There are major errors in [1] in the derivation of closed-form expressions for the spectral efficiency (SE) and large-scale fading decoding (LSFD) using different zero-forcing combining schemes for cell-free massive MIMO networks. In this comments paper, we first highlight those errors and then provide corresponding corrected expressions for the spectral efficiency and LSFD with these combining schemes. Furthermore, we provide numerical simulations to validate the proposed corrections by comparing the uplink SE with that obtained using the precoding vectors from [2] as combining vectors.

The uplink sum-throughput of distributed massive multiple-input-multiple-output (mMIMO) networks depends majorly on Access point (AP)-User Equipment (UE) association and power control. The AP-UE association and power control both are important problems in their own right in distributed mMIMO networks to improve scalability and reduce front-haul load of the network, and to enhance the system performance by mitigating the interference and boosting the desired signals, respectively. Unlike previous studies, which focused primarily on addressing these two problems separately, this work addresses the uplink sum-throughput maximization problem in distributed mMIMO networks by solving the joint AP-UE association and power control problem, while fulfilling Quality-of-Service (QoS) requirements of each UE. To improve scalability, we present an $l 1$-penalty function that delicately balances the trade-off between spectral efficiency (SE) and front-haul signaling load. Our proposed methodology leverages fractional programming, Lagrangian dual formation, and penalty functions to provide an elegant and effective iterative solution with guaranteed convergence. Extensive numerical simulations validate the efficacy of the proposed approach for maximizing sum-throughput while considering the aforementioned joint problem, demonstrating its superiority over approaches that address these problems individually. Furthermore, the results show that the introduced penalty function can help us effectively control the maximum front-haul load.

Pilot contamination is a critical issue in distributed massive MIMO networks, where the reuse of pilot sequences due to limited availability of orthogonal pilots for channel estimation leads to performance degradation. In this work, we propose a novel distributed pilot assignment scheme to effectively mitigate the impact of pilot contamination. Our proposed scheme not only has low convergence time, but it also enhances overall system performance with low signaling overhead. Results of extensive numerical simulations establish that the proposed scheme out-performs existing centralized and distributed schemes in terms of mitigating pilot contamination and significantly enhancing network throughput.

In distributed systems like large wireless sensor networks, dense indoor small cell networks, maintaining each node's logical clock synchronized to a common reference is challenging. Increasing data rates and required cooperation among nodes for high spectral efficiency, further exacerbate the required clock synchronization accuracy. Here, we propose a one way timing information distribution mechanism simplifying existing two way information exchange for clock synchronization. We view the problem at the synchronizing node as a Peer-to-Peer (P2P) continuously running process. A node with stable clock (master) transmits regular packets embedded with transmit time stamps. All synchronizing nodes within the master's communication range can listen and apply receive time stamp. Efficient Passive Minimum Variance Unbiased Estimator (PMVUE) or Passive Maximum Likelihood Estimator (PMLE) are applied to jointly estimate clock offset and skew to correct the local clock. Random component of the link delay is assumed to be Gaussian distributed. Cramer-Rao Lower Bound (CRLB) guarantees that PMVUE and PMLE are efficient. We also propose a less accurate PMLE method, Independent PMLE (IPMLE) to remove dependency on random delay distribution parameter and show through simulations the high accuracy of our algorithms.

In this paper, we consider wireless communication systems where user equipments (UEs) monitor packet-data traffic characteristics and adopt Discontinuous Reception (DRX) to conserve battery power. With DRX, the receiver circuitry is configured to toggle between on (active) and off (inactive) states for specified durations, depending on the packet arrival process. Arriving at an appropriate DRX configuration remains a challenging research issue, especially when multiple applications generate traffic. Our objective in this work is to provide a practical mechanism for selecting a suitable DRX configuration. First, we derive an analytical expression for the expected maximum time (delay) required for a packet arriving during the offduration to be serviced for any arrival process. Second, we obtain an estimate for the on-duration, Ton* for which the expected delay is below a certain threshold. Using Ton*, we compute the active duration, Tactivein each DRX cycle by considering the timers specified in 3rdGeneration Partnership Project (3GPP) Release 10 and for a given interarrival time distribution between packets. Finally, using our analysis, we propose a pragmatic algorithm and show how to select an appropriate DRX configuration which will lead to high power efficiency with acceptable buffer requirements. Through extensive analysis and simulations both with general arrival processes and real-time traces, we show that our algorithm can lead to significant extension in battery life at the UE.

In this paper, we show that the asymptotic probability density function (pdf) of the maxima of n independent and identically distributed (i.i.d.) sums of independent non-identically (i.n.i.d.) distributed gamma random variables (RVs) is a Gumbel pdf using Extreme Value Theory (EVT). We will also show that the asymptotic pdf of the maxima of n i.i.d. sums of correlated gamma RVs is a Gumbel pdf. Some applications in wireless communication are discussed where the maxima of n i.i.d. sums of correlated gamma RVs and maxima of n i.i.d. sums of i.n.i.d. gamma RVs arise. We discuss the utility of our results in the context of these applications.

Analytical expressions for scheduling gain and spectral efficiency of the proportional fair and maximum rate scheduling algorithms for Orthogonal Frequency Division Multiple Access (OFDMA) based systems are derived for the following cases: a) multipath Rayleigh and multipath Nakagami fading, b) Composite channel models which model the combined effect of both small scale and large scale fading. It is shown using Extreme Value Theory (EVT) that the asymptotic distribution of the maxima of the received signal to noise power (SNR) across all the users converges to a Gumbel distribution for both cases. Therefore, we use the Gumbel distribution to derive expressions for both the spectral efficiency and scheduling gain. The scheduling gains obtained through numerical integration (whenever tractable) and simulations match with the analytical values obtained using the EVT based expressions. We also discuss how the moments and order statistics of the Gumbel distribution can be used to study other metrics of the scheduling algorithms.

In this paper, we study the Foschini Miljanic algorithm, which was originally proposed in a static channel environment. We investigate the algorithm in a random channel environment, study its convergence properties and apply the Gerschgorin theorem to derive sufficient conditions for the convergence of the algorithm. We apply the Foschini and Miljanic algorithm to cellular networks and derive sufficient conditions for the convergence of the algorithm in distribution and validate the results with simulations. In cellular networks, the conditions which ensure convergence in distribution can be easily verified.