A novel semi-orthogonal transmission method, the pilot data superimposed dynamic inflow (PDSDI) method, is proposed to tackle the challenges of massive access in grant-free random access wireless communication systems. This approach aims to balance large-scale user access while mitigating performance degradation associated with non-orthogonal pilot sequences. The traditional orthogonal transmission scheme is also analyzed as a baseline for comparison. Uplink rate expressions in closed form are obtained under two common channel estimation methods, that is, least squares and minimum mean square error. The analysis investigates the asymptotic behavior of important system parameters, including RiceanK-factor, antenna numbersM, and symbol power, revealing that the sum uplink rate converges to log2M, regardless of theK-factor. In addition, a parameter optimization model is developed to maximize system performance by adjusting critical variables, such as antenna number and power scaling factors, ensuring improved system capacity and efficiency. The simulation results confirm that the PDSDI method greatly exceeds the performance of the traditional scheme with respect to uplink rate and user access capacity. This work presents a practical framework for optimizing system parameters in future large-scale wireless networks.

To keep up with the continuously growing coverage demands and attain true global coverage, the deployment of multi-layered low Earth orbit satellite constellations is necessary. Next-generation mega satellite constellations are expected to rely on inter-satellite links to relay information, which will enable fast and reliable communications between the different satellite nodes in free-space and facilitate the utilization of coverage diversity modes that can further enhance the quality-of-service provided in the network. However, materializing these high performing systems is challenging due to the complexity of the network architecture which may require long and complex simulation processes during design. In this article, we develop theoretical modeling for the probability of coverage for various diversity modes in mega satellite constellations by leveraging tools from stochastic geometry. We first develop analytical models for conventional single-shell networks and then extend these models to incorporate multi-shell networks. The analysis is validated using Monte-Carlo simulations which show a close fit to the analytical models derived. Moreover, the analytical models provide a performance baseline that is comparable to practical networks that rely on regular network architectures such as SpaceX’s Starlink. This allows network operators to devise expansion strategies to cater for expanding demands and gain insights into the performance of the network as more shells are introduced into the network.

The spectral efficiency in cell-free massive MIMO with grant-free random access under Ricean fading, utilizing a linear maximal-ratio combining detector, is the focus of this paper. Firstly, explicit expressions for the effective signal-to-interference-plus-noise ratio (SINR) are introduced, using the least squares (LS) and minimum mean squared error (MMSE) estimation methods, valid for varying Ricean K-factor and for varying numbers of access point antennas M. Furthermore, we drive the asymptotic SINR expressions under extreme conditions of infinite Ricean K-factor, infinite M, and the appropriate scaling of the users’ power. The corresponding analysis shows that as M approaches infinity, the sum uplink rate converges to $\log _{2} M$, regardless of the Ricean K-factor. Particularly, in the case of LS estimation, reducing each user’s transmit power by $1 / \sqrt{M}$ maintains the rate, converging to a definitive value irrespective of the Ricean K-factor. In the case of MMSE estimation, power is able to be scaled down by $1 / \sqrt{M}$ to achieve it when Ricean K-factor is zero, whereas the power of nonzero Ricean K-factor necessitates a reduction by $1 / M$. Finally, simulations confirm all theoretical outcomes.

Cell-free (CF) massive multiple-input multiple-output (MIMO), as a promising network architecture for beyond the fifth generation (5G), has a great potential to support grant-free (GF) transmission for machine-type communication (MTC). To shed light on this subject, this work aims to model and evaluate the performance of GF transmission in CF massive MIMO under a realistic network deployment scenario, where the spatial locations of both access points (APs) and devices are assumed to be random in nature. In particular, by capitalizing on the distinctive CF network architecture and features, we design a new two-disk based geometric model for GF transmission, which facilitates analysis and understanding in CF massive MIMO. Based on the proposed two-disk model, we derive an approximated closed-form expression for the access success probability by leveraging on techniques from stochastic geometry, and investigate the impact of different key system parameters on the network performance, which have not been presented previously. To highlight the performance superiority of CF massive MIMO, we further provide a comparative analysis by using an analogous single-disk model in an equivalent co-located massive MIMO network. Simulation results verify our analysis and demonstrate that CF massive MIMO is able to significantly outperform its co-located counterpart in terms of access success probability and provide robust performance against increased access density, which well suits to crowd scenarios.

