Large Number Of User Equipments Research Articles

Grant-free random access in pilot-reused multiceli massive MIMO systems with backoff mechanisms

Grant-free random access (RA) is attractive for future network due to the minimized access delay. In this paper, we investigate the grantfree RA in multicell massive multiple-input multiple-output (MIMO) systems with pilot reuse. With backoff mechanism, user equipments (UEs) in each cell are randomly activated, and active UEs randomly select orthogonal pilots from a predefined pilot pool, which results in a random pilot contamination among cells. With the help of indicators that capture the uncertainties of UE activation and pilot selection, we derive a closed-form approximation of the spectral efficiency per cell after averaging over the channel fading as well as UEs' random behaviors. Based on the analysis, the optimal backoff parameter and pilot length that maximize the spectral efficiency can be obtained. We find that the backoff mechanism is necessary for the system with large number of UEs, as it can bring significant gains on the spectral efficiency. Moreover, as UE number grows, more backoff time is needed.

A Bayesian Approach to Characterize Unknown Interference Power in Wireless Networks

The existence of unknown interference is a prevalent problem in wireless communication networks. Especially in multi-user multiple-input multiple-output (MIMO) networks, where a large number of user equipments are served on the same time-frequency resources, the outage performance may be dominated by the unknown interference arising from scheduling variations in neighboring cells. In this letter, we propose a Bayesian method for modeling the unknown interference power in the uplink of a cellular network. Numerical results show that our method accurately models the distribution of the unknown interference power and can be effectively used for rate adaptation with guaranteed target outage performance.

Age of Information in Downlink Systems: Broadcast or Unicast Transmission?

We analytically decide whether the broadcast transmission scheme or the unicast transmission scheme achieves the optimal age of information (AoI) performance of a multiuser system where a base station (BS) generates and transmits status updates to multiple user equipments (UEs). In the broadcast transmission scheme, the status update for all UEs is jointly encoded into a packet for transmission, while in the unicast transmission scheme, the status update for each UE is encoded individually and transmitted by following the round robin policy. For both transmission schemes, we examine three packet management strategies, namely the non-preemption strategy, the preemption in buffer strategy, and the preemption in serving strategy. We first derive new closed-form expressions for the average AoI achieved by two transmission schemes with three packet management strategies. Based on them, we compare the AoI performance of two transmission schemes in two systems, namely, the remote control system and the dynamic system. Aided by simulation results, we verify our analysis and investigate the impact of system parameters on the average AoI. For example, the unicast transmission scheme is more appropriate for the system with a large number of UEs. Otherwise, the broadcast transmission scheme is more appropriate.

G-SC-IRSA: Graph-Based Spatially Coupled IRSA for Age-Critical Grant-Free Massive Access

In this paper, we focus on a grant-free massive access setup and analyze its age of information (AoI), where a large number of user equipments (UEs) are randomly activated and attempt transmitting status update packets to a base station (BS) over a common shared channel. To support this age-critical grant-free massive access, we propose a graph-based spatially coupled irregular repetition slotted ALOHA (G-SC-IRSA) random access protocol, which utilizes the pseudo-random access pattern (PRAP), coupled frames, and sliding window decoder (SWD) to improve the packet loss rate (PLR) and AoI performance. Specifically, we derive the approximate expressions to the normalized average AoI (AAoI) as a function of the PRAP and system load. Then, we establish the problem of minimizing the AAoI under the G-SC-IRSA protocol. Furthermore, we utilize the density evolution (DE) with a bipartite graph to evaluate the system load threshold of G-SC-IRSA in asymptotic regime, and achieve an optimal degree distribution via the differential evolution algorithm, and finally obtain the optimal PRAP with PEG algorithm. Simulation results validate the accuracy of our theoretical derivations, and show the G-SC-IRSA can achieve the minimum AAoI with the optimal PRAP, and outperforms the existing benchmark schemes in terms of PLR and AAoI.

