New technologies that incorporate affordable storage at the edge, such as caching, are anticipated to facilitate the growth of wireless communication and accommodate increased traffic. In this paper, we address the challenge of adapting the original coded caching scheme to time-varying channels through a low-complexity approach. The approach categorizes graph nodes into two groups: those requiring modifications and those that remain unchanged. When a user's code rate increases, a merge process is applied to the associated graph vertices, while a split process is implemented for users with a decreased code rate. Subsequently, the graph is adaptively colored to facilitate continuous transmission. The efficacy and performance of the proposed scheme are demonstrated through simulation results.

New technologies that incorporate affordable storage at the edge, such as caching, are anticipated to facilitate the growth of wireless communication and accommodate increased traffic. In this paper, the focus is on designing an adaptive coded caching schema in dynamic scenarios with respect to the users mobility and their continuous requests. The proposed technique is based on the original coded caching schema that can be seen as index coding problem and modeled as a conflict graph. The approach is to adapt the conflict graph to any request and apply the necessary coloring in a adaptive way. A greedy approach is used, comparing the performance to the Greedy Randomized Adaptive Search Procedure (GRASP) algorithm proposed by Vettigli et al.. The aim of the adaptive greedy based technique is improving the performance while significantly reducing the complexity of the algorithm. Simulation results highlight the performance of the proposed technique.

The envisioned Internet of Things (IoT) will involve a massive deployment of objects connected through wireless cells. While commercial solutions are already available, the fundamental limits of such networks in terms of node density, achievable rates or reliability are not known. To address this question, this paper uses a large scale Multiple Access Channel (MAC) to model IoT nodes randomly distributed over the coverage area of a unique base station. The traffic is represented by an information rate spatial density ρ(x). This model, referred to as the Spatial Continuum Multiple Access Channel, is defined as the asymptotic limit of a sequence of discrete MACs. The access capacity region of this channel is defined as the set of achievable information rate spatial densities achievable with vanishing transmission errors and under a sum-power constraint. Simulation results validate the model and show that this fundamental limit theoretically achievable when all nodes transmit simultaneously over an infinite time, may be reached even with a relatively small number of simultaneous transmitters (typically around 20 nodes) which gives credibility to the model. The results also highlight the potential interest of non-orthogonal transmissions for IoT uplink transmissions when compared to an ideal time sharing strategy.

Modern wireless communication systems rely on limited feedback for Fast Link Adaptation (FLA). FLA in presence of advanced receivers needs new physical layer abstractions (PHY abstractions) that are able to capture their performance in order to derive the limited feedback metrics (e.g., Channel Quality Indicators). Recently, a PHY abstraction was proposed for the class of iterative linear minimum mean square error based soft interference cancellation and decoding also referred to as turbo Linear Codeword Interference Cancellation (turbo L-CWIC) receivers in 3GPP. This PHY abstraction needs to be calibrated and a first one-dimensional (1D) calibration method was proposed. In this paper, we revisit the calibration method by proposing a more rigorous framework to minimize the predicted throughput error that dramatically influences the FLA mechanisms. We also suggest a novel multi- dimensional calibration approach. Monte Carlo simulations show that it outperforms the 1D at the expense of the knowledge of the number of independent fading blocks experienced by a codeword.

We consider the problem of estimating a deterministic finite alphabet vector f from underdetermined measurements y = A f , where A is a given (random) n × N matrix. Two new convex optimization methods are introduced for the recovery of finite alphabet signals via ℓ1-norm minimization. The first method is based on regularization. In the second approach, the problem is formulated as the recovery of sparse signals after a suitable sparse transform. The regularization-based method is less complex than the transform-based one. When the alphabet size p equals 2 and (n, N) grows proportionally, the conditions under which the signal will be recovered with high probability are the same for the two methods. When p > 2, the behavior of the transform-based method is established. Experimental results support this theoretical result and show that the transform method outperforms the regularization-based one.

This paper addresses the problem of decoding and precoding in the K-user MIMO interference channels. At the receiver side, a joint decoding of the interference and the desired signal is able to improve the receive diversity order. At the transmitter side, we introduce a joint linear precoding design that maximizes the joint cut-off rate, known as a tight lower bound on the joint mutual information for high signal-to-noise ratio (SNR). We also derive a closed-form solution of the precoding matrices that maximizes the mutual information when the SNR is close to zero. This solution is characterized by its low computational complexity, and only requires a local channel state information knowledge at the transmitters. Our simulation results show that decoding interference jointly with the desired signal results in a significant improvement of the receive diversity order. Also a substantial bit error rate and sum-rate improvements are illustrated using the proposed precoding designs.

The degree of freedom of the Single Input Single Output (SISO) fading interference channel is asymptotically upperbounded by K/2. This upperbound can be achieved using the Interference Alignment approach (IA), proposed by Cadambe et al., In this work, a new optimized design of the IA scheme is presented. It involves introducing, for each user, a combination matrix so as to maximize the sum rate of the network. The optimal design is obtained via an iterative algorithm proposed in the K-user IA network, and a convergence to a local optimum is achieved. Numerical results enable us to evaluate the performance of the new algorithm and to compare it with other designs.