Enabling ultrareliable low-latency communication (URLLC) with stringent requirements for transmitting data packets (e.g., 99.999% reliability and 1-ms latency) presents considerable uplink transmission challenges. For each packet transmission over dynamically allocated network radio resources, the conventional random access protocols are based on a request-grant scheme. This induces excessive latency and necessitates reliable control signaling, resulting in overhead. To address these problems, grant-free (GF) solutions are proposed in the fifth-generation (5G) new radio (NR). In this article, an overview and vision of the state of the art in enabling GF URLLC are presented. In particular, we first provide a comprehensive review of NR specifications and techniques for URLLC, discuss underlying principles, and highlight impeding issues of enabling GF URLLC. Furthermore, we briefly explain two key phenomena of massive multiple-input–multiple-output (mMIMO) (i.e., channel hardening and favorable propagation) and build several deep insights into how celebrated mMIMO features can be exploited to address the issues and enhance the performance of GF URLLC. Moving further ahead, we examine the potential of cell-free (CF) mMIMO and analyze its distinctive features and benefits over mMIMO to resolve GF URLLC issues. Finally, we identify future research directions and challenges in enabling GF URLLC with CF mMIMO.

Massive multiple input multiple output (mMIMO) assisted grant-free random access (RA) (mGFRA) has been considered a promising RA scheme for future machine-type communication (MTC). In mGFRA, the nature that RA user equipments (UEs) blindly interplay each other in the presence of preamble collision degrades the performance of RA UEs. To address the issue, a mMIMO assisted semi-grant-free RA (mSGFRA) scheme is considered in this paper, where a downlink feedback based on the preamble detection after the preamble phase is introduced. With such a feedback, RA UEs experiencing preamble collision are enforced to keep silent in data-transmission phase, which in turn enhances the performance of RA UEs without experiencing preamble collision. To understand the performance behaviours of mSGFRA, we first analyse the preamble detection performance in mSGFRA and reveal that accurate collided-preamble detection can be achieved with the assistance of mMIMO. Then, we analyse and compare the performance of mSGFRA and mGFRA in terms of success probability. Simulation results validate theoretical analysis and confirm the potential superiority of mSGFRA to mGFRA.

In this paper, an improved Tabu-search preamble assignment algorithm is proposed in cell-free massive multiple input multiple output (MIMO) to find the optimal preamble assignment solution with a low complexity. Different from the existing Tabu-search algorithm, the proposed one exploits the large-scale fading knowledge to refine the solution space so that only a small number of solutions that are more likely to mitigate preamble collision are explored. As a result, the complexity of Tabu search for preamble assignment can be significantly reduced. Simulation results demonstrate that the proposed algorithm is able to provide a good tradeoff between the performance and complexity compared to the state-of-the-art algorithms. In particular, it can achieve an over 50% complexity reduction with only a negligible performance loss in terms of the achievable sum rate compared to the existing Tabu-search algorithm.

Preamble resources are scarce and precious in random access (RA), which need to be efficiently utilized to support critical machine-type communication (MTC) with stringent access requirements. In this article, we study a grant-free RA scenario for critical MTC in the context of massive multiple-input–multiple-output (MIMO). To enhance the access reliability of delay-sensitive devices within a predefined latency budget, two dynamic preamble-resource partitioning (DPP) schemes are proposed. Particularly, by leveraging massive MIMO, we first analytically investigate the feasibility of DPP in the considered RA scenario and demonstrate its performance superiority to the conventional scheme. Based on the analytical results, we then propose a greedy DPP scheme that performs locally optimal preamble-resource partitioning in each RA slot. To find the globally optimal DPP solution, we further propose a reinforcement learning (RL)-based scheme by modeling the considered RA scenario as a Markov decision process. Simulation results show the practicality and effectiveness of the proposed DPP schemes. In particular, to achieve a target access failure rate of$1\times 10^{-2}$, the proposed RL-based scheme is able to enhance the RA traffic load by 42% and improve the preamble resource utilization by over 25% compared to the conventional baseline scheme.