Multi-Agent Reinforcement Learning With Measured Difference Reward for Multi-Association in Ultra-Dense mmWave Network

Millimeter Wave (mmWave) communication technology is anticipated to play a vital role in meeting the growing demand for the scarce bandwidth in wireless communications. However, mmWave networks are highly susceptible to blockage. Thus, some mitigation techniques, such as multi-connectivity, need to be considered. Densely deploying mmWave base stations (mBSs) to form an ultra-dense network (UDN) also helps. With a mix of different technologies, optimally allocating resources becomes challenging. In this paper, we study mmWave user multi-association in a two-tier heterogeneous ultra-dense network (HetUDN) with a relatively large number of user equipments (UEs). We propose a framework of multi-agent reinforcement learning (MARL) to tackle the complicated optimization problem, leveraging its adaptivity to the communication environment. The proposed scheme considers mmWave beam-division based multi-connectivity and takes advantage of a macro base station (MBS) for indirect cooperation among agents (UEs). In particular, we borrow a credit-assignment technique called difference reward (DR) to deal with a relatively large MARL system with a large action space, which, to the best of our knowledge, is the first time to apply MARL with DR in user association. Furthermore, the proposed schemes are scalable mainly due to fixed observation dimensions and individual actions taken by UEs independently, ensuring that the operation is independent of the numbers of mBSs and UEs. Numerical results suggest that the two MARL schemes with measured DR could achieve a good balance between energy efficiency and QoS outage, and the one using extended DR (EDR) offers additional performance improvement.

Energy Efficiency of Uplink Cell-Free Massive MIMO With Transmit Power Control in Measured Propagation Channel

Cell-free massive MIMO (CF-mMIMO) provides wireless connectivity for a large number of user equipments (UEs) using access points (APs) distributed across a wide area with high spectral efficiency (SE). The energy efficiency (EE) of the uplink is determined by (i) the transmit power control (TPC) algorithms, (ii) the numbers, configurations, and locations of the APs and the UEs, and (iii) the propagation channels between the APs and the UEs. This paper investigates all three aspects, based on extensive (~30,000 possible AP locations and 128 possible UE locations) channel measurement data at 3.5 GHz. We compare three different TPC algorithms, namely maximization of transmit power (max-power), maximization of minimum SE (max-min SE), and maximization of minimum EE (max-min EE) while guaranteeing a target SE. We also compare various antenna arrangements including fully-distributed and semi-distributed systems, where APs can be located on a regular grid or randomly, and the UEs can be placed in clusters or far apart. Overall, we show that the max-min EE TPC is highly effective in improving the uplink EE, especially when no UE within a set of served UEs is in a bad channel condition and when the BS antennas are fully-distributed.

Joint time delay and energy optimization with intelligent overclocking in edge computing

With the rapid growth of user equipment (UE), the amount of data transmitted over networks has become enormous, exerting immense pressure on backbone networks and central cloud infrastructures. Simultaneously, corresponding applications requiring high energy consumption and low latency have multiplied the requirements for UE. Mobile edge computing (MEC) has been proposed to support the offloading of UE tasks to edge clouds for execution. The implementation of MEC requires fast data transmission between UE and edge servers, and the emerging 5G network appears to render this technology possible. In this paper, considering a large number of UE, a fixed MEC server, and an advanced intelligent network, we suggest an intelligent overclocking mechanism for the MEC server that operates for an intelligently calculated period to allow it to leverage more computing power without introducing additional hardware resources for a certain period of time. We jointly manage task offloading, server resource allocation, and overclocking to minimize the system-wide computation overhead and other risks. The proposed optimization problem is a mixed-integer nonlinear programming problem that is divided into three subproblems: offloading decision, resource allocation, and overclocking decision. We solve these subproblems using non-convex techniques and provide an iterative algorithm to obtain a heuristic solution for the original problem. Finally, simulation results show that the overclocked MEC server has lower system-wide computation overhead, faster task processing, and more offloaded UE as compared with the case without overclocking.

Average Vector-Symbol Error Rate Closed-Form Expression for ML Group Detection Receivers in Large MU-MIMO Channels With Transmit Correlation

