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.

We address the problem of distributed resource allocation for multicast communication in device-to-device (D2D) enabled underlay cellular networks. The optimal resource allocation is crucial for maximizing the performance of such networks, which are limited by the severe co-channel interference between cellular users (CU) and D2D multicast groups. However, finding such optimal allocation for networks with a large number of CUs and D2D users is challenging. Therefore, we propose a pragmatic scheme that allocates resources distributively, reducing signaling overhead and improving network scalability. Numerical simulations establish the efficacy of the proposed solution in improving the overall system throughout, compared to various existing schemes.

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.

Device-to-device (D2D) multicast communication is considered as a potential solution to improve the spectral efficiency of cellular networks. This paper focuses on resource allocation in underlay D2D multicast networks where multiple D2D multicast groups (MGs) share the uplink frequency channels with multiple cellular users (CUs). An optimization problem that maximizes the system throughput while fulfilling the maximum power constraint of every mobile user and ensuring a certain level of quality of service (QoS) to every CU and D2D multicast group is formulated. This formulation leads to mixed integer non-linear programming (MINLP), which is computationally intractable for large scale networks. Therefore, to find a feasible solution, we propose a channel sharing algorithm which determines how many MGs can share a channel with certain QoS guarantees to CU and D2D MGs. Then, we propose a power allocation algorithm that maximizes the system throughput while satisfying the various constraints. The impact of geographical spread of MGs, number of MGs, maximum available transmission power, and QoS requirements of every CU on achievable system throughput is analyzed. Numerical results show the efficacy of proposed model in terms of D2D MG's throughput and spectrum efficiency.

Energy efficient networking has gained momentum in past one decade due to the Internet's ever increasing share in world's total energy consumption. It is crucial to study and reduce the energy consumption of tasks that are very heavily used in the Internet. In this paper, we focus on one such task, file distribution, and study its energy efficiency. We prove lower bounds on energy consumption for different scenarios that are relevant in today's Internet and design schemes that achieve the lower bounds when all the hosts have equal upload and equal download capacities. We evaluate our theoretical results numerically to generalized scenarios as well. We show that our schemes of file distribution can save as much as 50% energy compared to other energy efficient P2P methods proposed in the literature.

Device-to-Device (D2D) multicast communication is emerging as a practical solution to alleviate severe capacity crunch in data-centric wireless networks and to encourage backhaul-free communication directly among devices with similar content requirement. Resource allocation in multicast networks is a critical issue that impacts both, the network throughput and spectrum efficiency. We devise an uplink resource reuse strategy for multiple multicast D2D groups and multiple cellular users (CUs), with the objective of maximizing the sum throughput, while guaranteeing a certain level of quality of service (QoS) to CUs and D2D users. We establish the efficacy of the proposed scheme for variable group sizes and geographical spread.

The potential of device-to-device (D2D) technology to support multicast services in LTE-Advanced networks has been recently realized. D2D communication brings great benefits to cellular networks in terms of enhanced spectral efficiency and larger coverage by enabling the devices to communicate directly with each other. D2D communication may lead to improved network capacity by sharing resources with cellular users (CUs). However, the resulting mutual interference may decrease or even outweigh the gain of D2D communication. In this paper, we propose a scheme to minimize the interference among D2D and CUs through a resource allocation scheme. We formulate the uplink resource allocation problem where a D2D multicast group can reuse resources of CUs under the constraint that the signal to interference plus noise ratio (SINR) requirements of CUs and D2D users are satisfied. We analyze a joint power and channel allocation scheme to maximize the total throughput of CUs and D2D users. The performance of D2D communication depends on maximum power constraint for the D2D users. Simulation results establish the efficacy of the proposed scheme.

The problem of characterizing the optimal rate achievable with analog network coding (ANC) for a unicast communication over general wireless relay networks is computationally hard. A relay node performing ANC scales and forwards its input signals. The source-destination channel in such communication scenarios is, in general, an intersymbol interference (ISI) channel which leads to the single-letter characterization of the optimal rate in terms of an optimization problem with nonconvex, non closed-form objective function and non-convex constraints. For a special class of such networks, called layered networks, a few key results and insights are however available. To gain insights into the nature of the optimal solution and to construct low-complexity schemes to characterize the optimal rate for general wireless relay networks, we need (1) network topologies that are regular enough for analysis, yet general enough to capture essential characteristics of general wireless relay networks, and (2) schemes to approximate the objective function in closed-form without significantly compromising the performance. Towards these two goals, this work proposes (1) nonlinear chain networks, and (2) two approximation schemes. We show that their combination allows us to tightly characterize the optimal ANC rate with low computational complexity for a much larger class of general wireless relay networks than possible with existing schemes.