In this article, a Co-existing Preamble and Data (CoPD) random access approach for machine-type communications (MTC) is proposed and studied in massive multiple input multiple output (MIMO) systems. Unlike conventional grant-free random access in which preambles are time-multiplexed with data, the proposed approach enables CoPD transmissions by different groups of devices, which leverages favourable propagation of massive MIMO and successive interference cancellation (SIC) technique. To fully understand the performance behavior of the proposed approach, the error characteristics of channel estimation (CE) due to SIC error propagation are investigated. To avoid the CE error variance growing arbitrarily with time that leads to data transmission failure, key conditions, with respect to preamble length$L$, traffic intensity$\lambda $, and average number of data packets per active device$\bar b$, are derived. Under the conditions, we demonstrate that the throughput of the proposed CoPD approach cannot be arbitrarily high by increasing$\bar b$and$\lambda $even though the antenna array size, denoted by$M$, grows arbitrarily in massive MIMO. Accordingly, its maximum throughput and success probability are theoretically obtained. Simulation results verify our analysis results and confirm that the maximum throughput of the proposed CoPD approach can be at least 4 times than that of the conventional one. Furthermore, its success probability approaches 1 when$M \to \infty $as long as the derived conditions are met.

Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA). In this paper, by leveraging distributed massive multiple input multiple output (mMIMO) together with machine learning, a novel machine learning based framework solution is proposed to address the preamble collision problem in GFRA. The key idea is to identify and employ the neighboring access points (APs) of a collided RA UE for its data decoding rather than all the APs, so that the mutual interference among collided RA UEs can be effectively mitigated. To this end, we first design a tailored deep neural network (DNN) to enable the preamble multiplicity estimation in GFRA, where an energy detection (ED) method is also proposed for performance comparison. With the estimated preamble multiplicity, we then propose a K-means AP clustering algorithm to cluster the neighboring APs of collided RA UEs and organize each AP cluster to decode the received data individually. Simulation results show that a decent performance of preamble multiplicity estimation in terms of accuracy and reliability can be achieved by the proposed DNN, and confirm that the proposed schemes are effective in preamble collision resolution in GFRA, which are able to achieve a near-optimal performance in terms of uplink achievable rate per collided RA UE, and offer significant performance improvement over traditional schemes.

In this paper, by considering the average mean squared error (AMSE) of channel estimation, we primarily obtain the closed-from expressions of the probability density function (PDF) and cumulative distribution function of AMSE for the least squares (LS)/minimum mean squared error (MMSE) estimation method as the line-of-sight (LOS) component is known, where the asymptotic analysis is executed in Rayleigh fading and strong LOS conditions. Secondly, the closed-form expressions for the expectation of AMSE ( Expamse) and variance of AMSE ( Varamse) are acquired, where Varamseis inversely proportional to the number of antennas ( M). As M becomes infinite, the PDF of AMSE at Expamsehas an order of root M. When the pilot power decreases with M in a power law, the LS case keeps deteriorating while the MMSE case converges to a constant which basically depends on the Rician K-factor. Next, the spectral efficiency is investigated by considering AMSE. When Expamseaccelerates, the spectral efficiency of the LS method keeps dropping and that of the MMSE method firstly is degraded and then is improved to a constant except Rayleigh fading. Finally, all results are validated via simulations.

Ambient backscatter communication (AmBC) has been introduced to address communication and power efficiency issues for short-range and low-power Internet-of-Things (IoT) applications. On the other hand, reconfigurable intelligent surface (RIS) has been recently proposed as a promising approach that can control the propagation environment especially in indoor communication environments. In this paper, we propose a new AmBC model over ambient orthogonal-frequency-division-multiplexing (OFDM) subcarriers in the frequency domain in conjunction with RIS for short-range communication scenarios. A tag transmits one bit per each OFDM subcarrier broadcasted from a WiFi access point. Then, RIS augments the signal quality at a reader by compensating the phase distortion effect of multipath channel on the incident signal. We also exploit the special spectrum structure of OFDM to transmit more data over its squeezed orthogonal subcarriers in the frequency domain. Consequently, the proposed method improves the bit-error-rate (BER) performance and provides a higher data rate compared to existing AmBC methods. Analytical and numerical evaluations show the superior performance of the proposed approach in terms of BER and data rate.

