Interference Cancellation Technique Research Articles

Multistage interference cancellation for cyclic interleaved frequency division multiplexing

AbstractCyclic interleaved frequency division multiplexing (CIFDM), a variant of IFDM, has recently been proposed. While CIFDM employs cyclic interleaving at the transmitter to make multipath components resolvable at the receiver, the current approach of matched filtering followed by multipath combining does not fully exploit the diversity available. This is primarily because the correlation residues among the codes have a significant impact on multipath resolution. As a solution, we introduce a novel multipath successive interference cancellation (SIC) technique for CIFDM, which replaces the conventional matched filtering approach. We have examined the performance of this proposed CIFDM‐SIC technique and compared it with the conventional CIFDM‐matched filter bank and IFDM schemes. Our simulation results clearly demonstrate the superiority of the proposed scheme over the existing ones.

Wireless Powered Cooperative NOMA With Alamouti Coding and Selection Relaying

Nonorthogonal multiple access (NOMA) can facilitate simultaneous data transmissions towards multiple users by using the superposition coding and successive interference cancelation techniques, which can greatly improve the spectrum efficiency. Cooperative relaying and space-time coding can promisingly improve the communication robustness of poor-quality links by achieving the space-diversity gain. Energy harvesting (EH) can prolong the lifetime of energy-limited terminals and make them work continuously. In order to enhance the spectrum efficiency as well as the communication quality, we enable a cluster of EH relays to assist the data transmissions from a base station (BS) to two far-users by using the Alamouti coding based cooperative NOMA strategy. The relays are capable of harvesting wireless energy from a power beacon as well as BS by using time-switching or power-splitting method. According to the energy status and the data decoding status, one relay is selected in a distributed manner according to either Max-min, Max-sum, or Random criterion. We analyze the transmission success probability and the system throughput performance. Extensive simulations are performed to compare the performance of different EH-based space-time coded cooperative NOMA with various relay selection schemes as well as the counterpart orthogonal transmission schemes.

Analysis of Multiuser Detectors and Performance improvement in DS-CDMA system using Multistage Multiuser Detection Techniques

The Direct Sequence Code Division Multiple Access (DS-CDMA) system faces several disruptions, this is most crucial of which is the Multi Access Interference (MAI) caused by its users. The efficiency of the system gradually declines as every quantity rises, and the MAI rises, especially in faded environments. This work proposes a multiple-phase multiuser identification approach called Differencing Partial Parallel Interference Cancellation (DPPIC), which improves the overall efficiency. The methods known as Differencing Parallel Interference Cancellation (DPIC) and Partial Parallel Interference Cancellation (PPIC) are combined in this methodology. Current solutions for Parallel Interference Cancellation (PIC) and PPIC have enhanced overall effectiveness; however, this has come at the expense of increasing the complexity of computation. As the variety of consecutive stages grows, the MAI falls. Using the DPIC approach may reduce the computational burden without improving system functionality. The use of the Partial Differencing Parallel Interference Cancellation (PDPIC) technique can enhance system performance while lowering the level of complexity. According to the simulation findings, Bit Error Rate (BER) vs normalized signal strength (i.e., Eb / N0) performs more effectively for the suggested DPPIC approach than for PIC, the PPIC, and PDPIC.

Nonlinear energy harvesting based alternate cooperative nonorthogonal multiple access with adaptive interference cancellation

We consider an energy-limited network, where a source node intends to communicate with two energy harvesting (EH) near-users and one far-user. We design a nonlinear EH-based alternate cooperative nonorthogonal multiple access (CNOMA) scheme, where the source node alternately superimposes the data of each near-user and the far-user and broadcasts the composite signal. When one near-user decodes the data of both the far-user and itself by using the adaptive interference cancellation technique, the other near-user can harvest energy from the received signal. The two near-users alternately and adaptively forward data to the far-user. We properly model the energy statuses of both the two near-users by defining a group of unequally-spaced discrete energy levels. We analyze the average success probability and throughput of each user, and use the differential evolution (DE) algorithm to efficiently achieve the maximum system throughput by jointly optimizing both the time and power allocation factors. We also present two benchmark EH-based transmission schemes for comparison. Extensive simulations are conducted to validate our theoretical analysis and reveal the impacts of various parameters.

