Holographic multiple-input multiple-output (HMIMO) communication systems utilize spatially-constrained arrays equipped with large number of antennas (NoA) to benefit from increased spatial multiplexing and spatial resolution gains. We consider a multi-user HMIMO system under an electromagnetic wave compliant channel model, and study its energy-efficiency (EE). We first derive closed-form expressions of the ergodic achievable rates under maximum ratio transmission (MRT) and zero-forcing (ZF) precoding in the downlink, and maximum ratio combining (MRC) and ZF combining in the uplink, implemented at the base station (BS) with reduced complexity dictated by the number of degrees-of-freedom (DoF) offered by the channel. Using these expressions, we formulate an EE maximization problem with respect to the power allocation (PA) and the NoA arranged within spatially-constrained HMIMO surfaces at the BS and users, and solve it using an alternating optimization algorithm. For fixed PA, the optimal NoA is derived as the solution of two analytical equations, while for fixed NoA we use sequential fractional programming to obtain the optimal PA. Numerical results yield useful insights into the EE performance in different operating regimes and under different side-lengths of HMIMO surfaces. The presented results show that under MRT and MRC in the downlink and uplink respectively, deploying more antennas in excess of the number of DoF increases the EE in low power budget (noise-limited) regime, whereas fixing the NoA to the number of DoF maximizes the EE in higher power budget (interference-limited) regime. On the other hand, under ZF precoders and combiners, the NoA that achieve the optimal EE is larger than the DoF under all power budgets, with the number of additional antennas decreasing with increasing power budget.

The paper provides a new perspective on peak- and average-constrained Gaussian channels. Such channels model optical wireless communication (OWC) systems which employ intensity-modulation with direct detection (IM/DD). First, the paper proposes a new, capacity-preserving vector binary channel (VBC) model, consisting of dependent binary noisy bit-pipes. Then, to simplify coding over this VBC, the paper proposes coding schemes with varying levels of complexity, building on the capacity of binary-symmetric channels (BSC) and channels with state. The achievable rates are compared to capacity and capacity bounds, showing that coding for the BSC with state over the VBC achieves rates close to capacity at moderate to high signal-to-noise ratio (SNR), whereas simpler schemes achieve lower rates at lower complexity. The presented coding schemes are realizable using capacity-achieving codes for binary-input channels, such as polar codes. Numerical results are provided to validate the theoretical results and demonstrate the applicability of the proposed schemes.

We consider resource allocation for offloading computational-intensive tasks in a mobile-edge computing (MEC) system, where each loT device's task can be processed at the MEC server, access points (APs), or locally at loT device itself. The envisioned system has both cooperative APs with a computing capability and multiple radio resource blocks (RRBs) and a MEC server. We aim to study the trade-off between minimizing the energy consumption and maximizing the effective system capacity, which is the number of loT devices with a successful task processing. For this objective, we exploit non-orthogonal multiple access (NOMA) to schedule a set of loT devices to the set of MEC's subcarriers and RRBs of the APs. Due to the intractability of the energy consumption minimization problem, we split it into two sub-problems. The first sub-problem considers the offloading decision and local computation allocation, while the second sub-problem considers the loT device scheduling and power allocation. Leveraging graph-theory, we propose an approach for solving the two sub-problems. Numerical results are presented to depict the trade-off between minimizing the energy consumption and maximizing the effective system capacity of the proposed approach over benchmark schemes.

This paper develops a binary decomposition for the peak-constrained intensity-modulation with direct-detection (IM/DD) channel. Then, it uses this decomposition, which we call a Vector Binary Channel (VBC), to propose simple binary coding schemes. The VBC consists of$N$bit-pipes and, different from the Avestimehr-Diggavi-Tse linear deterministic model, captures interactions between bit-pipes thus preserves capacity. Two practical coding schemes are then designed for the VBC. The first is an Independent Coding scheme which encodes independently and decodes successively while ignoring the dependence between noises on different bit-pipes. The second improves upon the first by exploiting the knowledge of noises on previously decoded bit-pipes as a state for the current decoding operation. The achievable rates of the schemes are compared numerically with capacity, and shown to perform well with the second scheme approaching capacity for signal-to-noise ratio (SNR) > 2.5 dB. The schemes can be implemented using practical capacity-achieving codes for the binary symmetric channel (BSC) such as polar codes. Methods to improve the schemes at low-SNR are discussed towards the end of the paper.