Massive multiple-input multiple-output (mMIMO) based grant-free random access (RA) (mGFRA) has been studied for massive machine-type communication (mMTC). In the existing works of mGFRA, it is implicitly assumed that each RA user equipment (UE) in mMTC has the same data-packet size. However, this may not be the case in practice as RA UEs in different applications can have different numbers of bits to send, which may result in data packets of different sizes. In addition to that, preamble collision is a key issue that degrades the performance of mGFRA when orthogonal preamble is used. In this article, we aim to address the preamble collision issue under a practical mGFRA scenario that consists of two different types of RA UEs (with short and large data packets). Specifically, by exploiting different sizes of data packets, we propose a triangular non-orthogonal RA (T-NORA) scheme, where the preamble transmission of RA UEs with small-sized data packet is embedded into the data transmission of RA UEs with large-sized data packet so that the preamble collision between two different types of RA UEs can be avoided. As a result, an improved performance over conventional mGFRA can be achieved by T-NORA with the assistance of mMIMO. Through analytical and simulation results, we can confirm that the proposed T-NORA scheme outperforms the conventional mGFRA scheme.

Massive MIMO based grant-free random access (mGFRA) is a promising RA technique for massive access with low signaling overhead. However, due to a limited orthogonal preamble size, preamble collision is a key factor that constrains the success probability of mGFRA. In this paper, we examine the potential of applying non-orthogonal preambles to mitigate the issue, where two typical non-orthogonal sequences, i.e., Gaussian distribution sequences and Zadoff-Chu (ZC) sequences, are taken into account. Asymptotic behaviors of the success probabilities with the non-orthogonal preamble sequences are analyzed and compared using derived closed-forms. Through the analysis we reveal that ZC sequences outperform Gaussian ones. However, though non-orthogonal sequences are able to reduce or even alleviate the preamble collision, they do not necessarily provide better performance than the orthogonal counterpart. In addition, regarding orthogonal sequences as a single-root case of ZC sequences, we show that there exists an optimal number of ZC roots that maximizes the success probability for mGFRA, which is low-bounded by orthogonal baseline. Simulation results evaluate the performance of non-orthogonal sequences in mGFRA under various practical system parameters, and validate our analysis.

Massive multiple-input multiple-output (MIMO) opens up new avenues for enabling highly efficient random access (RA) by offering abundance of spatial degrees of freedom. In this paper, we investigate the grant-free RA with massive MIMO and derive the analytic expressions of success probability of the grant-free RA for conjugate beamforming and zero-forcing beamforming techniques. With the derived analytic expressions, we further shed light on the impact of system parameters on the success probability. Simulation results verify the accuracy of the analyses. It is confirmed that the grant-free RA with massive MIMO is an attractive RA technique with low signaling overhead that could simultaneously accommodate a number of RA users, which is multiple times the number of RA channels, with close-to-one success probability. In addition, when the number of antennas in massive MIMO is sufficiently large, we show that the number of orthogonal preambles would dominate the success probability.

Grant-free random access (RA) with massive MIMO is a promising RA technique that provides significant benefits in increasing the channel reuse efficiency with low signaling overhead. Since user equipment (UE) detection and channel estimation in grant-free RA rely solely on the received preambles, preamble designs that enable high success rate of UE detection and channel estimation are very much in need to ensure the performance gain of grant-free RA with massive MIMO. In this paper, a super preamble consisting of multiple consecutive preambles is proposed for the high success rate of grant-free RA with massive MIMO. With the proposed approach, the success of UE detection and channel estimation for a UE depends on two conditions: 1) it is a solvable UE, where we define the UE whose super preamble is not a linear combination of the other UEs' super preambles as a solvable UE and 2) its super preamble is detected. Accordingly, we theoretically analyze the solvable rate of the UEs with multiple preambles and propose a reliable UE detection algorithm to obtain the super preambles of the UEs by exploiting the quasi-orthogonality characteristic of massive MIMO. The theoretical analysis and simulation results show that turning a preamble into a super preamble consisting of two or three shorter preambles, the success rate of UE detection and channel estimation could be significantly increased using the proposed approach.