On-Demand Coordinated Spectrum and Resource Provisioning Under an Open C-RAN Architecture for Dense Small Cell Networks

Mobile networks have seen more than a thousand times traffic increases in the past decade. Network operators face an ever increasing demand and cost to deploy denser networks in order to maintain the quality of services. In view of this paradigm shift, an open cloud radio access network (C-RAN) service model is studied in this work. Integrating the cross-layer functions of spectrum resource sharing, channel and power allocation, and interference management in a C-RAN architecture, we explore the feasibility of providing a scalable yet efficient broadband wireless service with dense small cell networks (DSN). The proposed methods and architecture can be applied to DSN that support the functions of (further enhanced) inter-cell interference cancelation (feICIC/ICIC) techniques of 3GPP standard, and have the potential to provide a cost-effective solution for high-quality DSN. Simulation results across an area of 100 km <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> show that the proposed scheme can offer an aggregate downlink throughput of 21 Gbps over a maximum channel bandwidth of 20 MHz, which is 5 times the throughput with a typical ICIC method. Moreover, the service satisfaction degree can reach 85% under the proposed C-RAN architecture, making it a promising and cost-effective solution for future broadband wireless services.

Deep Reinforcement Learning-Based Power Allocation for Minimizing Age of Information and Energy Consumption in Multi-Input Multi-Output and Non-Orthogonal Multiple Access Internet of Things Systems.

Multi-input multi-output and non-orthogonal multiple access (MIMO-NOMA) Internet-of-Things (IoT) systems can improve channel capacity and spectrum efficiency distinctly to support real-time applications. Age of information (AoI) plays a crucial role in real-time applications as it determines the timeliness of the extracted information. In MIMO-NOMA IoT systems, the base station (BS) determines the sample collection commands and allocates the transmit power for each IoT device. Each device determines whether to sample data according to the sample collection commands and adopts the allocated power to transmit the sampled data to the BS over the MIMO-NOMA channel. Afterwards, the BS employs the successive interference cancellation (SIC) technique to decode the signal of the data transmitted by each device. The sample collection commands and power allocation may affect the AoI and energy consumption of the system. Optimizing the sample collection commands and power allocation is essential for minimizing both AoI and energy consumption in MIMO-NOMA IoT systems. In this paper, we propose the optimal power allocation to achieve it based on deep reinforcement learning (DRL). Simulations have demonstrated that the optimal power allocation effectively achieves lower AoI and energy consumption compared to other algorithms. Overall, the reward is reduced by 6.44% and 11.78% compared the to GA algorithm and random algorithm, respectively.

A V2I and V2V Collaboration Framework to Support Emergency Communications in ABS-Aided Internet of Vehicles

Providing reliable and flexible emergency communications for the Internet of Vehicles (IoV) is challenging in post-disaster scenarios, especially when communications infrastructures are unavailable or seriously damaged. Under such circumstances, it is highly demanded to deploy the air base station (ABS) to restore connectivity in disaster areas. Although the ABS can offer improved performance, the massive access of communications devices and the rising demand for many emergency-related services will bring new challenges to the ABS-aided IoV. In this paper, we propose a vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) collaboration framework to support emergency communications. Specifically, we incorporate non-orthogonal multiple access (NOMA) into the ABS-aided IoV. By using the successive interference cancellation (SIC) technique, V2I and V2V links can operate in four spectrum reuse modes, which are the extension of the conventional underlaying reuse mode. To fully exploit the advantages of the NOMA enhanced collaboration framework, we formulate a total power consumption minimization problem by jointly considering the mode selection, power control, and the channel state information (CSI) latency. The formulated problem is constrained by the reliability and rate requirements, and the decoding threshold of the SIC technique. Due to the NP-hard characteristics of the formulated problem, we decouple it first, and then adopt the graphical method to derive the analytical solution of optimal power control policies. Afterwards, we can get every feasible spectrum reuse mode. Among the feasible modes, we select the mode with minimum power consumption as the optimal mode. Finally, simulation results demonstrate that our proposed algorithm outperforms the current works in term of the total power consumption, e.g., by adopting the ABS and the NOMA technology, the total power consumption can be reduced by 22.3% and 40.9%, respectively.

