Unsourced Random Access

In massive access scenarios, an unknown subset of a very large number of wireless devices transmit small packets to the base station (BS) in an uncoordinated manner. These scenarios have been a hot research topic in the context of 5G/Beyond-5G as they pose many challenges and require the design of highly efficient and lightweight communication protocols. This has inspired the paradigm of unsourced random access (U-RA) as a way to simplify multi-user decoding and reduce overhead in the uplink. However U-RA, being mainly a physical layer approach, lacks the ability to identify and authenticate users, which needs to be taken into account. Another important functionality is the transmission of acknowledgements (ACK) in the downlink. The naïve method of sending a dedicated message to each user may not be feasible in the massive random access scenario, thus a solution is to provide jointly encoded ACKs, which can achieve much higher efficiency at the cost of introducing false positives. In this paper we consider a system that combines U-RA with joint ACKs transmitted in the downlink. We focus on the systematic description and analysis of the false positives, as well as the design options, and the associated trade-offs among reliability, rate of retransmissions and power efficiency.

We consider the problem of unsourced random access (U-RA), a grant-free uncoordinated form of random access, in a wireless channel with a massive MIMO base station equipped with a large number $M$ of antennas and a large number of wireless single-antenna devices (users). We consider a block fading channel model where the $M$-dimensional channel vector of each user remains constant over a coherence block containing $L$ signal dimensions in time-frequency. In the considered setting, the number of potential users $K_\text{tot}$ is much larger than $L$ but at each time slot only $K_a \ll K_\text{tot}$ of them are active. Previous results, based on compressed sensing, require that $K_a < L$, which is a bottleneck in massive deployment scenarios such as Internet-of-Things and U-RA. In the context of activity detection it is known that such a limitation can be overcome when the number of base station antennas $M$ is sufficiently large and a covariance based recovery algorithm is employed at the receiver. We show that, in the context of U-RA, the same concept allows to achieve high spectral efficiencies in the order of $\mathcal{O}(L \log L)$, although at an exponentially growing complexity. We show also that a concatenated coding scheme can be used to reduce the complexity to an acceptable level while still achieving total spectral efficiencies in the order of $\mathcal{O}(L/\log L)$.

Identification and authentication are two essential features for traditional random access protocols. In ALOHA-based random access, the packets usually include a field with a unique user address. However, when the number of users is massive and relatively small packets are transmitted, the overhead of including such field becomes restrictive. In unsourced random access (U-RA), the packets do not include any address field for the user, which maximizes the number of useful bits that are transmitted. However, by definition an U-RA protocol does not provide user identification. This letter presents a scheme that builds upon an underlying U-RA protocol and solves the problem of user identification and authentication. In our scheme, the users generate a message authentication code (MAC) that provides these functionalities without violating the main principle of unsourced random access: the selection of codewords from a common codebook is i.i.d. among all users.

In this paper, the massive random access scenario that a part of users transmit both common and user-specific information and the others transmit only common information is considered, and a mixed massive random access scheme is proposed. In the proposed scheme, the common information is transmitted by unsourced random access (URA) technique, and the user-specific information by index-modulated (IM)-aided sourced random access (SRA). Specifically, the concatenated scheme consisting of inner compressed sensing codebook and outer parity-check code is employed in URA, and IM-aided non-coherent grant-free non-orthogonal multiple access scheme is employed in SRA. The URA and SRA signals are superposed at the transmitter of users who transmit both common and user-specific information. At the receiver, first, the covariance-based non-Bayesian multiuser detection is employed to estimate the indices of active codewords related with common information in each block, and both user activity and transmit data related with user-specific information. Further, the estimated indices of active codewords in different blocks are stitched based on the outer-code parity to recover the common information. Finally, simulation results show that the proposed mixed scheme can work well in the system without channel state information including large- and small-scaling fading coefficients at the receiver.

