation results show that our proposed solution can not only achieve a 1.33-times spectral efficiency gain compared with state-of-the-art methods, but also provides 1.27 and 1.74 multiplexing benefits for computing and networking resources, respectively.

Novel detection schemes for generalized quadrature spatial modulation

SummarySpatial Modulation (SM) is a technology that incorporates digital modulation and coding with multiple antennas to achieve high data rates along with improved energy efficiency. Quadrature spatial modulation (QSM), which extends the transmitted signals to in‐phase and quadrature domains, has recently been proposed to improve spectral efficiency (SE) for SM. In this paper, we have considered a combination of Generalized QSM (GQSM) and Quasi Orthogonal Space Time Block Codes (QOSTBC). We propose a new detection scheme for the GQSM and use three different prevailing detection schemes for QOSTBC. We present Bit Error Rate (BER) versus average SNR performance for the combined system using M‐QAM constellations with quasi‐static spatially uncorrelated Rayleigh fading channels and computational complexity in terms of numbers of addition/multiplication and search time. We have compared the considered system with prevailing GQSM system and shown that the prevailing system, as per active antenna permutation (AAP) in GQSM, offers number of transmitted bits ranging from 2 to 9 whereas the proposed system offers number of transmitted bits ranging from 6 to 16 in the same bandwidth. Thus, there is a visible gain in SE. Furthermore, we have compared the BER performance of the considered system with the performance of maximum likelihood detector (MLD) and spatial modulation (SM). In the considered system, the QOSTBC improves the diversity gain. Hence, we have shown that the considered Scheme 3 substantially outperforms the SM. Further, the considered system with Scheme 3 has nearly similar BER performance with the maximum likelihood detector (MLD), which has higher computational complexity.