Rate-Splitting Multiple Access in Cache-Aided Cloud-Radio Access Networks

Rate-splitting multiple access (RSMA) has been recognized as a promising physical layer strategy for 6G. Motivated by the ever-increasing popularity of cache-enabled content delivery in wireless communications, this paper proposes an innovative multigroup multicast transmission scheme based on RSMA for cache-aided cloud-radio access networks (C-RAN). Our proposed scheme not only exploits the properties of content-centric communications and local caching at the base stations (BSs) but also incorporates RSMA to better manage interference in multigroup multicast transmission with statistical channel state information (CSI) known at the central processor (CP) and the BSs. At the RSMA-enabled cloud CP, the message of each multicast group is split into a private and a common part with the former private part being decoded by all users in the respective group and the latter common part being decoded by multiple users from other multicast groups. Common message decoding is done for the purpose of mitigating the interference. In this work, we jointly optimize the clustering of BSs and the precoding with the aim of maximizing the minimum rate among all multicast groups to guarantee fairness serving all groups. The problem is a mixed-integer nonlinear stochastic program (MINLSP), which is solved by a practical algorithm we propose including a heuristic clustering algorithm for assigning a set of BSs to serve each user followed by an efficient iterative algorithm that combines the sample average approximation (SAA) and weighted minimum mean square error (WMMSE) to solve the stochastic non-convex subproblem of precoder design. Numerical results show the explicit max-min rate gain of our proposed transmission scheme compared to the state-of-the-art trivial interference processing methods. Therefore, we conclude that RSMA is a promising technique for cache-aided C-RAN.

Reliable Multicast Based on Congestion-Aware Cache in ICN

Reliable multicast distribution is essential for some applications such as Internet of Things (IoT) alarm information and important file distribution. Traditional IP reliable multicast usually relies on multicast source retransmission for recovery losses, causing huge recovery delay and redundancy. Moreover, feedback implosion tends to occur towards multicast source as the number of receivers grows. Information-Centric Networking (ICN) is an emerging network architecture that is efficient in content distribution by supporting multicast and in-network caching. Although ubiquitous in-network caching provides nearby retransmission, the design of cache strategy greatly affects the performance of loss recovery. Therefore, how to recover losses efficiently and quickly is an urgent problem to be solved in ICN reliable multicast. In this paper, we first propose an overview architecture of ICN-based reliable multicast and formulate a problem using recovery delay as the optimization target. Based on the architecture, we present a Congestion-Aware Probabilistic Cache (CAPC) strategy to reduce recovery delay by caching recently transmitted chunks during multicast transmission. Then, we propose NACK feedback aggregation and recovery isolation scheme to decrease recovery overhead. Finally, experimental results show that our proposal can achieve fully reliable multicast and outperforms other approaches in recovery delay, cache hit ratio, transmission completion time, and overhead.

Huffman Coding and Multi-Generation Mixing Assisted Network Coding Based MAC for QoS-Centric Secure Data Communication over MANETs