We consider some computationally efficient and provably correct algorithms with near-optimal sample complexity for the problem of noisy nonadaptive group testing. Group testing involves grouping arbitrary subsets of items into pools. Each pool is then tested to identify the defective items, which are usually assumed to be sparse. We consider nonadaptive randomly pooling measurements, where pools are selected randomly and independently of the test outcomes. We also consider a model where noisy measurements allow for both some false negative and some false positive test outcomes (and also allow for asymmetric noise, and activation noise). We consider three classes of algorithms for the group testing problem (we call them specifically the coupon collector algorithm, the column matching algorithms, and the LP decoding algorithms-the last two classes of algorithms (versions of some of which had been considered before in the literature) were inspired by corresponding algorithms in the compressive sensing literature. The second and third of these algorithms have several flavors, dealing separately with the noiseless and noisy measurement scenarios. Our contribution is novel analysis to derive explicit sample-complexity bounds-with all constants expressly computed-for these algorithms as a function of the desired error probability, the noise parameters, the number of items, and the size of the defective set (or an upper bound on it). We also compare the bounds to information-theoretic lower bounds for sample complexity based on Fano's inequality and show that the upper and lower bounds are equal up to an explicitly computable universal constant factor (independent of problem parameters).

The problem of maximum rate achievable with analog network coding for a unicast communication over a layered wireless relay network with directed links is considered. A relay node performing analog network coding scales and forwards the signals received at its input. Recently this problem has been considered under two assumptions: (A) each relay node scales its received signal to the upper bound of its transmit power constraint, (B) the relay nodes in specific subsets of the network operate in the high-SNR regime. We establish that assumption (A), in general, leads to suboptimal end-to-end rate. We also characterize the performance of analog network coding in a class of symmetric layered networks without assumption (B). The key contribution of this work is a lemma that states that in a layered relay network a globally optimal set of scaling factors for the nodes that maximizes the end-to-end rate can be computed layer-by-layer. Specifically, a rate-optimal set of scaling factors for the nodes in a layer is the one that maximizes the sum-rate of the nodes in the next layer. This critical insight allows us to characterize analog network coding performance in network scenarios beyond those that can be analyzed using the existing approaches. We illustrate this by computing the maximum rate achievable with analog network coding in one particular layered network, in various communication scenarios.

We present computationally efficient and provably correct algorithms with near-optimal sample-complexity for noisy non-adaptive group testing. Group testing involves grouping arbitrary subsets of items into pools. Each pool is then tested to identify the defective items, which are usually assumed to be sparsely distributed. We consider random non-adaptive pooling where pools are selected randomly and independently of the test outcomes. Our noisy scenario accounts for both false negatives and false positives for the test outcomes. Inspired by compressive sensing algorithms we introduce four novel computationally efficient decoding algorithms for group testing, CBP via Linear Programming (CBP-LP), NCBP-LP (Noisy CBP-LP), and the two related algorithms NCBP-SLP+ and NCBP-SLP- (“Simple” NCBP-LP). The first of these algorithms deals with the noiseless measurement scenario, and the next three with the noisy measurement scenario. We derive explicit sample-complexity bounds - with all constants made explicit - for these algorithms as a function of the desired error probability; the noise parameters; the number of items; and the size of the defective set (or an upper bound on it). We show that the sample-complexities of our algorithms are near-optimal with respect to known information-theoretic bounds.