In this paper, we derive a closed-form expression for the evaluation of the average vectorsymbol error rate (VER) of group detection followed by maximum-likelihood (GD-ML) receivers in large multi-user multiple-input multiple-output (MU-MIMO) systems with transmit side correlated Rayleigh channels. We assume M antennas at the base station (BS), N closely-located, single-antenna user equipment (UEs) with load factor λ = N/M , and N ≫ 1; consequently, we evaluate the performance of GD-ML receivers as the load factor grows to unity. The derived expression requires a negligible correlation at the receive side of the communication channel. Hence, from a practical point-of-view, when considering scenarios with a large number of UEs, the derived analytical expression is generally more applicable for systems with a distributed massive number of BS antennas. Numerical results are provided to validate our derived expression. We observe that the GD-ML with Nu group size achieves a diversity order proportional to M - N + Nu. Moreover, we show that for small group sizes, the analytical and simulation results remain close, and at moderate to high signal-to-noise ratio (SNR), the derived expression very closely matches the simulations, whereas this match becomes perfect as the users' side correlation increases. We also demonstrate that GD-ML outperforms the zero-forcing (ZF) and minimum mean-squared error (MMSE) receivers, in terms of VER; where for high λ, GD-ML exploits the maximum spatial multiplexing gain. Moreover, in terms of floating-point operations (FLOPs), we show that GD-ML receivers have almost the same complexity as ZF and MMSE where the ML detection stage adds a negligible complexity compared to the channel matrix inversion operation.

FTGPHA: Fixed-Trajectory Group Pre-Handover Authentication Mechanism for Mobile Relays in 5G High-Speed Rail Networks

For high-speed rail networks, data transmission suffers from severe penetration loss and when the train moves from one base station to another, a large number of User Equipments (UEs) on board carry out the handover authentication procedure simultaneously, which incurs a lot of handover overheads. The introduction of Mobile Relay Node (MRN) can improve the link quality and decrease the handover overheads. However, MRNs still suffer from several protocol attacks and frequent handovers and thus, the introduction of MRNs deteriorates the handover success rate and handover performance. At the same time, considering the diversity of future 5G high-speed rail networks, in this paper, we propose two fixed-trajectory group pre-handover authentication schemes for MRN: the first proposed scheme FTGPHA1 which establishes most of the important security properties and costs low handover overheads, and the second proposed scheme FTGPHA2 which furnishes better security properties than the first one. In these two schemes, since all of the MRNs in the same train and the next base station can accomplish the handover authentication with the help of the donor software defined networking controller before the MRN arrives, the handover delay can be ignored and thus, uninterrupted services can be provided for UEs on board. The security and performance evaluations demonstrate that the two proposed schemes outperform other related schemes.

Generalized Coordinated Multipoint (GCoMP)-Enabled NOMA: Outage, Capacity, and Power Allocation

A novel generalized coordinated multi-point transmission (GCoMP)-enabled non-orthogonal multiple access (NOMA) scheme is proposed. In particular, distributed base stations (BSs) in a network coverage area cooperate on the downlink to serve a set of user equipments (UEs) using the same transmission frequency band. Furthermore, all UEs associated to a BS and using a particular frequency band forms a single NOMA cluster. The number of BSs serving a UE in a particular frequency band is referred to as the order of clustering (or order of BS cooperation). To evaluate the proposed scheme, we derive a closed-form expression for the probability of outage for a UE with different orders of BS cooperation. To obtain important insights on the performance of the proposed system, approximate (asymptotic) expressions for the probability of outage and outage capacity are derived considering both perfect and imperfect channel state information (CSI) estimation. We observe that improved spectral efficiency with a large number of UEs per NOMA cluster can be achieved by increasing the clustering order (i.e., number of cooperating BSs per UE). Furthermore, an optimal transmission power allocation scheme that jointly allocates transmission power fractions from all cooperating BSs to all connected UEs is developed.

Massive Wireless Random Access With Successive Decoding: Delay Analysis and Optimization

In Internet of Things, wireless access networks are required to support a large number of user equipments (UEs) in real time. With UEs’ frequently arrival and departure, plenty of packet collisions can occur. Successive decoding thus becomes a promising technique to support the massive connectivity as it is capable of recovering packets from mixed received signals. In this paper, we propose an adaptive framing with successive decoding (AFSD) frame structure to deal with the fluidity of UEs. In the AFSD structure, the frame length is adaptively adjusted based on the present UE number, and successive decoding is adopted to alleviate the packet loss caused by packet collision. To quantify its performance, we provide exact as well as asymptotic results for the average delay. The analytical results are validated by simulations, and further extended to practical systems with estimated UE number, which shows that knowledge of active UE number is useful for delay reduction. Optimizations on UE transmission probability and frame length are also presented. In particular, the numerical studies reveal that by using optimized parameters, the average delay is reduced significantly for a wide range of arrival rates, which validates the effectiveness of the AFSD structure.