Massive machine-type communications became the new communication paradigm of the future wireless networks, connecting millions of devices per one base station. Here, with the large number of sensors, device identification becomes infeasible, and existing grant-based transmission protocols should be replaced with grant-free ones or random access protocols. As a result, in this approach the same codebook should be used for all active users, which results in the unsourced random access (URA) paradigm, suitable for many Internet of Things (IoT) scenarios. This paper investigates URA problem over Rayleigh block-fading channels with multiple antenna receivers. We compare several Rayleigh fading channel MIMO approaches. A pilot-based treat interference as noise with successive interference cancellation (TIN-SIC) technique that works well in a MIMO environment with unknown channel coefficients is mainly considered. T− fold ALOHA scheme is used as random access technique. We compare our investigation with a coded compressed sensing (CCS) scheme. We describe our findings with the help of minimum energy per bit (E <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</inf> /N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> ) necessary to reach the desired per-user probability of error(PUPE).

Multiple access communication systems enable numerous users to share common communication resources, playing a crucial role in wireless networks. With the emergence of the sixth generation (6G) and beyond communication networks, supporting massive machine-type communications with sporadic activity patterns is expected to become a critical challenge. Unsourced random access (URA) has emerged as a promising paradigm to address this challenge by decoupling user identification from data transmission through the use of a common codebook. This survey offers a comprehensive overview of URA solutions, encompassing both theoretical foundations and practical applications. We present a systematic classification of URA solutions across three primary channel models: Gaussian multiple access channels (GMACs), single-antenna fading channels, and multiple-input multiple-output (MIMO) fading channels. For each category, we analyze and compare state-of-the-art solutions in terms of performance, complexity, and practical feasibility. Additionally, we discuss critical challenges such as interference management, computational complexity, and synchronization. The survey concludes with promising future research directions and potential methods to address existing limitations, providing a roadmap for researchers and practitioners in this rapidly evolving field.

Unsourced random access (URA) is a recently proposed multiple access paradigm tailored to the uplink channel of machine-type communication networks. By exploiting a strong connection between URA and compressed sensing, the massive multiple access problem may be cast as a compressed sensing (CS) problem, albeit one in exceedingly large dimensions. To efficiently handle the dimensionality of the problem, coded compressed sensing (CCS) has emerged as a pragmatic signal processing tool that, when applied to URA, offers good performance at low complexity. While CCS is effective at recovering a signal that is sparse with respect to a single basis, it is unable to jointly recover signals that are sparse with respect to separate bases. In this article, the CCS framework is extended to the demixing setting, yielding a novel technique called coded demixing. A generalized framework for coded demixing is presented and a low-complexity recovery algorithm based on approximate message passing (AMP) is developed. Coded demixing is applied to heterogeneous multi-class URA networks and traditional single-class networks. Its performance is analyzed and numerical simulations are presented to highlight the benefits of coded demixing.

We consider the unsourced random access problem on a Rayleigh block-fading AWGN channel with multiple receive antennas. Specifically, we treat the slow fading scenario where the coherence blocklength is large compared to the number of active users and a message can be transmitted in a single fading coherence block. Unsourced random access refers to a form of grant-free random access where users are constrained to use the same codebook and therefore are a priori indistinguishable. The receiver must recover the list of transmitted messages up to permutations. In this paper, we propose an approach based on splitting the user messages into two parts. First, a small block of bits selects a relatively short codeword from a common “pilot” codebook. Then the remaining message bits are encoded by a standard block code for the Gaussian channel. The receiver makes use of a multiple measurement vector approximate message passing (MMV-AMP) algorithm to estimate the active user channels from the “pilot” part, and then uses the estimated channels to perform coherent maximum ratio combining (MRC) to decode the second part. We provide an accurate closed-form approximated analysis of the proposed scheme. Furthermore, we analyze the MRC decoding when successive interference cancellation is performed over groups of users, striking an attractive tradeoff between complexity and performance. Finally, we investigate the impact of power control policies, taking into account the unique nature of massive random access. As a byproduct, we also present an extension of the MMV-AMP algorithm which allows pathloss coefficients to be treated as deterministic unknowns by performing maximum likelihood estimation in each step of the MMV-AMP algorithm.