MREDNet: A Multi-Residual Encoder-Decoder Network for recovering LFM interfered by intra-pulse retransmission interferences

Retransmission interference in radar and communication signal processing significantly threatens the received linear frequency modulation signal. With the advancement in the hardware capabilities of interference devices, they can intercept and retransmit signals within the duration of a single pulse. Therefore, we refer to this type of interference as intra-pulse retransmission interference, which includes retransmission modes smeared spectrum, interrupted sampling repeater jamming, and interrupted nonuniform sampling repeater jamming. Although traditional interference cancellation techniques offer optimal solutions under the assumption of fully known interference parameters, practical scenarios often involve unknown parameters, estimation errors, random parameters, and multiple interferences, leading to performance degradation. To address these problems, we propose a Multi-Residual Encoder-Decoder Network (MRDENet), a novel approach for effectively mitigating intra-pulse retransmission interferences and recovering the signal of linear frequency modulation. In contrast to parameter estimation and interference cancellation, MREDNet extracts crucial features of both the target and interference and performs reconstruction. More specifically, MREDNet acquires information about the retransmission pattern of interference and the modulation frequency through its multi-level residual shortcut connections. To address the issue of network parameters being real while the signals are complex, we devised decomplexification—a data preprocessing approach enabling neural networks for direct utilization in front-end complex signal processing. Besides, we evaluate the performance of MREDNet comprehensively. The experimental conditions encompassed single and multiple interferences, incomplete data sets, varying signal-to-noise ratios, and jamming-to-signal ratios. The computational simulations demonstrate that MREDNet successfully solves the problem of recovering linear frequency modulation signals interfered with by intra-pulse retransmission interference. According to the boundary condition of MREDNet, the proposed method exhibits valuable applications in real-world scenarios.

An UAV and EV based mobile edge computing system for total delay minimization

In this paper, we studied a collaborative mobile edge computing(MEC) system based on unmanned aerial vehicle(UAV) and electric vehicle(EV), in which the EV moves to the target area carrying the UAV to provide computing and offloading services to the user equipment(UE). We aimed at minimizing all the UE tasks delay by optimizing the task offloading ratio, the UAV hovering position, and the computing resource allocation of EV and UAV, respectively. The problem was formulated as a Nonlinear Programming(NLP) problem, and we decomposed it into EV related subproblem and UAV related subproblem by the Block-Coordinate Descent(BCD) method. For EV related subproblem, we obtained the optimal offloading ratio by making the computing time on the UAV equal to its offloading time, and proved the method is feasible. For the UAV related subproblem, we introduced the non-orthogonal multiple access(NOMA) and successive interference cancellation(SIC) techniques to improve the communication efficiency, and we obtained the optimal hovering position by using the successive convex approximation(SCA) technique twice to transfer this subproblem into a convex problem. Finally, an overall iterative algorithm was proposed. To verify the effectiveness of our algorithm, we compared our scheme with ‘PSO’, ‘GT’, ‘MC’ and benchmark algorithm.

Modeling and analysis for group delay mismatch effect on wideband adaptive spatial interference cancellation

The adaptive interference cancellation technique has been widely utilized in radar, GPS, data link, etc., systems to address challenges from external interference, such as co-site and hostile interference. Since the anti-jamming performance of the adaptive interference cancellation technique is sensitive to group delay mismatch between channels, the group delay mismatch becomes one of the main factors that limit the system’s anti-jamming capability. However, the traditional adaptive interference cancellation system’s mathematical model cannot quantitatively characterize the group delay mismatch effect on the wideband interference cancellation performance. In this paper, the mathematical model of the wideband adaptive spatial interference cancellation (ASIC) system is established, which considers the group delay mismatch, to quantitatively analyze the impact of group delay mismatch on the hostile interference cancellation. The mathematical model utilizes the weighted multi-tone signals to fit the wideband interference, and then, delay differences are attached to each tone signal to simulate the group delay mismatch. Then, the analytic expressions of weight and interference cancellation ratio are derived, which consider the interference bandwidth and group delay mismatch, to quantitatively analyze the group delay mismatch effect on the anti-jamming performance of the wideband ASIC system. Simulation results indicate that the theoretical analysis based on the mathematical model of wideband ASIC system are accurate, which can achieve the quantitative analysis of the group delay mismatch effect on the WIC performance.