The exponential rise in wireless transmission has gained widespread attention to meet major mobile communication purposes including, Internet of Things (IoT) and Machine-to-Machine (M2M) communications. Mobile Ad-hoc Network (MANET) has emerged as one of the most viable solution to meet aforesaid mobile communication purposes; however, being decentralized and infrastructure-less network it undergoes adversaries including dynamic topology and security breaches due to malicious node attachment during routing. On the contrary, data security and allied Quality-of-Service (QoS) provision are inevitable in NextGen communication systems. Unlike routing-based security measures, Medium Access Control (MAC) based approaches are found more effective for MANETs. However, most of the classical MAC designs either address QoS or security as standalone objective. Unlike existing MAC solutions, in this paper a state-of-art novel Huffman Coding and Multi-Generation Mixing (MGM) assisted random linear network model-based MAC design (HM2-MAC) is proposed for MANET. Realizing the robustness of the network coding algorithms towards reliable, secure and error-free multicast transmission, we designed HM2-MAC in such manner that Huffman coding helps securing the source data packets, while MGM concept enables reduction in redundant packets to make overall communication resource efficient and secure. Unlike redundant packet-per-generation, MGM concept helps in reducing redundant transmission and hence achieves resource efficiency. Since, in HM2-MAC model the coefficient matrix used to encode the data is known only to the sink, no intermediate node can decode it or can pollute it. It makes multicast transmission more secure over MANET. Additionally, the use of iterative buffer flush technique preserves resources or buffer to accommodate more data for transmission and hence higher throughput. Noticeably, error sensitive packetization and MGM control strengthens our proposed model to retain optimal performance. HM2-MAC has been applied as a sub-layer of native IEEE 802.11 MAC and hence retains backward compatibility towards real-world implementation. MATLAB based simulation revealed that our proposed HM2-MAC protocol achieved almost 99.6% throughput even under varying link-loss patterns, which reveals its robustness to ensure QoS delivery with secure data transmission in MANET.

Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission

Herein, we focus on energy efficiency optimization for massive multiple-input multiple-output (MIMO) downlink secure multicast transmission exploiting statistical channel state information (CSI). Privacy engineering in the field of communication is a hot issue under study. The common signal transmitted by the base station is multicast transmitted to multiple legitimate user terminals in our system, but an eavesdropper might eavesdrop this signal. To achieve the energy efficiency utility–privacy trade-off of multicast transmission, we set up the problem of maximizing the energy efficiency which is defined as the ratio of the secure transmit rate to the power consumption. To simplify the formulated nonconvex problem, we use a lower bound of the secure multicast rate as the molecule of the design objective. We then obtain the eigenvector of the optimal transmit covariance matrix into a closed-form, simplifying the matrix-valued multicast transmission strategy problem into a power allocation problem in the beam domain. By utilizing the Minorize-Maximize method, an iterative algorithm is proposed to decompose the secure energy efficiency optimization problem into a sequence of iterative fractional programming subproblems. By using Dinkelbach’s transform, each subproblem becomes an iterative problem with the concave objective function, and it can be solved by classical convex optimization. We guarantee the convergence of the two-level iterative algorithm that we propose. Besides, we reduce the computational complexity of the algorithm by substituting the design objective with its deterministic equivalent. The numerical results show that the approach we propose performs well compared with the conventional methods.

Energy Efficiency and Spectral Efficiency Tradeoff in Massive MIMO Multicast Transmission with Statistical CSI

As the core technology of 5G mobile communication systems, massive multi-input multi-output (MIMO) can dramatically enhance the energy efficiency (EE), as well as the spectral efficiency (SE), which meets the requirements of new applications. Meanwhile, physical layer multicast technology has gradually become the focus of next-generation communication technology research due to its capacity to efficiently provide wireless transmission from point to multipoint. The availability of channel state information (CSI), to a large extent, determines the performance of massive MIMO. However, because obtaining the perfect instantaneous CSI in massive MIMO is quite challenging, it is reasonable and practical to design a massive MIMO multicast transmission strategy using statistical CSI. In this paper, in order to optimize the system resource efficiency (RE) to achieve EE-SE balance, the EE-SE trade-offs in the massive MIMO multicast transmission are investigated with statistical CSI. Firstly, we formulate the eigenvectors of the RE optimization multicast covariance matrices of different user terminals in closed form, which illustrates that in the massive MIMO downlink, optimal RE multicast precoding is supposed to be done in the beam domain. On the basis of this viewpoint, the optimal RE precoding design is simplified into a resource efficient power allocation problem. Via invoking the quadratic transform, we propose an iterative power allocation algorithm, which obtains an adjustable and reasonable EE-SE tradeoff. Numerical simulation results reveal the near-optimal performance and the effectiveness of our proposed statistical CSI-assisted RE maximization in massive MIMO.