A communication scenario where a source communicates with a destination over a directed layered relay network is considered. Each relay performs analog network coding where it scales and forwards the signals received at its input. In this scenario, we address the question: What portion of the maximum end-to-end achievable rate can be maintained if only a fraction of relay nodes available at each layer are used? We consider, in particular, the Gaussian diamond network and a class of symmetric layered networks. For these networks we provide upper bounds on additive and multiplicative gaps between the optimal analog network coding performance when all N relays in each layer are used and when only k such relays are are used, k <; N (network simplification). We show that asymptotically (in source power), the additive gap increases at most logarithmically with ratio N/k and the number of layers, and the corresponding multiplicative gap increases at most linearly with ratio N/k and is independent of the number of layers in the layered network. To the best of our knowledge, this work offers the first characterization of the performance of network simplification in general layered amplify-and-forward relay networks. Further, unlike most of the current approximation results that attempt to bound optimal rates either within an additive gap or a multiplicative gap, our results suggest a new rate approximation scheme that allows for the simultaneous computation of additive and multiplicative gaps.

A general class of wireless relay networks with a single source-destination pair is considered. Intermediate nodes in the network employ an amplify-and-forward scheme to relay their input signals. In this case the overall input-output channel from the source via the relays to the destination effectively behaves as an intersymbol interference channel with colored noise. Unlike previous work we formulate the problem of the maximum achievable rate in this setting as an optimization problem with no assumption on the network size, topology, and signal-to-noise ratio. Previous work considered only scenarios wherein relays use all their power to amplify their received signals. We demonstrate that this may not always maximize the achievable rate in amplify-and-forward relay networks. The proposed formulation allows us to not only recover known results on the performance of the amplify-and-forward schemes for some simple relay networks but also characterize the performance of such schemes in more complex relay networks which cannot be addressed in a straightforward manner with existing approaches. Using cut-set arguments, we derive simple upper bounds on the capacity of general wireless relay networks. Through various examples, we show that a large class of amplify-and-forward relay networks can achieve rates within a constant factor of these upper bounds asymptotically in network parameters.

We provide a new unified framework, called "multiple correlated informants - single recipient" communication, to address the variations of the traditional Distributed Source Coding (DSC) problem. Different combinations of the assumptions about the communication scenarios and the objectives of communication result in different variations of the DSC problem. For each of these variations, the complexities of communication and computation of the optimal solution is determined by the combination of the underlying assumptions. In the proposed framework, we address the asymmetric, interactive, and lossless variant of the DSC problem, with various objectives of communication and provide optimal solutions for those. Also, we consider both, the worst-case and average-case scenarios.

We are concerned with maximizing the lifetime of a data-gathering wireless sensor network consisting of set of nodes directly communicating with a base-station. We model this scenario as the m-message interactive communication between multiple correlated informants (sensor nodes) and a recipient (base-station). With this framework, we show that m-message interactive communication can indeed enhance network lifetime. Both worst-case and average-case performances are considered.

We consider the energy consumed in radio transmission by a set of sensors forming a data gathering wireless network. Our objective is to enhance the lifetime of such networks by exploiting three system-level opportunities. First, the number of bits to be transmitted can be reduced by taking advantage of the redundancy induced by spatio-temporal correlation in sensor data. Second, channel coding allows us to reduce transmission energy at the cost of increased transmission time. Third, sensor nodes can be expected to operate collaboratively, allowing optimal management of distributed energy resources. Our main contribution lies in providing a framework to merge these ideas for energy conscious networking. We pose the problem of maximizing network lifetime as an optimal scheduling problem. We consider a special case where the data rate is linearly proportional to received signal power, investigating both static and dynamic scheduling strategies. The optimal static schedule turns out to have a very simple form. For the dynamic case, we obtain an integer linear program formulation to find the optimal strategy. We then propose an efficient algorithm that exploits the special nature of the problem setting to find the optimal solution quickly. Finally, we consider the general case where data rates and signal power need not be linearly related and propose an algorithm to find the optimal transmission times subject to the deadline constraint imposed by the system.

Packet arrival-delay variations and losses seriously affect the quality of voice delivered in VoIP. In this paper, using a time-scale modification algorithm, an integrated scheme is proposed to handle these impairments without introducing additional delays. This scheme provides flexible arrivaldelay cut-offs to late arriving packets, reducing the packet loss-rate at the receiver. Further, the lost packets are concealed effectively. Extensive simulations have shown that the proposed scheme delivers high-quality speech across widely varying packet arrival-delays and loss-rates. The proposed scheme is fully receiver-based and with its low computational complexity and generic nature, is applicable to any VoIP system.