Integration of NOMA With Reflecting Intelligent Surfaces: A Multi-Cell Optimization With SIC Decoding Errors

Reflecting intelligent surfaces (RIS) has gained significant attention due to its high energy and spectral efficiency in next-generation wireless networks. By using low-cost passive reflecting elements, RIS can smartly reconfigure the signal propagation to extend the wireless communication coverage. On the other hand, non-orthogonal multiple access (NOMA) has been proven as a key air interface technique for supporting massive connections over limited resources. Utilizing the superposition coding and successive interference cancellation (SIC) techniques, NOMA can multiplex multiple users over the same spectrum and time resources by allocating different power levels. This paper proposes a new optimization scheme in a multi-cell RIS-NOMA network to enhance the spectral efficiency under SIC decoding errors. In particular, the power budget of the base station and the transmit power of NOMA users while the passive beamforming of RIS is simultaneously optimized in each cell. Due to objective function and quality of service constraints, the joint problem is formulated as non-convex, which is very complex and challenging to obtain the optimal global solution. To reduce the complexity and make the problem tractable, we first decouple the original problem into two sub-problems for power allocation and passive beamforming. Then, the efficient solution of each sub-problem is obtained in two-steps. In the first-step of For power allocation sub-problem, we transform it to a convex problem by the inner approximation method and then solve it through a standard convex optimization solver in the second-step. Accordingly, in the first-step of passive beamforming, it is transformed into a standard semi-definite programming problem by successive convex approximation and different of convex programming methods. Then, penalty based method is used to achieve a Rank-1 solution for passive beamforming in second-step. Numerical results demonstrate the benefits of the proposed optimization scheme in the multi-cell RIS-NOMA network.

High-Bit-Rate Transmission in Visible Light Communication System Based on Adaptive Successive Interference Cancellation Technique

High-Bit-Rate Transmission in Visible Light Communication System Based on Adaptive Successive Interference Cancellation Technique

Large-Scale Rate-Splitting Multiple Access in Uplink UAV Networks: Effective Secrecy Throughput Maximization Under Limited Feedback Channel

Unmanned aerial vehicles (UAVs) are capable of improving the performance of next generation wireless systems. However, their communication performance is prone to both channel estimation errors and potential eavesdropping. Hence, we investigate the effective network secrecy throughput (ENST) of the uplink UAV network, in which rate-splitting multiple access (RSMA) is employed by each legitimate user for secure transmission under the scenario of massive access. To maximize the ENST, the transmission rate versus power allocation relationship is formulated as a max-min optimization problem, relying on realistic imperfect channel state information (CSI) of both the legitimate users and passive eavesdroppers ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Eves$</tex-math></inline-formula> ). In the model considered, each user transmits a superposition of two messages to a UAV base-stations (UAV-BS), each having different transmit power and the UAV-BS adopts a successive interference cancellation (SIC) technique to decode the received messages. Given the non-convexity of the problem, it is decoupled into a pair of sub-problems. In particular, we derive a closed form expression for the optimal rate-splitting fraction of each user. Then, given the optimal rate-splitting fraction of each user, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -constrainted transmit power of each user is calculated by harnessing sequential parametric convex approximation (SPCA) programming. Our simulation results confirm that the scheme conceived significantly improves the ENST compared to both the existing orthogonal and non-orthogonal benchmarks.