Fifth-generation mobile communication systems aim to support massive access for future wireless applications. Unfortunately, wireless resource scarcity in random access (RA) is a fundamental bottleneck for enabling massive access. To address this problem, we propose a virtual carrier sensing (VCS)-based RA scheme in massive MIMO systems. The essence of the proposed scheme lies in exploiting wireless spatial resources of uplink channels occupied by assigned user equipments (UEs) to increase channel resources for RA. With the proposed scheme, RA UEs are able to exploit the spatial resources that are approximately orthogonal to those of assigned UEs, thus sharing the uplink channel resource with assigned UEs without causing significant interference to them. Specifically, to ensure RA UEs avoid serious interference with assigned UEs, a base station (BS) sends tailored virtual carriers to RA UEs on behalf of assigned UEs. The RA UEs then conduct VCS to determine whether or not the uplink channel resource is available for RA. The closed-form approximations for probability of channel availability and uplink achievable rate with the proposed scheme are derived. Theoretical analysis and simulation results show that the proposed scheme is able to significantly increase channel resources of RA for massive access.

Energy efficiency is one of the most critical parameters in ultra-wideband (UWB) based wireless body area networks (WBANs). In this paper, the energy efficiency optimization problem is investigated for cooperative transmission with a single relay in UWB based WBANs. Two practical onbody transmission scenarios are taken into account, namely, along-torso scenario and around-torso scenario. With a proposed single-relay WBAN model, a joint optimal scheme for the energy efficiency optimization is developed, which not only derives the optimal power allocation but also seeks the corresponding optimal relay location for each scenario. Simulation results show that the utilization of a relay node is necessary for the energy efficient transmission in particular for the around-torso scenario and the relay location is an important parameter. With the joint optimal relay location and power allocation, the proposed scheme is able to achieve up to 30 times improvement compared to direct transmission in terms of the energy efficiency when the battery of the sensor node is very limited, which indicates that it is an effective way to prolong the network lifetime in WBANs.

In this paper, we investigate a distributed beamforming problem to optimize energy efficiency (EE) in ultra-wideband (UWB) based implant body area networks (IBANs). To evaluate the impact of relay location on the EE, a relay location based cooperative network model is proposed, where multiple on-body relays are employed to assist an implant node to communicate with a BAN coordinator. With the proposed model, the EE optimization problem is mathematically formulated as a non-convex optimization problem. Sequential quadratic programming (SQP) combined with scatter search are applied to find the corresponding optimal solution. Simulation results illustrate that the proposed beamforming scheme outperforms other transmission schemes. A remarkable improvement can be achieved not only in EE but also in spectral efficiency (SE) compared to direct transmission. Moreover, numerical examples show that the relay location has a significant impact on the EE performance.

In this paper, spectral efficiency (SE) is investigated for cooperative ultra-wideband (UWB) based on-body area networks (OBANs). To optimize SE for single-relay cooperation, an equivalent generic cooperative model in UWB based OBANs is established first. With the proposed model, joint optimal relay location and power allocation for cooperation is then derived to solve the SE maximization problem. Simulation results show that direct transmission is preferable for UWB based OBANs when the transmitter and receiver are located on the same side of the human body. However, the joint optimal cooperative transmission scheme can achieve a significant improvement on SE compared with direct transmission when the transmitter and receiver are located on the different sides of the human body, which indicates that cooperation is more feasible to be applied in this case due to its robustness to the significant path loss.

For non-contiguous (NC) OFDM based cognitive radio (CR) systems, schemes have been developed in literature to acquire spectrum synchronization information (SSI) with perfect carrier frequency offset (CFO) synchronization. However, OFDM is extremely sensitive to the CFO in practice, which leads to inter-carrier interference (ICI), hence degrading the spectrum synchronization performance for existing schemes. An accurate CFO estimation is therefore required before setting up the SSI. In this paper, we present a novel scheme based on the maximum likelihood (ML) algorithm to estimate the CFO for the NC-OFDM receiver when the SSI is unknown. A corresponding Cramér-Rao lower bound (CRB) with the ideal SSI is derived to demonstrate the efficiency of the proposed scheme. Simulation results show that the proposed scheme is robust against interference and achieves a satisfactory accuracy of estimation, which is close to the relevant CRB.