This paper investigates the massive random access for a huge amount of user devices served by a base station (BS) equipped with a massive number of antennas. We consider a grant-free unsourced random access (U-RA) scheme where all users possess the same codebook and the BS aims at declaring a list of transmitted codewords and recovering the messages sent by active users. Most of the existing works concentrate on applying U-RA in the oversimplified independent and identically distributed (i.i.d.) channels. In this paper, we consider a fairly general joint-correlated MIMO channel model with line-of-sight components for the realistic outdoor wireless propagation environments. We conduct the activity detection for the emitted codewords by performing an improved coordinate descent approach with Bayesian learning automaton to solve a covariance-based maximum likelihood estimation problem. The proposed algorithm exhibits a faster convergence rate than traditional descent approaches. We further employ a coupled coding scheme to resolve the issue that the dimensions of the common codebook expand exponentially with user payload size in the practical massive machine-type communications scenario. Our simulations reveal that to achieve an error probability of 0.05 for reliable communications in correlated channels, one must pay a 0.9 to 1.3 dB penalty comparing to the minimum signal to noise ratio needed in i.i.d. channels on condition that a sufficient number of receiving antennas is equipped at the BS.

In this study, a mixed massive random access scheme is considered where part of users transmit both common information and user-specific information, while others transmit only common information. In this scheme, common information is transmitted by index modulation (IM)–aided unsourced random access (URA), while user-specific information is by IM-aided sourced random access (SRA). Practically, IM-aided URA partitions channel blocks of one transmission frame into multiple groups and then employs the IM principle to activate only part of the channel blocks in each group. IM-aided SRA allocates multiple pilot sequences to each user and activates only one pilot sequence whose index carries the data information. At the receiver, the covariance-based maximum likelihood detection (CB-MLD) is employed to recover the active compressed sensing (CS) code words of URA and information of SRA jointly. To stitch the common information at different blocks of URA, a modified tree decoder is proposed to take the IM constraint into account. Furthermore, to relax the strict threshold requirement and improve the performance, an iterative CS detector and tree decoder are employed to decode the common information, where successive signal reconstruction and interference cancellation are utilized. Finally, computer simulations are given to demonstrate the performance of the proposed scheme.

The core requirement of massive Machine-Type Communication (mMTC) is to support reliable and fast access for an enormous number of machine-type devices (MTDs). In many practical applications, the base station (BS) only concerns the list of received messages instead of the source information, introducing the emerging concept of unsourced random access (URA). Although some massive multiple-input multiple-output (MIMO) URA schemes have been proposed recently, the unique propagation properties of millimeter-wave (mmWave) massive MIMO systems are not fully exploited in conventional URA schemes. In grant-free random access, the BS cannot perform receive beamforming independently as the identities of active users are unknown to the BS. Therefore, only the intrinsic beam division property can be exploited to improve the decoding performance. In this paper, a URA scheme based on beam-space tree decoding is proposed for mmWave massive MIMO system. Specifically, two beam-space tree decoders are designed based on hard decision and soft decision, respectively, to utilize the beam division property. They both leverage the beam division property to assist in discriminating the sub-blocks transmitted from different users. Besides, the first decoder can reduce the searching space, enjoying a low complexity. The second decoder exploits the advantage of list decoding to recover the miss-detected packets. Simulation results verify the superiority of the proposed URA schemes compared to the conventional URA schemes in terms of error probability.

Machine to machine (M2M) communication has raised significant interests. However, due to the massive number of machine type communication (MTC) devices that are anticipated to communicate using cellular networks, there is a major problem on efficient accommodation of the heavy Random Access (RA) loads from the MTC devices. Use of small cells has been specified to provide network densification by 3GPP. In this paper we investigate the use of small cells to support RA and the allocation of Zadoff-Chu sequences to the small cells, which are used to generate preambles for the RA procedure. Small cells can be deployed on demand to handle mainly RA loads from MTC devices, which may generate much less data traffic compared to human devices. It is demonstrated that, with small cell support, more random channel access opportunities are provided and this can effectively support a massive number of machine devices. Using both simulations and analytical model the proposed implementation is evaluated and compared to two existing random access schemes without small cell support (the basic random access scheme and the access class barring (ACB) scheme). It is observed that the capacity of the networks in terms of the number of supported machine devices with small cell support can be increased significantly. The proposed implementation shows large potential to handle random channel access for massive machine devices.