Perfect and imperfect successive interference cancellation for channel decoding in downlink visible light non-orthogonal multiple access network

This paper exploits perfect and imperfect-successive interference cancellation (SIC) technique for channel decoding in downlink visible light-non-orthogonal multiple access (VL-NOMA) communication networks. A light-emitting-diode (LED) is used as a carrier transmitting signal to the two users (near- and far-user) as they are placed at different locations in the indoor environment. The power allocation technique uses power levels to effectively distribute information to the two users while the superposition coding (SC) encodes the two signals into one simultaneously. The SIC decodes the interference notice in the sub-channels until each user receives a better signal strength with the same resources (at the same time and frequency). The imperfect SIC which is a practical scenario, is experienced during the decoding process when a fraction residue at the channel-information properties is left due to an SIC error. This is because the VL channel properties uncertainty creates an interference when the NOMA is deployed with imperfect SIC to decode the signal, while the NOMA with perfect SIC is taken based on known channel pieces of information. The NOMA communication systems with imperfect SIC performs better than that of the perfect SIC in terms of bit-error-rate (BER). We have observe changes in the near-user and far-user at the channel, then SC is performed. MATLAB software tool is used for the simulation results.

Jamming Signal Cancellation by Channel Inversion Power Control for Preserving Covert Communications

Uninformed jammers are used to facilitate covert communications between a transmitter and an intended receiver under the surveillance of a warden. In reality, the signals the uniformed jammer emits to make the warden’s decision uncertain have inadvertently interfered with the detection of the intended receiver. In this paper, we apply truncated channel inversion power control (TCIPC) to both the transmitter and the uninformed jammer. The TCIPC scheme used on the uninformed jammer may help the intended receiver remove jamming signals using the successive interference cancellation (SIC) technique. Under the assumption that the warden knows the channel coefficient between two intended transceivers and achieves the optimal detection power threshold, we form the optimization problem to maximize the effective transmission rate (ETR) under covertness and decoding constraints. With the aim of enhancing covertness-related performance, we achieve the optimal power control parameters and determine system parameter-related constraints required for the existence of these solutions. According to the simulations, the use of the TCIPC scheme on the uninformed jammer significantly improves covertness-related performance in comparison to that of random power control (RPC) and constant power control (CPC) schemes. In addition, simulation results show that, for the TCIPC scheme: 1) the maximum ETR tends to converge as the transmitter’s or the uninformed jammer’s maximum transmit power increases, and 2) there exists an optimal value of the transmitter’s predetermined transmission rate to achieve the optimal performance.

C-FD MAC: Constellation based Full-Duplex MAC protocol

Simultaneous transmission and reception of data packets in Full-Duplex (FD) communication can theoretically double the system throughput. However, efficient self-interference mitigation and symbol detection at the receiver is required to realize the full potential of FD communication in practical scenarios. In this paper, we propose a constellation-based FD medium access control (MAC) protocol (C-FD) for wireless local area networks (WLAN). In the proposed C-FD MAC protocol, the node and Access Point (AP) can exchange packets simultaneously by transmitting the symbols and using different modulation schemes. The superimposed constellation diagram has been used to decode the symbols efficiently at both ends. The novel feature of the C-FD MAC protocol is that it does not require any complicated interference cancellation techniques. The analytical results for Symbol Error Rate (SER), Packet Error Rate (PER), and throughput have been derived for the symmetrical and asymmetrical FD link considering the additive white Gaussian noise (AWGN) with Rayleigh fading. The proposed C-FD MAC protocol achieves nearly twice the throughput compared to Half duplex (HD) communication without compromising the SER. The analytical results have been validated with extensive simulation.

Fusion of artificial intelligence and game theory for resource allocation in non‐orthogonal multiple access‐assisted device‐to‐device cooperative communication

SummaryDevice‐to‐device (D2D) communication offers a low‐cost paradigm where two devices in close proximity can communicate without needing a base station (BS). It significantly improves radio resource allocation, channel gain, communication latency, and energy efficiency and offers cooperative communication to enhance the weak user's network coverage. The cellular mobile users (CMUs) share the spectral resources (e.g., power, channel, and spectrum) with D2D mobile users (DMUs), improving spectral efficiency. However, the reuse of radio resources causes various interferences, such as intercell and intracell interference, that degrade the performance of overall D2D communication. To overcome the aforementioned issues, this paper presents a fusion of AI and coalition game for secure resource allocation in non‐orthogonal multiple access (NOMA)‐based cooperative D2D communication. Here, NOMA uses the successive interference cancellation (SIC) technique to reduce the severe impact of interference from the D2D systems. Further, we utilized a coalition game theoretic model that efficiently and securely allocates the resources between CMUs and DMUs. However, in the coalition game, all DMUs participate in obtaining resources from CMUs, which increases the computational overhead of the overall system. For that, we employ artificial intelligence (AI) classifiers that bifurcate the DMUs based on their channel quality parameters, such as reference signal received power (RSRP), received signal strength indicator (RSSI), signal‐to‐noise ratio (SNR), and channel quality indicator (CQI). It only forwards the DMUs that have better channel quality parameters into the coalition game, thus reducing the computational overhead of the overall D2D communication. The performance of the proposed scheme is evaluated using various statistical metrics, for example, precision score, accuracy, recall, F1 score, overall sum rate, and secrecy capacity, where an accuracy of 99.38% is achieved while selecting DMUs for D2D communication.

