Contention-Based Nonorthogonal Massive Access with Massive MIMO

The sporadic communication character of massive machine-type communication systems provides natural advantages to utilize the principle of compressive sensing(CS).However,due to the high computational complexity of CS algorithms,CS-based contention-free access schemes have limited scalability and high computational complexity for massive access with user-specific pilots.To address these problems,in this paper,we propose a new contention-based scheme for CSbased massive access,which can support the sporadic access of massive devices(more than one million devices)with limited resources.Furthermore,an advanced receiver algorithm is designed to solve the optimal solutions for the proposed scheme,which utilizes various prior information to enhance the performance.In specific,the joint sparsity between the channel and data is used to improve the accuracy of pilot detection,and the information of modulation and cyclic redundancy check is exploited for channel correction to improve the performance of data recovery.The simulation results show that the proposed scheme can achieve improved active user detection performance and data recovery accuracy than existing methods.

Enhanced Power Choice Barring Scheme For Massive MTCs With Grant-Free NOMA

Massive machine-type communication(m MTC)is a typical application scenario of the fifth generation(5G)mobile communications.To keep the m MTC reliable and minimize the energy consumption of the m MTC devices,this paper proposes an enhanced power choice barring(EPCB)scheme based on the distributed layered grant-free non-orthogonal multiple access(NOMA)framework,where the cell is divided into different layers according to a predetermined power levels.The proposed EPCB scheme not only combines the grant-free strategy with the sleep mode to reduce the energy consumption,but also designs a power level choosing strategy to increase the access success probability.Simulation results show that when compared with existing schemes,the proposed EPCB scheme has better performance in the aspects of the access success probability and energy efficiency.

Massive Unsourced Random Access Under Carrier Frequency Offset

Unsourced random access(URA)is a new perspective of massive access which aims at supporting numerous machine-type users.With the appearance of carrier frequency offset(CFO),joint activity detection and channel estimation,which is vital for multiple-input and multiple-output URA,is a challenging task.To handle the phase corruption of channel measurements under CFO,a novel compressed sensing algorithm is proposed,leveraging the parametric bilinear generalized approximate message passing framework with a Markov chain support model that captures the block sparsity structure of the considered angular domain channel.An uncoupled transmission scheme is proposed to reduce system complexity,where slot-emitted messages are reorganized relying on clustering unique user channels.Simulation results reveal that the proposed transmission design for URA under CFO outperforms other potential methods.

Uplink Grant-Free Pattern Division Multiple Access (GF-PDMA) for 5G Radio Access

Massive machine type communication(m MTC) is one of the key application scenarios for the fifth generation mobile communication(5 G). Grant-free(GF) transmission can reduce the high signaling overhead in m MTC. Non-orthogonal multiple access(NMA) can support more users for m MTC than orthogonal frequency division multiple access(OFDMA). Applying GF transmission in NMA system becomes an active topic recently. The in-depth study on applying GF transmission in pattern division multiple access(PDMA), a competitive candidate scheme of NMA, is investigated in this paper. The definition, latency and allocation of resource and transmission mechanism for GF-PDMA are discussed in detail. The link-level and system-level evaluations are provided to verify the analysis. The analysis and simulation results demonstrate that the proposed GF-PDMA has lower latency than grant based PDMA(GB-PDMA), possesses strong scalability to confront collision and provides almost 2.15 times gain over GF-OFDMA in terms of supporting the number of active users in the system.

Distributed Throughput and Energy Efficient Resource Optimization When D2D and Massive MIMO Coexist

Fifth generation(5G)cellular networks intend to overcome the challenging demands posed by dynamic service quality requirements,which are not achieved by single network technology.The future cellular networks require efficient resource allocation and power control schemes that meet throughput and energy efficiency requirements when multiple technologies coexist and share network resources.In this paper,we optimize the throughput and energy efficiency(EE)performance for the coexistence of two technologies that have been identified for the future cellular networks,namely,massive multiple-input multiple-output(MIMO)and network-assisted device-to-device(D2D)communications.In such a hybrid network,the co/cross-tier interferences between cellular and D2D communications caused by spectrum sharing is a significant challenge.To this end,we formulate the average sum rate and EE optimization problem as mixed-integer non-linear programming(MINLP).We develop distributed resource allocation algorithms based on matching theory to alleviate interferences and optimize network performance.It is shown in this paper that the proposed algorithms converge to a stable matching and terminate after finite iterations.Matlab simulation results show that the proposed algorithms achieved more than 88%of the average transmission rate and 86%of the energy efficiency performance of the optimal matching with lower complexity.

Reconfigurable intelligent surface assisted grant-free massive access

Massive machine-type communications(mMTC)is envisioned to be one of the pivotal scenarios in the fifth-generation(5G)wireless communication,where the explosively emerging Internet-of-Things(IoT)applications have triggered the demand for services with low-latency and high-reliability.To this end,grant-free random access paradigm has been proposed as a promising enabler in simplifying the connection procedure and significantly reducing access latency.In this paper,we propose to leverage the burgeoning reconfigurable intelligent surface(RIS)for grant-free massive access working at millimeter-wave(mmWave)frequency to further boost access reliability.By attaching independently controllable phase shifts,reconfiguring,and refracting the propagation of incident electromagnetic waves,the deployed RISs could provide additional diversity gain and enhance the access channel conditions.On this basis,to address the challenging active device detection(ADD)and channel estimation(CE)problem,we develop a joint-ADDCE(JADDCE)method by resorting to the existing approximate message passing(AMP)algorithm with expectation maximization(EM)to extract the structured common sparsity in traffic behaviors and cascaded channel matrices.Finally,simulations are carried out to demonstrate the superiority of our proposed scheme.