In this paper, we study the problem of user activity detection and large-scale fading coefficient estimation in a random access wireless uplink with a massive MIMO base station with a large number $M$ of antennas and a large number of wireless single-antenna devices (users). We consider a block fading channel model where the $M$-dimensional channel vector of each user remains constant over a coherence block containing $L$ signal dimensions in time-frequency. In the considered setting, the number of potential users $K_\text{tot}$ is much larger than $L$ but at each time slot only $K_a<<K_\text{tot}$ of them are active. Previous results, based on compressed sensing, require that $K_a\leq L$, which is a bottleneck in massive deployment scenarios such as Internet-of-Things and unsourced random access. In this work we show that such limitation can be overcome when the number of base station antennas $M$ is sufficiently large. We also provide two algorithms. One is based on Non-Negative Least-Squares, for which the above scaling result can be rigorously proved. The other consists of a low-complexity iterative componentwise minimization of the likelihood function of the underlying problem. Finally, we use the discussed approximated ML algorithm as the decoder for the inner code in a concatenated coding scheme for unsourced random access, a grant-free uncoordinated multiple access scheme where all users make use of the same codebook, and the massive MIMO base station must come up with the list of transmitted messages irrespectively of the identity of the transmitters. We show that reliable communication is possible at any $E_b/N_0$ provided that a sufficiently large number of base station antennas is used, and that a sum spectral efficiency in the order of $\mathcal{O}(L\log(L))$ is achievable.

In this work we treat the unsourced random access problem on a Rayleigh block-fading AWGN channel with multiple receive antennas. Specifically, we consider the slowly fading scenario where the coherence block-length is large compared to the number of active users and the message can be transmitted in one coherence block. Unsourced random access refers to a form of grant-free random access where users are considered to be a-priori indistinguishable and the receiver recovers a list of transmitted messages up to permutation. In this work we show that, when the coherence block length is large enough, a conventional approach based on the transmission of non-orthogonal pilot sequences with subsequent channel estimation and Maximum-Ratio-Combining (MRC) provides a simple energy-efficient solution whose performance can be well approximated in closed form. For the finite block-length simulations we use a randomly sub-sampled DFT matrix as pilot matrix, a low-complexity approximate message passing algorithm for activity detection and a state-of-the-art polar code with a successive-cancellation-list decoder as single-user error correction code. These simulations prove the scalability of the presented approach and the quality of the analysis.

This paper proposes a unified semi-blind detection framework for sourced and unsourced random access (RA), which enables next-generation ultra-reliable low-latency communications (URLLC) with a massive number of devices. Specifically, the active devices transmit their uplink access signals in a grant-free manner to realize ultra-low access latency. Meanwhile, the base station aims to achieve ultra-reliable data detection under severe inter-device interference without exploiting explicit channel state information (CSI). We first propose an efficient transmitter design, where a small amount of reference information (RI) is embedded in the access signal to resolve the inherent ambiguities incurred by the unknown CSI. At the receiver, we further develop a successive interference cancellation-based semi-blind detection scheme, where a bilinear generalized approximate message passing algorithm is utilized for joint channel and signal estimation (JCSE), while the embedded RI is exploited for ambiguity elimination. Particularly, a rank selection approach and a RI-aided initialization strategy are incorporated to reduce the algorithmic computational complexity and to enhance the JCSE reliability, respectively. Besides, four enabling techniques are integrated to satisfy the stringent latency and reliability requirements of massive URLLC. Numerical results demonstrate that the proposed semi-blind detection framework offers a better scalability-latency-reliability tradeoff than the state-of-the-art detection schemes dedicated to sourced or unsourced RA.