Performance Modeling and Analysis of BASUR-CARQ Protocol Based on Interrelay Interference Cancellation

To recover the performance loss of nodes in half-duplex mode, this paper proposes a buffer-aided successive relay protocol based on cooperative automatic repeat request mechanism (BASUR-CARQ). Based on the fact that interrelay interference (IRI) will occur when relay nodes transmit or receive simultaneously, an interference cancellation operator is proposed to determine whether the interference is eliminated to reduce the outage probability. Moreover, a delay model for data frame transmission is proposed based on CARQ mechanism, and a closed-form expression for the average delay is derived. A 6-state discrete-time Markov chain (DTMC) model is developed to obtain the system throughput, and a closed-form expression for the system energy efficiency under M-ary modulation is derived. Finally, the simulation results show that with the setting of parameters that can balance the main performance, the delay performance of BASUR-CARQ protocol is significantly enhanced compared to the traditional protocols, and the throughput of BASUR-CARQ protocol is also optimized at high signal-to-noise ratio (SNR). Meanwhile, the energy efficiency of BASUR-CARQ protocol is significantly improved for the successive relay communication system without interference cancellation technique.

On the outage performance of multi-user NOMA system with adaptive SIC

The performance of non-orthogonal multiple-access (NOMA) is significantly determined by the successive interference cancellation (SIC) technique. However, the interference redundancy can happen during the NOMA receiver performing SIC. To tackle this issue, this paper proposes a novel adaptive successive interference cancellation (ASIC) method. Specifically, the decoding error is considered as interference during the SIC process, and an adaptive filter with various weights is introduced for detection error mitigation. For the multi-user downlink NOMA system, the outage probability is analyzed under conventional and the proposed SIC methods. Simulation results verify our analysis, which also demonstrate the superiority of the proposed ASIC method.

An Investigation of the Optimum Power Allocation Technique in the MIMO-NOMA Network with the Deep Neural Network and Depth Limited Search Algorithm

The non-orthogonal multiple-access (NOMA) technique affords massive user scaling, industrial use for mission-critical communication, and multi-user shared in a single resource block. Because of these merits, it is a scheme that future technologies will prefer over orthogonal multiple access (OMA). This research study focuses on a comparative analysis of several power allocation algorithms (PAA) to identify the PAA that is most suited for the “MIMO-NOMA” technology. The investigation is done using two different methods. First, an analysis is conducted to take into account direct approaches, followed by PAA and the Deep Neural Network (DNN) and the Depth Limited Search Algorithm (DLSA). Both techniques are tested considering two-user and multi-user scenarios in a MIMO-NOMA network. Optimum power allocation (OPA) to the weaker user terminal is a complex task in a real environment; moreover, the appropriate distribution of access point (AP) source power is constrained by the successive interference cancellation (SIC) technique. The above issues can be resolved with the help of the Deep Neural Network (DNN) and DLSA, where the stronger signal user gets minimal power and the poorer signal user gets optimal power. Based on the DLSA, the “DNN-MIMO-NOMA” system helps user terminals to receive their signals more accurately and without noise. For the successful implementation of SIC, the DLSA combines MIMO-NOMA with deep learning. The location of the user terminal is predicted by the DLS and depending on where the user terminal is located; optimal power allocation is done. The simulation results show that the PAA, combining DNN and the DLSA, outperforms traditional power allocation technique.