Spectrum cartography is the process of constructing a map showing Radio Frequency signal strength over a finite geographical area. Multiple research groups have recently proposed to use spectrum cartography in the context of discovering spectrum holes in space that can be exploited locally in cognitive radio networks. In our novel approach, we exploit the sparsity of primary users in space to formulate the cartography process as a compressive sensing problem. Further, we present a novel algorithm for solving the cartography problem that builds on the well-known Orthogonal Matching Pursuit algorithm. We evaluate the performance of our approach by simulating a cognitive radio network where primary users are low power wireless microphones. Our simulation results show a significant improvement in reconstruction error, in comparison to two existing compressive sensing based methods.

For non-contiguous OFDM (NC-OFDM) based communication systems, especially NC-OFDM-based cognitive ratio (CR) systems, one of critical challenges is to establish spectrum synchronization between the transmitter and receiver before data transmission. In [5], an a posterior probability (APP) detection algorithm has been proposed for the receiver to detect the subchannels occupied by the transmitter (active subchannels). However, the encoding scheme adopted in [5] is a concatenation of a convolutional coder and a low rate repetition coder so that the system code rate is only 1/4 when half of the subcarriers are active. In this paper, we consider using low density parity-check (LDPC) codes to improve the system data transmission rate. Furthermore, with the channel model a of NC-OFDM-based communication system, we employ density evolution algorithm to obtain good degree distribution pairs and adopt a modified progressive edge-growth (PEG) algorithm to construct the parity-check matrix for the LDPC code. Simulation results show that our optimized LDPC code for NC-OFDM-based communication systems could obtain satisfactory error performance at a high data transmission rate (system code rate of 1/2).

For non-contiguous OFDM (NC-OFDM) based cognitive radio systems, one challenge is to establish the spectrum synchronization before the data transmission over an idle channel. In this paper, we propose two novel detection schemes, i.e., hard-decision-based detection (HDD) and soft-decision-based detection (SDD) schemes, to detect NC-OFDM symbols without the information of out-of-band spectrum synchronization. The key idea of the proposed schemes is to employ the received training symbol to calculate a posterior probability (APP) of each subchannel's being active. Specifically, for the proposed HDD scheme, the secondary receiver detects whether a subchannel is active or not based on its APP. When the APP of a subchannel is larger than a preset threshold, it will be employed for the data detection. Moreover, we derive the miss detection and false alarm probabilities of the hard decision based detection of active subchannels, respectively. For the proposed SDD scheme, all subchannels are employed for the data detection, in which as a priori information of the subchannels' being active, all APPs are taken into account in the bit metric calculation. Theoretical analysis and simulation results show that the proposed schemes can provide satisfactory performance.

For non-contiguous OFDM (NC-OFDM) based cognitive radio (CR) systems, one of huge challenges is to establish spectrum synchronization before data transmissions. In this paper, we propose a novel active subchannel detection scheme for the NC-OFDM receiver without out-of-band spectrum synchronization information. The key idea of the proposed scheme is to employ the received training symbol to calculate the a posterior probability (APP) of each subchannel being active, and then APPs are used to make a decision on which subchannels are active and to be processed in data detection. Furthermore, performance analysis of active subchannel detection is given. Both theoretical analysis and simulation results show that the proposed scheme can provide satisfactory performance of the symbol error rate (SER) when the interference from primary users is considered.

In this paper, we consider the weighted sum throughput maximization (WSTM) problem of multicell network with full spectral reuse. The WSTM problem is a nonconvex combinatorial problem, and difficult to tackle in practice. Thus, we present and discuss three low-complexity suboptimal algorithms, which solve the WSTM problem via joint user scheduling and power control (USPC) among a set of coordinating cells. Extensive system level simulations show that all the proposed algorithms have close weighted sum throughput (WST) performances to that of exhaustive search (ES). Furthermore, the proposed distributed iterative (DI) algorithm, which enjoys both distributed operation and fast convergence, is the most attractive one in practical systems.