In this paper, we firstly derive two approximations of the achievable uplink rate with the perfect/imperfect channel state information (CSI) in cell-free massive multi-input multi-output (MIMO) systems, and all these approximations are not only in the simple, but also converge into the classical bounds achieved in conventional massive MIMO systems where the base-station (BS) antennas are co-located. It is worth noting that the obtained two approximations with perfect CSI could be regarded as the special cases of the obtained two approximations with imperfect CSI when the pilot sequence power becomes infinite, respectively. Moreover, the theory analysis shows that all obtained approximations with perfect/imperfect CSI have an asymptotic lower bound α2log2L thanks to the extra distance diversity offered by massively distributed antennas, where L is the number of BS antennas and the path-loss factor α>2, except for the free space environment. Obviously, these results indicate that the cell-free massive MIMO system has huge potential of spectral efficiency than the conventional massive MIMO system with the asymptotically tight bound log2L.

In this paper, we propose a relative channel estimation error (RCEE) metric, and derive closed-form expressions for its expectation Exprcee and the achievable uplink rate holding for any number of base station antennas M, with the least squares (LS) and minimum mean squared error (MMSE) methods. It is found that RCEE and Exprcee converge to the same constant value when M → ∞, which renders the pilot power allocation (PPA) substantially simplified and a PPA algorithm is proposed to minimize the average Exprcee per user under a total pilot power budget F in multi-cell massive multipleinput multiple-output systems. Numerical results show that the PPA algorithm brings considerable gains for the LS estimation compared with equal PPA (EPPA), while the gains are significant only with large frequency reuse factor (FRF) for the MMSE estimation. Moreover, for large FRF and large F, the performance of the LS approaches to that of the MMSE. Besides, a scheduling strategy is proposed to allocate pilot power in the whole system, which can approach the optimal performance. For the achievable uplink rate, the PPA scheme and improves the minimum achievable uplink rate compared with the EPPA scheme.

Federated learning (FL) is promising in enabling large-scale model training by massive devices without exposing their local datasets. However, due to limited wireless resources, traditional cloud-based FL system suffers from the bottleneck of communication overhead in core network. Fueled by this issue, we consider a hierarchical FL system and formulate a joint problem of edge aggregation interval control and resource allocation to minimize the weighted sum of training loss and training latency. To quantify the learning performance, an upper bound of the average global gradient deviation, in terms of the edge aggregation interval, the training latency, and the number of successfully participating devices, is derived. Then an alternative problem is formulated, which can be decoupled into an edge aggregation interval control problem and a resource allocation problem, and solved by an iterative optimization algorithm. Specifically, given the resource allocation strategy, a relaxation and rounding method is proposed to optimize the edge aggregation interval. The problem of resource allocation including training time allocation and bandwidth allocation is solved separately based on the convex optimization theory. Simulation results show that the proposed algorithm, compared to the benchmarks, can achieve higher learning performance with lower training latency, and is capable of adaptively adjusting the edge aggregation interval and the resource allocation strategy according to the training process.

This paper analyzes the channel estimation performance of massive multiple-input multiple-output (MIMO) internet of things (IoT) systems with Ricean fading. First, by utilizing the least squares (LS) and minimum mean squared error (MMSE) estimation methods, we consider the relative channel estimation error (RCEE) between the IoT device and base-station, and provide the approximations of the expectation of RCEE (Exprcee). Then, it is found that when the number of antennas M becomes infinite, pilot contamination (PC) exists in both cases. However, for MMSE case, Exprcee scales down by the inverse of Ricean K-factor, and hence PC phenomenon disappears with a large Ricean K-factor. Moreover, as M∞, the power scaling laws show that the pilot sequence power can be scaled down proportionally to 1/Mα (α>0) with the MMSE case, where the performance is determined only by the Ricean K-factor. Next, the channel hardening and favorable propagation effects are examined via analyzing the approximations of the variance of RCEE (Varrcee). Analysis implies that Varrcee decreases by 1/M when M∞. For a large Ricean K-factor, Varrcee approaches a non-zero constant for the LS case and scales down by the inverse of the square of Ricean K-factor for the MMSE case. Finally, all results are verified via Monte-Carlo simulations.

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.

This paper investigates a task assignment and resource optimization problem in an integrated multi-unmanned aerial vehicle (multi-UAV) system, where multi-UAVs are rationally assigned to complete detection, tracking, and communication tasks. Specifically, we formulate a joint task chain, power, and UAV trajectory optimization problem to maximize the sum of detection, tracking, and communication performance under the constraints of task, UAV, resource, and task web. To address the proposed NP-hard problem, we decompose it into three subproblems, i.e., the task chain optimization problem, the power optimization problem, and the UAV trajectory optimization problem. We then propose a task chain selection algorithm to more efficiently address the task chain optimization problem by transforming invalid solutions into valid ones. The other two subproblems are both solved by the successive convex approximation (SCA) algorithm. Based on these three algorithms, an inner-outer iterative optimization (IOIO) algorithm is proposed to iteratively solve the original problem with polynomial time complexity. Simulation results demonstrate that the proposed algorithm achieves better performance compared to other baseline schemes.